WorldWideScience

Sample records for model successfully predicted

  1. Methodology for Designing Models Predicting Success of Infertility Treatment

    OpenAIRE

    Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

    2016-01-01

    Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...

  2. Prediction models for successful external cephalic version: a systematic review.

    Science.gov (United States)

    Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

    2015-12-01

    To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

  3. Prediction models for successful external cephalic version: a systematic review

    NARCIS (Netherlands)

    Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M.; Molkenboer, Jan F. M.; van der Post, Joris A. M.; Mol, Ben W.; Kok, Marjolein

    2015-01-01

    To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015.

  4. External Validation of a Prediction Model for Successful External Cephalic Version

    NARCIS (Netherlands)

    de Hundt, Marcella; Vlemmix, Floortje; Kok, Marjolein; van der Steeg, Jan W.; Bais, Joke M.; Mol, Ben W.; van der Post, Joris A.

    2012-01-01

    We sought external validation of a prediction model for the probability of a successful external cephalic version (ECV). We evaluated the performance of the prediction model with calibration and discrimination. For clinical practice, we developed a score chart to calculate the probability of a

  5. Development of a Predictive Model for Induction Success of Labour

    Directory of Open Access Journals (Sweden)

    Cristina Pruenza

    2018-03-01

    Full Text Available Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements or less frequently, social (elective inductions for convenience. The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn, costs (medication, hospitalization, qualified staff and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the

  6. Economic sustainability in franchising: a model to predict franchisor success or failure

    OpenAIRE

    Calderón Monge, Esther; Pastor Sanz, Ivan .; Huerta Zavala, Pilar Angélica

    2017-01-01

    As a business model, franchising makes a major contribution to gross domestic product (GDP). A model that predicts franchisor success or failure is therefore necessary to ensure economic sustainability. In this study, such a model was developed by applying Lasso regression to a sample of franchises operating between 2002 and 2013. For franchises with the highest likelihood of survival, the franchise fees and the ratio of company-owned to franchised outlets were suited to the age ...

  7. Modeling student success in engineering education

    Science.gov (United States)

    Jin, Qu

    In order for the United States to maintain its global competitiveness, the long-term success of our engineering students in specific courses, programs, and colleges is now, more than ever, an extremely high priority. Numerous studies have focused on factors that impact student success, namely academic performance, retention, and/or graduation. However, there are only a limited number of works that have systematically developed models to investigate important factors and to predict student success in engineering. Therefore, this research presents three separate but highly connected investigations to address this gap. The first investigation involves explaining and predicting engineering students' success in Calculus I courses using statistical models. The participants were more than 4000 first-year engineering students (cohort years 2004 - 2008) who enrolled in Calculus I courses during the first semester in a large Midwestern university. Predictions from statistical models were proposed to be used to place engineering students into calculus courses. The success rates were improved by 12% in Calculus IA using predictions from models developed over traditional placement method. The results showed that these statistical models provided a more accurate calculus placement method than traditional placement methods and help improve success rates in those courses. In the second investigation, multi-outcome and single-outcome neural network models were designed to understand and to predict first-year retention and first-year GPA of engineering students. The participants were more than 3000 first year engineering students (cohort years 2004 - 2005) enrolled in a large Midwestern university. The independent variables include both high school academic performance factors and affective factors measured prior to entry. The prediction performances of the multi-outcome and single-outcome models were comparable. The ability to predict cumulative GPA at the end of an engineering

  8. Improving student success using predictive models and data visualisations

    Directory of Open Access Journals (Sweden)

    Hanan Ayad

    2012-08-01

    Full Text Available The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50–60%. At the college level in the US only 30% of students graduate from 2-year colleges in 3 years or less and approximately 50% graduate from 4-year colleges in 5 years or less. A basic challenge in delivering global education, therefore, is improving student success. By student success we mean improving retention, completion and graduation rates. In this paper we describe a Student Success System (S3 that provides a holistic, analytical view of student academic progress.1 The core of S3 is a flexible predictive modelling engine that uses machine intelligence and statistical techniques to identify at-risk students pre-emptively. S3 also provides a set of advanced data visualisations for reaching diagnostic insights and a case management tool for managing interventions. S3's open modular architecture will also allow integration and plug-ins with both open and proprietary software. Powered by learning analytics, S3 is intended as an end-to-end solution for identifying at-risk students, understanding why they are at risk, designing interventions to mitigate that risk and finally closing the feedback look by tracking the efficacy of the applied intervention.

  9. Psychosocial Factors Predicting First-Year College Student Success

    Science.gov (United States)

    Krumrei-Mancuso, Elizabeth J.; Newton, Fred B.; Kim, Eunhee; Wilcox, Dan

    2013-01-01

    This study made use of a model of college success that involves students achieving academic goals and life satisfaction. Hierarchical regressions examined the role of six psychosocial factors for college success among 579 first-year college students. Academic self-efficacy and organization and attention to study were predictive of first semester…

  10. Using data mining to predict success in a weight loss trial.

    Science.gov (United States)

    Batterham, M; Tapsell, L; Charlton, K; O'Shea, J; Thorne, R

    2017-08-01

    Traditional methods for predicting weight loss success use regression approaches, which make the assumption that the relationships between the independent and dependent (or logit of the dependent) variable are linear. The aim of the present study was to investigate the relationship between common demographic and early weight loss variables to predict weight loss success at 12 months without making this assumption. Data mining methods (decision trees, generalised additive models and multivariate adaptive regression splines), in addition to logistic regression, were employed to predict: (i) weight loss success (defined as ≥5%) at the end of a 12-month dietary intervention using demographic variables [body mass index (BMI), sex and age]; percentage weight loss at 1 month; and (iii) the difference between actual and predicted weight loss using an energy balance model. The methods were compared by assessing model parsimony and the area under the curve (AUC). The decision tree provided the most clinically useful model and had a good accuracy (AUC 0.720 95% confidence interval = 0.600-0.840). Percentage weight loss at 1 month (≥0.75%) was the strongest predictor for successful weight loss. Within those individuals losing ≥0.75%, individuals with a BMI (≥27 kg m -2 ) were more likely to be successful than those with a BMI between 25 and 27 kg m -2 . Data mining methods can provide a more accurate way of assessing relationships when conventional assumptions are not met. In the present study, a decision tree provided the most parsimonious model. Given that early weight loss cannot be predicted before randomisation, incorporating this information into a post randomisation trial design may give better weight loss results. © 2017 The British Dietetic Association Ltd.

  11. Validation of prediction model for successful vaginal birth after Cesarean delivery based on sonographic assessment of hysterotomy scar.

    Science.gov (United States)

    Baranov, A; Salvesen, K Å; Vikhareva, O

    2018-02-01

    To validate a prediction model for successful vaginal birth after Cesarean delivery (VBAC) based on sonographic assessment of the hysterotomy scar, in a Swedish population. Data were collected from a prospective cohort study. We recruited non-pregnant women aged 18-35 years who had undergone one previous low-transverse Cesarean delivery at ≥ 37 gestational weeks and had had no other uterine surgery. Participants who subsequently became pregnant underwent transvaginal ultrasound examination of the Cesarean hysterotomy scar at 11 + 0 to 13 + 6 and at 19 + 0 to 21 + 6 gestational weeks. Thickness of the myometrium at the thinnest part of the scar area was measured. After delivery, information on pregnancy outcome was retrieved from hospital records. Individual probabilities of successful VBAC were calculated using a previously published model. Predicted individual probabilities were divided into deciles. For each decile, observed VBAC rates were calculated. To assess the accuracy of the prediction model, receiver-operating characteristics curves were constructed and the areas under the curves (AUC) were calculated. Complete sonographic data were available for 120 women. Eighty (67%) women underwent trial of labor after Cesarean delivery (TOLAC) with VBAC occurring in 70 (88%) cases. The scar was visible in all 80 women at the first-trimester scan and in 54 (68%) women at the second-trimester scan. AUC was 0.44 (95% CI, 0.28-0.60) among all women who underwent TOLAC and 0.51 (95% CI, 0.32-0.71) among those with the scar visible sonographically at both ultrasound examinations. The prediction model demonstrated poor accuracy for prediction of successful VBAC in our Swedish population. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

  12. Predicting the success of IVF: external validation of the van Loendersloot's model.

    Science.gov (United States)

    Sarais, Veronica; Reschini, Marco; Busnelli, Andrea; Biancardi, Rossella; Paffoni, Alessio; Somigliana, Edgardo

    2016-06-01

    Is the predictive model for IVF success proposed by van Loendersloot et al. valid in a different geographical and cultural context? The model discriminates well but was less accurate than in the original context where it was developed. Several independent groups have developed models that combine different variables with the aim of estimating the chance of pregnancy with IVF but only four of them have been externally validated. One of these four, the van Loendersloot's model, deserves particular attention and further investigation for at least three reasons; (i) the reported area under the receiver operating characteristics curve (c-statistics) in the temporal validation setting was the highest reported to date (0.68), (ii) the perspective of the model is clinically wise since it includes variables obtained from previous failed cycles, if any, so it can be applied to any women entering an IVF cycle, (iii) the model lacks external validation in a geographically different center. Retrospective cohort study of women undergoing oocyte retrieval for IVF between January 2013 and December 2013 at the infertility unit of the Fondazione Ca' Granda, Ospedale Maggiore Policlinico of Milan, Italy. Only the first oocyte retrieval cycle performed during the study period was included in the study. Women with previous IVF cycles were excluded if the last one before the study cycle was in another center. The main outcome was the cumulative live birth rate per oocytes retrieval. Seven hundred seventy-two women were selected. Variables included in the van Loendersloot's model and the relative weights (beta) were used. The variable resulting from this combination (Y) was transformed into a probability. The discriminatory capacity was assessed using the c-statistics. Calibration was made using a logistic regression that included Y as the unique variable and live birth as the outcome. Data are presented using both the original and the calibrated models. Performance was evaluated

  13. Linear genetic programming application for successive-station monthly streamflow prediction

    Science.gov (United States)

    Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

    2014-09-01

    In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

  14. Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

    Science.gov (United States)

    Calvert, Carol Elaine

    2014-01-01

    This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

  15. Predicting Ranger Assessment and Selection Program 1 Success and Optimizing Class Composition

    Science.gov (United States)

    2017-06-01

    Healthcare Specialist) 149 150 68X ( Mental Health Specialist) 1 74 74D (Chemical Operations Specialist) 15 15 88 88M (Motor Transport Operator) 27 27 89...regression and partition tree models to identify significant factors that contribute to a candidate’s success at RASP1 and predict graduation rates. We...tree models to identify significant factors that contribute to a candidate’s success at RASP1 and predict graduation rates. We use an integer linear

  16. Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

    Science.gov (United States)

    Chen, Weisheng; Sun, Cheng; Wei, Ru; Zhang, Yanlin; Ye, Heng; Chi, Ruibin; Zhang, Yichen; Hu, Bei; Lv, Bo; Chen, Lifang; Zhang, Xiunong; Lan, Huilan; Chen, Chunbo

    2018-01-01

    Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion. © 2016 American Society for Parenteral and Enteral Nutrition.

  17. INSIGHTS FROM MACHINE-LEARNED DIET SUCCESS PREDICTION.

    Science.gov (United States)

    Weber, Ingmar; Achananuparp, Palakorn

    2016-01-01

    To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction. Our findings include both expected results, such as the token "mcdonalds" or the category "dessert" being indicative for being over the calories goal, but also less obvious ones such as the difference between pork and poultry concerning dieting success, or the use of the "quick added calories" functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.

  18. Developing and validating a model to predict the success of an IHCS implementation: the Readiness for Implementation Model

    Science.gov (United States)

    Gustafson, David H; Hawkins, Robert P; Brennan, Patricia F; Dinauer, Susan; Johnson, Pauley R; Siegler, Tracy

    2010-01-01

    Objective To develop and validate the Readiness for Implementation Model (RIM). This model predicts a healthcare organization's potential for success in implementing an interactive health communication system (IHCS). The model consists of seven weighted factors, with each factor containing five to seven elements. Design Two decision-analytic approaches, self-explicated and conjoint analysis, were used to measure the weights of the RIM with a sample of 410 experts. The RIM model with weights was then validated in a prospective study of 25 IHCS implementation cases. Measurements Orthogonal main effects design was used to develop 700 conjoint-analysis profiles, which varied on seven factors. Each of the 410 experts rated the importance and desirability of the factors and their levels, as well as a set of 10 different profiles. For the prospective 25-case validation, three time-repeated measures of the RIM scores were collected for comparison with the implementation outcomes. Results Two of the seven factors, ‘organizational motivation’ and ‘meeting user needs,’ were found to be most important in predicting implementation readiness. No statistically significant difference was found in the predictive validity of the two approaches (self-explicated and conjoint analysis). The RIM was a better predictor for the 1-year implementation outcome than the half-year outcome. Limitations The expert sample, the order of the survey tasks, the additive model, and basing the RIM cut-off score on experience are possible limitations of the study. Conclusion The RIM needs to be empirically evaluated in institutions adopting IHCS and sustaining the system in the long term. PMID:20962135

  19. Prediction Modeling for Academic Success in Professional Master's Athletic Training Programs

    Science.gov (United States)

    Bruce, Scott L.; Crawford, Elizabeth; Wilkerson, Gary B.; Rausch, David; Dale, R. Barry; Harris, Martina

    2016-01-01

    Context: A common goal of professional education programs is to recruit the students best suited for the professional career. Selection of students can be a difficult process, especially if the number of qualified candidates exceeds the number of available positions. The ability to predict academic success in any profession has been a challenging…

  20. Introduction of an Evaluation Tool to Predict the Probability of Success of Companies: The Innovativeness, Capabilities and Potential Model (ICP

    Directory of Open Access Journals (Sweden)

    Michael Lewrick

    2009-05-01

    Full Text Available Successful innovation requires management and in this paper a model to help manage the innovation process is presented. This model can be used to audit the management capability to innovate and to monitor how sales increase is related to innovativeness. The model was developed from a study of companies in the high technology cluster around Munich and validated using statistical procedures. The model was found to be effective at predicting the success or otherwise of the innovation strategy pursued by the company. The use of this model and how it can be used to identify areas for improvement are documented in this paper.

  1. Predicting Commissary Store Success

    Science.gov (United States)

    2014-12-01

    stores or if it is possible to predict that success. Multiple studies of private commercial grocery consumer preferences , habits and demographics have...appropriate number of competitors due to the nature of international cultures and consumer preferences . 2. Missing Data Four of the remaining stores

  2. Predicting Success Study Using Students GPA Category

    Directory of Open Access Journals (Sweden)

    Awan Setiawan

    2015-07-01

    Full Text Available Abstract. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government. Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students’ performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the  university may recommend actions to be taken to improve the student  achievement index and graduation rates.Keywords: graduation, segmentation, quadrant GPA, data mining, modeling algorithms

  3. Predicting Success Study Using Students GPA Category

    Directory of Open Access Journals (Sweden)

    Awan Setiawan

    2015-06-01

    Full Text Available Abstract. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government. Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students’ performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the university may recommend actions to be taken to improve the student achievement index and graduation rates. Keywords: graduation, segmentation, quadrant GPA, data mining, modeling algorithms

  4. Predicting red wolf release success in the southeastern United States

    Science.gov (United States)

    van Manen, Frank T.; Crawford, Barron A.; Clark, Joseph D.

    2000-01-01

    Although the red wolf (Canis rufus) was once found throughout the southeastern United States, indiscriminate killing and habitat destruction reduced its range to a small section of coastal Texas and Louisiana. Wolves trapped from 1973 to 1980 were taken to establish a captive breeding program that was used to repatriate 2 mainland and 3 island red wolf populations. We collected data from 320 red wolf releases in these areas and classified each as a success or failure based on survival and reproductive criteria, and whether recaptures were necessary to resolve conflicts with humans. We evaluated the relations between release success and conditions at the release sites, characteristics of released wolves, and release procedures. Although <44% of the variation in release success was explained, model performance based on jackknife tests indicated a 72-80% correct prediction rate for the 4 operational models we developed. The models indicated that success was associated with human influences on the landscape and the level of wolf habituation to humans prior to release. We applied the models to 31 prospective areas for wolf repatriation and calculated an index of release success for each area. Decision-makers can use these models to objectively rank prospective release areas and compare strengths and weaknesses of each.

  5. Predicting Rehabilitation Success Rate Trends among Ethnic Minorities Served by State Vocational Rehabilitation Agencies: A National Time Series Forecast Model Demonstration Study

    Science.gov (United States)

    Moore, Corey L.; Wang, Ningning; Washington, Janique Tynez

    2017-01-01

    Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts…

  6. An excitable cortex and memory model successfully predicts new pseudopod dynamics.

    Directory of Open Access Journals (Sweden)

    Robert M Cooper

    Full Text Available Motile eukaryotic cells migrate with directional persistence by alternating left and right turns, even in the absence of external cues. For example, Dictyostelium discoideum cells crawl by extending distinct pseudopods in an alternating right-left pattern. The mechanisms underlying this zig-zag behavior, however, remain unknown. Here we propose a new Excitable Cortex and Memory (EC&M model for understanding the alternating, zig-zag extension of pseudopods. Incorporating elements of previous models, we consider the cell cortex as an excitable system and include global inhibition of new pseudopods while a pseudopod is active. With the novel hypothesis that pseudopod activity makes the local cortex temporarily more excitable--thus creating a memory of previous pseudopod locations--the model reproduces experimentally observed zig-zag behavior. Furthermore, the EC&M model makes four new predictions concerning pseudopod dynamics. To test these predictions we develop an algorithm that detects pseudopods via hierarchical clustering of individual membrane extensions. Data from cell-tracking experiments agrees with all four predictions of the model, revealing that pseudopod placement is a non-Markovian process affected by the dynamics of previous pseudopods. The model is also compatible with known limits of chemotactic sensitivity. In addition to providing a predictive approach to studying eukaryotic cell motion, the EC&M model provides a general framework for future models, and suggests directions for new research regarding the molecular mechanisms underlying directional persistence.

  7. Statistical and Machine Learning Models to Predict Programming Performance

    OpenAIRE

    Bergin, Susan

    2006-01-01

    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  8. Disentangling the Predictive Validity of High School Grades for Academic Success in University

    Science.gov (United States)

    Vulperhorst, Jonne; Lutz, Christel; de Kleijn, Renske; van Tartwijk, Jan

    2018-01-01

    To refine selective admission models, we investigate which measure of prior achievement has the best predictive validity for academic success in university. We compare the predictive validity of three core high school subjects to the predictive validity of high school grade point average (GPA) for academic achievement in a liberal arts university…

  9. Factors predicting labor induction success: a critical analysis.

    Science.gov (United States)

    Crane, Joan M G

    2006-09-01

    Because of the risk of failed induction of labor, a variety of maternal and fetal factors as well as screening tests have been suggested to predict labor induction success. Certain characteristics of the woman (including parity, age, weight, height and body mass index), and of the fetus (including birth weight and gestational age) are associated with the success of labor induction; with parous, young women who are taller and lower weight having a higher rate of induction success. Fetuses with a lower birth weight or increased gestational age are also associated with increased induction success. The condition of the cervix at the start of induction is an important predictor, with the modified Bishop score being a widely used scoring system. The most important element of the Bishop score is dilatation. Other predictors, including transvaginal ultrasound (TVUS) and biochemical markers [including fetal fibronectin (fFN)] have been suggested. Meta-analyses of studies identified from MEDLINE, PubMed, and EMBASE and published from 1990 to October 2005 were performed evaluating the use of TVUS and fFN in predicting labor induction success in women at term with singleton gestations. Both TVUS and Bishop score predicted successful induction [likelihood ratio (LR)=1.82, 95% confidence interval (CI)=1.51-2.20 and LR=2.10, 95%CI=1.67-2.64, respectively]. As well, fFN and Bishop score predicted successful induction (LR=1.49, 95%CI=1.20-1.85, and LR=2.62, 95%CI=1.88-3.64, respectively). Although TVUS and fFN predicted successful labor induction, neither has been shown to be superior to Bishop score. Further research is needed to evaluate these potential predictors and insulin-like growth factor binding protein-1 (IGFBP-1), another potential biochemical marker.

  10. Extracting falsifiable predictions from sloppy models.

    Science.gov (United States)

    Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

    2007-12-01

    Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

  11. Prediction of Success of External Cephalic Version after 36 Weeks

    NARCIS (Netherlands)

    Kok, Marjolein; van der Steeg, Jan Willem; van der Post, Joris A. M.; Mol, Ben W. J.

    2011-01-01

    We aimed to develop a predictive model for the chance of a successful external cephalic version (ECV). We performed a prospective cohort study of women with a singleton fetus in breech presentation with a gestational age of 36 weeks or more. Data on parity, maternal age, body mass index, ethnicity,

  12. Predicting Success in an Online Course Using Expectancies, Values, and Typical Mode of Instruction

    Science.gov (United States)

    Zimmerman, Whitney Alicia

    2017-01-01

    Expectancies of success and values were used to predict success in an online undergraduate-level introductory statistics course. Students who identified as primarily face-to-face learners were compared to students who identified as primarily online learners. Expectancy value theory served as a model. Expectancies of success were operationalized as…

  13. Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon

    Science.gov (United States)

    Holan, S.H.; Davis, G.M.; Wildhaber, M.L.; DeLonay, A.J.; Papoulias, D.M.

    2009-01-01

    The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed. ?? Journal compilation 2009 Royal Statistical Society.

  14. Predicting success of catheter drainage in infected necrotizing pancreatitis

    NARCIS (Netherlands)

    Hollemans, Robbert A.; Bollen, Thomas L.; Van Brunschot, Sandra; Bakker, Olaf J.; Ali, Usama Ahmed; Van Goor, Harry; Boermeester, Marja A.; Gooszen, Hein G.; Besselink, Marc G.; Van Santvoort, Hjalmar C.

    2016-01-01

    Introduction: At least 30% of patients with infected necrotizing pancreatitis are successfully treated with catheter drainage alone. It is currently not possible to predict which patients also need necrosectomy. We evaluated predictive factors for successful catheter drainage. Methods: This was a

  15. Predicting Success of Catheter Drainage in Infected Necrotizing Pancreatitis

    NARCIS (Netherlands)

    Hollemans, R.A.; Bollen, T.L.; Brunschot, S. van; Bakker, O.J.; Ali, U. Ahmed; Goor, H. van; Boermeester, M.A.; Gooszen, H.G.; Besselink, M.G.; Santvoort, H.C. van

    2016-01-01

    INTRODUCTION: At least 30% of patients with infected necrotizing pancreatitis are successfully treated with catheter drainage alone. It is currently not possible to predict which patients also need necrosectomy. We evaluated predictive factors for successful catheter drainage. METHODS: This was a

  16. Some uses of predictive probability of success in clinical drug development

    Directory of Open Access Journals (Sweden)

    Mauro Gasparini

    2013-03-01

    Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

  17. Predicting Success of Catheter Drainage in Infected Necrotizing Pancreatitis

    NARCIS (Netherlands)

    Hollemans, Robbert A.; Bollen, Thomas L.; van Brunschot, Sandra; Bakker, Olaf J.; Ahmed Ali, Usama; van Goor, Harry; Boermeester, Marja A.; Gooszen, Hein G.; Besselink, Marc G.; van Santvoort, Hjalmar C.

    2016-01-01

    At least 30% of patients with infected necrotizing pancreatitis are successfully treated with catheter drainage alone. It is currently not possible to predict which patients also need necrosectomy. We evaluated predictive factors for successful catheter drainage. This was a post hoc analysis of 130

  18. A Data mining Technique for Analyzing and Predicting the success of Movie

    Science.gov (United States)

    Meenakshi, K.; Maragatham, G.; Agarwal, Neha; Ghosh, Ishitha

    2018-04-01

    In real world prediction models and mechanisms can be used to predict the success of a movie. The proposed work aims to develop a system based upon data mining techniques that may help in predicting the success of a movie in advance thereby reducing certain level of uncertainty. An attempt is made to predict the past as well as the future of movie for the purpose of business certainty or simply a theoretical condition in which decision making [the success of the movie] is without risk, because the decision maker [movie makers and stake holders] has all the information about the exact outcome of the decision, before he or she makes the decision [release of the movie]. With over two million spectators a day and films exported to over 100 countries, the impact of Bollywood film industry is formidable We gather a series of interesting facts and relationships using a variety of data mining techniques. In particular, we concentrate on attributes relevant to the success prediction of movies, such as whether any particular actors or actresses are likely to help a movie to succeed. The paper additionally reports on the techniques used, giving their implementation and utility. Additionally, we found some attention-grabbing facts, such as the budget of a movie isn't any indication of how well-rated it'll be, there's a downward trend within the quality of films over time, and also the director and actors/actresses involved in the movie.

  19. Dive characteristics can predict foraging success in Australian fur seals (Arctocephalus pusillus doriferus as validated by animal-borne video

    Directory of Open Access Journals (Sweden)

    Beth L. Volpov

    2016-03-01

    Full Text Available Dive characteristics and dive shape are often used to infer foraging success in pinnipeds. However, these inferences have not been directly validated in the field with video, and it remains unclear if this method can be applied to benthic foraging animals. This study assessed the ability of dive characteristics from time-depth recorders (TDR to predict attempted prey capture events (APC that were directly observed on animal-borne video in Australian fur seals (Arctocephalus pusillus doriferus, n=11. The most parsimonious model predicting the probability of a dive with ≥1 APC on video included only descent rate as a predictor variable. The majority (94% of the 389 total APC were successful, and the majority of the dives (68% contained at least one successful APC. The best model predicting these successful dives included descent rate as a predictor. Comparisons of the TDR model predictions to video yielded a maximum accuracy of 77.5% in classifying dives as either APC or non-APC or 77.1% in classifying dives as successful verses unsuccessful. Foraging intensity, measured as either total APC per dive or total successful APC per dive, was best predicted by bottom duration and ascent rate. The accuracy in predicting total APC per dive varied based on the number of APC per dive with maximum accuracy occurring at 1 APC for both total (54% and only successful APC (52%. Results from this study linking verified foraging dives to dive characteristics potentially opens the door to decades of historical TDR datasets across several otariid species.

  20. Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study).

    Science.gov (United States)

    Thomas, Diana M; Ivanescu, Andrada E; Martin, Corby K; Heymsfield, Steven B; Marshall, Kaitlyn; Bodrato, Victoria E; Williamson, Donald A; Anton, Stephen D; Sacks, Frank M; Ryan, Donna; Bray, George A

    2015-03-01

    Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds. The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1. Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing model. The AUC statistic quantified the ROC curve's capacity to classify participants likely to lose models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated. Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50). The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention. © 2015 American Society for Nutrition.

  1. An internally validated prognostic model for success in revision stapes surgery for otosclerosis.

    Science.gov (United States)

    Wegner, Inge; Vincent, Robert; Derks, Laura S M; Rauh, Simone P; Heymans, Martijn W; Stegeman, Inge; Grolman, Wilko

    2018-03-09

    To develop a prediction model that can accurately predict the chance of success following revision stapes surgery in patients with recurrent or persistent otosclerosis at 2- to 6-months follow-up and to validate this model internally. A retrospective cohort study of prospectively gathered data in a tertiary referral center. The associations of 11 prognostic factors with treatment success were tested in 705 cases using multivariable logistic regression analysis with backward selection. Success was defined as a mean air-bone gap closure to 10 dB or less. The most relevant predictors were used to derive a clinical prediction rule to determine the probability of success. Internal validation by means of bootstrapping was performed. Model performance indices, including the Hosmer-Lemeshow test, the area under the receiver operating characteristics curve (AUC), and the explained variance were calculated. Success was achieved in 57.7% of cases at 2- to 6-months follow-up. Certain previous surgical techniques, primary causes of failure leading up to revision stapes surgery, and positions of the prosthesis placed during revision surgery were associated with higher success percentages. The clinical prediction rule performed moderately well in the original dataset (Hosmer-Lemeshow P = .78; AUC = 0.73; explained variance = 22%), which slightly decreased following internal validation by means of bootstrapping (AUC = 0.69; explained variance = 13%). Our study established the importance of previous surgical technique, primary cause of failure, and type of the prosthesis placed during the revision surgery in predicting the probability of success following stapes surgery at 2- to 6-months follow-up. 2b. Laryngoscope, 2018. © 2018 The American Laryngological, Rhinological and Otological Society, Inc.

  2. Long-term success and failure with SG is predictable by 3 months: a multivariate model using simple office markers.

    Science.gov (United States)

    Cottam, Austin; Billing, Josiah; Cottam, Daniel; Billing, Peter; Cottam, Samuel; Zaveri, Hinali; Surve, Amit

    2017-08-01

    Despite being the most common surgery in the United States, little is known about predicting weight loss success and failure with sleeve gastrectomy (SG). Papers that have been published are inconclusive. We decided to use multivariate analysis from 2 practices to design a model to predict weight loss outcomes using data widely available to any surgical practice at 3 months to determine weight loss outcomes at 1 year. Two private practices in the United States. A retrospective review of 613 patients from 2 bariatric institutions were included in this study. Co-morbidities and other preoperative characteristics were gathered, and %EWL was calculated for 1, 3, and 12 months. Excess weight loss (%EWL)failure. Multiple variate analysis was used to find factors that affect %EWL at 12 months. Preoperative sleep apnea, preoperative diabetes, %EWL at 1 month, and %EWL at 3 months all affect %EWL at 1 year. The positive predictive value and negative predictive value of our model was 72% and 91%, respectively. Sensitivity and specificity were 71% and 91%, respectively. One-year results of the SG can be predicted by diabetes, sleep apnea, and weight loss velocity at 3 months postoperatively. This can help surgeons direct surgical or medical interventions for patients at 3 months rather than at 1 year or beyond. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

  3. Using landscape disturbance and succession models to support forest management

    Science.gov (United States)

    Eric J. Gustafson; Brian R. Sturtevant; Anatoly S. Shvidenko; Robert M. Scheller

    2010-01-01

    Managers of forested landscapes must account for multiple, interacting ecological processes operating at broad spatial and temporal scales. These interactions can be of such complexity that predictions of future forest ecosystem states are beyond the analytical capability of the human mind. Landscape disturbance and succession models (LDSM) are predictive and...

  4. Authentic Leadership and Emotional Intelligence: Predicting Student Success

    Science.gov (United States)

    Jasso, Sonia Lizette

    2016-01-01

    Student success has been predicted conservatively, using academic, demographic, and economic variables. Since many colleges are feeling the pressure to produce more graduates, student success is at the forefront of all universities. This study looks to find a relationship between traditional and non-traditional variables. The objective of the…

  5. Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

    Directory of Open Access Journals (Sweden)

    Shandilya Sharad

    2012-10-01

    Full Text Available Abstract Background Ventricular Fibrillation (VF is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2, using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA technique. Methods A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold. Results The integrative model performs real-time, short-term (7.8 second analysis of the Electrocardiogram (ECG. For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC Area Under the Curve (AUC of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64

  6. Electronic Commerce Success Model: A Search for Multiple Criteria

    Directory of Open Access Journals (Sweden)

    Didi Achjari

    2004-01-01

    Full Text Available The current study attempts to develop and examine framework of e-commerce success. In order to obtain comprehensive and robust measures, the framework accomodates key factors that are identified in the literature concerning the success of electronic commerce. The structural model comprises of four exogenous variables (Internal Driver, Internal Impediment, External Driver and Exgternal Impediment and one endogenous variable (Electornic Commerce Success eith 24 observed variables. The study that was administered within large Australian companies using questionaire survey concluded that benefits for both internal organization and external parties from the use of e-commerce were the main factor tro predict perceived and/or expected success of electronic commerce.

  7. Factors predicting successful discontinuation of continuous renal replacement therapy.

    Science.gov (United States)

    Katayama, S; Uchino, S; Uji, M; Ohnuma, T; Namba, Y; Kawarazaki, H; Toki, N; Takeda, K; Yasuda, H; Izawa, J; Tokuhira, N; Nagata, I

    2016-07-01

    This multicentre, retrospective observational study was conducted from January 2010 to December 2010 to determine the optimal time for discontinuing continuous renal replacement therapy (CRRT) by evaluating factors predictive of successful discontinuation in patients with acute kidney injury. Analysis was performed for patients after CRRT was discontinued because of renal function recovery. Patients were divided into two groups according to the success or failure of CRRT discontinuation. In multivariate logistic regression analysis, urine output at discontinuation, creatinine level and CRRT duration were found to be significant variables (area under the receiver operating characteristic curve for urine output, 0.814). In conclusion, we found that higher urine output, lower creatinine and shorter CRRT duration were significant factors to predict successful discontinuation of CRRT.

  8. "Eyeball test" of thermographic patterns for predicting a successful lateral infraclavicular block.

    Science.gov (United States)

    Andreasen, Asger M; Linnet, Karen E; Asghar, Semera; Rothe, Christian; Rosenstock, Charlotte V; Lange, Kai H W; Lundstrøm, Lars H

    2017-11-01

    Increased distal skin temperature can be used to predict the success of lateral infraclavicular (LIC) block. We hypothesized that an "eyeball test" of specific infrared thermographic patterns after LIC block could be used to determine block success. In this observational study, five observers trained in four distinct thermographic patterns independently evaluated thermographic images of the hands of 40 patients at baseline and at one-minute intervals for 30 min after a LIC block. Sensitivity, specificity, and predictive values of a positive and a negative test were estimated to evaluate the validity of specific thermographic patterns for predicting a successful block. Sensory and motor block of the musculocutaneous, radial, ulnar, and median nerves defined block success. Fleiss' kappa statistics of multiple interobserver agreements were used to evaluate reliability. As a diagnostic test, the defined specific thermographic patterns of the hand predicted a successful block with increasing accuracy over the 30-min observation period. Block success was predicted with a sensitivity of 92.4% (95% confidence interval [CI], 86.8 to 96.2) and with a specificity of 84.0% (95% CI, 70.3 to 92.4) at min 30. The Fleiss' kappa for the five observers was 0.87 (95% CI, 0.77 to 0.96). We conclude that visual evaluation by an eyeball test of specific thermographic patterns of the blocked hands may be useful as a valid and reliable diagnostic test for predicting a successful LIC block.

  9. Attempting to Predict Success in the Qualifying Round of the International Chemistry Olympiad

    Science.gov (United States)

    Urhahne, Detlef; Ho, Lok Hang; Parchmann, Ilka; Nick, Sabine

    2012-01-01

    The aim of this study was trying to predict success in the qualifying round for the International Chemistry Olympiad (IChO) on the basis of the expectancy-value model of achievement motivation by Eccles et al. The investigation with 52 participants, including 14 females, was conducted during the third of four qualifying rounds of the IChO in…

  10. Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study)123

    Science.gov (United States)

    Ivanescu, Andrada E; Martin, Corby K; Heymsfield, Steven B; Marshall, Kaitlyn; Bodrato, Victoria E; Williamson, Donald A; Anton, Stephen D; Sacks, Frank M; Ryan, Donna; Bray, George A

    2015-01-01

    Background: Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds. Objective: The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1. Design: Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve’s capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated. Results: Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50). Conclusions: The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention. The POUNDS Lost study was registered at clinicaltrials.gov as NCT00072995. PMID:25733628

  11. Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

    Science.gov (United States)

    Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error model predicts the exposure of

  12. Modeling Success: Using Preenrollment Data to Identify Academically At-Risk Students

    Science.gov (United States)

    Gansemer-Topf, Ann M.; Compton, Jonathan; Wohlgemuth, Darin; Forbes, Greg; Ralston, Ekaterina

    2015-01-01

    Improving student success and degree completion is one of the core principles of strategic enrollment management. To address this principle, institutional data were used to develop a statistical model to identify academically at-risk students. The model employs multiple linear regression techniques to predict students at risk of earning below a…

  13. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  14. Initialization and Predictability of a Coupled ENSO Forecast Model

    Science.gov (United States)

    Chen, Dake; Zebiak, Stephen E.; Cane, Mark A.; Busalacchi, Antonio J.

    1997-01-01

    The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

  15. Diaphragmatic excursion: does it predict successful weaning from mechanical ventilation?

    International Nuclear Information System (INIS)

    Hayat, A.; Khalil, A.

    2017-01-01

    To measure the diaphragmatic excursion and its outcome on weani ng from mechanical ventilation. Study Design: Cross-sectional comparative study. Place and Duration of Study: Medical Intensive Care Unit (ICU), Military Hospital (MH), Rawalpindi, Pakistan, from January to December 2014. Methodology: Diaphragmatic excursion (DE) in cm was measured through ultrasound by marking liver and spleen displacement in patients who fulfilled the criteria of removal from ventilatory support. The patients were followed up for 48 hours and classified according to the outcome as successful weaning and weaning failure. Results: Out of 100 cases, 76 patients had a successful weaning while 24 had a failed weaning outcome. At a diaphragmatic excursion of 1.2 cm and more, out of 67 cases, 60 had a successful weaning (89.55%) while 7 cases (10.45%) had a weaning failure. At an excursion of less than 1. 2 cm, 17 out of 33 cases (51.5%) had successful weaning while 16 (48.48%) had weaning failure. At this cut off point (1.2 cm), the sensitivity and specificity for successful weaning were 78.95% and 70.83%, respectively. The positive and negative likelihood ratio (LR) for these values being 2.70 and 0.29, respectively. The positive predictive value was 82.35% and negative predictive value 60.00%. Conclusion: Ultrasonographic measurement of diaphragmatic excursion is a good method for predicting weaning outcome from mechanical ventilation. (author)

  16. Student nurse selection and predictability of academic success: The Multiple Mini Interview project.

    Science.gov (United States)

    Gale, Julia; Ooms, Ann; Grant, Robert; Paget, Kris; Marks-Maran, Di

    2016-05-01

    With recent reports of public enquiries into failure to care, universities are under pressure to ensure that candidates selected for undergraduate nursing programmes demonstrate academic potential as well as characteristics and values such as compassion, empathy and integrity. The Multiple Mini Interview (MMI) was used in one university as a way of ensuring that candidates had the appropriate numeracy and literacy skills as well as a range of communication, empathy, decision-making and problem-solving skills as well as ethical insights and integrity, initiative and team-work. To ascertain whether there is evidence of bias in MMIs (gender, age, nationality and location of secondary education) and to determine the extent to which the MMI is predictive of academic success in nursing. A longitudinal retrospective analysis of student demographics, MMI data and the assessment marks for years 1, 2 and 3. One university in southwest London. One cohort of students who commenced their programme in September 2011, including students in all four fields of nursing (adult, child, mental health and learning disability). Inferential statistics and a Bayesian Multilevel Model. MMI in conjunction with MMI numeracy test and MMI literacy test shows little or no bias in terms of ages, gender, nationality or location of secondary school education. Although MMI in conjunction with numeracy and literacy testing is predictive of academic success, it is only weakly predictive. The MMI used in conjunction with literacy and numeracy testing appears to be a successful technique for selecting candidates for nursing. However, other selection methods such as psychological profiling or testing of emotional intelligence may add to the extent to which selection methods are predictive of academic success on nursing. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Mental models accurately predict emotion transitions.

    Science.gov (United States)

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  18. Mental models accurately predict emotion transitions

    Science.gov (United States)

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  19. Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

    Science.gov (United States)

    Mokhtari, Fatemeh; Rejeski, W Jack; Zhu, Yingying; Wu, Guorong; Simpson, Sean L; Burdette, Jonathan H; Laurienti, Paul J

    2018-06-01

    More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine. Although the identification of biochemical and metabolic phenotypes are one potential direction of research, neurobiological measures may prove useful as substantial behavioral change is necessary to achieve success in a lifestyle intervention. In the present study, we use dynamic brain networks from functional magnetic resonance imaging (fMRI) data to prospectively identify individuals most likely to succeed in a behavioral weight loss intervention. Brain imaging was performed in overweight or obese older adults (age: 65-79 years) who participated in an 18-month lifestyle weight loss intervention. Machine learning and functional brain networks were combined to produce multivariate prediction models. The prediction accuracy exceeded 95%, suggesting that there exists a consistent pattern of connectivity which correctly predicts success with weight loss at the individual level. Connectivity patterns that contributed to the prediction consisted of complex multivariate network components that substantially overlapped with known brain networks that are associated with behavior emergence, self-regulation, body awareness, and the sensory features of food. Future work on independent datasets and diverse populations is needed to corroborate our findings. Additionally, we believe that efforts can begin to

  20. Collegiate Student-Athletes' Academic Success: Academic Communication Apprehension's Impact on Prediction Models

    Science.gov (United States)

    James, Kai'Iah A.

    2010-01-01

    This dissertation study examines the impact of traditional and non-cognitive variables on the academic prediction model for a sample of collegiate student-athletes. Three hundred and fifty-nine NCAA Division IA male and female student-athletes, representing 13 sports, including football and Men's and Women's Basketball provided demographic…

  1. Modeling the prediction of business intelligence system effectiveness.

    Science.gov (United States)

    Weng, Sung-Shun; Yang, Ming-Hsien; Koo, Tian-Lih; Hsiao, Pei-I

    2016-01-01

    Although business intelligence (BI) technologies are continually evolving, the capability to apply BI technologies has become an indispensable resource for enterprises running in today's complex, uncertain and dynamic business environment. This study performed pioneering work by constructing models and rules for the prediction of business intelligence system effectiveness (BISE) in relation to the implementation of BI solutions. For enterprises, effectively managing critical attributes that determine BISE to develop prediction models with a set of rules for self-evaluation of the effectiveness of BI solutions is necessary to improve BI implementation and ensure its success. The main study findings identified the critical prediction indicators of BISE that are important to forecasting BI performance and highlighted five classification and prediction rules of BISE derived from decision tree structures, as well as a refined regression prediction model with four critical prediction indicators constructed by logistic regression analysis that can enable enterprises to improve BISE while effectively managing BI solution implementation and catering to academics to whom theory is important.

  2. Pediatric extracorporeal shock wave lithotripsy: Predicting successful outcomes

    Directory of Open Access Journals (Sweden)

    Sean McAdams

    2010-01-01

    Full Text Available Extracorporeal shock wave lithotripsy (ESWL is currently a first-line procedure of most upper urinary tract stones <2 cm of size because of established success rates, its minimal invasiveness and long-term safety with minimal complications. Given that alternative surgical and endourological options exist for the management of stone disease and that ESWL failure often results in the need for repeat ESWL or secondary procedures, it is highly desirable to identify variables predicting successful outcomes of ESWL in the pediatric population. Despite numerous reports and growing experience, few prospective studies and guidelines for pediatric ESWL have been completed. Variation in the methods by which study parameters are measured and reported can make it difficult to compare individual studies or make definitive recommendations. There is ongoing work and a need for continuing improvement of imaging protocols in children with renal colic, with a current focus on minimizing exposure to ionizing radiation, perhaps utilizing advancements in ultrasound and magnetic resonance imaging. This report provides a review of the current literature evaluating the patient attributes and stone factors that may be predictive of successful ESWL outcomes along with reviewing the role of pre-operative imaging and considerations for patient safety.

  3. A Model for Predicting Student Performance on High-Stakes Assessment

    Science.gov (United States)

    Dammann, Matthew Walter

    2010-01-01

    This research study examined the use of student achievement on reading and math state assessments to predict success on the science state assessment. Multiple regression analysis was utilized to test the prediction for all students in grades 5 and 8 in a mid-Atlantic state. The prediction model developed from the analysis explored the combined…

  4. Prediction model for initial point of net vapor generation for low-flow boiling

    International Nuclear Information System (INIS)

    Sun Qi; Zhao Hua; Yang Ruichang

    2003-01-01

    The prediction of the initial point of net vapor generation is significant for the calculation of phase distribution in sub-cooled boiling. However, most of the investigations were developed in high-flow boiling, and there is no common model that could be successfully applied for the low-flow boiling. A predictive model for the initial point of net vapor generation for low-flow forced convection and natural circulation is established here, by the analysis of evaporation and condensation heat transfer. The comparison between experimental data and calculated results shows that this model can predict the net vapor generation point successfully in low-flow sub-cooled boiling

  5. Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

    Science.gov (United States)

    Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

    2014-01-01

    Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.

  6. Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

    Directory of Open Access Journals (Sweden)

    Ralph Olusola Aluko

    2016-12-01

    Full Text Available In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

  7. Blinded Observer Evaluation of Distal Skin Temperature for Predicting Lateral Infraclavicular Block Success

    DEFF Research Database (Denmark)

    Asghar, Semera; Lange, Kai H W; Lundstrøm, Lars H

    2015-01-01

    as a diagnostic test for predicting a successful lateral infraclavicular block. METHODS: Blinded observers investigated temperature difference between the blocked and the nonblocked hands of 40 patients. Sensitivity, specificity, and predictive values of a positive and a negative test were estimated......BACKGROUND: Changes in digit skin temperature may be used to predict and determine upper limb nerve block success. We investigated whether a temperature difference between the blocked and the nonblocked hands, simply registered by touching the skin of the 5th and 2nd digit was valid and reliable...... for evaluating the validity of a temperature difference for predicting a successful lateral infraclavicular block defined by sensory and motor block of all 4 major nerves (musculocutaneous, radial, ulnar, and median nerves). κ statistics of interobserver agreement were used for evaluating the reliability...

  8. Modeling Seizure Self-Prediction: An E-Diary Study

    Science.gov (United States)

    Haut, Sheryl R.; Hall, Charles B.; Borkowski, Thomas; Tennen, Howard; Lipton, Richard B.

    2013-01-01

    Purpose A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, “How likely are you to experience a seizure [time frame]”? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy. PMID:24111898

  9. WHAT PREDICTS A SUCCESSFUL LIFE? A LIFE-COURSE MODEL OF WELL-BEING*

    Science.gov (United States)

    Layard, Richard; Clark, Andrew E.; Cornaglia, Francesca; Powdthavee, Nattavudh; Vernoit, James

    2014-01-01

    Policy-makers who care about well-being need a recursive model of how adult life-satisfaction is predicted by childhood influences, acting both directly and (indirectly) through adult circumstances. We estimate such a model using the British Cohort Study (1970). We show that the most powerful childhood predictor of adult life-satisfaction is the child’s emotional health, followed by the child’s conduct. The least powerful predictor is the child’s intellectual development. This may have implications for educational policy. Among adult circumstances, family income accounts for only 0.5% of the variance of life-satisfaction. Mental and physical health are much more important. PMID:25422527

  10. Are traditional cognitive tests useful in predicting clinical success?

    Science.gov (United States)

    Gray, Sarah A; Deem, Lisa P; Straja, Sorin R

    2002-11-01

    The purpose of this research was to determine the predictive value of the Dental Admission Test (DAT) for clinical success using Ackerman's theory of ability determinants of skilled performance. The Ackerman theory is a valid, reliable schema in the applied psychology literature used to predict complex skill acquisition. Inconsistent stimulus-response skill acquisition depends primarily on determinants of cognitive ability. Consistent information-processing tasks have been described as "automatic," in which stimuli and responses are mapped in a manner that allows for complete certainty once the relationships have been learned. It is theorized that the skills necessary for success in the clinical component of dental schools involve a significant amount of automatic processing demands and, as such, student performance in the clinics should begin to converge as task practice is realized and tasks become more consistent. Subtest scores of the DAT of four classes were correlated with final grades in nine clinical courses. Results showed that the DAT subtest scores played virtually no role with regard to the final clinical grades. Based on this information, the DAT scores were determined to be of no predictive value in clinical achievement.

  11. Factors predictive of successful learning in postgraduate medical education

    NARCIS (Netherlands)

    Smits, P. B. A.; Verbeek, J. H. A. M.; Nauta, M. C. E.; ten Cate, Th J.; Metz, J. C. M.; van Dijk, F. J. H.

    2004-01-01

    PURPOSE To establish which personal and contextual factors are predictive of successful outcomes in postgraduate medical education. METHOD We performed a follow-up study of 118 doctors on a postgraduate occupational health training programme on the management of mental health problems. The following

  12. Successful modeling?

    Science.gov (United States)

    Lomnitz, Cinna

    Tichelaar and Ruff [1989] propose to “estimate model variance in complicated geophysical problems,” including the determination of focal depth in earthquakes, by means of unconventional statistical methods such as bootstrapping. They are successful insofar as they are able to duplicate the results from more conventional procedures.

  13. Successful emotion regulation is predicted by amygdala activity and aspects of personality: A latent variable approach.

    Science.gov (United States)

    Morawetz, Carmen; Alexandrowicz, Rainer W; Heekeren, Hauke R

    2017-04-01

    The experience of emotions and their cognitive control are based upon neural responses in prefrontal and subcortical regions and could be affected by personality and temperamental traits. Previous studies established an association between activity in reappraisal-related brain regions (e.g., inferior frontal gyrus and amygdala) and emotion regulation success. Given these relationships, we aimed to further elucidate how individual differences in emotion regulation skills relate to brain activity within the emotion regulation network on the one hand, and personality/temperamental traits on the other. We directly examined the relationship between personality and temperamental traits, emotion regulation success and its underlying neuronal network in a large sample (N = 82) using an explicit emotion regulation task and functional MRI (fMRI). We applied a multimethodological analysis approach, combing standard activation-based analyses with structural equation modeling. First, we found that successful downregulation is predicted by activity in key regions related to emotion processing. Second, the individual ability to successfully upregulate emotions is strongly associated with the ability to identify feelings, conscientiousness, and neuroticism. Third, the successful downregulation of emotion is modulated by openness to experience and habitual use of reappraisal. Fourth, the ability to regulate emotions is best predicted by a combination of brain activity and personality as well temperamental traits. Using a multimethodological analysis approach, we provide a first step toward a causal model of individual differences in emotion regulation ability by linking biological systems underlying emotion regulation with descriptive constructs. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  14. Orchiopexy for intra-abdominal testes: factors predicting success.

    Science.gov (United States)

    Stec, Andrew A; Tanaka, Stacy T; Adams, Mark C; Pope, John C; Thomas, John C; Brock, John W

    2009-10-01

    Intra-abdominal testes can be treated with several surgical procedures. We evaluated factors influencing the outcome of orchiopexy for intra-abdominal testis. We retrospectively reviewed 156 consecutive orchiopexies performed for intra-abdominal testis, defined as a nonpalpable testis on examination and located in the abdomen at surgery. All surgical approaches were included in the study. Primary outcome was the overall success rate and secondary outcomes were success based on surgical approach, age and a patent processus vaginalis. Success was considered a testis with normal texture and size compared to the contralateral testis at followup. Multivariate analysis was performed to determine factors predictive of success. The overall success rate of all orchiopexies was 79.5%. Median patient age at orchiopexy was 12 months and mean followup was 16 months. Of the patients 117 had a patent processus vaginalis at surgery. One-stage abdominal orchiopexy was performed in 92 testes with 89.1% success. Of these cases 32 were performed laparoscopically with 96.9% success. One-stage Fowler-Stephens orchiopexy was performed in 27 testes and 2-stage Fowler-Stephens orchiopexy was performed in 37 with success in 63.0% and 67.6%, respectively. Multivariate analysis revealed that 1-stage orchiopexy without vessel division had more successful outcomes than 1 and 2-stage Fowler-Stephens orchiopexy (OR 0.24, p = 0.007 and 0.29, p = 0.19, respectively). Neither age at surgery nor an open internal ring was significant (p = 0.49 and 0.12, respectively). The overall success of orchiopexy for intra-abdominal testis is 79.5%. While patient selection remains a critical factor, 1-stage orchiopexy without vessel division was significantly more successful and a laparoscopic approach was associated with the fewest failures for intra-abdominal testes.

  15. Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression

    Directory of Open Access Journals (Sweden)

    Glauco H.S. Mendes

    2013-09-01

    Full Text Available Critical success factors in new product development (NPD in the Brazilian small and medium enterprises (SMEs are identified and analyzed. Critical success factors are best practices that can be used to improve NPD management and performance in a company. However, the traditional method for identifying these factors is survey methods. Subsequently, the collected data are reduced through traditional multivariate analysis. The objective of this work is to develop a logistic regression model for predicting the success or failure of the new product development. This model allows for an evaluation and prioritization of resource commitments. The results will be helpful for guiding management actions, as one way to improve NPD performance in those industries.

  16. Predicting field weed emergence with empirical models and soft computing techniques

    Science.gov (United States)

    Seedling emergence is the most important phenological process that influences the success of weed species; therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict seedling e...

  17. Developing a risk prediction model for the functional outcome after hip arthroscopy.

    Science.gov (United States)

    Stephan, Patrick; Röling, Maarten A; Mathijssen, Nina M C; Hannink, Gerjon; Bloem, Rolf M

    2018-04-19

    Hip arthroscopic treatment is not equally beneficial for every patient undergoing this procedure. Therefore, the purpose of this study was to develop a clinical prediction model for functional outcome after surgery based on preoperative factors. Prospective data was collected on a cohort of 205 patients having undergone hip arthroscopy between 2011 and 2015. Demographic and clinical variables and patient reported outcome (PRO) scores were collected, and considered as potential predictors. Successful outcome was defined as either a Hip Outcome Score (HOS)-ADL score of over 80% or improvement of 23%, defined by the minimal clinical important difference, 1 year after surgery. The prediction model was developed using backward logistic regression. Regression coefficients were converted into an easy to use prediction rule. The analysis included 203 patients, of which 74% had a successful outcome. Female gender (OR: 0.37 (95% CI 0.17-0.83); p = 0.02), pincer impingement (OR: 0.47 (95% CI 0.21-1.09); p = 0.08), labral tear (OR: 0.46 (95% CI 0.20-1.06); p = 0.07), HOS-ADL score (IQR OR: 2.01 (95% CI 0.99-4.08); p = 0.05), WHOQOL physical (IQR OR: 0.43 (95% CI 0.22-0.87); p = 0.02) and WHOQOL psychological (IQR OR: 2.40 (95% CI 1.38-4.18); p = prediction model of successful functional outcome 1 year after hip arthroscopy. The model's discriminating accuracy turned out to be fair, as 71% (95% CI: 64-80%) of the patients were classified correctly. The developed prediction model can predict the functional outcome of patients that are considered for a hip arthroscopic intervention, containing six easy accessible preoperative risk factors. The model can be further improved trough external validation and/or adding additional potential predictors.

  18. Success Prediction: A DDC Bibliography. December 1949--December 1971.

    Science.gov (United States)

    Defense Documentation Center, Alexandria, VA.

    This document contains bibliographic information, descriptive terms, and abstracts for 145 technical reports on the general subject of success prediction. The bibliography includes reports on development of individuals during military training, peer evaluation, biographical inventory, and the validity of tests which may be used as predictors of…

  19. Catalytic cracking models developed for predictive control purposes

    Directory of Open Access Journals (Sweden)

    Dag Ljungqvist

    1993-04-01

    Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.

  20. Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.

    Science.gov (United States)

    Alshurafa, Nabil; Sideris, Costas; Pourhomayoun, Mohammad; Kalantarian, Haik; Sarrafzadeh, Majid; Eastwood, Jo-Ann

    2017-03-01

    Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support. Many participants benefitted from this RHM system. In response to the variance in participants' success, we developed a framework to identify classification schemes that predicted successful and unsuccessful participants. We analyzed both contextual baseline features and data from the first month of intervention such as activity, blood pressure, and questionnaire responses transmitted through the smartphone. A prediction tool can aid clinicians and scientists in identifying participants who may optimally benefit from the RHM system. Targeting therapies could potentially save healthcare costs, clinician, and participant time and resources. Our classification scheme yields RHM outcome success predictions with an F-measure of 91.9%, and identifies behaviors during the first month of intervention that help determine outcome success. We also show an improvement in prediction by using intervention-based smartphone data. Results from the WHHS study demonstrates that factors such as the variation in first month intervention response to the consumption of nuts, beans, and seeds in the diet help predict patient RHM protocol outcome success in a group of young Black women ages 25-45.

  1. Pediatric extracorporeal shock wave lithotripsy: Predicting successful outcomes.

    Science.gov (United States)

    McAdams, Sean; Shukla, Aseem R

    2010-10-01

    Extracorporeal shock wave lithotripsy (ESWL) is currently a first-line procedure of most upper urinary tract stones ionizing radiation, perhaps utilizing advancements in ultrasound and magnetic resonance imaging. This report provides a review of the current literature evaluating the patient attributes and stone factors that may be predictive of successful ESWL outcomes along with reviewing the role of pre-operative imaging and considerations for patient safety.

  2. Prediction of strong earthquake motions on rock surface using evolutionary process models

    International Nuclear Information System (INIS)

    Kameda, H.; Sugito, M.

    1984-01-01

    Stochastic process models are developed for prediction of strong earthquake motions for engineering design purposes. Earthquake motions with nonstationary frequency content are modeled by using the concept of evolutionary processes. Discussion is focused on the earthquake motions on bed rocks which are important for construction of nuclear power plants in seismic regions. On this basis, two earthquake motion prediction models are developed, one (EMP-IB Model) for prediction with given magnitude and epicentral distance, and the other (EMP-IIB Model) to account for the successive fault ruptures and the site location relative to the fault of great earthquakes. (Author) [pt

  3. Psychological approach to successful ageing predicts future quality of life in older adults

    Directory of Open Access Journals (Sweden)

    Iliffe Steve

    2011-03-01

    Full Text Available Abstract Background Public policies aim to promote well-being, and ultimately the quality of later life. Positive perspectives of ageing are underpinned by a range of appraoches to successful ageing. This study aimed to investigate whether baseline biological, psychological and social aproaches to successful ageing predicted future QoL. Methods Postal follow-up in 2007/8 of a national random sample of 999 people aged 65 and over in 1999/2000. Of 496 valid addresses of survivors at follow-up, the follow-up response rate was 58% (287. Measures of the different concepts of successful ageing were constructed using baseline indicators. They were assessed for their ability to independently predict quality of life at follow-up. Results Few respondents achieved all good scores within each of the approaches to successful ageing. Each approach was associated with follow-up QoL when their scores were analysed continuously. The biomedical (health approach failed to achieve significance when the traditional dichotomous cut-off point for successfully aged (full health, or not (less than full health, was used. In multiple regression analyses of the relative predictive ability of each approach, only the psychological approach (perceived self-efficacy and optimism retained significance. Conclusion Only the psychological approach to successful ageing independently predicted QoL at follow-up. Successful ageing is not only about the maintenance of health, but about maximising one's psychological resources, namely self-efficacy and resilience. Increasing use of preventive care, better medical management of morbidity, and changing lifestyles in older people may have beneficial effects on health and longevity, but may not improve their QoL. Adding years to life and life to years may require two distinct and different approaches, one physical and the other psychological. Follow-up health status, number of supporters and social activities, and self-rated active ageing

  4. Predicting the establishment success of introduced target species in grassland restoration by functional traits.

    Science.gov (United States)

    Engst, Karina; Baasch, Annett; Bruelheide, Helge

    2017-09-01

    Species-rich semi-natural grasslands are highly endangered habitats in Central Europe and numerous restoration efforts have been made to compensate for the losses in the last decades. However, some plant species could become more easily established than others. The establishment success of 37 species was analyzed over 6 years at two study sites of a restoration project in Germany where hay transfer and sowing of threshing material in combination with additional sowing were applied. The effects of the restoration method applied, time since the restoration took place, traits related to germination, dispersal, and reproduction, and combinations of these traits on the establishment were analyzed. While the specific restoration method of how seeds were transferred played a subordinate role, the establishment success depended in particular on traits such as flower season or the lifeform. Species flowering in autumn, such as Pastinaca sativa and Serratula tinctoria , became established better than species flowering in other seasons, probably because they could complete their life cycle, resulting in increasingly stronger seed pressure with time. Geophytes, like Allium angulosum and Galium boreale , became established very poorly, but showed an increase with study duration. For various traits, we found significant trait by method and trait by year interactions, indicating that different traits promoted establishment under different conditions. Using a multi-model approach, we tested whether traits acted in combination. For the first years and the last year, we found that models with three traits explained establishment success better than models with a single trait or two traits. While traits had only an additive effect on the establishment success in the first years, trait interactions became important thereafter. The most important trait was the season of flowering, which occurred in all best models from the third year onwards. Overall, our approach revealed the

  5. Predicting success on the certification examinations of the American Board of Anesthesiology.

    Science.gov (United States)

    McClintock, Joseph C; Gravlee, Glenn P

    2010-01-01

    Currently, residency programs lack objective predictors for passing the sequenced American Board of Anesthesiology (ABA) certification examinations on the first attempt. Our hypothesis was that performance on the ABA/American Society of Anesthesiologists In-Training Examination (ITE) and other variables can predict combined success on the ABA Part 1 and Part 2 examinations. The authors studied 2,458 subjects who took the ITE immediately after completing the first year of clinical anesthesia training and took the ABA Part 1 examination for primary certification immediately after completing residency training 2 yr later. ITE scores and other variables were used to predict which residents would complete the certification process (passing the ABA Part 1 and Part 2 examinations) in the shortest possible time after graduation. ITE scores alone accounted for most of the explained variation in the desired outcome of certification in the shortest possible time. In addition, almost half of the observed variation and most of the explained variance in ABA Part 1 scores was accounted for by ITE scores. A combined model using ITE scores, residency program accreditation cycle length, country of medical school, and gender best predicted which residents would complete the certification examinations in the shortest possible time. The principal implication of this study is that higher ABA/ American Society of Anesthesiologists ITE scores taken at the end of the first clinical anesthesia year serve as a significant and moderately strong predictor of high performance on the ABA Part 1 (written) examination, and a significant predictor of success in completing both the Part 1 and Part 2 examinations within the calendar year after the year of graduation from residency. Future studies may identify other predictors, and it would be helpful to identify factors that predict clinical performance as well.

  6. Validation of the close-to-delivery prediction model for vaginal birth after cesarean delivery in a Middle Eastern cohort.

    Science.gov (United States)

    Abdel Aziz, Ahmed; Abd Rabbo, Amal; Sayed Ahmed, Waleed A; Khamees, Rasha E; Atwa, Khaled A

    2016-07-01

    To validate a prediction model for vaginal birth after cesarean (VBAC) that incorporates variables available at admission for delivery among Middle Eastern women. The present prospective cohort study enrolled women at 37weeks of pregnancy or more with cephalic presentation who were willing to attempt a trial of labor (TOL) after a single prior low transverse cesarean delivery at Al-Jahra Hospital, Kuwait, between June 2013 and June 2014. The predicted success rate of VBAC determined via the close-to-delivery prediction model of Grobman et al. was compared between participants whose TOL was and was not successful. Among 203 enrolled women, 140 (69.0%) had successful VBAC. The predicted VBAC success rate was higher among women with successful TOL (82.4%±13.1%) than among those with failed TOL (67.7%±18.3%; P30%-40% to >90%-100%, the actual success rate was 20%, 30.7%, 38.5%, 59.1%, 71.4%, 76%, and 84.5%, respectively (r=0.98, P=0.013). The close-to-delivery prediction model was found to be applicable to Middle Eastern women and might predict VBAC success rates, thereby decreasing morbidities associated with failed TOL. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

  7. Robust human body model injury prediction in simulated side impact crashes.

    Science.gov (United States)

    Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

    2016-01-01

    This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

  8. Computationally efficient model predictive control algorithms a neural network approach

    CERN Document Server

    Ławryńczuk, Maciej

    2014-01-01

    This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

  9. Prediction models in in vitro fertilization; where are we? A mini review

    Directory of Open Access Journals (Sweden)

    Laura van Loendersloot

    2014-05-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in 1978, over five million babies have been born worldwide using IVF. Contrary to the perception of many, IVF does not guarantee success. Almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. The decision to start or pursue with IVF is challenging due to the high cost, the burden of the treatment, and the uncertain outcome. In optimal counseling on chances of a pregnancy with IVF, prediction models may play a role, since doctors are not able to correctly predict pregnancy chances. There are three phases of prediction model development: model derivation, model validation, and impact analysis. This review provides an overview on predictive factors in IVF, the available prediction models in IVF and provides key principles that can be used to critically appraise the literature on prediction models in IVF. We will address these points by the three phases of model development.

  10. QSAR models for prediction of chromatographic behavior of homologous Fab variants.

    Science.gov (United States)

    Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

    2017-06-01

    While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R 2  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  11. Predicting occupancy for pygmy rabbits in Wyoming: an independent evaluation of two species distribution models

    Science.gov (United States)

    Germaine, Stephen S.; Ignizio, Drew; Keinath, Doug; Copeland, Holly

    2014-01-01

    Species distribution models are an important component of natural-resource conservation planning efforts. Independent, external evaluation of their accuracy is important before they are used in management contexts. We evaluated the classification accuracy of two species distribution models designed to predict the distribution of pygmy rabbit Brachylagus idahoensis habitat in southwestern Wyoming, USA. The Nature Conservancy model was deductive and based on published information and expert opinion, whereas the Wyoming Natural Diversity Database model was statistically derived using historical observation data. We randomly selected 187 evaluation survey points throughout southwestern Wyoming in areas predicted to be habitat and areas predicted to be nonhabitat for each model. The Nature Conservancy model correctly classified 39 of 77 (50.6%) unoccupied evaluation plots and 65 of 88 (73.9%) occupied plots for an overall classification success of 63.3%. The Wyoming Natural Diversity Database model correctly classified 53 of 95 (55.8%) unoccupied plots and 59 of 88 (67.0%) occupied plots for an overall classification success of 61.2%. Based on 95% asymptotic confidence intervals, classification success of the two models did not differ. The models jointly classified 10.8% of the area as habitat and 47.4% of the area as nonhabitat, but were discordant in classifying the remaining 41.9% of the area. To evaluate how anthropogenic development affected model predictive success, we surveyed 120 additional plots among three density levels of gas-field road networks. Classification success declined sharply for both models as road-density level increased beyond 5 km of roads per km-squared area. Both models were more effective at predicting habitat than nonhabitat in relatively undeveloped areas, and neither was effective at accounting for the effects of gas-energy-development road networks. Resource managers who wish to know the amount of pygmy rabbit habitat present in an

  12. A structural equation model to integrate changes in functional strategies during old-field succession.

    Science.gov (United States)

    Vile, Denis; Shipley, Bill; Garnier, Eric

    2006-02-01

    From a functional perspective, changes in abundance, and ultimately species replacement, during succession are a consequence of integrated suites of traits conferring different relative ecological advantages as the environment changes over time. Here we use structural equations to model the interspecific relationships between these integrated functional traits using 34 herbaceous species from a Mediterranean old-field succession and thus quantify the notion of a plant strategy. We measured plant traits related to plant vegetative and reproductive size, leaf functioning, reproductive phenology, seed mass, and production on 15 individuals per species monitored during one growing season. The resulting structural equation model successfully accounts for the pattern of trait covariation during the first 45 years post-abandonment using just two forcing variables: time since site abandonment and seed mass; no association between time since field abandonment and seed mass was observed over these herbaceous stages of secondary succession. All other predicted traits values are determined by these two variables and the cause-effect linkage between them. Adding pre-reproductive vegetative mass as a third forcing variable noticeably increased the predictive power of the model. Increasing the time after abandonment favors species with increasing life span and pre-reproductive biomass and decreasing specific leaf area. Allometric coefficients relating vegetative and reproductive components of plant size were in accordance with allometry theory. The model confirmed the trade-off between seed mass and seed number. Maximum plant height and seed mass were major determinants of reproductive phenology. Our results show that beyond verbal conceptualization, plant ecological strategies can be quantified and modeled.

  13. Using an admissions exam to predict student success in an ADN program.

    Science.gov (United States)

    Gallagher, P A; Bomba, C; Crane, L R

    2001-01-01

    Nursing faculty strive to admit students who are likely to successfully complete the nursing curriculum and pass NCLEX-RN. The high cost of academic preparation and the nursing shortage make this selection process even more critical. The authors discuss how one community college nursing program examined academic achievement measures to determine how well they predicted student success. Results provided faculty with useful data to improve the success and retention of nursing.

  14. Predicting landscape vegetation dynamics using state-and-transition simulation models

    Science.gov (United States)

    Colin J. Daniel; Leonardo. Frid

    2012-01-01

    This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...

  15. Multivariate statistical models for disruption prediction at ASDEX Upgrade

    International Nuclear Information System (INIS)

    Aledda, R.; Cannas, B.; Fanni, A.; Sias, G.; Pautasso, G.

    2013-01-01

    In this paper, a disruption prediction system for ASDEX Upgrade has been proposed that does not require disruption terminated experiments to be implemented. The system consists of a data-based model, which is built using only few input signals coming from successfully terminated pulses. A fault detection and isolation approach has been used, where the prediction is based on the analysis of the residuals of an auto regressive exogenous input model. The prediction performance of the proposed system is encouraging when it is applied to the same set of campaigns used to implement the model. However, the false alarms significantly increase when we tested the system on discharges coming from experimental campaigns temporally far from those used to train the model. This is due to the well know aging effect inherent in the data-based models. The main advantage of the proposed method, with respect to other data-based approaches in literature, is that it does not need data on experiments terminated with a disruption, as it uses a normal operating conditions model. This is a big advantage in the prospective of a prediction system for ITER, where a limited number of disruptions can be allowed

  16. Successful N2 leptogenesis with flavour coupling effects in realistic unified models

    International Nuclear Information System (INIS)

    Bari, Pasquale Di; King, Stephen F.

    2015-01-01

    In realistic unified models involving so-called SO(10)-inspired patterns of Dirac and heavy right-handed (RH) neutrino masses, the lightest right-handed neutrino N 1 is too light to yield successful thermal leptogenesis, barring highly fine tuned solutions, while the second heaviest right-handed neutrino N 2 is typically in the correct mass range. We show that flavour coupling effects in the Boltzmann equations may be crucial to the success of such N 2 dominated leptogenesis, by helping to ensure that the flavour asymmetries produced at the N 2 scale survive N 1 washout. To illustrate these effects we focus on N 2 dominated leptogenesis in an existing model, the A to Z of flavour with Pati-Salam, where the neutrino Dirac mass matrix may be equal to an up-type quark mass matrix and has a particular constrained structure. The numerical results, supported by analytical insight, show that in order to achieve successful N 2 leptogenesis, consistent with neutrino phenomenology, requires a ''flavour swap scenario'' together with a less hierarchical pattern of RH neutrino masses than naively expected, at the expense of some mild fine-tuning. In the considered A to Z model neutrino masses are predicted to be normal ordered, with an atmospheric neutrino mixing angle well into the second octant and the Dirac phase δ≅ 20 o , a set of predictions that will be tested in the next years in neutrino oscillation experiments. Flavour coupling effects may be relevant for other SO(10)-inspired unified models where N 2 leptogenesis is necessary

  17. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  18. Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection.

    Science.gov (United States)

    Wu, Yu-Chung; Wei, Nien-Chih; Hung, Jung-Jyh; Yeh, Yi-Chen; Su, Li-Jen; Hsu, Wen-Hu; Chou, Teh-Ying

    2017-10-03

    Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I-IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB ( p model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

  19. Prediction of successful trial of labour in patients with a previous caesarean section

    International Nuclear Information System (INIS)

    Shaheen, N.; Khalil, S.; Iftikhar, P.

    2014-01-01

    Objective: To determine the prediction rate of success in trial of labour after one previous caesarean section. Methods: The cross-sectional study was conducted at the Department of Obstetrics and Gynaecology, Cantonment General Hospital, Rawalpindi, from January 1, 2012 to January 31, 2013, and comprised women with one previous Caesarean section and with single alive foetus at 37-41 weeks of gestation. Women with more than one Caesarean section, unknown site of uterine scar, bony pelvic deformity, placenta previa, intra-uterine growth restriction, deep transverse arrest in previous labour and non-reassuring foetal status at the time of admission were excluded. Intrapartum risk assessment included Bishop score at admission, rate of cervical dilatation and scar tenderness. SPSS 21 was used for statistical analysis. Results: Out of a total of 95 women, the trial was successful in 68 (71.6%). Estimated foetal weight and number of prior vaginal deliveries had a high predictive value for successful trial of labour after Caesarean section. Estimated foetal weight had an odds ratio of 0.46 (p<0.001), while number of prior vaginal deliveries had an odds ratio of 0.85 with (p=0.010). Other factors found to be predictive of successful trial included Bishop score at the time of admission (p<0.037) and rate of cervical dilatation in the first stage of labour (p<0.021). Conclusion: History of prior vaginal deliveries, higher Bishop score at the time of admission, rapid rate of cervical dilatation and lower estimated foetal weight were predictive of a successful trial of labour after Caesarean section. (author)

  20. Humor Ability Reveals Intelligence, Predicts Mating Success, and Is Higher in Males

    Science.gov (United States)

    Greengross, Gil; Miller, Geoffrey

    2011-01-01

    A good sense of humor is sexually attractive, perhaps because it reveals intelligence, creativity, and other "good genes" or "good parent" traits. If so, intelligence should predict humor production ability, which in turn should predict mating success. In this study, 400 university students (200 men and 200 women) completed…

  1. Predictive modelling for startup and investor relationship based on crowdfunding platform data

    Science.gov (United States)

    Alamsyah, Andry; Buono Asto Nugroho, Tri

    2018-03-01

    Crowdfunding platform is a place where startup shows off publicly their idea for the purpose to get their project funded. Crowdfunding platform such as Kickstarter are becoming popular today, it provides the efficient way for startup to get funded without liabilities, it also provides variety project category that can be participated. There is an available safety procedure to ensure achievable low-risk environment. The startup promoted project must accomplish their funded goal target. If they fail to reach the target, then there is no investment activity take place. It motivates startup to be more active to promote or disseminate their project idea and it also protect investor from losing money. The study objective is to predict the successfulness of proposed project and mapping investor trend using data mining framework. To achieve the objective, we proposed 3 models. First model is to predict whether a project is going to be successful or failed using K-Nearest Neighbour (KNN). Second model is to predict the number of successful project using Artificial Neural Network (ANN). Third model is to map the trend of investor in investing the project using K-Means clustering algorithm. KNN gives 99.04% model accuracy, while ANN best configuration gives 16-14-1 neuron layers and 0.2 learning rate, and K-Means gives 6 best separation clusters. The results of those models can help startup or investor to make decision regarding startup investment.

  2. Output from Statistical Predictive Models as Input to eLearning Dashboards

    Directory of Open Access Journals (Sweden)

    Marlene A. Smith

    2015-06-01

    Full Text Available We describe how statistical predictive models might play an expanded role in educational analytics by giving students automated, real-time information about what their current performance means for eventual success in eLearning environments. We discuss how an online messaging system might tailor information to individual students using predictive analytics. The proposed system would be data-driven and quantitative; e.g., a message might furnish the probability that a student will successfully complete the certificate requirements of a massive open online course. Repeated messages would prod underperforming students and alert instructors to those in need of intervention. Administrators responsible for accreditation or outcomes assessment would have ready documentation of learning outcomes and actions taken to address unsatisfactory student performance. The article’s brief introduction to statistical predictive models sets the stage for a description of the messaging system. Resources and methods needed to develop and implement the system are discussed.

  3. Predictive Modelling and Time: An Experiment in Temporal Archaeological Predictive Models

    OpenAIRE

    David Ebert

    2006-01-01

    One of the most common criticisms of archaeological predictive modelling is that it fails to account for temporal or functional differences in sites. However, a practical solution to temporal or functional predictive modelling has proven to be elusive. This article discusses temporal predictive modelling, focusing on the difficulties of employing temporal variables, then introduces and tests a simple methodology for the implementation of temporal modelling. The temporal models thus created ar...

  4. Threat-related selective attention predicts treatment success in childhood anxiety disorders.

    Science.gov (United States)

    Legerstee, Jeroen S; Tulen, Joke H M; Kallen, Victor L; Dieleman, Gwen C; Treffers, Philip D A; Verhulst, Frank C; Utens, Elisabeth M W J

    2009-02-01

    The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. Participants consisted of 131 children with anxiety disorders (aged 8-16 years), who received standardized cognitive-behavioral therapy. At pretreatment, a pictorial dot-probe task was administered to assess selective attention. Both at pretreatment and posttreatment, diagnostic status of the children was evaluated with a semistructured clinical interview (the Anxiety Disorders Interview Schedule for Children). Selective attention for severely threatening pictures at pretreatment assessment was predictive of treatment success. Examination of the specific components of selective attention revealed that nonresponders showed difficulties to disengage their attention away from severe threat. Treatment responders showed a tendency not to engage their attention toward severe threat. Age was not associated with selective attention and treatment success. Threat-related selective attention is a significant predictor of treatment success in children with anxiety disorders. Clinically anxious children with difficulties disengaging their attention away from severe threat profit less from cognitive-behavioral therapy. For these children, additional training focused on learning to disengage attention away from anxiety-arousing stimuli may be beneficial.

  5. Predictive modeling of reactive wetting and metal joining.

    Energy Technology Data Exchange (ETDEWEB)

    van Swol, Frank B.

    2013-09-01

    The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.

  6. Body Condition Indices Predict Reproductive Success but Not Survival in a Sedentary, Tropical Bird

    OpenAIRE

    Milenkaya, Olga; Catlin, Daniel H.; Legge, Sarah; Walters, Jeffrey R.

    2015-01-01

    Body condition may predict individual fitness because those in better condition have more resources to allocate towards improving their fitness. However, the hypothesis that condition indices are meaningful proxies for fitness has been questioned. Here, we ask if intraspecific variation in condition indices predicts annual reproductive success and survival. We monitored a population of Neochmia phaeton (crimson finch), a sedentary, tropical passerine, for reproductive success and survival ove...

  7. Ecological prediction with nonlinear multivariate time-frequency functional data models

    Science.gov (United States)

    Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

    2013-01-01

    Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

  8. Plasmonic Light Trapping in Thin-Film Solar Cells: Impact of Modeling on Performance Prediction

    Directory of Open Access Journals (Sweden)

    Alberto Micco

    2015-06-01

    Full Text Available We present a comparative study on numerical models used to predict the absorption enhancement in thin-film solar cells due to the presence of structured back-reflectors exciting, at specific wavelengths, hybrid plasmonic-photonic resonances. To evaluate the effectiveness of the analyzed models, they have been applied in a case study: starting from a U-shaped textured glass thin-film, µc-Si:H solar cells have been successfully fabricated. The fabricated cells, with different intrinsic layer thicknesses, have been morphologically, optically and electrically characterized. The experimental results have been successively compared with the numerical predictions. We have found that, in contrast to basic models based on the underlying schematics of the cell, numerical models taking into account the real morphology of the fabricated device, are able to effectively predict the cells performances in terms of both optical absorption and short-circuit current values.

  9. Predicting Student Success in College: What Does the Research Say?

    Science.gov (United States)

    Merante, Joseph A.

    1983-01-01

    Reviews various methods for predicting college success: correlation of students' high school grades, achievement test scores, and class rank with characteristics of the institution to be attended; examination of demographic variables such as age, sex, birth order, income, parents' education, religious and ethnic background, and geographic factors;…

  10. Using plural modeling for predicting decisions made by adaptive adversaries

    International Nuclear Information System (INIS)

    Buede, Dennis M.; Mahoney, Suzanne; Ezell, Barry; Lathrop, John

    2012-01-01

    Incorporating an appropriate representation of the likelihood of terrorist decision outcomes into risk assessments associated with weapons of mass destruction attacks has been a significant problem for countries around the world. Developing these likelihoods gets at the heart of the most difficult predictive problems: human decision making, adaptive adversaries, and adversaries about which very little is known. A plural modeling approach is proposed that incorporates estimates of all critical uncertainties: who is the adversary and what skills and resources are available to him, what information is known to the adversary and what perceptions of the important facts are held by this group or individual, what does the adversary know about the countermeasure actions taken by the government in question, what are the adversary's objectives and the priorities of those objectives, what would trigger the adversary to start an attack and what kind of success does the adversary desire, how realistic is the adversary in estimating the success of an attack, how does the adversary make a decision and what type of model best predicts this decision-making process. A computational framework is defined to aggregate the predictions from a suite of models, based on this broad array of uncertainties. A validation approach is described that deals with a significant scarcity of data.

  11. Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

    Science.gov (United States)

    Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

    2014-01-01

    Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set

  12. How Predictive Analytics and Choice Architecture Can Improve Student Success

    Science.gov (United States)

    Denley, Tristan

    2014-01-01

    This article explores the challenges that students face in navigating the curricular structure of post-secondary degree programs, and how predictive analytics and choice architecture can play a role. It examines Degree Compass, a course recommendation system that successfully pairs current students with the courses that best fit their talents and…

  13. Threat-Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

    NARCIS (Netherlands)

    J.S. Legerstee (Jeroen); J.H.M. Tulen (Joke); V.L. Kallen (Victor); G.C. Dieleman (Gwen); P.D.A. Treffers (Philip); F.C. Verhulst (Frank); E.M.W.J. Utens (Elisabeth)

    2009-01-01

    textabstractAbstract OBJECTIVE: The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders

  14. Threat-related selective attention predicts treatment success in childhood anxiety disorders

    NARCIS (Netherlands)

    Legerstee, Jeroen S.; Tulen, Joke H. M.; Kallen, Victor L.; Dieleman, Gwen C.; Treffers, Philip D. A.; Verhulst, Frank C.; Utens, Elisabeth M. W. J.

    2009-01-01

    The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. Participants

  15. Individual differences in episodic memory abilities predict successful prospective memory output monitoring.

    Science.gov (United States)

    Hunter Ball, B; Pitães, Margarida; Brewer, Gene A

    2018-02-07

    Output monitoring refers to memory for one's previously completed actions. In the context of prospective memory (PM) (e.g., remembering to take medication), failures of output monitoring can result in repetitions and omissions of planned actions (e.g., over- or under-medication). To be successful in output monitoring paradigms, participants must flexibly control attention to detect PM cues as well as engage controlled retrieval of previous actions whenever a particular cue is encountered. The current study examined individual differences in output monitoring abilities in a group of younger adults differing in attention control (AC) and episodic memory (EM) abilities. The results showed that AC ability uniquely predicted successful cue detection on the first presentation, whereas EM ability uniquely predicted successful output monitoring on the second presentation. The current study highlights the importance of examining external correlates of PM abilities and contributes to the growing body of research on individual differences in PM.

  16. Predicting Student Success from the "LASSI for Learning Online" (LLO)

    Science.gov (United States)

    Carson, Andrew D.

    2011-01-01

    This study tested the degree to which subscales of the "LASSI for Learning Online" (LLO) (Weinstein & Palmer, 2006), a measure of learning strategies and study skills, predict student success in the form of passing grades, using a combination of large training (N = 4,409) and cross-validation (N = 3,203) samples. Discriminant function analysis…

  17. Predicting birth weight with conditionally linear transformation models.

    Science.gov (United States)

    Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

    2016-12-01

    Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

  18. Prediction of shot success for basketball free throws: visual search strategy.

    Science.gov (United States)

    Uchida, Yusuke; Mizuguchi, Nobuaki; Honda, Masaaki; Kanosue, Kazuyuki

    2014-01-01

    In ball games, players have to pay close attention to visual information in order to predict the movements of both the opponents and the ball. Previous studies have indicated that players primarily utilise cues concerning the ball and opponents' body motion. The information acquired must be effective for observing players to select the subsequent action. The present study evaluated the effects of changes in the video replay speed on the spatial visual search strategy and ability to predict free throw success. We compared eye movements made while observing a basketball free throw by novices and experienced basketball players. Correct response rates were close to chance (50%) at all video speeds for the novices. The correct response rate of experienced players was significantly above chance (and significantly above that of the novices) at the normal speed, but was not different from chance at both slow and fast speeds. Experienced players gazed more on the lower part of the player's body when viewing a normal speed video than the novices. The players likely detected critical visual information to predict shot success by properly moving their gaze according to the shooter's movements. This pattern did not change when the video speed was decreased, but changed when it was increased. These findings suggest that temporal information is important for predicting action outcomes and that such outcomes are sensitive to video speed.

  19. Predicting athletic success motivation using mental skin and emotional intelligence and its components in male athletes.

    Science.gov (United States)

    Kajbafnezhad, H; Ahadi, H; Heidarie, A; Askari, P; Enayati, M

    2012-10-01

    The aim of this study was to predict athletic success motivation by mental skills, emotional intelligence and its components. The research sample consisted of 153 male athletes who were selected through random multistage sampling. The subjects completed the Mental Skills Questionnaire, Bar-On Emotional Intelligence questionnaire and the perception of sport success questionnaire. Data were analyzed using Pearson correlation coefficient and multiple regressions. Regression analysis shows that between the two variables of mental skill and emotional intelligence, mental skill is the best predictor for athletic success motivation and has a better ability to predict the success rate of the participants. Regression analysis results showed that among all the components of emotional intelligence, self-respect had a significantly higher ability to predict athletic success motivation. The use of psychological skills and emotional intelligence as an mediating and regulating factor and organizer cause leads to improved performance and can not only can to help athletes in making suitable and effective decisions for reaching a desired goal.

  20. Body Condition Indices Predict Reproductive Success but Not Survival in a Sedentary, Tropical Bird.

    Directory of Open Access Journals (Sweden)

    Olga Milenkaya

    Full Text Available Body condition may predict individual fitness because those in better condition have more resources to allocate towards improving their fitness. However, the hypothesis that condition indices are meaningful proxies for fitness has been questioned. Here, we ask if intraspecific variation in condition indices predicts annual reproductive success and survival. We monitored a population of Neochmia phaeton (crimson finch, a sedentary, tropical passerine, for reproductive success and survival over four breeding seasons, and sampled them for commonly used condition indices: mass adjusted for body size, muscle and fat scores, packed cell volume, hemoglobin concentration, total plasma protein, and heterophil to lymphocyte ratio. Our study population is well suited for this research because individuals forage in common areas and do not hold territories such that variation in condition between individuals is not confounded by differences in habitat quality. Furthermore, we controlled for factors that are known to impact condition indices in our study population (e.g., breeding stage such that we assessed individual condition relative to others in the same context. Condition indices that reflect energy reserves predicted both the probability of an individual fledging young and the number of young produced that survived to independence, but only during some years. Those that were relatively heavy for their body size produced about three times more independent young compared to light individuals. That energy reserves are a meaningful predictor of reproductive success in a sedentary passerine supports the idea that energy reserves are at least sometimes predictors of fitness. However, hematological indices failed to predict reproductive success and none of the indices predicted survival. Therefore, some but not all condition indices may be informative, but because we found that most indices did not predict any component of fitness, we question the ubiquitous

  1. Body Condition Indices Predict Reproductive Success but Not Survival in a Sedentary, Tropical Bird.

    Science.gov (United States)

    Milenkaya, Olga; Catlin, Daniel H; Legge, Sarah; Walters, Jeffrey R

    2015-01-01

    Body condition may predict individual fitness because those in better condition have more resources to allocate towards improving their fitness. However, the hypothesis that condition indices are meaningful proxies for fitness has been questioned. Here, we ask if intraspecific variation in condition indices predicts annual reproductive success and survival. We monitored a population of Neochmia phaeton (crimson finch), a sedentary, tropical passerine, for reproductive success and survival over four breeding seasons, and sampled them for commonly used condition indices: mass adjusted for body size, muscle and fat scores, packed cell volume, hemoglobin concentration, total plasma protein, and heterophil to lymphocyte ratio. Our study population is well suited for this research because individuals forage in common areas and do not hold territories such that variation in condition between individuals is not confounded by differences in habitat quality. Furthermore, we controlled for factors that are known to impact condition indices in our study population (e.g., breeding stage) such that we assessed individual condition relative to others in the same context. Condition indices that reflect energy reserves predicted both the probability of an individual fledging young and the number of young produced that survived to independence, but only during some years. Those that were relatively heavy for their body size produced about three times more independent young compared to light individuals. That energy reserves are a meaningful predictor of reproductive success in a sedentary passerine supports the idea that energy reserves are at least sometimes predictors of fitness. However, hematological indices failed to predict reproductive success and none of the indices predicted survival. Therefore, some but not all condition indices may be informative, but because we found that most indices did not predict any component of fitness, we question the ubiquitous interpretation of

  2. Prediction Model of Interval Grey Numbers with a Real Parameter and Its Application

    Directory of Open Access Journals (Sweden)

    Bo Zeng

    2014-01-01

    Full Text Available Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.

  3. Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

  4. Predicting Academic Success from Academic Motivation and Learning Approaches in Classroom Teaching Students

    Science.gov (United States)

    Çetin, Baris

    2015-01-01

    Our aim was to determine whether learning approaches and academic motivation together predict academic success of classroom teaching students. The sample of the study included 536 students (386 female, 150 male) studying at the Classroom Teaching Division of Canakkale 18 Mart University. Our research was designed as a prediction study. Data was…

  5. Predictive Score Card in Lumbar Disc Herniation: Is It Reflective of Patient Surgical Success after Discectomy?

    Directory of Open Access Journals (Sweden)

    Parisa Azimi

    Full Text Available Does the Finneson-Cooper score reflect the true value of predicting surgical success before discectomy? The aim of this study was to identify reliable predictors for surgical success two year after surgery for patients with LDH. Prospective analysis of 154 patients with LDH who underwent single-level lumbar discectomy was performed. Pre- and post-surgical success was assessed by the Oswestry Disability Index (ODI over a 2-year period. The Finneson-Cooper score also was used for evaluation of the clinical results. Using the ODI, surgical success was defined as a 30% (or more improvement on the ODI score from the baseline. The ODI was considered the gold standard in this study. Finally, the sensitivity, specificity, and positive and negative predictive power of the Finneson-Cooper score in predicting surgical success were calculated. The mean age of the patients was 49.6 (SD = 9.3 years and 47.4% were male. Significant improvement from the pre- to post-operative ODI scores was observed (P < 0.001. Post-surgical success was 76.0% (n = 117. The patients' rating on surgical success assessments by the ODI discriminated well between sub-groups of patients who differed with respect to the Finneson-Cooper score. Regarding patients' surgical success, the sensitivity, specificity, and accuracy of the Finneson-Cooper ratings correlated with success rate. The findings indicated that the Finneson-Cooper score was reflective of surgical success before discectomy.

  6. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  7. Predicting Educational Success and Attrition in Problem-Based Learning: Do First Impressions Count?

    Science.gov (United States)

    Wijnia, Lisette; Loyens, Sofie M. M.; Derous, Eva; Koendjie, Nitaasha S.; Schmidt, Henk G.

    2014-01-01

    This study examines whether tutors (N?=?15) in a problem-based learning curriculum were able to predict students' success in their first year and their entire bachelor programme. Tutors were asked to rate each student in their tutorial group in terms of the chance that this student would successfully finish their first year and the entire…

  8. Multi-state succession in wetlands: a novel use of state and transition models

    Science.gov (United States)

    Zweig, Christa L.; Kitchens, Wiley M.

    2009-01-01

    The complexity of ecosystems and mechanisms of succession are often simplified by linear and mathematical models used to understand and predict system behavior. Such models often do not incorporate multivariate, nonlinear feedbacks in pattern and process that include multiple scales of organization inherent within real-world systems. Wetlands are ecosystems with unique, nonlinear patterns of succession due to the regular, but often inconstant, presence of water on the landscape. We develop a general, nonspatial state and transition (S and T) succession conceptual model for wetlands and apply the general framework by creating annotated succession/management models and hypotheses for use in impact analysis on a portion of an imperiled wetland. The S and T models for our study area, Water Conservation Area 3A South (WCA3), Florida, USA, included hydrologic and peat depth values from multivariate analyses and classification and regression trees. We used the freeware Vegetation Dynamics Development Tool as an exploratory application to evaluate our S and T models with different management actions (equal chance [a control condition], deeper conditions, dry conditions, and increased hydrologic range) for three communities: slough, sawgrass (Cladium jamaicense), and wet prairie. Deeper conditions and increased hydrologic range behaved similarly, with the transition of community states to deeper states, particularly for sawgrass and slough. Hydrology is the primary mechanism for multi-state transitions within our study period, and we show both an immediate and lagged effect on vegetation, depending on community state. We consider these S and T succession models as a fraction of the framework for the Everglades. They are hypotheses for use in adaptive management, represent the community response to hydrology, and illustrate which aspects of hydrologic variability are important to community structure. We intend for these models to act as a foundation for further restoration

  9. Computerized mathematical model for prediction of resin/fiber composite properties

    International Nuclear Information System (INIS)

    Lowe, K.A.

    1985-01-01

    A mathematical model has been developed for the design and optimization of resin formulations. The behavior of a fiber-reinforced cured resin matrix can be predicted from constituent properties of the formulation and fiber when component interaction is taken into account. A computer implementation of the mathematical model has been coded to simulate resin/fiber response and generate expected values for any definable properties of the composite. The algorithm is based on multistage regression techniques and the manipulation of n-order matrices. Excellent correlation between actual test values and predicted values has been observed for physical, mechanical, and qualitative properties of resin/fiber composites. Both experimental and commercial resin systems with various fiber reinforcements have been successfully characterized by the model. 6 references, 3 figures, 2 tables

  10. Computerized heat balance models to predict performance of operating nuclear power plants

    International Nuclear Information System (INIS)

    Breeding, C.L.; Carter, J.C.; Schaefer, R.C.

    1983-01-01

    The use of computerized heat balance models has greatly enhanced the decision making ability of TVA's Division of Nuclear Power. These models are utilized to predict the effects of various operating modes and to analyze changes in plant performance resulting from turbine cycle equipment modifications with greater speed and accuracy than was possible before. Computer models have been successfully used to optimize plant output by predicting the effects of abnormal condenser circulating water conditions. They were utilized to predict the degradation in performance resulting from installation of a baffle plate assembly to replace damaged low-pressure blading, thereby providing timely information allowing an optimal economic judgement as to when to replace the blading. Future use will be for routine performance test analysis. This paper presents the benefits of utility use of computerized heat balance models

  11. Perceived Academic Control and Academic Emotions Predict Undergraduate University Student Success: Examining Effects on Dropout Intention and Achievement.

    Science.gov (United States)

    Respondek, Lisa; Seufert, Tina; Stupnisky, Robert; Nett, Ulrike E

    2017-01-01

    The present study addressed concerns over the high risk of university students' academic failure. It examined how perceived academic control and academic emotions predict undergraduate students' academic success, conceptualized as both low dropout intention and high achievement (indicated by GPA). A cross-sectional survey was administered to 883 undergraduate students across all disciplines of a German STEM orientated university. The study additionally compared freshman students ( N = 597) vs. second-year students ( N = 286). Using structural equation modeling, for the overall sample of undergraduate students we found that perceived academic control positively predicted enjoyment and achievement, as well as negatively predicted boredom and anxiety. The prediction of dropout intention by perceived academic control was fully mediated via anxiety. When taking perceived academic control into account, we found no specific impact of enjoyment or boredom on the intention to dropout and no specific impact of all three academic emotions on achievement. The multi-group analysis showed, however, that perceived academic control, enjoyment, and boredom among second-year students had a direct relationship with dropout intention. A major contribution of the present study was demonstrating the important roles of perceived academic control and anxiety in undergraduate students' academic success. Concerning corresponding institutional support and future research, the results suggested distinguishing incoming from advanced undergraduate students.

  12. PREDICTABILITY OF FINANCIAL CRISES: TESTING K.R.L. MODEL IN THE CASE OF TURKEY

    Directory of Open Access Journals (Sweden)

    Zeynep KARACOR

    2012-06-01

    Full Text Available The aim of this study is to test predictability of 2007 Global Economic Crisis which hit Turkey by the help of macroeconomic data of Turkey. K.R.L. model is used to test the predictability. By the method of analyzing various leading early warning indicators, the success of the model in forecasting the crises is surveyed. The findings do not support K.R.L. models. Possible reasons for this are stated at the article.

  13. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  14. Formability prediction for AHSS materials using damage models

    Science.gov (United States)

    Amaral, R.; Santos, Abel D.; José, César de Sá; Miranda, Sara

    2017-05-01

    Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches.

  15. Formability prediction for AHSS materials using damage models

    International Nuclear Information System (INIS)

    Amaral, R.; Miranda, Sara; Santos, Abel D.; José, César de Sá

    2017-01-01

    Advanced high strength steels (AHSS) are seeing an increased use, mostly due to lightweight design in automobile industry and strict regulations on safety and greenhouse gases emissions. However, the use of these materials, characterized by a high strength to weight ratio, stiffness and high work hardening at early stages of plastic deformation, have imposed many challenges in sheet metal industry, mainly their low formability and different behaviour, when compared to traditional steels, which may represent a defying task, both to obtain a successful component and also when using numerical simulation to predict material behaviour and its fracture limits. Although numerical prediction of critical strains in sheet metal forming processes is still very often based on the classic forming limit diagrams, alternative approaches can use damage models, which are based on stress states to predict failure during the forming process and they can be classified as empirical, physics based and phenomenological models. In the present paper a comparative analysis of different ductile damage models is carried out, in order numerically evaluate two isotropic coupled damage models proposed by Johnson-Cook and Gurson-Tvergaard-Needleman (GTN), each of them corresponding to the first two previous group classification. Finite element analysis is used considering these damage mechanics approaches and the obtained results are compared with experimental Nakajima tests, thus being possible to evaluate and validate the ability to predict damage and formability limits for previous defined approaches. (paper)

  16. Fire spread in chaparral – a comparison of laboratory data and model predictions in burning live fuels

    Science.gov (United States)

    David R. Weise; Eunmo Koo; Xiangyang Zhou; Shankar Mahalingam; Frédéric Morandini; Jacques-Henri Balbi

    2016-01-01

    Fire behaviour data from 240 laboratory fires in high-density live chaparral fuel beds were compared with model predictions. Logistic regression was used to develop a model to predict fire spread success in the fuel beds and linear regression was used to predict rate of spread. Predictions from the Rothermel equation and three proposed changes as well as two physically...

  17. Predicting, deciding, learning: can one evaluate the 'success' of national climate scenarios?

    International Nuclear Information System (INIS)

    Hulme, Mike; Dessai, Suraje

    2008-01-01

    Scenarios may be understood as products and/or processes. Viewing scenario exercises as productive tends to emphasize their tangibility: scenario products may acquire value unrelated to the processes of their creation. Viewing scenario exercises as procedural tends to emphasize their modes of formation: the process of constructing scenarios may have benefits irrespective of the value of ensuing products. These two framings yield different expectations about how one might evaluate the 'success' or otherwise of scenario exercises. We illustrate three approaches to evaluating the success or otherwise of scenarios using the example of the series of national UK climate scenarios published between 1991 and 2002. These are: predictive success (has the future turned out as envisaged?), decision success (have 'good' decisions subsequently been made?) and learning success (have scenarios proved engaging and enabled learning?). We reflect on the different ways the 'success' of national climate scenarios might be evaluated and on the relationship between the productive and procedural dimensions of scenario exercises.

  18. A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

    Science.gov (United States)

    Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey

    2017-11-01

    As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.

  19. Recent Successes of Wave/Turbulence Driven Models of Solar Wind Acceleration

    Science.gov (United States)

    Cranmer, S. R.; Hollweg, J. V.; Chandran, B. D.; van Ballegooijen, A. A.

    2010-12-01

    A key obstacle in the way of producing realistic simulations of the Sun-heliosphere system is the lack of a first-principles understanding of coronal heating. Also, it is still unknown whether the solar wind is "fed" through flux tubes that remain open (and are energized by footpoint-driven wavelike fluctuations) or if mass and energy are input intermittently from closed loops into the open-field regions. In this presentation, we discuss self-consistent models that assume the energy comes from solar Alfven waves that are partially reflected, and then dissipated, by magnetohydrodynamic turbulence. These models have been found to reproduce many of the observed features of the fast and slow solar wind without the need for artificial "coronal heating functions" used by earlier models. For example, the models predict a variation with wind speed in commonly measured ratios of charge states and elemental abundances that agrees with observed trends. This contradicts a commonly held assertion that these ratios can only be produced by the injection of plasma from closed-field regions on the Sun. This presentation also reviews two recent comparisons between the models and empirical measurements: (1) The models successfully predict the amplitude and radial dependence of Faraday rotation fluctuations (FRFs) measured by the Helios probes for heliocentric distances between 2 and 15 solar radii. The FRFs are a particularly sensitive test of turbulence models because they depend not only on the plasma density and Alfven wave amplitude in the corona, but also on the turbulent correlation length. (2) The models predict the correct sense and magnitude of changes seen in the polar high-speed solar wind by Ulysses from the previous solar minimum (1996-1997) to the more recent peculiar minimum (2008-2009). By changing only the magnetic field along the polar magnetic flux tube, consistent with solar and heliospheric observations at the two epochs, the model correctly predicts that the

  20. Predictive models to determine imagery strategies employed by children to judge hand laterality.

    Science.gov (United States)

    Spruijt, Steffie; Jongsma, Marijtje L A; van der Kamp, John; Steenbergen, Bert

    2015-01-01

    A commonly used paradigm to study motor imagery is the hand laterality judgment task. The present study aimed to determine which strategies young children employ to successfully perform this task. Children of 5 to 8 years old (N = 92) judged laterality of back and palm view hand pictures in different rotation angles. Response accuracy and response duration were registered. Response durations of the trials with a correct judgment were fitted to a-priori defined predictive sinusoid models, representing different strategies to successfully perform the hand laterality judgment task. The first model predicted systematic changes in response duration as a function of rotation angle of the displayed hand. The second model predicted that response durations are affected by biomechanical constraints of hand rotation. If observed data could be best described by the first model, this would argue for a mental imagery strategy that does not involve motor processes to solve the task. The second model reflects a motor imagery strategy to solve the task. In line with previous research, we showed an age-related increase in response accuracy and decrease in response duration in children. Observed data for both back and palm view showed that motor imagery strategies were used to perform hand laterality judgments, but that not all the children use these strategies (appropriately) at all times. A direct comparison of response duration patterns across age sheds new light on age-related differences in the strategies employed to solve the task. Importantly, the employment of the motor imagery strategy for successful task performance did not change with age.

  1. Baseline Gray- and White Matter Volume Predict Successful Weight Loss in the Elderly

    Science.gov (United States)

    Mokhtari, Fatemeh; Paolini, Brielle M.; Burdette, Jonathan H.; Marsh, Anthony P.; Rejeski, W. Jack; Laurienti, Paul J.

    2016-01-01

    Objective The purpose of this study is to investigate if structural brain phenotypes can be used to predict weight loss success following behavioral interventions in older adults that are overweight or obese and have cardiometabolic dysfunction. Methods A support vector machine (SVM) with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter (GM) and white matter (WM) volume from 52 individuals that completed the intervention and a magnetic resonance imaging session. Results The SVM resulted in an average classification accuracy of 72.62 % based on GM and WM volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82. Conclusions Our findings suggest that baseline brain structure is able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss is an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss. PMID:27804273

  2. A Model of Successful School Leadership from the International Successful School Principalship Project

    Directory of Open Access Journals (Sweden)

    David Gurr

    2015-03-01

    Full Text Available The International Successful School Principalship Project (ISSPP has been actively conducting research about the work of successful principals since 2001. Findings from four project books and eight models derived from this project are synthesised into a model of successful school leadership. Building on Gurr, Drysdale and Mulford’s earlier model, the work of school leaders is described as engaging within the school context to influence student and school outcomes through interventions in teaching and learning, school capacity building, and the wider context. The qualities a leader brings to their role, a portfolio approach to using leadership ideas, constructing networks, collaborations and partnerships, and utilising accountability and evaluation for evidence-informed improvement, are important additional elements. The model is applicable to all in leadership roles in schools.

  3. Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

    Science.gov (United States)

    van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

    2018-06-01

    In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

  4. Do successful agers live longer? The Vitality 90+ study.

    Science.gov (United States)

    Nosraty, Lily; Enroth, Linda; Raitanen, Jani; Hervonen, Antti; Jylhä, Marja

    2015-02-01

    To discover whether successful aging, understood as a multidimensional concept, predicts further survival in very old people. The population sample consisted of 1,370 persons aged 90 or over in the Vitality 90+ study. Four alternative models of successful aging were constructed, each of them consisting of physical, psychological, and social dimensions. Mortality was followed up after 4 and 7 years. Three out of four models significantly predicted survival at both follow-ups for the whole group. Separately, "success" in the physical, psychological, and social components was also associated with higher survival. The associations were stronger in women than in men. Successful aging, measured using physical, psychological, and social dimensions, predicts the length of future life in nonagenarians. © The Author(s) 2014.

  5. Hounsfield unit density accurately predicts ESWL success.

    Science.gov (United States)

    Magnuson, William J; Tomera, Kevin M; Lance, Raymond S

    2005-01-01

    Extracorporeal shockwave lithotripsy (ESWL) is a commonly used non-invasive treatment for urolithiasis. Helical CT scans provide much better and detailed imaging of the patient with urolithiasis including the ability to measure density of urinary stones. In this study we tested the hypothesis that density of urinary calculi as measured by CT can predict successful ESWL treatment. 198 patients were treated at Alaska Urological Associates with ESWL between January 2002 and April 2004. Of these 101 met study inclusion with accessible CT scans and stones ranging from 5-15 mm. Follow-up imaging demonstrated stone freedom in 74.2%. The overall mean Houndsfield density value for stone-free compared to residual stone groups were significantly different ( 93.61 vs 122.80 p ESWL for upper tract calculi between 5-15mm.

  6. Encopresis in children. Outcome and predictive factors of successful management.

    Science.gov (United States)

    Mohammed, Adnan A; Mekael, Farag M

    2012-06-01

    To elucidate our experience and outcome in the management of childhood encopresis, and to emphasize the factors that may predict successful management. This prospective study was carried out between September 2003 and September 2011 in the Department of Pediatric Surgery, Al-Thoura Teaching Hospital, Al-Beida and Al-Butnan Medical Teaching Center, Tobruk, Libya. One hundred and thirty-two patients (117 male, 15 female) took part of the study. The male and female ratio was 7.8:1. The participants were patients aged 4-9 years. There were 30 (22.7%) patients between 4-5 years, 61 (46.2%) between 6-7 years, and 41 (31%) between 8-9 years. Nonretentive encopresis patients were 36 (27.2%) (Group I) and 96 (72.8%) patients had retentive encopresis (Group II). Patients with low fluid intake were 87 (65.9%) and low fiber diet were 91 (68.9%). Patients with delayed toilet training were 99 (75%). The total rate of successful conservative treatment was 70.5%. The rate of successful treatment in Group I was 94.4% and in Group II was 61.5%. We observed 18.2% of the patients had recurrence of encopresis. The factors found to predict good resolution rate after medical treatment included: cooperation of the parent and patient, female gender, ages above 5 years, and non-retentive encopresis. Encopresis remains a problem for the parents and the patients. Clinical evaluation is indispensable. Good outcome can be achieved effectively. Cooperative parents and patient, female gender, age above 5 years, and nonretentive encopresis are predictors for good response to medical treatment.

  7. Induction of labour: clinical predictive factors for success and failure.

    Science.gov (United States)

    Batinelli, Laura; Serafini, Andrea; Nante, Nicola; Petraglia, Felice; Severi, Filiberto Maria; Messina, Gabriele

    2018-04-01

    Induction of labour (IOL) is a widely-used practice in obstetrics. Our aim was to evaluate predictors of vaginal delivery in postdate pregnancies induced with prostaglandins. We conducted a retrospective cross-sectional study with analytic component. A total of 145 women, admitted for IOL after the 41st week of gestation, were induced with a vaginal pessary releasing prostaglandins. Type of delivery, whether vaginal or caesarean, was the outcome. Several maternal and foetal variables were investigated. The Kaplan-Maier curves, monovariate and a multivariate logistic regression were carried out. In our population, 80.7% of women had vaginal delivery after the induction. Multiparity and a high Bishop score at the beginning of the IOL were protective factors for a vaginal delivery (respectively OR 0.16, p = .028 and OR 0.62, p = .034) while age >35 years, and the foetal birth weight >3500 g at the birth, resulted in being risk factors for caesarean section (respectively OR 4.20, p = .006 and OR 3.63, p = .013). IMPACT STATEMENT What is already known on this subject: Induction of labour (IOL) is a widely used practice in obstetrics. Scientific literature shows several predictors of successful induction, although there is no unanimity except for 'multiparity' and 'favourable Bishop score' which are associated with positive outcome of the induction. The main difficulty in finding other predictive factors is the heterogeneity of this field (different local protocols in each hospital, type of induction, populations and outcomes chosen in each study). In addition to that, populations are not always comparable due to the different gestation. For this reason, we decided to select a specific population of women, such as low risk postterm pregnancies induced with prostaglandins, in order to detect possible predictive factors for the success of the IOL for women with uncomplicated pregnancies. What the results of this study add: Our study agrees with existing

  8. Threat Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

    Science.gov (United States)

    Legerstee, Jeroen S.; Tulen, Joke H. M.; Kallen, Victor L.; Dieleman, Gwen C.; Treffers, Philip D. A.; Verhulst, Frank C.; Utens, Elisabeth M. W. J.

    2009-01-01

    Threat-related selective attention was found to predict the success of the treatment of childhood anxiety disorders through administering a pictorial dot-probe task to 131 children with anxiety disorders prior to cognitive behavioral therapy. The diagnostic status of the subjects was evaluated with a semistructured clinical interview at both pre-…

  9. Value of College Education Mediating the Predictive Effects of Causal Attributions on Academic Success

    Science.gov (United States)

    Dong, Ying; Stupnisky, Robert H.; Obade, Masela; Gerszewski, Tammy; Ruthig, Joelle C.

    2015-01-01

    Causal attributions (explanations for outcomes) have been found to predict college students' academic success; however, not all students attributing success or failure to adaptive (i.e., controllable) causes perform well in university. Eccles et al.'s ("Achievement and achievement motives." W.H. Freeman, San Francisco, pp 75-145, 1983)…

  10. Successful External Cephalic Version: Factors Predicting Vaginal Birth

    Science.gov (United States)

    Lim, Pei Shan; Ng, Beng Kwang; Ali, Anizah; Shafiee, Mohamad Nasir; Kampan, Nirmala Chandralega; Mohamed Ismail, Nor Azlin; Omar, Mohd Hashim; Abdullah Mahdy, Zaleha

    2014-01-01

    Purpose. To determine the maternal and fetal outcomes of successful external cephalic version (ECV) as well as factors predicting vaginal birth. Methods. The ECV data over a period of three years at Universiti Kebangsaan Malaysia Medical Centre (UKMMC) between 1 September 2008 and 30 September 2010 was reviewed. Sixty-seven patients who had successful ECV were studied and reviewed for maternal, fetal, and labour outcomes. The control group comprised patients with cephalic singletons of matching parity who delivered following the index cases. Results. The mean gestational age at ECV was 263 ± 6.52 days (37.5 weeks ± 6.52 days). Spontaneous labour and transient cardiotocographic (CTG) changes were the commonest early adverse effects following ECV. The reversion rate was 7.46%. The mean gestational age at delivery of the two groups was significantly different (P = 0.000) with 277.9 ± 8.91 days and 269.9 ± 9.68 days in the study group and control groups, respectively. The study group needed significantly more inductions of labour. They required more operative deliveries, had more blood loss at delivery, a higher incidence of meconium-stained liquor, and more cord around the neck. Previous flexed breeches had a threefold increase in caesarean section rate compared to previous extended breeches (44.1% versus 15.2%, P = 0.010). On the contrary, an amniotic fluid index (AFI) of 13 or more is significantly associated with a higher rate of vaginal birth (86.8% versus 48.3%, P = 0.001). Conclusions. Patients with successful ECV were at higher risk of carrying the pregnancy beyond 40 weeks and needing induction of labour, with a higher rate of caesarean section and higher rates of obstetrics complications. Extended breech and AFI 13 or more were significantly more likely to deliver vaginally postsuccessful ECV. This additional information may be useful to caution a patient with breech that ECV does not bring them to behave exactly like a normal cephalic, so that they

  11. Successful External Cephalic Version: Factors Predicting Vaginal Birth

    Directory of Open Access Journals (Sweden)

    Pei Shan Lim

    2014-01-01

    Full Text Available Purpose. To determine the maternal and fetal outcomes of successful external cephalic version (ECV as well as factors predicting vaginal birth. Methods. The ECV data over a period of three years at Universiti Kebangsaan Malaysia Medical Centre (UKMMC between 1 September 2008 and 30 September 2010 was reviewed. Sixty-seven patients who had successful ECV were studied and reviewed for maternal, fetal, and labour outcomes. The control group comprised patients with cephalic singletons of matching parity who delivered following the index cases. Results. The mean gestational age at ECV was 263±6.52 days (37.5 weeks ± 6.52 days. Spontaneous labour and transient cardiotocographic (CTG changes were the commonest early adverse effects following ECV. The reversion rate was 7.46%. The mean gestational age at delivery of the two groups was significantly different (P=0.000 with 277.9±8.91 days and 269.9±9.68 days in the study group and control groups, respectively. The study group needed significantly more inductions of labour. They required more operative deliveries, had more blood loss at delivery, a higher incidence of meconium-stained liquor, and more cord around the neck. Previous flexed breeches had a threefold increase in caesarean section rate compared to previous extended breeches (44.1% versus 15.2%, P=0.010. On the contrary, an amniotic fluid index (AFI of 13 or more is significantly associated with a higher rate of vaginal birth (86.8% versus 48.3%, P=0.001. Conclusions. Patients with successful ECV were at higher risk of carrying the pregnancy beyond 40 weeks and needing induction of labour, with a higher rate of caesarean section and higher rates of obstetrics complications. Extended breech and AFI 13 or more were significantly more likely to deliver vaginally postsuccessful ECV. This additional information may be useful to caution a patient with breech that ECV does not bring them to behave exactly like a normal cephalic, so that they

  12. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  13. Predicting typhoon-induced storm surge tide with a two-dimensional hydrodynamic model and artificial neural network model

    Science.gov (United States)

    Chen, W.-B.; Liu, W.-C.; Hsu, M.-H.

    2012-12-01

    Precise predictions of storm surges during typhoon events have the necessity for disaster prevention in coastal seas. This paper explores an artificial neural network (ANN) model, including the back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) algorithms used to correct poor calculations with a two-dimensional hydrodynamic model in predicting storm surge height during typhoon events. The two-dimensional model has a fine horizontal resolution and considers the interaction between storm surges and astronomical tides, which can be applied for describing the complicated physical properties of storm surges along the east coast of Taiwan. The model is driven by the tidal elevation at the open boundaries using a global ocean tidal model and is forced by the meteorological conditions using a cyclone model. The simulated results of the hydrodynamic model indicate that this model fails to predict storm surge height during the model calibration and verification phases as typhoons approached the east coast of Taiwan. The BPNN model can reproduce the astronomical tide level but fails to modify the prediction of the storm surge tide level. The ANFIS model satisfactorily predicts both the astronomical tide level and the storm surge height during the training and verification phases and exhibits the lowest values of mean absolute error and root-mean-square error compared to the simulated results at the different stations using the hydrodynamic model and the BPNN model. Comparison results showed that the ANFIS techniques could be successfully applied in predicting water levels along the east coastal of Taiwan during typhoon events.

  14. MODEL OF TRAINING OF SUCCESS IN LIFE

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    Екатерина Александровна Лежнева

    2014-04-01

    Full Text Available The article explains the importance of the development of motive to succeed in adolescence. It is determined the value of the motive to achieve success in the further development of the teenager: a motive to achieve effective internal forces mobilized for the implementation of successful operation ensures the active involvement of teenagers in social and interpersonal relationships. As the primary means of motive development success is considered training. The author provides a definition of "training for success in life," creates a model of training for success in life, and describes its units (targeted, informative, technological, productive, reveals the successful development of the technology life strategy used during the training (self-presentation, targets, incentives, subject-orientation. The author pays attention to the need for a future psychologist to develop teenagers’ motive to achieve success through the mastery of competence in constructing a model of training for success in life, and its implementation in the course of professional activities. The main means of training students of psychology to the use of training success in life identified the additional educational programs and psychological section.DOI: http://dx.doi.org/10.12731/2218-7405-2013-9-77

  15. Impaction and Prediction: Does Ureteral Wall Thickness Affect the Success of Medical Expulsive Therapy in Pediatric Ureteral Stones?

    Science.gov (United States)

    Tuerxun, Aierken; Batuer, Abudukahaer; Erturhan, Sakip; Eryildirim, Bilal; Camur, Emre; Sarica, Kemal

    2017-01-01

    The study aimed to evaluate the predictive value of ureteral wall thickness (UWT) and stone-related parameters for medical expulsive therapy (MET) success with an alpha blocker in pediatric upper ureteral stones. A total of 35 children receiving MET ureteral stones (Hounsfield unit), degree of hydronephrosis, and UWT were evaluated with patient demographics and recorded. The possible predictive value of these parameters in success rates and time to stone expulsion were evaluated in a comparative manner between the 2 groups. The overall mean patient age and stone size values were 5.40 ± 0.51 years and 6.24 ± 0.28 mm, respectively. Regarding the predictive values of these parameters for the success of MET, while stone size and UWT were found to be highly predictive for MET success, patients age, body mass index, stone density, and degree of hydronephrosis had no predictive value on this aspect. Our findings indicated that some stone and anatomical factors may be used to predict the success of MET in pediatric ureteral stones in an effective manner. With this approach, unnecessary use of these drugs that may cause a delay in removing the stone will be avoided, and the possible adverse effects of obstruction as well as stone-related clinical symptoms could be minimized. © 2017 S. Karger AG, Basel.

  16. Can brain responses to movie trailers predict success?

    OpenAIRE

    Boksem, Maarten

    2015-01-01

    textabstractDecades of research have shown that much of our mental processing occurs at the subconscious level, including the decisions we make as consumers. These subconscious processes explain why we so often fail to accurately predict our own future choices. Often what we think we want has little or no bearing on the choices we actually make. Now a new study provides the first evidence that brain measures can provide significant added value to models for predicting consumer choice.

  17. Predicting Time Series Outputs and Time-to-Failure for an Aircraft Controller Using Bayesian Modeling

    Science.gov (United States)

    He, Yuning

    2015-01-01

    Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.

  18. A predictive coding account of bistable perception - a model-based fMRI study.

    Science.gov (United States)

    Weilnhammer, Veith; Stuke, Heiner; Hesselmann, Guido; Sterzer, Philipp; Schmack, Katharina

    2017-05-01

    In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work

  19. A predictive coding account of bistable perception - a model-based fMRI study.

    Directory of Open Access Journals (Sweden)

    Veith Weilnhammer

    2017-05-01

    Full Text Available In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together

  20. Prediction of successful treatment by extracorporeal shock wave lithotripsy based on crystalluriacomposition correlations of urinary calculi

    Directory of Open Access Journals (Sweden)

    Nadia Messaoudi

    2015-12-01

    Full Text Available Objective: To provide correlations between crystalluria and chemical structure of calculi in situ to help making decision in the use of the extracorporeal shock wave lithotripsy (ESWL. Methods: A crystalluria study was carried out on 644 morning urines of 172 nephrolithiasis patients (111 males and 61 females, and 235 of them were in situ stone carriers. After treating by ESWL, the recovered calculi have been analyzed by Fourier transform infrared spectroscopy and their compositions were correlated to the nature of urinary crystals. Results: We obtained successful treatment for 109 patients out of 157 and 63 patients out of 78 with stones had a treatment failure (33.2%. The correlations showed that for the overwhelming crystalluria containing calcium oxalate dihydrate (COD with mixed crystals without calcium oxalate monohydrate, we should have 68% to 88 % success rate. However, the obtained result was 79%. Similarly, for crystalluria with COD + calcium oxalate monohydrate ± carbapatite, the prediction was 11% to 45% and the result was approximately 39%. When the majority of crystalluria was calcium phosphate, the prediction of 50% to 80% was confirmed by 71% success rate. For those majority containing magnesium ammonium phosphate hexahydrate (struvite ± diammonium urate ± COD, we predicted between 80% to 100%, and the result gave a success rate of 84%. Conclusions: The analysis of crystalluria of morning urine can help to know the composition of calculi in situ and can predict the success rate of ESWL for maximum efficiency.

  1. Developing a stochastic traffic volume prediction model for public-private partnership projects

    Science.gov (United States)

    Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

    2017-11-01

    Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

  2. Impact of Stakeholder Psychological Empowerment on Project Success

    Directory of Open Access Journals (Sweden)

    Herry Pintardi Chandra

    2011-05-01

    Full Text Available The relationship between psychological empowerment of stakeholders and project success is an important thing that must be known by project manager. This research developed and tested the model to predict how well the impact of stakeholder psychological empowerment on project success. Stakeholder psychological empowerment was defined to have five indicator variables covering intrinsic motivation, opportunity to perform, ability to perform, task behaviors, and contextual behaviors. Meanwhile, project success can be measured by cost performance, time performance, quality performance, profitability, and customer satisfaction. In this study, it was hypothesized that stakeholder psychological empowerment influenced project success. Based on the data obtained from a questionnaire survey carried out to 204 respondents, structural equation modeling (SEM was used for predicting the performance of project success. It was found that stakeholder psychological empowerment influenced project success, especially on the ability to perform of stakeholders.

  3. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  4. Comparison of clinical utility between diaphragm excursion and thickening change using ultrasonography to predict extubation success

    Science.gov (United States)

    Yoo, Jung-Wan; Lee, Seung Jun; Lee, Jong Deog; Kim, Ho Cheol

    2018-01-01

    Background/Aims Both diaphragmatic excursion and change in muscle thickening are measured using ultrasonography (US) to assess diaphragm function and mechanical ventilation weaning outcomes. However, which parameter can better predict successful extubation remains to be determined. The aim of this study was to compare the clinical utility of these two diaphragmatic parameters to predict extubation success. Methods This study included patients subjected to extubation trial in the medical or surgical intensive care unit of a university-affiliated hospital from May 2015 through February 2016. Diaphragm excursion and percent of thickening change (Δtdi%) were measured using US within 24 hours before extubation. Results Sixty patients were included, and 78.3% (47/60) of these patients were successfully extubated, whereas 21.7% (13/60) were not. The median degree of excursion was greater in patients with extubation success than in those with extubation failure (1.65 cm vs. 0.8 cm, p success had a greater Δtdi% than those with extubation failure (42.1% vs. 22.5%, p = 0.03). The areas under the receiver operating curve for excursion and Δtdi% were 0.836 (95% confidence interval [CI], 0.717 to 0.919) and 0.698 (95% CI, 0.566 to 0.810), respectively (p = 0.017). Conclusions Diaphragm excursion seems more accurate than a change in the diaphragm thickness to predict extubation success. PMID:29050461

  5. Hormone levels predict individual differences in reproductive success in a passerine bird.

    Science.gov (United States)

    Ouyang, Jenny Q; Sharp, Peter J; Dawson, Alistair; Quetting, Michael; Hau, Michaela

    2011-08-22

    Hormones mediate major physiological and behavioural components of the reproductive phenotype of individuals. To understand basic evolutionary processes in the hormonal regulation of reproductive traits, we need to know whether, and during which reproductive phases, individual variation in hormone concentrations relates to fitness in natural populations. We related circulating concentrations of prolactin and corticosterone to parental behaviour and reproductive success during both the pre-breeding and the chick-rearing stages in both individuals of pairs of free-living house sparrows, Passer domesticus. Prolactin and baseline corticosterone concentrations in pre-breeding females, and prolactin concentrations in pre-breeding males, predicted total number of fledglings. When the strong effect of lay date on total fledgling number was corrected for, only pre-breeding baseline corticosterone, but not prolactin, was negatively correlated with the reproductive success of females. During the breeding season, nestling provisioning rates of both sexes were negatively correlated with stress-induced corticosterone levels. Lastly, individuals of both sexes with low baseline corticosterone before and high baseline corticosterone during breeding raised the most offspring, suggesting that either the plasticity of this trait contributes to reproductive success or that high parental effort leads to increased hormone concentrations. Thus hormone concentrations both before and during breeding, as well as their seasonal dynamics, predict reproductive success, suggesting that individual variation in absolute concentrations and in plasticity is functionally significant, and, if heritable, may be a target of selection.

  6. Construction cost prediction model for conventional and sustainable college buildings in North America

    Directory of Open Access Journals (Sweden)

    Othman Subhi Alshamrani

    2017-03-01

    Full Text Available The literature lacks in initial cost prediction models for college buildings, especially comparing costs of sustainable and conventional buildings. A multi-regression model was developed for conceptual initial cost estimation of conventional and sustainable college buildings in North America. RS Means was used to estimate the national average of construction costs for 2014, which was subsequently utilized to develop the model. The model could predict the initial cost per square feet with two structure types made of steel and concrete. The other predictor variables were building area, number of floors and floor height. The model was developed in three major stages, such as preliminary diagnostics on data quality, model development and validation. The developed model was successfully tested and validated with real-time data.

  7. Noncognitive Variables to Predict Academic Success among Junior Year Baccalaureate Nursing Students

    Science.gov (United States)

    Smith, Ellen M. T.

    2017-01-01

    An equitable predictor of academic success is needed as nursing education strives toward comprehensive preparation of diverse nursing students. The purpose of this study was to discover how Sedlacek's (2004a) Noncognitive Questionnaire (NCQ) and Duckworth & Quinn's (2009) Grit-S predicted baccalaureate nursing student academic performance and…

  8. Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

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    Swagata Payra

    2014-01-01

    Full Text Available The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

  9. Predicting Defects Using Information Intelligence Process Models in the Software Technology Project.

    Science.gov (United States)

    Selvaraj, Manjula Gandhi; Jayabal, Devi Shree; Srinivasan, Thenmozhi; Balasubramanie, Palanisamy

    2015-01-01

    A key differentiator in a competitive market place is customer satisfaction. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer satisfaction should be considered as a part of business strategy. The associated project parameters should be proactively managed and the project outcome needs to be predicted by a technical manager. There is lot of focus on the end state and on minimizing defect leakage as much as possible. Focus should be on proactively managing and shifting left in the software life cycle engineering model. Identify the problem upfront in the project cycle and do not wait for lessons to be learnt and take reactive steps. This paper gives the practical applicability of using predictive models and illustrates use of these models in a project to predict system testing defects thus helping to reduce residual defects.

  10. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India); Gupta, Shikha; Rai, Premanjali [Academy of Scientific and Innovative Research, Council of Scientific and Industrial Research, New Delhi (India); Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001 (India)

    2013-10-15

    abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: • Global robust models constructed for carcinogenicity prediction of diverse chemicals. • Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. • PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. • Developed interspecies PNN/GRNN models for carcinogenicity prediction. • Proposed models can be used as tool to predict carcinogenicity of new chemicals.

  11. Intraoperative Factors that Predict the Successful Placement of Essure Microinserts.

    Science.gov (United States)

    Arthuis, Chloé J; Simon, Emmanuel G; Hébert, Thomas; Marret, Henri

    To determine whether the number of coils visualized in the uterotubal junction at the end of hysteroscopic microinsert placement predicts successful tubal occlusion. Cohort retrospective study (Canadian Task Force classification II-2). Department of obstetrics and gynecology in a teaching hospital. One hundred fifty-three women underwent tubal microinsert placement for permanent birth control from 2010 through 2014. The local institutional review board approved this study. Three-dimensional transvaginal ultrasound (3D TVU) was routinely performed 3 months after hysteroscopic microinsert placement to check position in the fallopian tube. The correlation between the number of coils visible at the uterotubal junction at the end of the hysteroscopic microinsert placement procedure and the device position on the 3-month follow-up 3D TVU in 141 patients was evaluated. The analysis included 276 microinserts placed during hysteroscopy. The median number of coils visible after the hysteroscopic procedure was 4 (interquartile range, 3-5). Devices for 30 patients (21.3%) were incorrectly positioned according to the 3-month follow-up 3D TVU, and hysterosalpingography was recommended. In those patients the median number of coils was in both the right (interquartile range, 2-4) and left (interquartile range, 1-3) uterotubal junctions. The number of coils visible at the uterotubal junction at the end of the placement procedure was the only factor that predicted whether the microinsert was well positioned at the 3-month 3D TVU confirmation (odds ratio, .44; 95% confidence interval, .28-.63). When 5 or more coils were visible, no incorrectly placed microinsert could be seen on the follow-up 3D TVU; the negative predictive value was 100%. No pregnancies were reported. The number of coils observed at the uterotubal junction at the time of microinsert placement should be considered a significant predictive factor of accurate and successful microinsert placement. Copyright © 2017

  12. A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

    Directory of Open Access Journals (Sweden)

    Paulino José García Nieto

    2016-05-01

    Full Text Available Remaining useful life (RUL estimation is considered as one of the most central points in the prognostics and health management (PHM. The present paper describes a nonlinear hybrid ABC–MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS, which have been successfully adopted for regression problems, with the artificial bee colony (ABC technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC–MARS-based model from the remaining measured parameters (input variables for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC–MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.

  13. Predicting therapy success for treatment as usual and blended treatment in the domain of depression

    NARCIS (Netherlands)

    van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

    2017-01-01

    In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients

  14. Modeling N Cycling during Succession after Forest Disturbance: an Analysis of N Mining and Retention Hypothesis

    Science.gov (United States)

    Zhou, Z.; Ollinger, S. V.; Ouimette, A.; Lovett, G. M.; Fuss, C. B.; Goodale, C. L.

    2017-12-01

    Dissolved inorganic nitrogen losses at the Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA, have declined in recent decades, a pattern that counters expectations based on prevailing theory. An unbalanced ecosystem nitrogen (N) budget implies there is a missing component for N sink. Hypotheses to explain this discrepancy include increasing rates of denitrification and accumulation of N in mineral soil pools following N mining by plants. Here, we conducted a modeling analysis fused with field measurements of N cycling, specifically examining the hypothesis relevant to N mining and retention in mineral soils. We included simplified representations of both mechanisms, N mining and retention, in a revised ecosystem process model, PnET-SOM, to evaluate the dynamics of N cycling during succession after forest disturbance at the HBEF. The predicted N mining during the early succession was regulated by a metric representing a potential demand of extra soil N for large wood growth. The accumulation of nitrate in mineral soil pools was a function of the net aboveground biomass accumulation and soil N availability and parameterized based on field 15N tracer incubation data. The predicted patterns of forest N dynamics were consistent with observations. The addition of the new algorithms also improved the predicted DIN export in stream water with an R squared of 0.35 (Ppay back the mined N in mineral soils. Predicted ecosystem N balance showed that N gas loss could account for 14-46% of the total N deposition, the soil mining about 103% during the early succession, and soil retention about 35% at the current forest stage at the HBEF.

  15. Clinical models are inaccurate in predicting bile duct stones in situ for patients with gallbladder.

    Science.gov (United States)

    Topal, B; Fieuws, S; Tomczyk, K; Aerts, R; Van Steenbergen, W; Verslype, C; Penninckx, F

    2009-01-01

    The probability that a patient has common bile duct stones (CBDS) is a key factor in determining diagnostic and treatment strategies. This prospective cohort study evaluated the accuracy of clinical models in predicting CBDS for patients who will undergo cholecystectomy for lithiasis. From October 2005 until September 2006, 335 consecutive patients with symptoms of gallstone disease underwent cholecystectomy. Statistical analysis was performed on prospective patient data obtained at the time of first presentation to the hospital. Demonstrable CBDS at the time of endoscopic retrograde cholangiopancreatography (ERCP) or intraoperative cholangiography (IOC) was considered the gold standard for the presence of CBDS. Common bile duct stones were demonstrated in 53 patients. For 35 patients, ERCP was performed, with successful stone clearance in 24 of 30 patients who had proven CBDS. In 29 patients, IOC showed CBDS, which were managed successfully via laparoscopic common bile duct exploration, with stone extraction at the time of cholecystectomy. Prospective validation of the existing model for CBDS resulted in a predictive accuracy rate of 73%. The new model showed a predictive accuracy rate of 79%. Clinical models are inaccurate in predicting CBDS in patients with cholelithiasis. Management strategies should be based on the local availability of therapeutic expertise.

  16. A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations

    NARCIS (Netherlands)

    Yamamoto, Yumi; Valitalo, Pyry A.; van den Berg, Dirk-Jan; Hartman, Robin; van den Brink, Willem; Wong, Yin Cheong; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Bakshi, Suruchi; Aranzana-Climent, Vincent; Marchand, Sandrine; Dahyot-Fizelier, Claire; Couet, William; Danhof, Meindert; van Hasselt, Johan G. C.; de lange, Elizabeth C. M.

    Purpose Predicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human

  17. Second-to-fourth digit ratio predicts success among high-frequency financial traders.

    Science.gov (United States)

    Coates, John M; Gurnell, Mark; Rustichini, Aldo

    2009-01-13

    Prenatal androgens have important organizing effects on brain development and future behavior. The second-to-fourth digit length ratio (2D:4D) has been proposed as a marker of these prenatal androgen effects, a relatively longer fourth finger indicating higher prenatal androgen exposure. 2D:4D has been shown to predict success in highly competitive sports. Yet, little is known about the effects of prenatal androgens on an economically influential class of competitive risk taking-trading in the financial world. Here, we report the findings of a study conducted in the City of London in which we sampled 2D:4D from a group of male traders engaged in what is variously called "noise" or "high-frequency" trading. We found that 2D:4D predicted the traders' long-term profitability as well as the number of years they remained in the business. 2D:4D also predicted the sensitivity of their profitability to increases both in circulating testosterone and in market volatility. Our results suggest that prenatal androgens increase risk preferences and promote more rapid visuomotor scanning and physical reflexes. The success and longevity of traders exposed to high levels of prenatal androgens further suggests that financial markets may select for biological traits rather than rational expectations.

  18. [Succession caused by beaver (Castor fiber L.) life activity: II. A refined Markov model].

    Science.gov (United States)

    Logofet; Evstigneev, O I; Aleinikov, A A; Morozova, A O

    2015-01-01

    The refined Markov model of cyclic zoogenic successions caused by beaver (Castor fiber L.) life activity represents a discrete chain of the following six states: flooded forest, swamped forest, pond, grassy swamp, shrubby swamp, and wet forest, which correspond to certain stages of succession. Those stages are defined, and a conceptual scheme of probable transitions between them for one time step is constructed from the knowledge of beaver behaviour in small river floodplains of "Bryanskii Les" Reserve. We calibrated the corresponding matrix of transition probabilities according to the optimization principle: minimizing differences between the model outcome and reality; the model generates a distribution of relative areas corresponding to the stages of succession, that has to be compared to those gained from case studies in the Reserve during 2002-2006. The time step is chosen to equal 2 years, and the first-step data in the sum of differences are given various weights, w (between 0 and 1). The value of w = 0.2 is selected due to its optimality and for some additional reasons. By the formulae of finite homogeneous Markov chain theory, we obtained the main results of the calibrated model, namely, a steady-state distribution of stage areas, indexes of cyclicity, and the mean durations (M(j)) of succession stages. The results of calibration give an objective quantitative nature to the expert knowledge of the course of succession and get a proper interpretation. The 2010 data, which are not involved in the calibration procedure, enabled assessing the quality of prediction by the homogeneous model in short-term (from the 2006 situation): the error of model area distribution relative to the distribution observed in 2010 falls into the range of 9-17%, the best prognosis being given by the least optimal matrices (rejected values of w). This indicates a formally heterogeneous nature of succession processes in time. Thus, the refined version of the homogeneous Markov chain

  19. Impact of bacterial activity on turnover of insoluble hydrophobic substrates (phenanthrene and pyrene)-Model simulations for prediction of bioremediation success.

    Science.gov (United States)

    Rein, Arno; Adam, Iris K U; Miltner, Anja; Brumme, Katja; Kästner, Matthias; Trapp, Stefan

    2016-04-05

    Many attempts for bioremediation of polycyclic aromatic hydrocarbon (PAH) contaminated sites failed in the past, but the reasons for this failure are not well understood. Here we apply and improve a model for integrated assessment of mass transfer, biodegradation and residual concentrations for predicting the success of remediation actions. First, we provide growth parameters for Mycobacterium rutilum and Mycobacterium pallens growing on phenanthrene (PHE) or pyrene (PYR) degraded the PAH completely at all investigated concentrations. Maximum metabolic rates vmax and growth rates μ were similar for the substrates PHE and PYR and for both strains. The investigated Mycobacterium species were not superior in PHE degradation to strains investigated earlier with this method. Real-world degradation scenario simulations including diffusive flux to the microbial cells indicate: that (i) bioaugmentation only has a small, short-lived effect; (ii) Increasing sorption shifts the remaining PAH to the adsorbed/sequestered PAH pool; (iii) mobilizing by solvents or surfactants resulted in a significant decrease of the sequestered PAH, and (iv) co-metabolization e.g. by compost addition can contribute significantly to the reduction of PAH, because active biomass is maintained at a high level by the compost. The model therefore is a valuable contribution to the assessment of potential remediation action at PAH-polluted sites. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  1. Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese

    DEFF Research Database (Denmark)

    Martinez Rios, Veronica; Østergaard, Nina Bjerre; Rosshaug, Per Sand

    .43. The acceptable simulation zone method showed the new model for cottage cheese to successfully predict growth of psychrotolerant pseudomonads at both constant and dynamic temperature storage conditions. The new models can be used together with the Food Spoilage and Safety Predictor (FSSP) software to predict......Mathematical models were developed and evaluated for growth of psychrotolerant pseudomonads in chilled milk and cottage cheese with cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic acid and sorbic acid. A simplified cardinal parameter growth model...... was developed based on growth in broth. Subsequently, the reference growth rate parameter (μref at 25 °C) was fitted to a total of 35 growth rates from cottage cheese with cultured cream dressing. Growth rate models for milk and cottage cheese were evaluated by comparison with data from literature and new...

  2. Academic Life Satisfaction Scale (ALSS) and Its Effectiveness in Predicting Academic Success

    Science.gov (United States)

    Kumar, P.K. Sudheesh; P., Dileep

    2006-01-01

    This study is undertaken to examine the effectiveness of a newly constructed psychometric instrument to assess Academic Life Satisfaction along with the components of Emotional Intelligence. The Academic Life Satisfaction Scale is used to predict the scholastic achievement as an index of Academic success. The investigators found that Academic Life…

  3. Threat-Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

    OpenAIRE

    Legerstee, Jeroen; Tulen, Joke; Kallen, Victor; Dieleman, Gwen; Treffers, Philip; Verhulst, Frank; Utens, Elisabeth

    2009-01-01

    textabstractAbstract OBJECTIVE: The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. METHOD: Participants consisted of 131 children with anxiety disorders (aged 8-16 years), who received standardized cognitive-behavioral therapy. At pretreatment, a pictorial dot-pr...

  4. Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

    Science.gov (United States)

    Kennedy, Curtis E; Turley, James P

    2011-10-24

    Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

  5. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  6. Low Motivational Incongruence Predicts Successful EEG Resting-state Neurofeedback Performance in Healthy Adults.

    Science.gov (United States)

    Diaz Hernandez, Laura; Rieger, Kathryn; Koenig, Thomas

    2018-05-15

    Neurofeedback is becoming increasingly sophisticated and widespread, although predictors of successful performance still remain scarce. Here, we explored the possible predictive value of psychological factors and report the results obtained from a neurofeedback training study designed to enhance the self-regulation of spontaneous EEG microstates of a particular type (microstate class D). Specifically, we were interested in life satisfaction (including motivational incongruence), body awareness, personality and trait anxiety. These variables were quantified with questionnaires before neurofeedback. Individual neurofeedback success was established by means of linear mixed models that accounted for the amount of observed target state (microstate class D contribution) as a function of time and training condition: baseline, training and transfer (results shown in Diaz Hernandez et al.). We found a series of significant negative correlations between motivational incongruence and mean percentage increase of microstate D during the condition transfer, across-sessions (36% of common variance) and mean percentage increase of microstate D during the condition training, within-session (42% of common variance). There were no significant correlations related to other questionnaires, besides a trend in a sub-scale of the Life Satisfaction questionnaire. We conclude that motivational incongruence may be a potential predictor for neurofeedback success, at least in the current protocol. The finding may be explained by the interfering effect on neurofeedback performance produced by incompatible simultaneously active psychological processes, which are indirectly measured by the Motivational Incongruence questionnaire. Copyright © 2016. Published by Elsevier Ltd.

  7. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  8. Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

    Science.gov (United States)

    Lei, Yu; Guo, Min; Zhao, Danning; Cai, Hongbing; Hu, Dandan

    2016-03-01

    A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

  9. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  10. The morphology of midcingulate cortex predicts frontal-midline theta neurofeedback success

    Directory of Open Access Journals (Sweden)

    Stefanie eEnriquez-Geppert

    2013-08-01

    Full Text Available Humans differ in their ability to learn how to control their own brain activity by neurofeedback. However, neural mechanisms underlying these inter-individual differences, which may determine training success and associated cognitive enhancement, are not well understood. Here, it is asked whether neurofeedback success of frontal-midline (fm theta, an oscillation related to higher cognitive functions, could be predicted by the morphology of brain structures known to be critically involved in fm-theta generation. Nineteen young, right-handed participants underwent magnetic resonance imaging of T1-weighted brain images, and took part in an individualized, eight-session neurofeedback training in order to learn how to enhance activity in their fm-theta frequency band. Initial training success, measured at the second training session, was correlated with the final outcome measure. We found that the inferior, superior and middle frontal cortices were not associated with training success. However, volume of the midcingulate cortex as well as volume and concentration of the underlying white matter structures act as predictor variables for the general responsiveness to training. These findings suggest a neuroanatomical foundation for the ability to learn to control one’s own brain activity.

  11. Using Standardized Tests to Identify Prior Knowledge Necessary for Success in Algebra: A Predictive Analysis

    Science.gov (United States)

    Jensen, Jennifer

    2014-01-01

    This study sought to determine if there is a relationship between students' scores on the eighth-grade Indiana State Test of Education Progress Plus (ISTEP+) exam and success on Indiana's Algebra End-of-Course Assessment (ECA). Additionally, it sought to determine if algebra success could be significantly predicted by the achievement in one or…

  12. Determinants of construction project success in India

    CERN Document Server

    Jha, Kumar Neeraj

    2013-01-01

    This study presents exploratory work and seeks to identify and evaluate the success and failure factors that could form a guideline for further study and to some extent help professionals to understand some critical aspects that impact project performance concerning construction in India. A total of 55 attributes affecting the performance of construction projects are analysed in terms of their level of influence on four key performance criteria – schedule, cost, quality, and no disputes – using a two-stage questionnaire survey. These attributes are then further analysed, interpreted and evaluated.   Based on the critical success factors obtained from the study, a neural network model-based predictive model for project performance has been developed. The performance prediction models have been derived for all four project performance criteria. Further, a hypothesis that ‘project success’ is influenced by ‘success traits’ has also been formulated. The hypothesized positive inter-relationships betwe...

  13. Bike and run pacing on downhill segments predict Ironman triathlon relative success.

    Science.gov (United States)

    Johnson, Evan C; Pryor, J Luke; Casa, Douglas J; Belval, Luke N; Vance, James S; DeMartini, Julie K; Maresh, Carl M; Armstrong, Lawrence E

    2015-01-01

    Determine if performance and physiological based pacing characteristics over the varied terrain of a triathlon predicted relative bike, run, and/or overall success. Poor self-regulation of intensity during long distance (Full Iron) triathlon can manifest in adverse discontinuities in performance. Observational study of a random sample of Ironman World Championship athletes. High performing and low performing groups were established upon race completion. Participants wore global positioning system and heart rate enabled watches during the race. Percentage difference from pre-race disclosed goal pace (%off) and mean HR were calculated for nine segments of the bike and 11 segments of the run. Normalized graded running pace (accounting for changes in elevation) was computed via analysis software. Step-wise regression analyses identified segments predictive of relative success and HP and LP were compared at these segments to confirm importance. %Off of goal velocity during two downhill segments of the bike (HP: -6.8±3.2%, -14.2±2.6% versus LP: -1.2±4.2%, -5.1±11.5%; p<0.020) and %off from NGP during one downhill segment of the run (HP: 4.8±5.2% versus LP: 33.3±38.7%; p=0.033) significantly predicted relative performance. Also, HP displayed more consistency in mean HR (141±12 to 138±11 bpm) compared to LP (139±17 to 131±16 bpm; p=0.019) over the climb and descent from the turn-around point during the bike component. Athletes who maintained faster relative speeds on downhill segments, and who had smaller changes in HR between consecutive up and downhill segments were more successful relative to their goal times. Copyright © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  14. Does Trait Emotional Intelligence Predict Unique Variance in Early Career Success Beyond IQ and Personality?

    OpenAIRE

    Haro García, José Manuel de; Castejón Costa, Juan Luis

    2014-01-01

    In order to determine the contribution of emotional intelligence (EI) to career success, in this study, we analyzed the relationship between trait EI (TEI), general mental ability (GMA), the big five personality traits, and career success indicators, in a sample of 130 graduates who were in the early stages of their careers. Results from hierarchical regression analyses indicated that TEI, and especially its dimension “repair,” has incremental validity in predicting one of the career success ...

  15. Predictive Local Composition Models for Solid/Liquid Equilibrium in n-Alkane Systems: Wilson Equation for Multicomponent Systems

    DEFF Research Database (Denmark)

    Coutinho, João A.P.; Stenby, Erling Halfdan

    1996-01-01

    The predictive local composition model is applied to multicomponent hydrocarbon systems with long-chain n-alkanes as solutes. The results show that it can successfully be extended to highorder systems and accurately predict the solid appearance temperature, also known as cloud point, in solutions...

  16. Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

    Science.gov (United States)

    Rybinska, Anna; Sosnowska, Anita; Barycki, Maciej; Puzyn, Tomasz

    2016-02-01

    Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ({{Q}}_{{EXT}}2 = 0.87). However, PM7-based model has comparable values of quality parameters ({{Q}}_{{EXT}}2 = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

  17. [Active ageing and success: A brief history of conceptual models].

    Science.gov (United States)

    Petretto, Donatella Rita; Pili, Roberto; Gaviano, Luca; Matos López, Cristina; Zuddas, Carlo

    2016-01-01

    The aim of this paper is to analyse and describe different conceptual models of successful ageing, active and healthy ageing developed in Europe and in America in the 20° century, starting from Rowe and Kahn's original model (1987, 1997). A narrative review was conducted on the literature on successful ageing. Our review included definition of successful ageing from European and American scholars. Models were found that aimed to describe indexes of active and healthy ageing, models devoted to describe processes involved in successful ageing, and additional views that emphasise subjective and objective perception of successful ageing. A description is also given of critiques on previous models and remedies according to Martin et al. (2014) and strategies for successful ageing according to Jeste and Depp (2014). The need is discussed for the enhancement of Rowe and Kahn's model and other models with a more inclusive, universal description of ageing, incorporating scientific evidence regarding active ageing. Copyright © 2015 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.

  18. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

  19. A Case Study Using Modeling and Simulation to Predict Logistics Supply Chain Issues

    Science.gov (United States)

    Tucker, David A.

    2007-01-01

    Optimization of critical supply chains to deliver thousands of parts, materials, sub-assemblies, and vehicle structures as needed is vital to the success of the Constellation Program. Thorough analysis needs to be performed on the integrated supply chain processes to plan, source, make, deliver, and return critical items efficiently. Process modeling provides simulation technology-based, predictive solutions for supply chain problems which enable decision makers to reduce costs, accelerate cycle time and improve business performance. For example, United Space Alliance, LLC utilized this approach in late 2006 to build simulation models that recreated shuttle orbiter thruster failures and predicted the potential impact of thruster removals on logistics spare assets. The main objective was the early identification of possible problems in providing thruster spares for the remainder of the Shuttle Flight Manifest. After extensive analysis the model results were used to quantify potential problems and led to improvement actions in the supply chain. Similarly the proper modeling and analysis of Constellation parts, materials, operations, and information flows will help ensure the efficiency of the critical logistics supply chains and the overall success of the program.

  20. Forecasting success via early adoptions analysis: A data-driven study.

    Directory of Open Access Journals (Sweden)

    Giulio Rossetti

    Full Text Available Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

  1. Forecasting success via early adoptions analysis: A data-driven study.

    Science.gov (United States)

    Rossetti, Giulio; Milli, Letizia; Giannotti, Fosca; Pedreschi, Dino

    2017-01-01

    Innovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don't. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.

  2. Effects of Test Conditions on APA Rutting and Prediction Modeling for Asphalt Mixtures

    Directory of Open Access Journals (Sweden)

    Hui Wang

    2017-01-01

    Full Text Available APA rutting tests were conducted for six kinds of asphalt mixtures under air-dry and immersing conditions. The influences of test conditions, including load, temperature, air voids, and moisture, on APA rutting depth were analyzed by using grey correlation method, and the APA rutting depth prediction model was established. Results show that the modified asphalt mixtures have bigger rutting depth ratios of air-dry to immersing conditions, indicating that the modified asphalt mixtures have better antirutting properties and water stability than the matrix asphalt mixtures. The grey correlation degrees of temperature, load, air void, and immersing conditions on APA rutting depth decrease successively, which means that temperature is the most significant influencing factor. The proposed indoor APA rutting prediction model has good prediction accuracy, and the correlation coefficient between the predicted and the measured rutting depths is 96.3%.

  3. Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

    Directory of Open Access Journals (Sweden)

    Elizabeth A. Becker

    2016-02-01

    Full Text Available Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”, but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE, observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast.

  4. Hope of Success and Fear of Failure Predicting Academic Procrastination Students Who Working on a Thesis

    Directory of Open Access Journals (Sweden)

    Sari Zakiah Akmal

    2017-08-01

    Full Text Available Students, who are working on the thesis, have some difficulties caused by internal and external factors. Those problems can disrupt the completion of their thesis, such as the tendency to do academic procrastination. Increasing achievement motivation can reduce academic procrastination. This study aims to look at the role of achievement motivation (hope of success and fear of failure in predicting academic procrastination. The study used a quantitative approach by distributing academic procrastination and achievement motivation questionnaires. The study involved 182 students who were working on a thesis as samples, which were obtained by using accidental sampling technique. Data were analyzed using multiple regressions. It showed that the hope of success and fear of failure have a significant role in predicting academic procrastination (R2 = 13.8%, F = 14,356, p <0.05. The hope of success can decrease academic procrastination, while fear of failure can improve it. Thus, interventions to reduce academic procrastination can be delivered by increasing students hope of success.

  5. Predicting Species-Resolved Macronutrient Acquisition during Succession in a Model Phototrophic Biofilm Using an Integrated ‘Omics Approach

    Directory of Open Access Journals (Sweden)

    Stephen R. Lindemann

    2017-06-01

    Full Text Available The principles governing acquisition and interspecies exchange of nutrients in microbial communities and how those exchanges impact community productivity are poorly understood. Here, we examine energy and macronutrient acquisition in unicyanobacterial consortia for which species-resolved genome information exists for all members, allowing us to use multi-omic approaches to predict species’ abilities to acquire resources and examine expression of resource-acquisition genes during succession. Metabolic reconstruction indicated that a majority of heterotrophic community members lacked the genes required to directly acquire the inorganic nutrients provided in culture medium, suggesting high metabolic interdependency. The sole primary producer in consortium UCC-O, cyanobacterium Phormidium sp. OSCR, displayed declining expression of energy harvest, carbon fixation, and nitrate and sulfate reduction proteins but sharply increasing phosphate transporter expression over 28 days. Most heterotrophic members likewise exhibited signs of phosphorus starvation during succession. Though similar in their responses to phosphorus limitation, heterotrophs displayed species-specific expression of nitrogen acquisition genes. These results suggest niche partitioning around nitrogen sources may structure the community when organisms directly compete for limited phosphate. Such niche complementarity around nitrogen sources may increase community diversity and productivity in phosphate-limited phototrophic communities.

  6. Multi-target QSPR modeling for simultaneous prediction of multiple gas-phase kinetic rate constants of diverse chemicals

    Science.gov (United States)

    Basant, Nikita; Gupta, Shikha

    2018-03-01

    The reactions of molecular ozone (O3), hydroxyl (•OH) and nitrate (NO3) radicals are among the major pathways of removal of volatile organic compounds (VOCs) in the atmospheric environment. The gas-phase kinetic rate constants (kO3, kOH, kNO3) are thus, important in assessing the ultimate fate and exposure risk of atmospheric VOCs. Experimental data for rate constants are not available for many emerging VOCs and the computational methods reported so far address a single target modeling only. In this study, we have developed a multi-target (mt) QSPR model for simultaneous prediction of multiple kinetic rate constants (kO3, kOH, kNO3) of diverse organic chemicals considering an experimental data set of VOCs for which values of all the three rate constants are available. The mt-QSPR model identified and used five descriptors related to the molecular size, degree of saturation and electron density in a molecule, which were mechanistically interpretable. These descriptors successfully predicted three rate constants simultaneously. The model yielded high correlations (R2 = 0.874-0.924) between the experimental and simultaneously predicted endpoint rate constant (kO3, kOH, kNO3) values in test arrays for all the three systems. The model also passed all the stringent statistical validation tests for external predictivity. The proposed multi-target QSPR model can be successfully used for predicting reactivity of new VOCs simultaneously for their exposure risk assessment.

  7. A clinical nomogram to predict the successful shock wave lithotripsy of renal and ureteral calculi.

    Science.gov (United States)

    Wiesenthal, Joshua D; Ghiculete, Daniela; Ray, A Andrew; Honey, R John D'A; Pace, Kenneth T

    2011-08-01

    Although shock wave lithotripsy is dependent on patient and stone related factors, there are few reliable algorithms predictive of its success. In this study we develop a comprehensive nomogram to predict renal and ureteral stone shock wave lithotripsy outcomes. During a 5-year period data from patients treated at our lithotripsy unit were reviewed. Analysis was restricted to patients with a solitary renal or ureteral calculus 20 mm or less. Demographic, stone, patient, treatment and 3-month followup data were collected from a prospective database. All patients were treated using the Philips Lithotron® lithotripter. A total of 422 patients (69.7% male) were analyzed. Mean stone size was 52.3±39.3 mm2 for ureteral stones and 78.9±77.3 mm2 for renal stones, with 95 (43.6%) of the renal stones located in the lower pole. The single treatment success rates for ureteral and renal stones were 60.3% and 70.2%, respectively. On univariate analysis predictors of shock wave lithotripsy success, regardless of stone location, were age (p=0.01), body mass index (p=0.01), stone size (pstone density (pstone distance (pstone area and skin to stone distance were significant predictors with an AUC of 0.75. For ureteral calculi predictive factors included body mass index and stone size (AUC 0.70). Patient and stone parameters have been identified to create a nomogram that predicts shock wave lithotripsy outcomes using the Lithotron lithotripter, which can facilitate optimal treatment based decisions and provide patients with more accurate single treatment success rates for shock wave lithotripsy tailored to patient specific situations. Copyright © 2011 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  8. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    Science.gov (United States)

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  9. Youth's Causal Beliefs About Success: Socioeconomic Differences and Prediction of Early Career Development.

    Science.gov (United States)

    Kay, Joseph S; Shane, Jacob; Heckhausen, Jutta

    2017-10-01

    Youth's career attainment is associated with socioeconomic background, but may also be related to their beliefs about causes of success. Relationships between 17-year-olds' socioeconomic status (SES) and causal beliefs about success, and whether these beliefs predict career attainment after completing a vocational or university degree were examined using data from the German Socio-Economic Panel Study (n = 997, 48.5% female). Youth with higher SES parents and those who attended higher levels of high schools were less likely to believe that success in society is due to external causes, but SES was unrelated to the belief that success is due to personal merit or ability. Youth who believe that success is due to external causes attained lower income, occupational prestige, and job autonomy, and slower increases in income over time. There were also significant indirect effects of youth's parents' SES and their own high school levels on career attainment through such external causal beliefs; merit beliefs, by contrast, were largely unrelated to career attainment. These results suggest that beliefs about external causes of success may uniquely contribute to the transmission and maintenance of SES across generations and over time.

  10. Vaginal birth after caesarean section prediction models: a UK comparative observational study.

    Science.gov (United States)

    Mone, Fionnuala; Harrity, Conor; Mackie, Adam; Segurado, Ricardo; Toner, Brenda; McCormick, Timothy R; Currie, Aoife; McAuliffe, Fionnuala M

    2015-10-01

    Primarily, to assess the performance of three statistical models in predicting successful vaginal birth in patients attempting a trial of labour after one previous lower segment caesarean section (TOLAC). The statistically most reliable models were subsequently subjected to validation testing in a local antenatal population. A retrospective observational study was performed with study data collected from the Northern Ireland Maternity Service Database (NIMATs). The study population included all women that underwent a TOLAC (n=385) from 2010 to 2012 in a regional UK obstetric unit. Data was collected from the Northern Ireland Maternity Service Database (NIMATs). Area under the curve (AUC) and correlation analysis was performed. Of the three prediction models evaluated, AUC calculations for the Smith et al., Grobman et al. and Troyer and Parisi Models were 0.74, 0.72 and 0.65, respectively. Using the Smith et al. model, 52% of women had a low risk of caesarean section (CS) (predicted VBAC >72%) and 20% had a high risk of CS (predicted VBAC <60%), of whom 20% and 63% had delivery by CS. The fit between observed and predicted outcome in this study cohort using the Smith et al. and Grobman et al. models were greatest (Chi-square test, p=0.228 and 0.904), validating both within the population. The Smith et al. and Grobman et al. models could potentially be utilized within the UK to provide women with an informed choice when deciding on mode of delivery after a previous CS. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

  11. Mentoring Support and Power: A Three Year Predictive Field Study on Protege Networking and Career Success

    Science.gov (United States)

    Blickle, Gerhard; Witzki, Alexander H.; Schneider, Paula B.

    2009-01-01

    Career success of early employees was analyzed from a power perspective and a developmental network perspective. In a predictive field study with 112 employees mentoring support and mentors' power were assessed in the first wave, employees' networking was assessed after two years, and career success (i.e. income and hierarchical position) and…

  12. Predicting Successful Completion Using Student Delay Indicators in Undergraduate Self-Paced Online Courses

    Science.gov (United States)

    Lim, Janine M.

    2016-01-01

    Self-paced online courses meet flexibility and learning needs of many students, but skepticism persists regarding the quality and the tendency for students to procrastinate in self-paced courses. Research is needed to understand procrastination and delay patterns of students in online self-paced courses to predict successful completion and…

  13. Predicting Collateral Status With Magnetic Resonance Perfusion Parameters: Probabilistic Approach With a Tmax-Derived Prediction Model.

    Science.gov (United States)

    Lee, Mi Ji; Son, Jeong Pyo; Kim, Suk Jae; Ryoo, Sookyung; Woo, Sook-Young; Cha, Jihoon; Kim, Gyeong-Moon; Chung, Chin-Sang; Lee, Kwang Ho; Bang, Oh Young

    2015-10-01

    Good collateral flow is an important predictor for favorable responses to recanalization therapy and successful outcomes after acute ischemic stroke. Magnetic resonance perfusion-weighted imaging (MRP) is widely used in patients with stroke. However, it is unclear whether the perfusion parameters and thresholds would predict collateral status. The present study evaluated the relationship between hypoperfusion severity and collateral status to develop a predictive model for good collaterals using MRP parameters. Patients who were eligible for recanalization therapy that underwent both serial diffusion-weighted imaging and serial MRP were enrolled into the study. A collateral flow map derived from MRP source data was generated through automatic postprocessing. Hypoperfusion severity, presented as proportions of every 2-s Tmax strata to the entire hypoperfusion volume (Tmax≥2 s), was compared between patients with good and poor collaterals. Prediction models for good collaterals were developed with each Tmax strata proportion and cerebral blood volumes. Among 66 patients, 53 showed good collaterals based on MRP-based collateral grading. Although no difference was noted in delays within 16 s, more severe Tmax delays (Tmax16-18 s, Tmax18-22 s, Tmax22-24 s, and Tmax>24 s) were associated with poor collaterals. The probability equation model using Tmax strata proportion demonstrated high predictive power in a receiver operating characteristic analysis (area under the curve=0.9303; 95% confidence interval, 0.8682-0.9924). The probability score was negatively correlated with the volume of infarct growth (P=0.030). Collateral status is associated with more severe Tmax delays than previously defined. The present Tmax severity-weighted model can determine good collaterals and subsequent infarct growth. © 2015 American Heart Association, Inc.

  14. Female song rate and structure predict reproductive success in a socially monogamous bird.

    Directory of Open Access Journals (Sweden)

    Dianne Heather Brunton

    2016-03-01

    Full Text Available Bird song is commonly regarded as a male trait that has evolved through sexual selection. However, recent research has prompted a re-evaluation of this view by demonstrating that female song is an ancestral and phylogenetically widespread trait. Species with female song provide opportunities to study selective pressures and mechanisms specific to females within the wider context of social competition. We investigated the relationship between reproductive success and female song performance in the New Zealand bellbird (Anthornis melanura, a passerine resident year round in New Zealand temperate forests. We monitored breeding behavior and song over three years on Tiritiri Matangi Island. Female bellbirds contributed significantly more towards parental care than males (solely incubating young and provisioning chicks at more than twice the rate of males. Female song rate in the vicinity of the nest was higher than that of males during incubation and chick-rearing stages but similar during early-nesting and post-breeding stages. Using GLMs, we found that female song rates during both incubation and chick-rearing stages strongly predicted the number of fledged chicks. However, male song rate and male and female chick provisioning rates had no effect on fledging success. Two measures of female song complexity (number of syllable types and the number of transitions between different syllable types were also good predictors of breeding success (GLM on PC scores. In contrast, song duration, the total number of syllables, and the number of ‘stutter’ syllables per song were not correlated with fledging success. It is unclear why male song rate was not associated with reproductive success and we speculate that extra-pair paternity might play a role. While we have previously demonstrated that female bellbird song is important in intrasexual interactions, we clearly demonstrate here that female song predicts reproductive success. These results, with others

  15. Prediction of Marginal Mass Required for Successful Islet Transplantation

    Science.gov (United States)

    Papas, Klearchos K.; Colton, Clark K.; Qipo, Andi; Wu, Haiyan; Nelson, Rebecca A.; Hering, Bernhard J.; Weir, Gordon C.; Koulmanda, Maria

    2013-01-01

    Islet quality assessment methods for predicting diabetes reversal (DR) following transplantation are needed. We investigated two islet parameters, oxygen consumption rate (OCR) and OCR per DNA content, to predict transplantation outcome and explored the impact of islet quality on marginal islet mass for DR. Outcomes in immunosuppressed diabetic mice were evaluated by transplanting mixtures of healthy and purposely damaged rat islets for systematic variation of OCR/DNA over a wide range. The probability of DR increased with increasing transplanted OCR and OCR/DNA. On coordinates of OCR versus OCR/DNA, data fell into regions in which DR occurred in all, some, or none of the animals with a sharp threshold of around 150-nmol/min mg DNA. A model incorporating both parameters predicted transplantation outcome with sensitivity and specificity of 93% and 94%, respectively. Marginal mass was not constant, depended on OCR/DNA, and increased from 2,800 to over 100,000 islet equivalents/kg body weight as OCR/DNA decreased. We conclude that measurements of OCR and OCR/DNA are useful for predicting transplantation outcome in this model system, and OCR/DNA can be used to estimate the marginal mass required for reversing diabetes. Because human clinical islet preparations in a previous study had OCR/DNA values in the range of 100–150-nmol/min mg DNA, our findings suggest that substantial improvement in transplantation outcome may accompany increasedOCR/DNAin clinical islet preparations. PMID:20233002

  16. The Drivers of Success in Business Model Transformation

    Directory of Open Access Journals (Sweden)

    Nenad Savič

    2016-01-01

    Full Text Available Existing empirical literature on business models is still inconclusive about the key drivers of successful business model transformation. The paper explores this issue by using a single longitudinal case study design in combination with grounded theory approach on a medium-sized, high-tech and globally oriented company. Based on on-site visits, interviews and secondary documentation data analysis, the study identifies six generic drivers of successful business model transformation: transformational leadership, discovery driven decision-making, industry improvement – customer specific orientation, content-oriented communication, self-initiative collaborators, and phased separation strategy. The new drivers supplement our existing knowledge on how successful transformation takes place and add to existing drivers, while extensive discussion of their implications may help the managers to execute business transformations more effectively.

  17. Multi-model analysis in hydrological prediction

    Science.gov (United States)

    Lanthier, M.; Arsenault, R.; Brissette, F.

    2017-12-01

    Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

  18. Conceptual Software Reliability Prediction Models for Nuclear Power Plant Safety Systems

    International Nuclear Information System (INIS)

    Johnson, G.; Lawrence, D.; Yu, H.

    2000-01-01

    The objective of this project is to develop a method to predict the potential reliability of software to be used in a digital system instrumentation and control system. The reliability prediction is to make use of existing measures of software reliability such as those described in IEEE Std 982 and 982.2. This prediction must be of sufficient accuracy to provide a value for uncertainty that could be used in a nuclear power plant probabilistic risk assessment (PRA). For the purposes of the project, reliability was defined to be the probability that the digital system will successfully perform its intended safety function (for the distribution of conditions under which it is expected to respond) upon demand with no unintended functions that might affect system safety. The ultimate objective is to use the identified measures to develop a method for predicting the potential quantitative reliability of a digital system. The reliability prediction models proposed in this report are conceptual in nature. That is, possible prediction techniques are proposed and trial models are built, but in order to become a useful tool for predicting reliability, the models must be tested, modified according to the results, and validated. Using methods outlined by this project, models could be constructed to develop reliability estimates for elements of software systems. This would require careful review and refinement of the models, development of model parameters from actual experience data or expert elicitation, and careful validation. By combining these reliability estimates (generated from the validated models for the constituent parts) in structural software models, the reliability of the software system could then be predicted. Modeling digital system reliability will also require that methods be developed for combining reliability estimates for hardware and software. System structural models must also be developed in order to predict system reliability based upon the reliability

  19. Model of key success factors for Business Intelligence implementation

    Directory of Open Access Journals (Sweden)

    Peter Mesaros

    2016-07-01

    Full Text Available New progressive technologies recorded growth in every area. Information-communication technologies facilitate the exchange of information and it facilitates management of everyday activities in enterprises. Specific modules (such as Business Intelligence facilitate decision-making. Several studies have demonstrated the positive impact of Business Intelligence to decision-making. The first step is to put in place the enterprise. The implementation process is influenced by many factors. This article discusses the issue of key success factors affecting to successful implementation of Business Intelligence. The article describes the key success factors for successful implementation and use of Business Intelligence based on multiple studies. The main objective of this study is to verify the effects and dependence of selected factors and proposes a model of key success factors for successful implementation of Business Intelligence. Key success factors and the proposed model are studied in Slovak enterprises.

  20. Final Report Coupling in silico microbial models with reactive transport models to predict the fate of contaminants in the subsurface.

    Energy Technology Data Exchange (ETDEWEB)

    Lovley, Derek R.

    2012-10-31

    This project successfully accomplished its goal of coupling genome-scale metabolic models with hydrological and geochemical models to predict the activity of subsurface microorganisms during uranium bioremediation. Furthermore, it was demonstrated how this modeling approach can be used to develop new strategies to optimize bioremediation. The approach of coupling genome-scale metabolic models with reactive transport modeling is now well enough established that it has been adopted by other DOE investigators studying uranium bioremediation. Furthermore, the basic principles developed during our studies will be applicable to much broader investigations of microbial activities, not only for other types of bioremediation, but microbial metabolism in diversity of environments. This approach has the potential to make an important contribution to predicting the impact of environmental perturbations on the cycling of carbon and other biogeochemical cycles.

  1. Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

    Science.gov (United States)

    Zhang, Chengxin; Mortuza, S M; He, Baoji; Wang, Yanting; Zhang, Yang

    2018-03-01

    We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successfully folds three medium-size FM targets that have more than 150 residues, even though the interplay between the two pipelines still awaits further optimization. Newly developed sequence-based contact prediction by NeBcon plays a critical role to enhance the quality of models, particularly for FM targets, by the new pipelines. The inclusion of NeBcon predicted contacts as restraints in the QUARK simulations results in an average TM-score of 0.41 for the best in top five predicted models, which is 37% higher than that by the QUARK simulations without contacts. In particular, there are seven targets that are converted from non-foldable to foldable (TM-score >0.5) due to the use of contact restraints in the simulations. Another additional feature in the current pipelines is the local structure quality prediction by ResQ, which provides a robust residue-level modeling error estimation. Despite the success, significant challenges still remain in ab initio modeling of multi-domain proteins and folding of β-proteins with complicated topologies bound by long-range strand-strand interactions. Improvements on domain boundary and long-range contact prediction, as well as optimal use of the predicted contacts and multiple threading alignments, are critical to address these issues seen in the CASP12 experiment. © 2017 Wiley Periodicals, Inc.

  2. Prediction of Success in External Cephalic Version under Tocolysis: Still a Challenge.

    Science.gov (United States)

    Vaz de Macedo, Carolina; Clode, Nuno; Mendes da Graça, Luís

    2015-01-01

    External cephalic version is a procedure of fetal rotation to a cephalic presentation through manoeuvres applied to the maternal abdomen. There are several prognostic factors described in literature for external cephalic version success and prediction scores have been proposed, but their true implication in clinical practice is controversial. We aim to identify possible factors that could contribute to the success of an external cephalic version attempt in our population. We retrospectively examined 207 consecutive external cephalic version attempts under tocolysis conducted between January 1997 and July 2012. We consulted the department's database for the following variables: race, age, parity, maternal body mass index, gestational age, estimated fetal weight, breech category, placental location and amniotic fluid index. We performed descriptive and analytical statistics for each variable and binary logistic regression. External cephalic version was successful in 46.9% of cases (97/207). None of the included variables was associated with the outcome of external cephalic version attempts after adjustment for confounding factors. We present a success rate similar to what has been previously described in literature. However, in contrast to previous authors, we could not associate any of the analysed variables with success of the external cephalic version attempt. We believe this discrepancy is partly related to the type of statistical analysis performed. Even though there are numerous prognostic factors identified for the success in external cephalic version, care must be taken when counselling and selecting patients for this procedure. The data obtained suggests that external cephalic version should continue being offered to all eligible patients regardless of prognostic factors for success.

  3. Cross-Species Extrapolation of Models for Predicting Lead Transfer from Soil to Wheat Grain.

    Directory of Open Access Journals (Sweden)

    Ke Liu

    Full Text Available The transfer of Pb from the soil to crops is a serious food hygiene security problem in China because of industrial, agricultural, and historical contamination. In this study, the characteristics of exogenous Pb transfer from 17 Chinese soils to a popular wheat variety (Xiaoyan 22 were investigated. In addition, bioaccumulation prediction models of Pb in grain were obtained based on soil properties. The results of the analysis showed that pH and OC were the most important factors contributing to Pb uptake by wheat grain. Using a cross-species extrapolation approach, the Pb uptake prediction models for cultivar Xiaoyan 22 in different soil Pb levels were satisfactorily applied to six additional non-modeled wheat varieties to develop a prediction model for each variety. Normalization of the bioaccumulation factor (BAF to specific soil physico-chemistry is essential, because doing so could significantly reduce the intra-species variation of different wheat cultivars in predicted Pb transfer and eliminate the influence of soil properties on ecotoxicity parameters for organisms of interest. Finally, the prediction models were successfully verified against published data (including other wheat varieties and crops and used to evaluate the ecological risk of Pb for wheat in contaminated agricultural soils.

  4. Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R

    Science.gov (United States)

    Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.

    2015-10-01

    A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM.

  5. Implementation of model predictive control for resistive wall mode stabilization on EXTRAP T2R

    International Nuclear Information System (INIS)

    Setiadi, A C; Brunsell, P R; Frassinetti, L

    2015-01-01

    A model predictive control (MPC) method for stabilization of the resistive wall mode (RWM) in the EXTRAP T2R reversed-field pinch is presented. The system identification technique is used to obtain a linearized empirical model of EXTRAP T2R. MPC employs the model for prediction and computes optimal control inputs that satisfy performance criterion. The use of a linearized form of the model allows for compact formulation of MPC, implemented on a millisecond timescale, that can be used for real-time control. The design allows the user to arbitrarily suppress any selected Fourier mode. The experimental results from EXTRAP T2R show that the designed and implemented MPC successfully stabilizes the RWM. (paper)

  6. Transparent predictive modelling of the twin screw granulation process using a compensated interval type-2 fuzzy system.

    Science.gov (United States)

    AlAlaween, Wafa' H; Khorsheed, Bilal; Mahfouf, Mahdi; Gabbott, Ian; Reynolds, Gavin K; Salman, Agba D

    2018-03-01

    In this research, a new systematic modelling framework which uses machine learning for describing the granulation process is presented. First, an interval type-2 fuzzy model is elicited in order to predict the properties of the granules produced by twin screw granulation (TSG) in the pharmaceutical industry. Second, a Gaussian mixture model (GMM) is integrated in the framework in order to characterize the error residuals emanating from the fuzzy model. This is done to refine the model by taking into account uncertainties and/or any other unmodelled behaviour, stochastic or otherwise. All proposed modelling algorithms were validated via a series of Laboratory-scale experiments. The size of the granules produced by TSG was successfully predicted, where most of the predictions fit within a 95% confidence interval. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  8. Predictive Modeling in Race Walking

    Directory of Open Access Journals (Sweden)

    Krzysztof Wiktorowicz

    2015-01-01

    Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

  9. Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University

    Directory of Open Access Journals (Sweden)

    Muluken Alemu Yehuala

    2015-04-01

    Full Text Available Abstract This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University students datasets. CRISP-DM Cross Industry Standard Process for Data mining is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from students data. These patterns can be seen in relation to different variables in the students records. The classification rule generation process is based on the decision tree and Bayes as a classification technique and the generated rules were studied and evaluated. Data collected from MSEXCEL files and it has been preprocessed for model building. Models were built and tested by using a sample dataset of 11873 regular undergraduate students. Analysis is done by using WEKA 3.7 application software. The research results offer a helpful and constructive recommendations to the academic planners in universities of learning to enhance their decision making process. This will also aid in the curriculum structure and modification in order to improve students academic performance. Students able to decide about their field of study before they are enrolled in specific field of study based on the previous experience taken from the research-findings. The research findings indicated that EHEECE Ethiopian Higher Education Entrance Certificate Examination result Sex Number of students in a class number of courses given in a semester and field of study are the major factors affecting the student performances. So on the bases of the research findings the level of student success will increase and it is possible to prevent educational institutions from serious financial strains.

  10. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  11. Model-free and model-based reward prediction errors in EEG.

    Science.gov (United States)

    Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

    2018-05-24

    Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

  12. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

    Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

  13. A deep learning-based multi-model ensemble method for cancer prediction.

    Science.gov (United States)

    Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

    2018-01-01

    Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Predictive modeling of deep-sea fish distribution in the Azores

    Science.gov (United States)

    Parra, Hugo E.; Pham, Christopher K.; Menezes, Gui M.; Rosa, Alexandra; Tempera, Fernando; Morato, Telmo

    2017-11-01

    Understanding the link between fish and their habitat is essential for an ecosystem approach to fisheries management. However, determining such relationship is challenging, especially for deep-sea species. In this study, we applied generalized additive models (GAMs) to relate presence-absence and relative abundance data of eight economically-important fish species to environmental variables (depth, slope, aspect, substrate type, bottom temperature, salinity and oxygen saturation). We combined 13 years of catch data collected from systematic longline surveys performed across the region. Overall, presence-absence GAMs performed better than abundance models and predictions made for the observed data successfully predicted the occurrence of the eight deep-sea fish species. Depth was the most influential predictor of all fish species occurrence and abundance distributions, whereas other factors were found to be significant for some species but did not show such a clear influence. Our results predicted that despite the extensive Azores EEZ, the habitats available for the studied deep-sea fish species are highly limited and patchy, restricted to seamounts slopes and summits, offshore banks and island slopes. Despite some identified limitations, our GAMs provide an improved knowledge of the spatial distribution of these commercially important fish species in the region.

  15. Developing a theoretical model and questionnaire survey instrument to measure the success of electronic health records in residential aged care.

    Science.gov (United States)

    Yu, Ping; Qian, Siyu

    2018-01-01

    Electronic health records (EHR) are introduced into healthcare organizations worldwide to improve patient safety, healthcare quality and efficiency. A rigorous evaluation of this technology is important to reduce potential negative effects on patient and staff, to provide decision makers with accurate information for system improvement and to ensure return on investment. Therefore, this study develops a theoretical model and questionnaire survey instrument to assess the success of organizational EHR in routine use from the viewpoint of nursing staff in residential aged care homes. The proposed research model incorporates six variables in the reformulated DeLone and McLean information systems success model: system quality, information quality, service quality, use, user satisfaction and net benefits. Two variables training and self-efficacy were also incorporated into the model. A questionnaire survey instrument was designed to measure the eight variables in the model. After a pilot test, the measurement scale was used to collect data from 243 nursing staff members in 10 residential aged care homes belonging to three management groups in Australia. Partial least squares path modeling was conducted to validate the model. The validated EHR systems success model predicts the impact of the four antecedent variables-training, self-efficacy, system quality and information quality-on the net benefits, the indicator of EHR systems success, through the intermittent variables use and user satisfaction. A 24-item measurement scale was developed to quantitatively evaluate the performance of an EHR system. The parsimonious EHR systems success model and the measurement scale can be used to benchmark EHR systems success across organizations and units and over time.

  16. New and Accurate Predictive Model for the Efficacy of Extracorporeal Shock Wave Therapy in Managing Patients With Chronic Plantar Fasciitis.

    Science.gov (United States)

    Yin, Mengchen; Chen, Ni; Huang, Quan; Marla, Anastasia Sulindro; Ma, Junming; Ye, Jie; Mo, Wen

    2017-12-01

    To identify factors for the outcome of a minimum clinically successful therapy and to establish a predictive model of extracorporeal shock wave therapy (ESWT) in managing patients with chronic plantar fasciitis. Randomized, controlled, prospective study. Outpatient of local medical center settings. Patients treated for symptomatic chronic plantar fasciitis between 2014 and 2016 (N=278). ESWT was performed by the principal authors to treat chronic plantar fasciitis. ESWT was administered in 3 sessions, with an interval of 2 weeks (±4d). In the low-, moderate-, and high-intensity groups, 2400 impulses total of ESWT with an energy flux density of 0.2, 0.4, and 0.6mJ/mm 2 , respectively (a rate of 8 impulses per second), were applied. The independent variables were patient age, sex, body mass index, affected side, duration of symptoms, Roles and Maudsley score, visual analog scale (VAS) score when taking first steps in the morning, edema, bone spurs, and intensity grade of ESWT. A minimal reduction of 50% in the VAS score was considered as minimum clinically successful therapy. The correlations between the achievement of minimum clinically successful therapy and independent variables were analyzed. The statistically significant factors identified were further analyzed by multivariate logistic regression, and the predictive model was established. The success rate of ESWT was 66.9%. Univariate analysis found that VAS score when taking first steps in the morning, edema, and the presence of heel spur in radiograph significantly affected the outcome of the treatment. Logistic regression drew the equation: minimum clinically successful therapy=(1+e [.011+42.807×heel spur+.109×edema+5.395×VAS score] ) -1 .The sensitivity of the predictive factors was 96.77%, 87.63%, and 86.02%, respectively. The specificity of the predictive factors was 45.65%, 42.39%, and 85.87%, respectively. The area under the curve of the predictive factors was .751, .650, and .859, respectively. The

  17. Predicting success for college students enrolled in an online, lab-based, biology course for non-majors

    Science.gov (United States)

    Foster, Regina

    Online education has exploded in popularity. While there is ample research on predictors of traditional college student success, little research has been done on effective methods of predicting student success in online education. In this study, a number of demographic variables including GPA, ACT, gender, age and others were examined to determine what, if any, role they play in successfully predicting student success in an online, lab-based biology for non-majors course. Within course variables such as participation in specific categories of assignment and frequency of online visits were also examined. Groups of students including Native American/Non-Native American and Digital Immigrants and Digital Natives and others were also examined to determine if overall course success differed significantly. Good predictors of online success were found to be GPA, ACT, previous course experience and frequency of online visits with the course materials. Additionally, students who completed more of the online assignments within the course were more successful. Native American and Non-Native American students were found to differ in overall course success significantly as well. Findings indicate student academic background, previous college experience and time spent with course materials are the most important factors in course success. Recommendations include encouraging enrollment advisors to advise students about the importance of maintaining high academic levels, previous course experience and spending time with course materials may impact students' choices for online courses. A need for additional research in several areas is indicated, including Native American and Non-Native American differences. A more detailed examination of students' previous coursework would also be valuable. A study involving more courses, a larger number of students and surveys from faculty who teach online courses would help improve the generalizability of the conclusions.

  18. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    Science.gov (United States)

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  19. Predictive factor analysis for successful performance of iris recognition-assisted dynamic rotational eye tracking during laser in situ keratomileusis.

    Science.gov (United States)

    Prakash, Gaurav; Ashok Kumar, Dhivya; Agarwal, Amar; Jacob, Soosan; Sarvanan, Yoga; Agarwal, Athiya

    2010-02-01

    To analyze the predictive factors associated with success of iris recognition and dynamic rotational eye tracking on a laser in situ keratomileusis (LASIK) platform with active assessment and correction of intraoperative cyclotorsion. Interventional case series. Two hundred seventy-five eyes of 142 consecutive candidates underwent LASIK with attempted iris recognition and dynamic rotational tracking on the Technolas 217z100 platform (Techolas Perfect Vision, St Louis, Missouri, USA) at a tertiary care ophthalmic hospital. The main outcome measures were age, gender, flap creation method (femtosecond, microkeratome, epi-LASIK), success of static rotational tracking, ablation algorithm, pulses, and depth; preablation and intraablation rotational activity were analyzed and evaluated using regression models. Preablation static iris recognition was successful in 247 eyes, without difference in flap creation methods (P = .6). Age (partial correlation, -0.16; P = .014), amount of pulses (partial correlation, 0.39; P = 1.6 x 10(-8)), and gender (P = .02) were significant predictive factors for the amount of intraoperative cyclodeviation. Tracking difficulties leading to linking the ablation with a new intraoperatively acquired iris image were more with femtosecond-assisted flaps (P = 2.8 x 10(-7)) and the amount of intraoperative cyclotorsion (P = .02). However, the number of cases having nonresolvable failure of intraoperative rotational tracking was similar in the 3 flap creation methods (P = .22). Intraoperative cyclotorsional activity depends on the age, gender, and duration of ablation (pulses delivered). Femtosecond flaps do not seem to have a disadvantage over microkeratome flaps as far as iris recognition and success of intraoperative dynamic rotational tracking is concerned. Copyright (c) 2010 Elsevier Inc. All rights reserved.

  20. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...

  1. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  2. Interaction of species traits and environmental disturbance predicts invasion success of aquatic microorganisms.

    Directory of Open Access Journals (Sweden)

    Elvira Mächler

    Full Text Available Factors such as increased mobility of humans, global trade and climate change are affecting the range of many species, and cause large-scale translocations of species beyond their native range. Many introduced species have a strong negative influence on the new local environment and lead to high economic costs. There is a strong interest to understand why some species are successful in invading new environments and others not. Most of our understanding and generalizations thereof, however, are based on studies of plants and animals, and little is known on invasion processes of microorganisms. We conducted a microcosm experiment to understand factors promoting the success of biological invasions of aquatic microorganisms. In a controlled lab experiment, protist and rotifer species originally isolated in North America invaded into a natural, field-collected community of microorganisms of European origin. To identify the importance of environmental disturbances on invasion success, we either repeatedly disturbed the local patches, or kept them as undisturbed controls. We measured both short-term establishment and long-term invasion success, and correlated it with species-specific life-history traits. We found that environmental disturbances significantly affected invasion success. Depending on the invading species' identity, disturbances were either promoting or decreasing invasion success. The interaction between habitat disturbance and species identity was especially pronounced for long-term invasion success. Growth rate was the most important trait promoting invasion success, especially when the species invaded into a disturbed local community. We conclude that neither species traits nor environmental factors alone conclusively predict invasion success, but an integration of both of them is necessary.

  3. Cut-Off Value for Pain Sensitivity Questionnaire in Predicting Surgical Success in Patients with Lumbar Disc Herniation.

    Directory of Open Access Journals (Sweden)

    Parisa Azimi

    Full Text Available Various factors related to predict surgical success were studied; however, a standard cut-off point for the Pain Sensitivity Questionnaire (PSQ measure has not yet been established for a favorable surgical outcome for lumbar disc herniation (LDH. This study was to find the optimal cut-off point on the PSQ to distinguish surgical success in patients with LDH. A total of 154 patients with LDH consecutively referred to our clinic were enrolled into this prospective study between February 2011 and January 2014. All participants completed the PSQ. Patients completed the Oswestry Disability Index (ODI score before surgery, and at 2 years after surgery. Surgical success was defined as a 13-point improvement from the baseline ODI scores. The cut-off value for PSQ was determined by the receiver-operating characteristic curve (ROC. The mean age of patients was 49.3±9.6 years, and there were 80 women. The mean time for follow-up assessment was 31±5 months (range 24-35. Post-surgical success was 79.9% (n = 123 at 2 years follow up. The mean score for the total PSQ, PSQ-minor, and PSQ-moderate were 6.0 (SD = 1.6, 5.4 (SD = 1.9 and 6.5 (SD = 1.7, respectively. Total PSQ score was also significantly correlated with the total scores of the ODI. The optimal total PSQ cut-off point was determined as > 5.2 to predict surgical success in LDH patients, with 80.0% sensitivity and 75.6% specificity (AUC-0.814, 95% CI 0.703-0.926. This study showed that the PSQ could be considered a parameter for predicting surgical success in patients with LDH, and can be useful in clinical practice.

  4. Successful and unsuccessful psychopaths: a neurobiological model.

    Science.gov (United States)

    Gao, Yu; Raine, Adrian

    2010-01-01

    Despite increasing interest in psychopathy research, surprisingly little is known about the etiology of non-incarcerated, successful psychopaths. This review provides an analysis of current knowledge on the similarities and differences between successful and unsuccessful psychopaths derived from five population sources: community samples, individuals from employment agencies, college students, industrial psychopaths, and serial killers. An initial neurobiological model of successful and unsuccessful psychopathy is outlined. It is hypothesized that successful psychopaths have intact or enhanced neurobiological functioning that underlies their normal or even superior cognitive functioning, which in turn helps them to achieve their goals using more covert and nonviolent methods. In contrast, in unsuccessful, caught psychopaths, brain structural and functional impairments together with autonomic nervous system dysfunction are hypothesized to underlie cognitive and emotional deficits and more overt violent offending.

  5. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  6. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

    Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

  7. Prognostic factors of successful tympanoplasty in pediatric patients: a cohort study

    Directory of Open Access Journals (Sweden)

    Boronat-Echeverría Nuria

    2012-06-01

    Full Text Available Abstract Background Tympanoplasty in children is a current and controversial theme. The success of tympanoplasty traditionally has been measured only by the post-operative integrity of the graft. Yet, there are other variables that may be used to determine success. The objectives of the present work were to analyze which factors are predictive of successful tympanoplasty in pediatric patients and to construct and validate a prognostic index that could be used as a tool to predict the success of tympanoplasty in children. Methods Setting. Department of Pediatric Otorhinolaryngology, tertiary-care hospital, Mexico City. Patients. Forty-eight patients, who were older that five years of age, had persistent perforation of the tympanic membrane, and had undergone tympanoplasty (January 2005–June 2008, were followed for a year. Main Outcome Measures. The factors tested for their value as predictors were the following: age at time of surgery, state of contralateral ear, previous adenoidectomy, cause of perforation, size of perforation, infection at the time of surgery, state of mucosa, age at first occurrence of perforation, presence of craniofacial dysmorphia, and surgical technique. These factors were compared with the criterion, success, which was defined as attaining three positive outcomes: 1 integrity of the implant or membrane; 2 minimum of 10-dB gain in the auditory threshold or, in the case of normal hearing, conservation of same; and 3 air-filled space in the middle ear. The best model was obtained through logistic regression analysis; the model was validated. Results The most balanced prediction model was that in which the three success criteria were included, with age, surgical technique, and infection at surgery being excluded as variables. The additional 12 pediatric cases used in the validation had a probability of success >0.425 (best cut-off level; two patients (17% had poor evolution. Conclusions This is the first study that

  8. Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

    OpenAIRE

    Namvar, Anahita; Naderpour, Mohsen

    2018-01-01

    As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be o...

  9. Predictive value of low tube voltage and dual-energy CT for successful shock wave lithotripsy: an in vitro study.

    Science.gov (United States)

    Largo, Remo; Stolzmann, Paul; Fankhauser, Christian D; Poyet, Cédric; Wolfsgruber, Pirmin; Sulser, Tullio; Alkadhi, Hatem; Winklhofer, Sebastian

    2016-06-01

    This study investigates the capabilities of low tube voltage computed tomography (CT) and dual-energy CT (DECT) for predicting successful shock wave lithotripsy (SWL) of urinary stones in vitro. A total of 33 urinary calculi (six different chemical compositions; mean size 6 ± 3 mm) were scanned using a dual-source CT machine with single- (120 kVp) and dual-energy settings (80/150, 100/150 Sn kVp) resulting in six different datasets. The attenuation (Hounsfield Units) of calculi was measured on single-energy CT images and the dual-energy indices (DEIs) were calculated from DECT acquisitions. Calculi underwent SWL and the number of shock waves for successful disintegration was recorded. The prediction of required shock waves regarding stone attenuation/DEI was calculated using regression analysis (adjusted for stone size and composition) and the correlation between CT attenuation/DEI and the number of shock waves was assessed for all datasets. The median number of shock waves for successful stone disintegration was 72 (interquartile range 30-361). CT attenuation/DEI of stones was a significant, independent predictor (P waves with the best prediction at 80 kVp (β estimate 0.576) (P waves ranged between ρ = 0.31 and 0.68 showing the best correlation at 80 kVp (P < 0.001). The attenuation of urinary stones at low tube voltage CT is the best predictor for successful stone disintegration, being independent of stone composition and size. DECT shows no added value for predicting the success of SWL.

  10. Predictive user modeling with actionable attributes

    NARCIS (Netherlands)

    Zliobaite, I.; Pechenizkiy, M.

    2013-01-01

    Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target

  11. Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

    Science.gov (United States)

    Gonzalez, Raul; Conn, Kathleen E.; Crosswell, Joey; Noble, Rachel

    2012-01-01

    Coastal and estuarine waters are the site of intense anthropogenic influence with concomitant use for recreation and seafood harvesting. Therefore, coastal and estuarine water quality has a direct impact on human health. In eastern North Carolina (NC) there are over 240 recreational and 1025 shellfish harvesting water quality monitoring sites that are regularly assessed. Because of the large number of sites, sampling frequency is often only on a weekly basis. This frequency, along with an 18–24 h incubation time for fecal indicator bacteria (FIB) enumeration via culture-based methods, reduces the efficiency of the public notification process. In states like NC where beach monitoring resources are limited but historical data are plentiful, predictive models may offer an improvement for monitoring and notification by providing real-time FIB estimates. In this study, water samples were collected during 12 dry (n = 88) and 13 wet (n = 66) weather events at up to 10 sites. Statistical predictive models for Escherichiacoli (EC), enterococci (ENT), and members of the Bacteroidales group were created and subsequently validated. Our results showed that models for EC and ENT (adjusted R2 were 0.61 and 0.64, respectively) incorporated a range of antecedent rainfall, climate, and environmental variables. The most important variables for EC and ENT models were 5-day antecedent rainfall, dissolved oxygen, and salinity. These models successfully predicted FIB levels over a wide range of conditions with a 3% (EC model) and 9% (ENT model) overall error rate for recreational threshold values and a 0% (EC model) overall error rate for shellfish threshold values. Though modeling of members of the Bacteroidales group had less predictive ability (adjusted R2 were 0.56 and 0.53 for fecal Bacteroides spp. and human Bacteroides spp., respectively), the modeling approach and testing provided information on Bacteroidales ecology. This is the first example of a set of successful statistical

  12. FUZZY MODELING BY SUCCESSIVE ESTIMATION OF RULES ...

    African Journals Online (AJOL)

    This paper presents an algorithm for automatically deriving fuzzy rules directly from a set of input-output data of a process for the purpose of modeling. The rules are extracted by a method termed successive estimation. This method is used to generate a model without truncating the number of fired rules, to within user ...

  13. Comparative analysis of insect succession data from Victoria (Australia) using summary statistics versus preceding mean ambient temperature models.

    Science.gov (United States)

    Archer, Mel

    2014-03-01

    Minimum postmortem interval (mPMI) can be estimated with preceding mean ambient temperature models that predict carrion taxon pre-appearance interval. But accuracy has not been compared with using summary statistics (mean ± SD of taxon arrival/departure day, range, 95% CI). This study collected succession data from ten experimental and five control (infrequently sampled) pig carcasses over two summers (n = 2 experimental, n = 1 control per placement date). Linear and exponential preceding mean ambient temperature models for appearance and departure times were constructed for 17 taxa/developmental stages. There was minimal difference in linear or exponential model success, although arrival models were more often significant: 65% of linear arrival (r2 = 0.09–0.79) and exponential arrival models (r2 = 0.05–81.0) were significant, and 35% of linear departure (r2 = 0.0–0.71) and exponential departure models (r2 = 0.0–0.72) were significant. Performance of models and summary statistics for estimating mPMI was compared in two forensic cases. Only summary statistics produced accurate mPMI estimates.

  14. Modeling antecedents of electronic medical record system implementation success in low-resource setting hospitals.

    Science.gov (United States)

    Tilahun, Binyam; Fritz, Fleur

    2015-08-01

    With the increasing implementation of Electronic Medical Record Systems (EMR) in developing countries, there is a growing need to identify antecedents of EMR success to measure and predict the level of adoption before costly implementation. However, less evidence is available about EMR success in the context of low-resource setting implementations. Therefore, this study aims to fill this gap by examining the constructs and relationships of the widely used DeLone and MacLean (D&M) information system success model to determine whether it can be applied to measure EMR success in those settings. A quantitative cross sectional study design using self-administered questionnaires was used to collect data from 384 health professionals working in five governmental hospitals in Ethiopia. The hospitals use a comprehensive EMR system since three years. Descriptive and structural equation modeling methods were applied to describe and validate the extent of relationship of constructs and mediating effects. The findings of the structural equation modeling shows that system quality has significant influence on EMR use (β = 0.32, P quality has significant influence on EMR use (β = 0.44, P service quality has strong significant influence on EMR use (β = 0.36, P effect of EMR use on user satisfaction was not significant. Both EMR use and user satisfaction have significant influence on perceived net-benefit (β = 0.31, P mediating factor in the relationship between service quality and EMR use (P effect on perceived net-benefit of health professionals. EMR implementers and managers in developing countries are in urgent need of implementation models to design proper implementation strategies. In this study, the constructs and relationships depicted in the updated D&M model were found to be applicable to assess the success of EMR in low resource settings. Additionally, computer literacy was found to be a mediating factor in EMR use and user satisfaction of

  15. Perceived emotional intelligence, general intelligence and early professional success: predictive and incremental validity

    Directory of Open Access Journals (Sweden)

    José-Manuel de Haro

    2014-05-01

    Full Text Available Although the study of factors affecting career success has shown connections between biographical and other aspects related to ability, knowledge and personality, few studies have examined the relationship between emotional intelligence and professional success at the initial career stage. When these studies were carried out, the results showed significant relationships between the dimensions of emotional intelligence (emotional self-awareness, self-regulation, social awareness or social skills and the level of professional competence. In this paper, we analyze the relationship between perceived emotional intelligence, measured by the Trait Meta-Mood Scale (TMMS-24 questionnaire, general intelligence assessed by the Cattell factor "g" test, scale 3, and extrinsic indicators of career success, in a sample of 130 graduates at the beginning of their careers. Results from hierarchical regression analysis indicate that emotional intelligence makes a specific contribution to the prediction of salary, after controlling the general intelligence effect. The perceived emotional intelligence dimensions of TMMS repair, TMMS attention and sex show a higher correlation and make a greater contribution to professional success than general intelligence. The implications of these results for the development of socio-emotional skills among University graduates are discussed.

  16. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

    Science.gov (United States)

    Son, S. W.; Lim, Y.; Kim, D.

    2017-12-01

    The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

  17. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

    Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

    2017-12-01

    Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

  18. Voice and Handgrip Strength Predict Reproductive Success in a Group of Indigenous African Females

    Science.gov (United States)

    Sorokowska, Agnieszka; Sorokowski, Piotr; Mberira, Mara; Bartels, Astrid; Gallup, Gordon G.

    2012-01-01

    Evolutionary accounts of human traits are often based on proxies for genetic fitness (e.g., number of sex partners, facial attractiveness). Instead of using proxies, actual differences in reproductive success is a more direct measure of Darwinian fitness. Certain voice acoustics such as fundamental frequency and measures of health such as handgrip strength correlate with proxies of fitness, yet there are few studies showing the relation of these traits to reproduction. Here, we explore whether the fundamental frequency of the voice and handgrip strength account for differences in actual reproduction among a population of natural fertility humans. Our results show that both fundamental frequency and handgrip strength predict several measures of reproductive success among a group of indigenous Namibian females, particularly amongst the elderly, with weight also predicting reproductive outcomes among males. These findings demonstrate that both hormonally regulated and phenotypic quality markers can be used as measures of Darwinian fitness among humans living under conditions that resemble the evolutionary environment of Homo sapiens. We also argue that these findings provide support for the Grandmother Hypothesis. PMID:22870251

  19. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  20. Factors for successful improvement of Swedish healthcare

    OpenAIRE

    Olsson, Jesper

    2005-01-01

    The Swedish OCM, developed by an Integrative Group Process, was found to be a valid model able to distinguish successful from unsuccessful organizations in terms of improvement. A majority of healthcare organizations applied the Internal Collaborative strategy which lacks the patient centered task alignment characterizing those organizations predicted to be successful by their relatively superior Swedish OCM score. Managers tend to overestimate the prospects of organizationa...

  1. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  2. Excellence in fleet combat replacement squadrons: predicting carrier qualification success

    OpenAIRE

    Smith, Martin P.

    1988-01-01

    This thesis presents a two-part analysis of excellence criteria for fleet combat replacement squadrons. Part one focuses on the qualitative issues and management techniques identified in outstanding fleet combat replacement squadrons. Part two develops and presents a regression model for predicting a fleet replacement squadron pilot's carrier qualification grade. The model was derived using standard linear regression techniques and the SPSSx software package of the Naval Postgraduate School. ...

  3. Establishing a Cloud Computing Success Model for Hospitals in Taiwan

    Science.gov (United States)

    Lian, Jiunn-Woei

    2017-01-01

    The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services. PMID:28112020

  4. Establishing a Cloud Computing Success Model for Hospitals in Taiwan.

    Science.gov (United States)

    Lian, Jiunn-Woei

    2017-01-01

    The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services.

  5. Establishing a Cloud Computing Success Model for Hospitals in Taiwan

    Directory of Open Access Journals (Sweden)

    Jiunn-Woei Lian PhD

    2017-01-01

    Full Text Available The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services.

  6. IS Success Model in E-Learning Context Based on Students' Perceptions

    Science.gov (United States)

    Freeze, Ronald D.; Alshare, Khaled A.; Lane, Peggy L.; Wen, H. Joseph

    2010-01-01

    This study utilized the Information Systems Success (ISS) model in examining e-learning systems success. The study was built on the premise that system quality (SQ) and information quality (IQ) influence system use and user satisfaction, which in turn impact system success. A structural equation model (SEM), using LISREL, was used to test the…

  7. [Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

    Science.gov (United States)

    Liu, Zun-lei; Yuan, Xing-wei; Yang, Lin-lin; Yan, Li-ping; Zhang, Hui; Cheng, Jia-hua

    2015-02-01

    Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P

  8. Long-term orbit prediction for China's Tiangong-1 spacecraft based on mean atmosphere model

    Science.gov (United States)

    Tang, Jingshi; Liu, Lin; Miao, Manqian

    Tiangong-1 is China's test module for future space station. It has gone through three successful rendezvous and dockings with Shenzhou spacecrafts from 2011 to 2013. For the long-term management and maintenance, the orbit sometimes needs to be predicted for a long period of time. As Tiangong-1 works in a low-Earth orbit with an altitude of about 300-400 km, the error in the a priori atmosphere model contributes significantly to the rapid increase of the predicted orbit error. When the orbit is predicted for 10-20 days, the error in the a priori atmosphere model, if not properly corrected, could induce the semi-major axis error and the overall position error up to a few kilometers and several thousand kilometers respectively. In this work, we use a mean atmosphere model averaged from NRLMSIS00. The a priori reference mean density can be corrected during precise orbit determination (POD). For applications in the long-term orbit prediction, the observations are first accumulated. With sufficiently long period of observations, we are able to obtain a series of the diurnal mean densities. This series bears the recent variation of the atmosphere density and can be analyzed for various periods. After being properly fitted, the mean density can be predicted and then applied in the orbit prediction. We show that the densities predicted with this approach can serve to increase the accuracy of the predicted orbit. In several 20-day prediction tests, most predicted orbits show semi-major axis errors better than 700m and overall position errors better than 600km.

  9. Promoting success or preventing failure: cultural differences in motivation by positive and negative role models.

    Science.gov (United States)

    Lockwood, Penelope; Marshall, Tara C; Sadler, Pamela

    2005-03-01

    In two studies, cross-cultural differences in reactions to positive and negative role models were examined. The authors predicted that individuals from collectivistic cultures, who have a stronger prevention orientation, would be most motivated by negative role models, who highlight a strategy of avoiding failure; individuals from individualistic cultures, who have a stronger promotion focus, would be most motivated by positive role models, who highlight a strategy of pursuing success. In Study 1, the authors examined participants' reported preferences for positive and negative role models. Asian Canadian participants reported finding negative models more motivating than did European Canadians; self-construals and regulatory focus mediated cultural differences in reactions to role models. In Study 2, the authors examined the impact of role models on the academic motivation of Asian Canadian and European Canadian participants. Asian Canadians were motivated only by a negative model, and European Canadians were motivated only by a positive model.

  10. Predicting College Success: Achievement, Demographic, and Psychosocial Predictors of First-Semester College Grade Point Average

    Science.gov (United States)

    Saltonstall, Margot

    2013-01-01

    This study seeks to advance and expand research on college student success. Using multinomial logistic regression analysis, the study investigates the contribution of psychosocial variables above and beyond traditional achievement and demographic measures to predicting first-semester college grade point average (GPA). It also investigates if…

  11. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  12. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

  13. Prediction of tectonic stresses and fracture networks with geomechanical reservoir models

    International Nuclear Information System (INIS)

    Henk, A.; Fischer, K.

    2014-09-01

    This project evaluates the potential of geomechanical Finite Element (FE) models for the prediction of in situ stresses and fracture networks in faulted reservoirs. Modeling focuses on spatial variations of the in situ stress distribution resulting from faults and contrasts in mechanical rock properties. In a first methodological part, a workflow is developed for building such geomechanical reservoir models and calibrating them to field data. In the second part, this workflow was applied successfully to an intensively faulted gas reservoir in the North German Basin. A truly field-scale geomechanical model covering more than 400km 2 was built and calibrated. It includes a mechanical stratigraphy as well as a network of 86 faults. The latter are implemented as distinct planes of weakness and allow the fault-specific evaluation of shear and normal stresses. A so-called static model describes the recent state of the reservoir and, thus, after calibration its results reveal the present-day in situ stress distribution. Further geodynamic modeling work considers the major stages in the tectonic history of the reservoir and provides insights in the paleo stress distribution. These results are compared to fracture data and hydraulic fault behavior observed today. The outcome of this project confirms the potential of geomechanical FE models for robust stress and fracture predictions. The workflow is generally applicable and can be used for modeling of any stress-sensitive reservoir.

  14. Prediction of tectonic stresses and fracture networks with geomechanical reservoir models

    Energy Technology Data Exchange (ETDEWEB)

    Henk, A.; Fischer, K. [TU Darmstadt (Germany). Inst. fuer Angewandte Geowissenschaften

    2014-09-15

    This project evaluates the potential of geomechanical Finite Element (FE) models for the prediction of in situ stresses and fracture networks in faulted reservoirs. Modeling focuses on spatial variations of the in situ stress distribution resulting from faults and contrasts in mechanical rock properties. In a first methodological part, a workflow is developed for building such geomechanical reservoir models and calibrating them to field data. In the second part, this workflow was applied successfully to an intensively faulted gas reservoir in the North German Basin. A truly field-scale geomechanical model covering more than 400km{sup 2} was built and calibrated. It includes a mechanical stratigraphy as well as a network of 86 faults. The latter are implemented as distinct planes of weakness and allow the fault-specific evaluation of shear and normal stresses. A so-called static model describes the recent state of the reservoir and, thus, after calibration its results reveal the present-day in situ stress distribution. Further geodynamic modeling work considers the major stages in the tectonic history of the reservoir and provides insights in the paleo stress distribution. These results are compared to fracture data and hydraulic fault behavior observed today. The outcome of this project confirms the potential of geomechanical FE models for robust stress and fracture predictions. The workflow is generally applicable and can be used for modeling of any stress-sensitive reservoir.

  15. A predictive modeling approach to increasing the economic effectiveness of disease management programs.

    Science.gov (United States)

    Bayerstadler, Andreas; Benstetter, Franz; Heumann, Christian; Winter, Fabian

    2014-09-01

    Predictive Modeling (PM) techniques are gaining importance in the worldwide health insurance business. Modern PM methods are used for customer relationship management, risk evaluation or medical management. This article illustrates a PM approach that enables the economic potential of (cost-) effective disease management programs (DMPs) to be fully exploited by optimized candidate selection as an example of successful data-driven business management. The approach is based on a Generalized Linear Model (GLM) that is easy to apply for health insurance companies. By means of a small portfolio from an emerging country, we show that our GLM approach is stable compared to more sophisticated regression techniques in spite of the difficult data environment. Additionally, we demonstrate for this example of a setting that our model can compete with the expensive solutions offered by professional PM vendors and outperforms non-predictive standard approaches for DMP selection commonly used in the market.

  16. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  17. Development of an Electronic Portfolio System Success Model: An Information Systems Approach

    Science.gov (United States)

    Balaban, Igor; Mu, Enrique; Divjak, Blazenka

    2013-01-01

    This research has two main goals: to develop an instrument for assessing Electronic Portfolio (ePortfolio) success and to build a corresponding ePortfolio success model using DeLone and McLean's information systems success model as the theoretical framework. For this purpose, we developed an ePortfolio success measurement instrument and structural…

  18. Process health management using success tree and empirical model

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Gyunyoung [Kyung Hee Univ., Yongin (Korea, Republic of); Kim, Suyoung [BNF Technology, Daejeon (Korea, Republic of); Sung, Wounkyoung [Korea South-East Power Co. Ltd., Seoul (Korea, Republic of)

    2012-03-15

    Interests on predictive or condition-based maintenance are heightening in power industries. The ultimate goal of the condition-based maintenance is to prioritize and optimize the maintenance resources by taking a reasonable decision-making process depending op plant's conditions. Such decision-making process should be able to not only observe the deviation from a normal state but also determine the severity or impact of the deviation on different levels such as a component, a system, or a plant. In order to achieve this purpose, a Plant Health Index (PHI) monitoring system was developed, which is operational in more than 10 units of large steam turbine cycles in Korea as well as desalination plants in Saudi Arabia as a proto-type demonstration. The PHI monitoring system has capability to detect whether the deviation between a measured and an estimated parameter which is the result of kernel regression using the accumulated operation data and the current plant boundary conditions (referred as an empirical model) is statistically meaningful. This deviation is converted into a certain index considering the margin to set points which are associated with safety. This index is referred as a PHI and the PHIs can be monitored for an individual parameter as well as a component, system, or plant level. In order to organize the PHIs at the component, system, or plant level, a success tree was developed. At the top of the success tree, the PHIs nodes in the middle of the success tree, the PHIs represent the health status of a component or a system. The concept and definition of the PHI, the key methodologies, the architecture of the developed system, and a practical case of using the PHI monitoring system are described in this article.

  19. Process health management using success tree and empirical model

    International Nuclear Information System (INIS)

    Heo, Gyunyoung; Kim, Suyoung; Sung, Wounkyoung

    2012-01-01

    Interests on predictive or condition-based maintenance are heightening in power industries. The ultimate goal of the condition-based maintenance is to prioritize and optimize the maintenance resources by taking a reasonable decision-making process depending op plant's conditions. Such decision-making process should be able to not only observe the deviation from a normal state but also determine the severity or impact of the deviation on different levels such as a component, a system, or a plant. In order to achieve this purpose, a Plant Health Index (PHI) monitoring system was developed, which is operational in more than 10 units of large steam turbine cycles in Korea as well as desalination plants in Saudi Arabia as a proto-type demonstration. The PHI monitoring system has capability to detect whether the deviation between a measured and an estimated parameter which is the result of kernel regression using the accumulated operation data and the current plant boundary conditions (referred as an empirical model) is statistically meaningful. This deviation is converted into a certain index considering the margin to set points which are associated with safety. This index is referred as a PHI and the PHIs can be monitored for an individual parameter as well as a component, system, or plant level. In order to organize the PHIs at the component, system, or plant level, a success tree was developed. At the top of the success tree, the PHIs nodes in the middle of the success tree, the PHIs represent the health status of a component or a system. The concept and definition of the PHI, the key methodologies, the architecture of the developed system, and a practical case of using the PHI monitoring system are described in this article

  20. Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

    Science.gov (United States)

    Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

    2016-02-19

    Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

  1. Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.

    Directory of Open Access Journals (Sweden)

    Michael Drexler

    Full Text Available Spatially explicit ecosystem models of all types require an initial allocation of biomass, often in areas where fisheries independent abundance estimates do not exist. A generalized additive modelling (GAM approach is used to describe the abundance of 40 species groups (i.e. functional groups across the Gulf of Mexico (GoM using a large fisheries independent data set (SEAMAP and climate scale oceanographic conditions. Predictor variables included in the model are chlorophyll a, sediment type, dissolved oxygen, temperature, and depth. Despite the presence of a large number of zeros in the data, a single GAM using a negative binomial distribution was suitable to make predictions of abundance for multiple functional groups. We present an example case study using pink shrimp (Farfantepenaeus duroarum and compare the results to known distributions. The model successfully predicts the known areas of high abundance in the GoM, including those areas where no data was inputted into the model fitting. Overall, the model reliably captures areas of high and low abundance for the large majority of functional groups observed in SEAMAP. The result of this method allows for the objective setting of spatial distributions for numerous functional groups across a modeling domain, even where abundance data may not exist.

  2. Testing predictions of forest succession using long-term measurements: 100 yrs of observations in the Oregon Cascades

    Science.gov (United States)

    Mark E. Harmon; Robert J. Pabst

    2015-01-01

    Question: Many predictions about forest succession have been based on chronosequences. Are these predictions – at the population, community and ecosystemlevel – consistent with long-termmeasurements in permanent plots? Location: Pseudotsuga menziesii (Mirb.) Franco dominated forest in western Oregon, US.Methods: Over a 100-yr period,...

  3. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  4. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  5. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  6. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  7. The UIS Model for Online Success

    Science.gov (United States)

    Bloemer, Bill

    2009-01-01

    This case study describes the philosophy underlying the delivery of online programs and courses at the University of Illinois-Springfield. The strategies used to implement the UIS model and the measures used to validate its success are outlined. These factors are reviewed in the context of the Sloan-C Five Pillars for quality learning environments.

  8. Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

    Science.gov (United States)

    Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

    2017-12-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

  9. OL-DEC-MDP Model for Multiagent Online Scheduling with a Time-Dependent Probability of Success

    Directory of Open Access Journals (Sweden)

    Cheng Zhu

    2014-01-01

    Full Text Available Focusing on the on-line multiagent scheduling problem, this paper considers the time-dependent probability of success and processing duration and proposes an OL-DEC-MDP (opportunity loss-decentralized Markov Decision Processes model to include opportunity loss into scheduling decision to improve overall performance. The success probability of job processing as well as the process duration is dependent on the time at which the processing is started. The probability of completing the assigned job by an agent would be higher when the process is started earlier, but the opportunity loss could also be high due to the longer engaging duration. As a result, OL-DEC-MDP model introduces a reward function considering the opportunity loss, which is estimated based on the prediction of the upcoming jobs by a sampling method on the job arrival. Heuristic strategies are introduced in computing the best starting time for an incoming job by each agent, and an incoming job will always be scheduled to the agent with the highest reward among all agents with their best starting policies. The simulation experiments show that the OL-DEC-MDP model will improve the overall scheduling performance compared with models not considering opportunity loss in heavy-loading environment.

  10. Evaluation of SAMe-TT2R2 Score on Predicting Success With Extended-Interval Warfarin Monitoring.

    Science.gov (United States)

    Hwang, Andrew Y; Carris, Nicholas W; Dietrich, Eric A; Gums, John G; Smith, Steven M

    2018-06-01

    In patients with stable international normalized ratios, 12-week extended-interval warfarin monitoring can be considered; however, predictors of success with this strategy are unknown. The previously validated SAMe-TT 2 R 2 score (considering sex, age, medical history, treatment, tobacco, and race) predicts anticoagulation control during standard follow-up (every 4 weeks), with lower scores associated with greater time in therapeutic range. To evaluate the ability of the SAMe-TT 2 R 2 score in predicting success with extended-interval warfarin follow-up in patients with previously stable warfarin doses. In this post hoc analysis of a single-arm feasibility study, baseline SAMe-TT 2 R 2 scores were calculated for patients with ≥1 extended-interval follow-up visit. The primary analysis assessed achieved weeks of extended-interval follow-up according to baseline SAMe-TT 2 R 2 scores. A total of 47 patients receiving chronic anticoagulation completed a median of 36 weeks of extended-interval follow-up. The median baseline SAMe-TT 2 R 2 score was 1 (range 0-5). Lower SAMe-TT 2 R 2 scores appeared to be associated with greater duration of extended-interval follow-up achieved, though the differences between scores were not statistically significant. No individual variable of the SAMe-TT 2 R 2 score was associated with achieved weeks of extended-interval follow-up. Analysis of additional patient factors found that longer duration (≥24 weeks) of prior stable treatment was significantly associated with greater weeks of extended-interval follow-up completed ( P = 0.04). Conclusion and Relevance: This pilot study provides limited evidence that the SAMe-TT 2 R 2 score predicts success with extended-interval warfarin follow-up but requires confirmation in a larger study. Further research is also necessary to establish additional predictors of successful extended-interval warfarin follow-up.

  11. Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

    Science.gov (United States)

    Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

    2005-12-01

    Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

  12. Predictive value of the transtheoretical model to smoking cessation in hospitalized patients with cardiovascular disease.

    Science.gov (United States)

    Chouinard, Maud-Christine; Robichaud-Ekstrand, Sylvie

    2007-02-01

    Several authors have questioned the transtheoretical model. Determining the predictive value of each cognitive-behavioural element within this model could explain the multiple successes reported in smoking cessation programmes. The purpose of this study was to predict point-prevalent smoking abstinence at 2 and 6 months, using the constructs of the transtheoretical model, when applied to a pooled sample of individuals who were hospitalized for a cardiovascular event. The study follows a predictive correlation design. Recently hospitalized patients (n=168) with cardiovascular disease were pooled from a randomized, controlled trial. Independent variables of the predictive transtheoretical model comprise stages and processes of change, pros and cons to quit smoking (decisional balance), self-efficacy, and social support. These were evaluated at baseline, 2 and 6 months. Compared to smokers, individuals who abstained from smoking at 2 and 6 months were more confident at baseline to remain non-smokers, perceived less pros and cons to continue smoking, utilized less consciousness raising and self-re-evaluation experiential processes of change, and received more positive reinforcement from their social network with regard to their smoke-free behaviour. Self-efficacy and stages of change at baseline were predictive of smoking abstinence after 6 months. Other variables found to be predictive of smoking abstinence at 6 months were an increase in self-efficacy; an increase in positive social support behaviour and a decrease of the pros within the decisional balance. The results partially support the predictive value of the transtheoretical model constructs in smoking cessation for cardiovascular disease patients.

  13. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    International Nuclear Information System (INIS)

    Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.; Contrera, Joseph F.

    2007-01-01

    , comprised primarily of pharmaceutical, industrial and some natural products developed under an FDA-MDL cooperative research and development agreement (CRADA). The predictive performance for this group of dietary natural products and the control group was 97% sensitivity and 80% concordance. Specificity was marginal at 53%. This study finds that the in silico QSAR analysis employing this software's rodent carcinogenicity database is capable of identifying the rodent carcinogenic potential of naturally occurring organic molecules found in the human diet with a high degree of sensitivity. It is the first study to demonstrate successful QSAR predictive modeling of naturally occurring carcinogens found in the human diet using an external validation test. Further test validation of this software and expansion of the training data set for dietary chemicals will help to support the future use of such QSAR methods for screening and prioritizing the risk of dietary chemicals when actual animal data are inadequate, equivocal, or absent

  14. Examining the patterns and dynamics of species abundance distributions in succession of forest communities by model selection

    Science.gov (United States)

    Luo, Shao-Ming; Chen, Ping; He, Xiao; Guo, Wei; Li, Bailian

    2018-01-01

    There are a few common species and many rare species in a biological community or a multi-species collection in given space and time. This hollow distribution curve is called species abundance distribution (SAD). Few studies have examined the patterns and dynamics of SADs during the succession of forest communities by model selection. This study explored whether the communities in different successional stages followed different SAD models and whether there existed a best SAD model to reveal their intrinsic quantitative features of structure and dynamics in succession. The abundance (the number of individuals) of each vascular plant was surveyed by quadrat sampling method from the tree, shrub and herb layers in two typical communities (i.e., the evergreen needle- and broad-leaved mixed forest and the monsoon evergreen broad-leaved forest) in southern subtropical Dinghushan Biosphere Reserve, South China. The sites of two forest communities in different successional stages are both 1 ha in area. We collected seven widely representative SAD models with obviously different function forms and transformed them into the same octave (log2) scale. These models are simultaneously confronted with eight datasets from four layers of two communities, and their goodness-of-fits to the data were evaluated by the chi-squared test, the adjusted coefficient of determination and the information criteria. The results indicated that: (1) the logCauchy model followed all the datasets and was the best among seven models; (2) the fitness of each model to the data was not directly related to the successional stage of forest community; (3) according to the SAD curves predicted by the best model (i.e., the logCauchy), the proportion of rare species decreased but that of common ones increased in the upper layers with succession, while the reverse was true in the lower layers; and (4) the difference of the SADs increased between the upper and the lower layers with succession. We concluded that

  15. Application of two forest succession models at sites in Northeast Germany

    International Nuclear Information System (INIS)

    Lasch, P.; Lindner, M.

    1995-06-01

    In order to simulate potential impacts of climate change on forests, two succession models were applied to sites in the Northeast German lowlands. The models, which had been developed for Alpine (FORECE) and Boreal (FORSKA) forests differ from each other in the way they represent tree growth processes and the impact of environmental factors on establishment and growth. Both models were adjusted and compared with each other at sites that are situated along an ecological gradient from maritime to subcontinental climate. These sites are extending the former environmental space of model application towards water limited conditions, which under a predicted climatic change may have increasing importance for European forests. First results showed that FORECE was unrealistically sensitive to changes in soil moisture. On the other hand, FORSKA generally simulated very low biomasses. Since the structure of FORSKA seemed to be better suited for the simulation of changing environmental conditions, this model was chosen for further model development, applications and sensitivity analyses. Among other changes, establishment rates were increased and some environmental response factors were analysed. The function of account for resource depletion was modified. After the modifications for Central European conditions were made, there was a decrease in performance for the Boreal site. Both simulated total biomasses and species composition had changed. We conclude, that with currently available models, realistic forest dynamics within different climatic zones of Europe cannot be simulated without more substantial model modifications. (orig.)

  16. A Validated Analytical Model for Availability Prediction of IPTV Services in VANETs

    Directory of Open Access Journals (Sweden)

    Bernd E. Wolfinger

    2014-12-01

    Full Text Available In vehicular ad hoc networks (VANETs, besides the original applications typically related to traffic safety, we nowadays can observe an increasing trend toward infotainment applications, such as IPTV services. Quality of experience (QoE, as observed by the end users of IPTV, is highly important to guarantee adequate user acceptance for the service. In IPTV, QoE is mainly determined by the availability of TV channels for the users. This paper presents an efficient and rather generally applicable analytical model that allows one to predict the blocking probability of TV channels, both for channel-switching-induced, as well as for handover-induced blocking events. We present the successful validation of the model by means of simulation, and we introduce a new measure for QoE. Numerous case studies illustrate how the analytical model and our new QoE measure can be applied successfully for the dimensioning of IPTV systems, taking into account the QoE requirements of the IPTV service users in strongly diverse traffic scenarios.

  17. Career success criteria and locus of control as indicators of adaptive readiness in the career adaptation model.

    OpenAIRE

    Zhou, W.; Guan, Y.; Xin, L.; Mak, M.C.K.; Deng, Y.

    2016-01-01

    The present research had two goals. The first goal was to identify additional individual characteristics that may contribute to adaptive readiness. The second goal was to test if these characteristics fit the career adaptation model of readiness to resources to responses. We examined whether career success criteria (measured at Time 1) and career locus of control (measured at Time 1) would contribute to adaptivity and predict university students’ career decision-making self-efficacy (measured...

  18. Predicting Student Success in a Major's Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores

    Science.gov (United States)

    Thompson, E. David; Bowling, Bethany V.; Markle, Ross E.

    2018-02-01

    Studies over the last 30 years have considered various factors related to student success in introductory biology courses. While much of the available literature suggests that the best predictors of success in a college course are prior college grade point average (GPA) and class attendance, faculty often require a valuable predictor of success in those courses wherein the majority of students are in the first semester and have no previous record of college GPA or attendance. In this study, we evaluated the efficacy of the ACT Mathematics subject exam and Lawson's Classroom Test of Scientific Reasoning in predicting success in a major's introductory biology course. A logistic regression was utilized to determine the effectiveness of a combination of scientific reasoning (SR) scores and ACT math (ACT-M) scores to predict student success. In summary, we found that the model—with both SR and ACT-M as significant predictors—could be an effective predictor of student success and thus could potentially be useful in practical decision making for the course, such as directing students to support services at an early point in the semester.

  19. Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

    Directory of Open Access Journals (Sweden)

    A. E. Sikorska

    2012-04-01

    Full Text Available Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

  20. Development and validation of the 3-D CFD model for CANDU-6 moderator temperature predictions

    International Nuclear Information System (INIS)

    Yoon, Churl; Rhee, Bo Wook; Min, Byung Joo

    2003-03-01

    A computational fluid dynamics model for predicting the moderator circulation inside the CANada Deuterium Uranium (CANDU) reactor vessel has been developed to estimate the local subcooling of the moderator in the vicinity of the Calandria tubes. The buoyancy effect induced by internal heating is accounted for by Boussinesq approximation. The standard κ-ε turbulence model associated with logarithmic wall treatment is applied to predict the turbulent jet flows from the inlet nozzles. The matrix of the Calandria tubes in the core region is simplified to porous media, in which an-isotropic hydraulic impedance is modeled using an empirical correlation of the frictional pressure loss. The governing equations are solved by CFX-4.4, a commercial CFD code developed by AEA technology. The CFD model has been successfully verified and validated against experimental data obtained in the Stern Laboratories Inc. (SLI) in Hamilton, Ontario

  1. Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

    Science.gov (United States)

    Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

    2016-01-01

    Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

  2. Mathematical modeling and computational prediction of cancer drug resistance.

    Science.gov (United States)

    Sun, Xiaoqiang; Hu, Bin

    2017-06-23

    Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of

  3. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Clinical Inquiry: What's the best way to predict the success of a trial of labor after a previous C-section?

    Science.gov (United States)

    Warren, Johanna B; Hamilton, Andrew

    2015-12-01

    Seven validated prospective scoring systems, and one unvalidated system, predict a successful TOLAC based on a variety of clinical factors. The systems use different outcome statistics, so their predictive accuracy can't be directly compared.

  5. Reproductive success is predicted by social dynamics and kinship in managed animal populations [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Saul J. Newman

    2016-05-01

    Full Text Available Kin and group interactions are important determinants of reproductive success in many species. Their optimization could, therefore, potentially improve the productivity and breeding success of managed populations used for agricultural and conservation purposes. Here we demonstrate this potential using a novel approach to measure and predict the effect of kin and group dynamics on reproductive output in a well-known species, the meerkat Suricata suricatta. Variation in social dynamics predicts 30% of the individual variation in reproductive success of this species in managed populations, and accurately forecasts reproductive output at least two years into the future. Optimization of social dynamics in captive meerkat populations doubles their projected reproductive output. These results demonstrate the utility of a quantitative approach to breeding programs informed by social and kinship dynamics. They suggest that this approach has great potential for improvements in the management of social endangered and agricultural species.

  6. Improving MJO Prediction and Simulation Using AGCM Coupled Ocean Model with Refined Vertical Resolution

    Science.gov (United States)

    Tu, Chia-Ying; Tseng, Wan-Ling; Kuo, Pei-Hsuan; Lan, Yung-Yao; Tsuang, Ben-Jei; Hsu, Huang-Hsiung

    2017-04-01

    Precipitation in Taiwan area is significantly influenced by MJO (Madden-Julian Oscillation) in the boreal winter. This study is therefore conducted by toggling the MJO prediction and simulation with a unique model structure. The one-dimensional TKE (Turbulence Kinetic Energy) type ocean model SIT (Snow, Ice, Thermocline) with refined vertical resolution near surface is able to resolve cool skin, as well as diurnal warm layer. SIT can simulate accurate SST and hence give precise air-sea interaction. By coupling SIT with ECHAM5 (MPI-Meteorology), CAM5 (NCAR) and HiRAM (GFDL), the MJO simulations in 20-yrs climate integrations conducted by three SIT-coupled AGCMs are significant improved comparing to those driven by prescribed SST. The horizontal resolutions in ECHAM5, CAM5 and HiRAM are 2-deg., 1-deg and 0.5-deg., respectively. This suggests that the improvement of MJO simulation by coupling SIT is AGCM-resolution independent. This study further utilizes HiRAM coupled SIT to evaluate its MJO forecast skill. HiRAM has been recognized as one of the best model for seasonal forecasts of hurricane/typhoon activity (Zhao et al., 2009; Chen & Lin, 2011; 2013), but was not as successful in MJO forecast. The preliminary result of the HiRAM-SIT experiment during DYNAMO period shows improved success in MJO forecast. These improvements of MJO prediction and simulation in both hindcast experiments and climate integrations are mainly from better-simulated SST diurnal cycle and diurnal amplitude, which is contributed by the refined vertical resolution near ocean surface in SIT. Keywords: MJO Predictability, DYNAMO

  7. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing

  8. Cephalometric variables predicting the long-term success or failure of combined rapid maxillary expansion and facial mask therapy.

    Science.gov (United States)

    Baccetti, Tiziano; Franchi, Lorenzo; McNamara, James A

    2004-07-01

    The aim of this study was to select a model of cephalometric variables to predict the results of early treatment of Class III malocclusion with rapid maxillary expansion and facemask therapy followed by comprehensive treatment with fixed appliances. Lateral cephalograms of 42 patients (20 boys, 22 girls) with Class III malocclusion were analyzed at the start of treatment (mean age 8 years 6 months +/- 2 years, at stage I in cervical vertebral maturation). All patients were reevaluated after a mean period of 6 years 6 months (at stage IV or V in cervical vertebral maturation) that included active treatment plus retention. At this time, the sample was divided into 2 groups according to occlusal criteria: a successful group (30 patients) and an unsuccessful group (12 patients). Discriminant analysis was applied to select pretreatment predictive variables of long-term treatment outcome. Stepwise variable selection of the cephalometric measurements at the first observation identified 3 predictive variables. Orthopedic treatment of Class III malocclusion might be unfavorable over the long term when a patient's pretreatment cephalometric records exhibit a long mandibular ramus (ie, increased posterior facial height), an acute cranial base angle, and a steep mandibular plane angle. On the basis of the equation generated by the multivariate statistical method, the outcome of interceptive orthopedic treatment for each new patient with Class III malocclusion can be predicted with a probability error of 16.7%.

  9. The predictive power of SIMION/SDS simulation software for modeling ion mobility spectrometry instruments

    Science.gov (United States)

    Lai, Hanh; McJunkin, Timothy R.; Miller, Carla J.; Scott, Jill R.; Almirall, José R.

    2008-09-01

    The combined use of SIMION 7.0 and the statistical diffusion simulation (SDS) user program in conjunction with SolidWorks® with COSMSOSFloWorks® fluid dynamics software to model a complete, commercial ion mobility spectrometer (IMS) was demonstrated for the first time and compared to experimental results for tests using compounds of immediate interest in the security industry (e.g., 2,4,6-trinitrotoluene, 2,7-dinitrofluorene, and cocaine). The effort of this research was to evaluate the predictive power of SIMION/SDS for application to IMS instruments. The simulation was evaluated against experimental results in three studies: (1) a drift:carrier gas flow rates study assesses the ability of SIMION/SDS to correctly predict the ion drift times; (2) a drift gas composition study evaluates the accuracy in predicting the resolution; (3) a gate width study compares the simulated peak shape and peak intensity with the experimental values. SIMION/SDS successfully predicted the correct drift time, intensity, and resolution trends for the operating parameters studied. Despite the need for estimations and assumptions in the construction of the simulated instrument, SIMION/SDS was able to predict the resolution between two ion species in air within 3% accuracy. The preliminary success of IMS simulations using SIMION/SDS software holds great promise for the design of future instruments with enhanced performance.

  10. Variation in circulating testosterone during mating predicts reproductive success in a wild songbird.

    Directory of Open Access Journals (Sweden)

    Beate Apfelbeck

    2016-08-01

    Full Text Available Testosterone is an important sex hormone and mediates reproduction in male vertebrates. There is ample evidence that testosterone coordinates the expression of physiological, morphological and behavioural traits during reproduction and many of these traits are under sexual selection. However, only few studies so far have examined if individual variation in testosterone is correlated with reproductive success. Because socially monogamous bird species pass through different phases within a breeding cycle and each of these phases requires the expression of different behaviours, the relation between testosterone and reproductive success could vary with breeding stage. Here we investigate the link between reproductive success and testosterone in European stonechats – a socially monogamous songbird with biparental care. Previous studies found that territorial aggression in breeding stonechats depends on testosterone and that testosterone levels peak during the mating phase. Thus, high testosterone levels during mating may influence reproductive success by promoting territorial aggression and mate guarding. We found that males with two breeding attempts produced a similar number of fledglings as males with three breeding attempts. However, males with two breeding attempts expressed higher levels of testosterone than males with just one or those with three breeding attempts, regardless of whether testosterone was measured during the mating or the parental phase of the first brood. Furthermore, testosterone levels during mating, but not during parenting correlated with the total annual number of fledglings. Thus, individual variation in levels of plasma testosterone predicted reproductive success in stonechats.

  11. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  12. Information Systems Success In Public Administration: Proposal For An Exploratory Model

    Directory of Open Access Journals (Sweden)

    Deyvison de Lima Oliveira

    2015-12-01

    Full Text Available Investments in Information Systems (IS have been significantly increasing and hence the relevance of the studies on the IS success is persistent. Delone and Mclean (2003 developed an IS successful model which is a benchmark for researches in the area, however, in the perspective of the public sector, studies are still rare. In this research it was sought to propose an exploratory model of successful IS in public administration, from the identification of each construct items of the Delone and McLean (2003 original model, through multiple case studies in three Municipalities and a Town Hall of the Southern Cone of Rondônia state, in Brazil. Based on the empirical research, it was found that the IS success factors in municipal public administration are close to those factors indicated in the reviewed literature, however, showing some particularities of the public sector. A model of successful factors and items of IS, from the confrontation between literature and empirical data, is presented in the end of this work.

  13. Profile and determinants of successful aging in the Ibadan Study of Ageing.

    Science.gov (United States)

    Gureje, Oye; Oladeji, Bibilola D; Abiona, Taiwo; Chatterji, Somnath

    2014-05-01

    To determine the profile and determinants of successful aging in a developing country characterized by low life expectancy and where successful agers may represent a unique group. Community-based cohort study. Eight contiguous states in the Yoruba-speaking region of Nigeria. A multistage clustered sampling of households was used to select a representative sample of individuals (N = 2,149) aged 65 and older at baseline. Nine hundred thirty were successfully followed for an average of 64 months between August 2003 and December 2009. Lifestyle and behavioral factors were assessed at baseline. Successful aging, defined using each of three models (absence of chronic health conditions, functional independence, and satisfaction with life), was assessed at follow-up. Between 16% and 75% of respondents could be classified as successful agers using one of the three models while 7.5% could be so classified using a combination of all the models. Correlations between the three models were small, ranging from 0.08 to 0.15. Different features predicted their outcomes, suggesting that they represent relatively independent trajectories of aging. Whichever model was used, more men than women tended to be classified as aging successfully. Men who aged successfully, using a combination of all the three models, were more likely never to have smoked (adjusted odds ratio (aOR) = 4.7, 95% confidence interval (CI) = 1.55-14.46) and to report, at baseline, having contacts with friends (aOR = 4.2, 95% CI = 1.0-18.76) or participating in community activities (aOR = 16.0, 95% CI = 1.23-204.40). In women, there was a nonlinear trend for younger age at baseline to predict this outcome. Modifiable social and lifestyle factors predicted successful aging in this population, suggesting that health promotion targeting behavior change may lead to tangible benefits for health and well-being in old age. © 2014, Copyright the Authors Journal compilation © 2014, The American Geriatrics Society.

  14. Success/Failure Prediction of Noninvasive Mechanical Ventilation in Intensive Care Units. Using Multiclassifiers and Feature Selection Methods.

    Science.gov (United States)

    Martín-González, Félix; González-Robledo, Javier; Sánchez-Hernández, Fernando; Moreno-García, María N

    2016-05-17

    This paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventilation (NIMV) in intensive care units. Data mining methods were employed to find out the factors influencing the success/failure of NIMV and to predict its results in future patients. These artificial intelligence-based methods have not been applied in this field in spite of the good results obtained in other medical areas. Feature selection methods provided the most influential variables in the success/failure of NIMV, such as NIMV hours, PaCO2 at the start, PaO2 / FiO2 ratio at the start, hematocrit at the start or PaO2 / FiO2 ratio after two hours. These methods were also used in the preprocessing step with the aim of improving the results of the classifiers. The algorithms provided the best results when the dataset used as input was the one containing the attributes selected with the CFS method. Data mining methods can be successfully applied to determine the most influential factors in the success/failure of NIMV and also to predict NIMV results in future patients. The results provided by classifiers can be improved by preprocessing the data with feature selection techniques.

  15. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  16. Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection

    Science.gov (United States)

    Zipkin, Elise F.; Grant, Evan H. Campbell; Fagan, William F.

    2012-01-01

    The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multi-species hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions of species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation dataset. We found that wetland hydroperiod (the length of time that a wetland holds water) as well as the occurrence state in the prior year were generally the most important factors in determining occupancy. The model with only habitat covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multi-species models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for

  17. Predictive QSPR Modelling for the Second Virial Coefficient of the Pure Organic Compounds.

    Science.gov (United States)

    Mokshyna, E; Polishchuk, P G; Nedostup, V I; Kuzmin, V E

    2015-01-01

    In this article we developed a system of the predictive models for the second virial coefficients of the pure compounds. Second virial coefficient is the property derived from the virial equation of state, and is of particular interest as it describes pair intermolecular interactions. The two-layer QSPR models were developed, which exploited the well-known physical equations and allowed us to include this information into traditional QSPR methodology. This shows some new perspectives for work with temperature-dependent properties. It was shown that 2D descriptors can be successfully used for modeling of complex thermodynamic properties like virial coefficients. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-09-10

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.

  19. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

  20. Project description and crowdfunding success: an exploratory study.

    Science.gov (United States)

    Zhou, Mi Jamie; Lu, Baozhou; Fan, Weiguo Patrick; Wang, G Alan

    2018-01-01

    Existing research on antecedent of funding success mainly focuses on basic project properties such as funding goal, duration, and project category. In this study, we view the process by which project owners raise funds from backers as a persuasion process through project descriptions. Guided by the unimodel theory of persuasion, this study identifies three exemplary antecedents (length, readability, and tone) from the content of project descriptions and two antecedents (past experience and past expertise) from the trustworthy cue of project descriptions. We then investigate their impacts on funding success. Using data collected from Kickstarter, a popular crowdfunding platform, we find that these antecedents are significantly associated with funding success. Empirical results show that the proposed model that incorporated these antecedents can achieve an accuracy of 73 % (70 % in F-measure). The result represents an improvement of roughly 14 percentage points over the baseline model based on informed guessing and 4 percentage points improvement over the mainstream model based on basic project properties (or 44 % improvement of mainstream's performance over informed guessing). The proposed model also has superior true positive and true negative rates. We also investigate the timeliness of project data and find that old project data is gradually becoming less relevant and losing predictive power to newly created projects. Overall, this study provides evidence that antecedents identified from project descriptions have incremental predictive power and can help project owners evaluate and improve the likelihood of funding success.

  1. A STUDY ON THE SUCCESSION MODEL OF FAMILY BUSINESSS

    OpenAIRE

    Hung-Jung Chang; Szu-Ju Lin

    2011-01-01

    Family business has to face issues such as ownership issue, governance structure issue and succession issue, etc. in enterprise development history. Among them, the succession issue is an important transition point in enterprise’s survival and development. It is thus thought of as one of the most important strategic and decision making issues in the enterprise. This article aims at investigating the succession model of Family business. First, reviews are done on the meaning of Family business...

  2. A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

    Science.gov (United States)

    Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

    2010-01-01

    The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…

  3. Research of combination model for prediction of the trend of outbreak of hepatitis B

    Directory of Open Access Journals (Sweden)

    Yin-ping CHEN

    2014-03-01

    Full Text Available Objective To establish a combination model of autoregressive integrated moving average model and the grey dynamics (ARIMA-GM of hepatitis B incidence rate (1/100 000 to predict the trend of outbreak of hepatitis B, as to provide a scientific basis for the early discovery of the infectious diseases for the performance of countermeasures of controlling its spread. Methods The monthly incidence of hepatitis B in Qian'an city, Hebei province, was collected from Jan 2004 to Dec 2012, and a model (ARIMA was reproduced with SPSS software. The GM (1,1 model was used to correct the residual sequence with a threshold value, and a combined forecasting model was reproduced. This combination model was used to predict the monthly incidence rate in this city in 2013. Results The model ARIMA(0,1,1(0,1,112 was established successfully and the residual sequence was a white noise sequence. Then the GM (1,1 model with a threshold of 3 was used to correct its residuals and obtain its nonlinear feature extraction of information. The forecasting model met required precision standards (C=0.673, P=0.877, the fitting accuracy of which was basically qualified. The results showed that the MAE, MAPE of the ARIMA-GM combined model were smaller than that of a single model, and the combined model could improve the prediction accuracy. Using the combined model to forecast the incidence of hepatitis B during Jan 2013 to Dec 2013, the overall trend was relatively consistent with the condition of previous years. Conclusion The ARIMA-GM combined model can better fit the incidence rate of hepatitis B with a greater accuracy than the seasonal ARIMA model. The prediction results can provide the reference for the early warning system of HBV. DOI: 10.11855/j.issn.0577-7402.2014.01.12

  4. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    Science.gov (United States)

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  5. Factors predicting recurrence in successfully treated cases of anisometropic amblyopia

    Directory of Open Access Journals (Sweden)

    Rohit Saxena

    2013-01-01

    Full Text Available Context: Recurrence after successful treatment of amblyopia is known and understanding the risk factors could help effective management. Aim: To measure incidence of recurrence in successfully treated cases of anisometropic amblyopia and evaluate factors predicting it. Settings and Design: Cohort Study at a tertiary level institution. Materials and Methods: Successfully treated anisometropic amblyopes aged 4−12 years were followed up for 1 year after stopping therapy. Best corrected visual acuity (BCVA, refractive error, stereoacuity and contrast sensitivity were evaluated at baseline and follow-up. Statistical Analysis: Intergroup analysis with appropriate tests: Chi-square test, Fisher′s exact test, Wilcoxon rank sum test and paired t-test. Results: One hundred and two patients with mean age at diagnosis 7.06 μ 1.81 years were followed-up for a mean duration of 1.0 μ 0.2 years. The mean pre-treatment BCVA (LogMAR score at diagnosis was 0.73 μ 0.36 units which improved to 0.20 μ 0.00 with treatment and after 1 year of stopping treatment was 0.22 μ 0.07. Thirteen (12.74% patients showed amblyopia recurrence during follow-up. Risk of recurrence was higher with older age of onset of treatment (6.64 μ 1.77 years without recurrence v/s 8.53 μ 1.39 years with recurrence, P = 0.0014. Greater extent of improvement of VA (P = 0.048 and final VA at stopping occlusion (P = 0.03 were associated with higher recurrence. Binocularity status or stereoacuity changes were not associated with risk of recurrence. Conclusions: Significant numbers of children suffer recurrence of amblyopia after stopping therapy. Older age, better BCVA after stopping therapy and greater magnitude of improvement in BCVA are important risk factors for recurrence. Careful follow-up is essential for early detection and management of recurrence.

  6. Factors predicting recurrence in successfully treated cases of anisometropic amblyopia

    Science.gov (United States)

    Saxena, Rohit; Puranik, Shraddha; Singh, Digvijay; Menon, Vimla; Sharma, Pradeep; Phuljhele, Swati

    2013-01-01

    Context: Recurrence after successful treatment of amblyopia is known and understanding the risk factors could help effective management. Aim: To measure incidence of recurrence in successfully treated cases of anisometropic amblyopia and evaluate factors predicting it. Settings and Design: Cohort Study at a tertiary level institution. Materials and Methods: Successfully treated anisometropic amblyopes aged 4−12 years were followed up for 1 year after stopping therapy. Best corrected visual acuity (BCVA), refractive error, stereoacuity and contrast sensitivity were evaluated at baseline and follow-up. Statistical Analysis: Intergroup analysis with appropriate tests: Chi-square test, Fisher's exact test, Wilcoxon rank sum test and paired t-test. Results: One hundred and two patients with mean age at diagnosis 7.06 ± 1.81 years were followed-up for a mean duration of 1.0 ± 0.2 years. The mean pre-treatment BCVA (LogMAR score) at diagnosis was 0.73 ± 0.36 units which improved to 0.20 ± 0.00 with treatment and after 1 year of stopping treatment was 0.22 ± 0.07. Thirteen (12.74%) patients showed amblyopia recurrence during follow-up. Risk of recurrence was higher with older age of onset of treatment (6.64 ± 1.77 years without recurrence v/s 8.53 ± 1.39 years with recurrence, P = 0.0014). Greater extent of improvement of VA (P = 0.048) and final VA at stopping occlusion (P = 0.03) were associated with higher recurrence. Binocularity status or stereoacuity changes were not associated with risk of recurrence. Conclusions: Significant numbers of children suffer recurrence of amblyopia after stopping therapy. Older age, better BCVA after stopping therapy and greater magnitude of improvement in BCVA are important risk factors for recurrence. Careful follow-up is essential for early detection and management of recurrence. PMID:24343594

  7. Factors predicting recurrence in successfully treated cases of anisometropic amblyopia.

    Science.gov (United States)

    Saxena, Rohit; Puranik, Shraddha; Singh, Digvijay; Menon, Vimla; Sharma, Pradeep; Phuljhele, Swati

    2013-11-01

    Recurrence after successful treatment of amblyopia is known and understanding the risk factors could help effective management. To measure incidence of recurrence in successfully treated cases of anisometropic amblyopia and evaluate factors predicting it. Cohort Study at a tertiary level institution. Successfully treated anisometropic amblyopes aged 4-12 years were followed up for 1 year after stopping therapy. Best corrected visual acuity (BCVA), refractive error, stereoacuity and contrast sensitivity were evaluated at baseline and follow-up. Intergroup analysis with appropriate tests: Chi-square test, Fisher's exact test, Wilcoxon rank sum test and paired t-test. One hundred and two patients with mean age at diagnosis 7.06 μ 1.81 years were followed-up for a mean duration of 1.0 μ 0.2 years. The mean pre-treatment BCVA (LogMAR score) at diagnosis was 0.73 μ 0.36 units which improved to 0.20 μ 0.00 with treatment and after 1 year of stopping treatment was 0.22 μ 0.07. Thirteen (12.74%) patients showed amblyopia recurrence during follow-up. Risk of recurrence was higher with older age of onset of treatment (6.64 μ 1.77 years without recurrence v/s 8.53 μ 1.39 years with recurrence, P = 0.0014). Greater extent of improvement of VA (P = 0.048) and final VA at stopping occlusion (P = 0.03) were associated with higher recurrence. Binocularity status or stereoacuity changes were not associated with risk of recurrence. Significant numbers of children suffer recurrence of amblyopia after stopping therapy. Older age, better BCVA after stopping therapy and greater magnitude of improvement in BCVA are important risk factors for recurrence. Careful follow-up is essential for early detection and management of recurrence.

  8. Prediction skill of rainstorm events over India in the TIGGE weather prediction models

    Science.gov (United States)

    Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

    2017-12-01

    Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

  9. Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks.

    Science.gov (United States)

    Ajmani, Subhash; Rogers, Stephen C; Barley, Mark H; Burgess, Andrew N; Livingstone, David J

    2010-09-17

    In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Molecular constraints on synaptic tagging and maintenance of long-term potentiation: a predictive model.

    Science.gov (United States)

    Smolen, Paul; Baxter, Douglas A; Byrne, John H

    2012-01-01

    Protein synthesis-dependent, late long-term potentiation (LTP) and depression (LTD) at glutamatergic hippocampal synapses are well characterized examples of long-term synaptic plasticity. Persistent increased activity of protein kinase M ζ (PKMζ) is thought essential for maintaining LTP. Additional spatial and temporal features that govern LTP and LTD induction are embodied in the synaptic tagging and capture (STC) and cross capture hypotheses. Only synapses that have been "tagged" by a stimulus sufficient for LTP and learning can "capture" PKMζ. A model was developed to simulate the dynamics of key molecules required for LTP and LTD. The model concisely represents relationships between tagging, capture, LTD, and LTP maintenance. The model successfully simulated LTP maintained by persistent synaptic PKMζ, STC, LTD, and cross capture, and makes testable predictions concerning the dynamics of PKMζ. The maintenance of LTP, and consequently of at least some forms of long-term memory, is predicted to require continual positive feedback in which PKMζ enhances its own synthesis only at potentiated synapses. This feedback underlies bistability in the activity of PKMζ. Second, cross capture requires the induction of LTD to induce dendritic PKMζ synthesis, although this may require tagging of a nearby synapse for LTP. The model also simulates the effects of PKMζ inhibition, and makes additional predictions for the dynamics of CaM kinases. Experiments testing the above predictions would significantly advance the understanding of memory maintenance.

  11. Predicting climate-induced range shifts: model differences and model reliability.

    Science.gov (United States)

    Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

    2006-01-01

    Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...

  12. Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT

    Science.gov (United States)

    Schliep, E. M.; Gelfand, A. E.; Holland, D. M.

    2015-12-01

    There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.

  13. Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

    Science.gov (United States)

    Aksoy, A.; Yuzugullu, O.

    2017-12-01

    Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

  14. Application of Pharmacokinetics Modelling to Predict Human Exposure of a Cationic Liposomal Subunit Antigen Vaccine System

    Directory of Open Access Journals (Sweden)

    Raj K. S. Badhan

    2017-12-01

    Full Text Available The pharmacokinetics of a liposomal subunit antigen vaccine system composed of the cationic lipid dimethyldioctadecylammonium bromide (DDA and the immunostimulatory agent trehalose 6,6-dibehenate (TDB (8:1 molar ratio combined with the Ag85B-ESAT-6 (H1 antigen were modelled using mouse in-vivo data. Compartment modelling and physiologically based pharmacokinetics (PBPK were used to predict the administration site (muscle and target site (lymph temporal concentration profiles and factors governing these. Initial estimates using compartmental modelling established that quadriceps pharmacokinetics for the liposome demonstrated a long half-life (22.6 days compared to the associated antigen (2.62 days. A mouse minimal-PBPK model was developed and successfully predicted quadriceps liposome and antigen pharmacokinetics. Predictions for the popliteal lymph node (PLN aligned well at earlier time-points. A local sensitivity analysis highlighted that the predicted AUCmuscle was sensitive to the antigen degradation constant kdeg (resulting in a 3-log change more so than the fraction escaping the quadriceps (fe (resulting in a 10-fold change, and the predicted AUCPLN was highly sensitive to fe. A global sensitivity analysis of the antigen in the muscle demonstrated that model predictions were within the 50th percentile for predictions and showed acceptable fits. To further translate in-vitro data previously generated by our group, the mouse minimal-PBPK model was extrapolated to humans and predictions made for antigen pharmacokinetics in muscle and PLN. Global analysis demonstrated that both kdeg and fe had a minimal impact on the resulting simulations in the muscle but a greater impact in the PLN. In summary, this study has predicted the in-vivo fate of DDA:TDB:H1 in humans and demonstrated the roles that formulation degradation and fraction escaping the depot site can play upon the overall depot effect within the site of administration.

  15. Basal follicle stimulating hormone and leptin on the day of hCG administration predict successful fertilization in in vitro fertilization

    Directory of Open Access Journals (Sweden)

    Andon Hestiantoro

    2016-04-01

    Full Text Available Background: Successful pregnancy in in vitro fertilization (IVF program depends on multiple factors. This study aimed to determine whether age, body mass index (BMI, basal follicle stimulating hormone (FSH, estradiol, and leptin on the day of trigger ovulation with human chorionic gonadotropin (hCG might be used as predictor for successful oocyte fertilization in in vitro fertilization (IVF program.Methods: This is a cross sectional study conducted in Yasmin Fertility Clinic, Cipto Mangunkusumo Hospital, Jakarta, Indonesia. Forty participating patients underwent IVF program, excluding smokers, patients with diabetic, morbid obesity, and severe oligospermia or azoospermia. Age, BMI, basal FSH, estradiol, leptin on the day of hCG administration, oocyte count on oocyte retrieval, the number of mature oocyte, and fertility rate were analyzed using bivariate and multivariate analysis to determine which eligible factors play role in predicting the successful of fertilization.Results: Significant correlation was found between basal FSH level and serum leptin/oocyte ratio on the day of hCG administration with successful fertilization. We found probability formula as follows: 1/(1+exp –(6.2 - 0.4(leptin serum/oocyte ratio - 0.8(basal FSH, with 77.8% sensitivity, 77.8% specificity, and AUC levels of 85.6% indicating strong predictability. Probability of successful fertilization related to basal FSH level of 5.90 mIU/mL and leptin serum/oocyte ratio of 3.98.Conclusion: The formula consisting of basal FSH and leptin serum/oocyte ratio on the day of trigger ovulation was capable in predicting the probability of successful fertilization in IVF procedure.

  16. Predictive Modeling of a Paradigm Mechanical Cooling Tower Model: II. Optimal Best-Estimate Results with Reduced Predicted Uncertainties

    Directory of Open Access Journals (Sweden)

    Ruixian Fang

    2016-09-01

    Full Text Available This work uses the adjoint sensitivity model of the counter-flow cooling tower derived in the accompanying PART I to obtain the expressions and relative numerical rankings of the sensitivities, to all model parameters, of the following model responses: (i outlet air temperature; (ii outlet water temperature; (iii outlet water mass flow rate; and (iv air outlet relative humidity. These sensitivities are subsequently used within the “predictive modeling for coupled multi-physics systems” (PM_CMPS methodology to obtain explicit formulas for the predicted optimal nominal values for the model responses and parameters, along with reduced predicted standard deviations for the predicted model parameters and responses. These explicit formulas embody the assimilation of experimental data and the “calibration” of the model’s parameters. The results presented in this work demonstrate that the PM_CMPS methodology reduces the predicted standard deviations to values that are smaller than either the computed or the experimentally measured ones, even for responses (e.g., the outlet water flow rate for which no measurements are available. These improvements stem from the global characteristics of the PM_CMPS methodology, which combines all of the available information simultaneously in phase-space, as opposed to combining it sequentially, as in current data assimilation procedures.

  17. Improving Rice Modeling Success Rate with Ternary Non-structural Fertilizer Response Model.

    Science.gov (United States)

    Li, Juan; Zhang, Mingqing; Chen, Fang; Yao, Baoquan

    2018-06-13

    Fertilizer response modelling is an important technical approach to realize metrological fertilization on rice. With the goal of solving the problems of a low success rate of a ternary quadratic polynomial model (TPFM) and to expand the model's applicability, this paper established a ternary non-structural fertilizer response model (TNFM) based on the experimental results from N, P and K fertilized rice fields. Our research results showed that the TNFM significantly improved the modelling success rate by addressing problems arising from setting the bias and multicollinearity in a TPFM. The results from 88 rice field trials in China indicated that the proportion of typical TNFMs that satisfy the general fertilizer response law of plant nutrition was 40.9%, while the analogous proportion of TPFMs was only 26.1%. The recommended fertilization showed a significant positive linear correlation between the two models, and the parameters N 0 , P 0 and K 0 that estimated the value of soil supplying nutrient equivalents can be used as better indicators of yield potential in plots where no N or P or K fertilizer was applied. The theoretical analysis showed that the new model has a higher fitting accuracy and a wider application range.

  18. Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage

    Directory of Open Access Journals (Sweden)

    Douglas Halamay

    2014-09-01

    Full Text Available This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps, can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.

  19. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  20. Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University

    OpenAIRE

    Muluken Alemu Yehuala

    2015-01-01

    Abstract This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University students datasets. CRISP-DM Cross Industry Standard Process for Data mining is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from students data. These patterns can be seen in relation to different variables in the students records. The ...

  1. Perfectionism and self-conscious emotions in British and Japanese students: Predicting pride and embarrassment after success and failure

    OpenAIRE

    Stoeber, Joachim; Kobori, Osamu; Tanno, Yoshihiko

    2013-01-01

    Regarding self-conscious emotions, studies have shown that different forms of perfectionism show different relationships with pride, shame, and embarrassment depending on success and failure. What is unknown is whether these relationships also show cultural variations. Therefore, we conducted a study investigating how self-oriented and socially prescribed perfectionism predicted pride and embarrassment after success and failure comparing 363 British and 352 Japanese students. Students were as...

  2. Developing a dimensional model for successful cognitive and emotional aging.

    Science.gov (United States)

    Vahia, Ipsit V; Thompson, Wesley K; Depp, Colin A; Allison, Matthew; Jeste, Dilip V

    2012-04-01

    There is currently a lack of consensus on the definition of successful aging (SA) and existing implementations have omitted constructs associated with SA. We used empirical methods to develop a dimensional model of SA that incorporates a wider range of associated variables, and we examined the relationship among these components using factor analysis and Bayesian Belief Nets. We administered a successful aging questionnaire comprising several standardized measures related to SA to a sample of 1948 older women enrolled in the San Diego site of the Women's Health Initiative study. The SA-related variables we included in the model were self-rated successful aging, depression severity, physical and emotional functioning, optimism, resilience, attitude towards own aging, self-efficacy, and cognitive ability. After adjusting for age, education and income, we fitted an exploratory factor analysis model to the SA-related variables and then, in order to address relationships among these factors, we computed a Bayesian Belief Net (BBN) using rotated factor scores. The SA-related variables loaded onto five factors. Based on the loading, we labeled the factors as follows: self-rated successful aging, cognition, psychosocial protective factors, physical functioning, and emotional functioning. In the BBN, self-rated successful aging emerged as the primary downstream factor and exhibited significant partial correlations with psychosocial protective factors, physical/general status and mental/emotional status but not with cognitive ability. Our study represents a step forward in developing a dimensional model of SA. Our findings also point to a potential role for psychiatry in improving successful aging by managing depressive symptoms and developing psychosocial interventions to improve self-efficacy, resilience, and optimism.

  3. Ultrasonographic evaluation of myometrial thickness and prediction of a successful external cephalic version.

    Science.gov (United States)

    Buhimschi, Catalin S; Buhimschi, Irina A; Wehrum, Mark J; Molaskey-Jones, Sherry; Sfakianaki, Anna K; Pettker, Christian M; Thung, Stephen; Campbell, Katherine H; Dulay, Antonette T; Funai, Edmund F; Bahtiyar, Mert O

    2011-10-01

    To test the hypothesis that myometrial thickness predicts the success of external cephalic version. Abdominal ultrasonographic scans were performed in 114 consecutive pregnant women with breech singletons before an external cephalic version maneuver. Myometrial thickness was measured by a standardized protocol at three sites: the lower segment, midanterior wall, and the fundal uterine wall. Independent variables analyzed in conjunction with myometrial thickness were: maternal age, parity, body mass index, abdominal wall thickness, estimated fetal weight, amniotic fluid index, placental thickness and location, fetal spine position, breech type, and delivery outcomes such as final mode of delivery and birth weight. Successful version was associated with a thicker ultrasonographic fundal myometrium (unsuccessful: 6.7 [5.5-8.4] compared with successful: 7.4 [6.6-9.7] mm, P=.037). Multivariate regression analysis showed that increased fundal myometrial thickness, high amniotic fluid index, and nonfrank breech presentation were the strongest independent predictors of external cephalic version success (Pexternal cephalic versions (fundal myometrial thickness: odds ratio [OR] 2.4, 95% confidence interval [CI] 1.1-5.2, P=.029; amniotic fluid index: OR 2.8, 95% CI 1.3-6.0, P=.008). Combining the two variables resulted in an absolute risk reduction for a failed version of 27.6% (95% CI 7.1-48.1) and a number needed to treat of four (95% CI 2.1-14.2). Fundal myometrial thickness and amniotic fluid index contribute to success of external cephalic version and their evaluation can be easily incorporated in algorithms before the procedure. III.

  4. Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction.

    Science.gov (United States)

    Iga, Katsumi; Kiriyama, Akiko

    2017-12-01

    Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF h ). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA i, CYP s) and victim-specific fractional CYP-isoform contributions to the clearance (vf m, CYP s)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

  5. Model predictive Controller for Mobile Robot

    OpenAIRE

    Alireza Rezaee

    2017-01-01

    This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

  6. Forecasting the Success of Implementing Sensors Advanced Manufacturing Technology

    Directory of Open Access Journals (Sweden)

    Cheng-Shih Su

    2014-08-01

    Full Text Available This paper is presented fuzzy preference relations approach to forecast the success of implementing sensors advanced manufacturing technology (AMT. In the manufacturing environment, performance measurement is based on different quantitative and qualitative factors. This study proposes an analytic hierarchical prediction model based on fuzzy preference relations to help the organizations become aware of the essential factors affecting the AMT implementation, forecasting the chance of successful implementing sensors AMT, as well as identifying the actions necessary before implementing sensors AMT. Then predicted success/failure values are obtained to enable organizations to decide whether to initiate sensors AMT, inhibit adoption or take remedial actions to increase the possibility of successful sensors AMT initiatives. This proposed approach is demonstrated with a real case study involving six influential factors assessed by nine evaluators solicited from a semiconductor engineering incorporation located in Taiwan.

  7. Deep Predictive Models in Interactive Music

    OpenAIRE

    Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

    2018-01-01

    Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

  8. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  9. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  10. Emerging approaches in predictive toxicology.

    Science.gov (United States)

    Zhang, Luoping; McHale, Cliona M; Greene, Nigel; Snyder, Ronald D; Rich, Ivan N; Aardema, Marilyn J; Roy, Shambhu; Pfuhler, Stefan; Venkatactahalam, Sundaresan

    2014-12-01

    Predictive toxicology plays an important role in the assessment of toxicity of chemicals and the drug development process. While there are several well-established in vitro and in vivo assays that are suitable for predictive toxicology, recent advances in high-throughput analytical technologies and model systems are expected to have a major impact on the field of predictive toxicology. This commentary provides an overview of the state of the current science and a brief discussion on future perspectives for the field of predictive toxicology for human toxicity. Computational models for predictive toxicology, needs for further refinement and obstacles to expand computational models to include additional classes of chemical compounds are highlighted. Functional and comparative genomics approaches in predictive toxicology are discussed with an emphasis on successful utilization of recently developed model systems for high-throughput analysis. The advantages of three-dimensional model systems and stem cells and their use in predictive toxicology testing are also described. © 2014 Wiley Periodicals, Inc.

  11. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  12. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  13. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation

    Science.gov (United States)

    Nampak, Haleh; Pradhan, Biswajeet; Manap, Mohammad Abd

    2014-05-01

    The objective of this paper is to exploit potential application of an evidential belief function (EBF) model for spatial prediction of groundwater productivity at Langat basin area, Malaysia using geographic information system (GIS) technique. About 125 groundwater yield data were collected from well locations. Subsequently, the groundwater yield was divided into high (⩾11 m3/h) and low yields (divided into a testing dataset 70% (42 wells) for training the model and the remaining 30% (18 wells) was used for validation purpose. To perform cross validation, the frequency ratio (FR) approach was applied into remaining groundwater wells with low yield to show the spatial correlation between the low potential zones of groundwater productivity. A total of twelve groundwater conditioning factors that affect the storage of groundwater occurrences were derived from various data sources such as satellite based imagery, topographic maps and associated database. Those twelve groundwater conditioning factors are elevation, slope, curvature, stream power index (SPI), topographic wetness index (TWI), drainage density, lithology, lineament density, land use, normalized difference vegetation index (NDVI), soil and rainfall. Subsequently, the Dempster-Shafer theory of evidence model was applied to prepare the groundwater potential map. Finally, the result of groundwater potential map derived from belief map was validated using testing data. Furthermore, to compare the performance of the EBF result, logistic regression model was applied. The success-rate and prediction-rate curves were computed to estimate the efficiency of the employed EBF model compared to LR method. The validation results demonstrated that the success-rate for EBF and LR methods were 83% and 82% respectively. The area under the curve for prediction-rate of EBF and LR methods were calculated 78% and 72% respectively. The outputs achieved from the current research proved the efficiency of EBF in groundwater

  14. Predicting water main failures using Bayesian model averaging and survival modelling approach

    International Nuclear Information System (INIS)

    Kabir, Golam; Tesfamariam, Solomon; Sadiq, Rehan

    2015-01-01

    To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in predicting the failure of water mains, uncertainty is inherent regardless of the quality and quantity of data used in the model. To improve the understanding of water main failure, a Bayesian framework is developed for predicting the failure of water mains considering uncertainties. In this study, Bayesian model averaging method (BMA) is presented to identify the influential pipe-dependent and time-dependent covariates considering model uncertainties whereas Bayesian Weibull Proportional Hazard Model (BWPHM) is applied to develop the survival curves and to predict the failure rates of water mains. To accredit the proposed framework, it is implemented to predict the failure of cast iron (CI) and ductile iron (DI) pipes of the water distribution network of the City of Calgary, Alberta, Canada. Results indicate that the predicted 95% uncertainty bounds of the proposed BWPHMs capture effectively the observed breaks for both CI and DI water mains. Moreover, the performance of the proposed BWPHMs are better compare to the Cox-Proportional Hazard Model (Cox-PHM) for considering Weibull distribution for the baseline hazard function and model uncertainties. - Highlights: • Prioritize rehabilitation and replacements (R/R) strategies of water mains. • Consider the uncertainties for the failure prediction. • Improve the prediction capability of the water mains failure models. • Identify the influential and appropriate covariates for different models. • Determine the effects of the covariates on failure

  15. The Integrated Medical Model: Statistical Forecasting of Risks to Crew Health and Mission Success

    Science.gov (United States)

    Fitts, M. A.; Kerstman, E.; Butler, D. J.; Walton, M. E.; Minard, C. G.; Saile, L. G.; Toy, S.; Myers, J.

    2008-01-01

    The Integrated Medical Model (IMM) helps capture and use organizational knowledge across the space medicine, training, operations, engineering, and research domains. The IMM uses this domain knowledge in the context of a mission and crew profile to forecast crew health and mission success risks. The IMM is most helpful in comparing the risk of two or more mission profiles, not as a tool for predicting absolute risk. The process of building the IMM adheres to Probability Risk Assessment (PRA) techniques described in NASA Procedural Requirement (NPR) 8705.5, and uses current evidence-based information to establish a defensible position for making decisions that help ensure crew health and mission success. The IMM quantitatively describes the following input parameters: 1) medical conditions and likelihood, 2) mission duration, 3) vehicle environment, 4) crew attributes (e.g. age, sex), 5) crew activities (e.g. EVA's, Lunar excursions), 6) diagnosis and treatment protocols (e.g. medical equipment, consumables pharmaceuticals), and 7) Crew Medical Officer (CMO) training effectiveness. It is worth reiterating that the IMM uses the data sets above as inputs. Many other risk management efforts stop at determining only likelihood. The IMM is unique in that it models not only likelihood, but risk mitigations, as well as subsequent clinical outcomes based on those mitigations. Once the mathematical relationships among the above parameters are established, the IMM uses a Monte Carlo simulation technique (a random sampling of the inputs as described by their statistical distribution) to determine the probable outcomes. Because the IMM is a stochastic model (i.e. the input parameters are represented by various statistical distributions depending on the data type), when the mission is simulated 10-50,000 times with a given set of medical capabilities (risk mitigations), a prediction of the most probable outcomes can be generated. For each mission, the IMM tracks which conditions

  16. Can maintaining cognitive function at 65 years old predict successful ageing 6 years later? The PROOF study.

    Science.gov (United States)

    Castro-Lionard, Karine; Thomas-Antérion, Catherine; Crawford-Achour, Emilie; Rouch, Isabelle; Trombert-Paviot, Béatrice; Barthélémy, Jean-Claude; Laurent, Bernard; Roche, Frédéric; Gonthier, Régis

    2011-03-01

    preservation of cognitive abilities is required to have a good quality of life. The predictive value of cognitive functioning at 65 years old on successful ageing 6 years later is not established. nine hundred and seventy-six questionnaires were sent by mail to a sample of healthy and voluntary French pensioners. Successful ageing was defined through health status and well-being. Cognitive abilities had been assessed 6 years earlier according to an objective method (Free and Cued Selective Recall Reminding Test (FCSRT), the Benton visual retention test and the similarities subtest of the Wechsler Adult Intelligence Scale-Revised) and a subjective one (Goldberg's anxiety scale, Mac Nair's scale and a Visual Analogue Scale to evaluate memory abilities change in the last 5 years). six hundred and eighty-six questionnaires could be analysed. The mean age was 72.9 ± 1.2 years old with 59% of women and 99% lived at home. Well-being was negatively correlated with the FCSRT (r = -0.08, P = 0.0318) but positively related with the Benton (r = 0.09, P = 0.0125) and the similarities tests (r = 0.09, P = 0.0118). There is a negative correlation between anxious and cognitive complaints measured at baseline, and successful ageing indicators 6 years later. preservation of cognitive abilities at the age of retirement can predict a successful ageing 6 years later. ClinicalTrials.gov Identifier: NCT00759304.

  17. Testing the predictive power of nuclear mass models

    International Nuclear Information System (INIS)

    Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

    2008-01-01

    A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool

  18. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  19. Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

    Science.gov (United States)

    Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

    2017-01-01

    Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

  20. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  1. Evaluating the impacts of slope aspect on forest dynamic succession in Northwest China based on FAREAST model

    Science.gov (United States)

    Hu, Shanshan; Ma, Jianyong; Shugart, Herman H.; Yan, Xiaodong

    2018-03-01

    Mountain forests provide the main water resources and lumber for Northwest China. The understanding of the differences in forests growing among individual slope aspects in mountainous regions is of great significance to the wise management and planning of these natural systems. The aim of this study was to investigate the impacts of slope aspect on forest dynamic succession in Northwest China by using the dynamic forest succession model (FAREAST). First, the simulated forest composition and vertical forest zonation produced by the model were compared against recorded data in three sub-regions of the Altai Mountains. The FAREAST model accurately reproduced the vertical zonation, forest composition, growth curves of the dominant species (Larix sibirica), and forest biomass in the Altai Mountains. Transitions along the forest zones of the Altai Mountains averaged about a 400 m difference between the northern and southern sites. Biomass for forests on north-facing slopes were 11.0, 15.3 and 55.9 t C ha-1 higher than for south-facing slopes in the Northeast, Central and Southeast sub-regions, respectively. Second, our analyses showed that the FAREAST model can be used to predict dynamic forest succession in Northwest China under the influence of slope and aspect. In the Altai Mountains, the north-facing slopes supported the best forest growth, followed by the west- and east-facing slopes. South-facing slopes consistently exhibited the lowest growth, biomass storage and forest diversity.

  2. A Multi-Stage Maturity Model for Long-Term IT Outsourcing Relationship Success

    Science.gov (United States)

    Luong, Ming; Stevens, Jeff

    2015-01-01

    The Multi-Stage Maturity Model for Long-Term IT Outsourcing Relationship Success, a theoretical stages-of-growth model, explains long-term success in IT outsourcing relationships. Research showed the IT outsourcing relationship life cycle consists of four distinct, sequential stages: contract, transition, support, and partnership. The model was…

  3. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  4. A New Prediction Model for Evaluating Treatment-Resistant Depression.

    Science.gov (United States)

    Kautzky, Alexander; Baldinger-Melich, Pia; Kranz, Georg S; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

    2017-02-01

    Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV. Treatment-resistant depression (TRD) was defined by a 17-item Hamilton Depression Rating Scale (HDRS) score ≥ 17 after at least 2 antidepressant trials of adequate dosage and length. Remission was defined by an HDRS score depressive episode, age at first antidepressant treatment, response to first antidepressant treatment, severity, suicidality, melancholia, number of lifetime depressive episodes, patients' admittance type, education, occupation, and comorbid diabetes, panic, and thyroid disorder. While single predictors could not reach a prediction accuracy much different from random guessing, by combining all predictors, we could detect resistance with an accuracy of 0.737 and remission with an accuracy of 0.850. Consequently, 65.5% of predictions for TRD and 77.7% for remission can be expected to be accurate. Using machine learning algorithms, we could demonstrate success rates of 0.737 for predicting TRD and 0.850 for predicting remission, surpassing predictive capabilities of clinicians. Our results strengthen data mining and suggest the benefit of focus on interaction-based statistics. Considering that all predictors can easily be obtained in a clinical setting, we hope that our model can be tested by other research groups. © Copyright 2017 Physicians Postgraduate Press, Inc.

  5. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  6. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  7. Emotional intelligence predicts success in medical school.

    Science.gov (United States)

    Libbrecht, Nele; Lievens, Filip; Carette, Bernd; Côté, Stéphane

    2014-02-01

    Accumulating evidence suggests that effective communication and interpersonal sensitivity during interactions between doctors and patients impact therapeutic outcomes. There is an important need to identify predictors of these behaviors, because traditional tests used in medical admissions offer limited predictions of "bedside manners" in medical practice. This study examined whether emotional intelligence would predict the performance of 367 medical students in medical school courses on communication and interpersonal sensitivity. One of the dimensions of emotional intelligence, the ability to regulate emotions, predicted performance in courses on communication and interpersonal sensitivity over the next 3 years of medical school, over and above cognitive ability and conscientiousness. Emotional intelligence did not predict performance on courses on medical subject domains. The results suggest that medical schools may better predict who will communicate effectively and show interpersonal sensitivity if they include measures of emotional intelligence in their admission systems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  8. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  9. Documenting a best practice model for successful female inmate ...

    African Journals Online (AJOL)

    Documenting a best practice model for successful female inmate and female ex ... of men and women within the prison and correctional services as well as inform and ... and beyond, with scope for transforming it into a robust business model.

  10. Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.

    Science.gov (United States)

    Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald

    2017-07-01

    The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

    Directory of Open Access Journals (Sweden)

    Jiří Štefanica

    2014-02-01

    Full Text Available Reliable prediction of NOx emissions can provide useful information for boiler design and fuel selection. Recently used kinetic prediction models for FBC boilers are overly complex and require large computing capacity. Even so, there are many uncertainties in the case of FBC boilers. An empirical modeling approach for NOx prediction has been used exclusively for PCC boilers. No reference is available for modifying this method for FBC conditions. This paper presents possible advantages of empirical modeling based prediction of NOx emissions for FBC boilers, together with a discussion of its limitations. Empirical models are reviewed, and are applied to operation data from FBC boilers used for combusting Czech lignite coal or coal-biomass mixtures. Modifications to the model are proposed in accordance with theoretical knowledge and prediction accuracy.

  12. Profile control simulations and experiments on TCV : A controller test environment and results using a model-based predictive controller

    NARCIS (Netherlands)

    Maljaars, E.; Felici, F.; Blanken, T.C.; Galperti, C.; Sauter, O.; de Baar, M.R.; Carpanese, F.; Goodman, T.P.; Kim, D.; Kim, S.H.; Kong, M.G.; Mavkov, B.; Merle, A.; Moret, J.M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.A.; Vu, N.M.T.

    2017-01-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

  13. Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

    NARCIS (Netherlands)

    Maljaars, B.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.; Vu, T.

    2017-01-01

    The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety

  14. A biodynamic model predicting waterborne lead bioaccumulation in Gammarus pulex: Influence of water chemistry and in situ validation

    International Nuclear Information System (INIS)

    Urien, N.; Uher, E.; Billoir, E.; Geffard, O.; Fechner, L.C.; Lebrun, J.D.

    2015-01-01

    Metals bioaccumulated in aquatic organisms are considered to be a good indicator of bioavailable metal contamination levels in freshwaters. However, bioaccumulation depends on the metal, the species, and the water chemistry that influences metal bioavailability. In the laboratory, a kinetic model was used to describe waterborne Pb bioaccumulated in Gammarus pulex. Uptake and elimination rate constants were successfully determined and the effect of Ca 2+ on Pb uptake was integrated into the model. Thereafter, accumulated Pb concentrations in organisms were predicted with the model and compared with those measured in native populations from the Seine watershed (France). The predictions had a good agreement with the bioaccumulation levels observed in native gammarids and particularly when the effect of calcium was considered. To conclude, kinetic parameters experimentally derived for Pb in G. pulex are applicable in environmental conditions. Moreover, the consideration of the water's chemistry is crucial for a reliable interpretation of bioaccumulation. - Highlights: • Kinetic model was used to describe waterborne Pb bioaccumulation in G. pulex. • Ca 2+ inhibits Pb uptake by G. pulex in the laboratory. • Model predictions were compared to bioaccumulated Pb in native G. pulex. • Model accurately predicts waterborne bioaccumulated Pb in gammarids. • Considering the influence of Ca 2+ improves the model predictions in the field. - An experimentally-derived kinetic model considering the effect of calcium was relevant to predict the waterborne Pb bioaccumulation in native Gammarus pulex

  15. Prediction of pipeline corrosion rate based on grey Markov models

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

    2009-01-01

    Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)

  16. Sweat loss prediction using a multi-model approach.

    Science.gov (United States)

    Xu, Xiaojiang; Santee, William R

    2011-07-01

    A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

  17. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Gupta, Shikha

    2014-03-15

    constructed (c) DTB and (d) DTF regression models to predict the T. pyriformis toxicity of diverse chemicals. - Highlights: • Ensemble learning (EL) based models constructed for toxicity prediction of chemicals • Predictive models used a few simple non-quantum mechanical molecular descriptors. • EL-based DTB/DTF models successfully discriminated toxic and non-toxic chemicals. • DTB/DTF regression models precisely predicted toxicity of chemicals in multi-species. • Proposed EL based models can be used as tool to predict toxicity of new chemicals.

  18. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

    International Nuclear Information System (INIS)

    Singh, Kunwar P.; Gupta, Shikha

    2014-01-01

    constructed (c) DTB and (d) DTF regression models to predict the T. pyriformis toxicity of diverse chemicals. - Highlights: • Ensemble learning (EL) based models constructed for toxicity prediction of chemicals • Predictive models used a few simple non-quantum mechanical molecular descriptors. • EL-based DTB/DTF models successfully discriminated toxic and non-toxic chemicals. • DTB/DTF regression models precisely predicted toxicity of chemicals in multi-species. • Proposed EL based models can be used as tool to predict toxicity of new chemicals

  19. Predictive modeling of multicellular structure formation by using Cellular Particle Dynamics simulations

    Science.gov (United States)

    McCune, Matthew; Shafiee, Ashkan; Forgacs, Gabor; Kosztin, Ioan

    2014-03-01

    Cellular Particle Dynamics (CPD) is an effective computational method for describing and predicting the time evolution of biomechanical relaxation processes of multicellular systems. A typical example is the fusion of spheroidal bioink particles during post bioprinting structure formation. In CPD cells are modeled as an ensemble of cellular particles (CPs) that interact via short-range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through integration of their equations of motion. CPD was successfully applied to describe and predict the fusion of 3D tissue construct involving identical spherical aggregates. Here, we demonstrate that CPD can also predict tissue formation involving uneven spherical aggregates whose volumes decrease during the fusion process. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

  20. Mathematical Modeling Projects: Success for All Students

    Science.gov (United States)

    Shelton, Therese

    2018-01-01

    Mathematical modeling allows flexibility for a project-based experience. We share details of our regular capstone course, successful for virtually 100% of our math majors for almost two decades. Our research-like approach in this course accommodates a variety of student backgrounds and interests, and has produced some award-winning student…

  1. Honey bee success predicted by landscape composition in Ohio, USA

    Directory of Open Access Journals (Sweden)

    DB Sponsler

    2015-03-01

    Full Text Available Foraging honey bees (Apis mellifera L. can routinely travel as far as several kilometers from their hive in the process of collecting nectar and pollen from floral patches within the surrounding landscape. Since the availability of floral resources at the landscape scale is a function of landscape composition, apiculturists have long recognized that landscape composition is a critical determinant of honey bee colony success. Nevertheless, very few studies present quantitative data relating colony success metrics to local landscape composition. We employed a beekeeper survey in conjunction with GIS-based landscape analysis to model colony success as a function of landscape composition in the State of Ohio, USA, a region characterized by intensive cropland, urban development, deciduous forest, and grassland. We found that colony food accumulation and wax production were positively related to cropland and negatively related to forest and grassland, a pattern that may be driven by the abundance of dandelion and clovers in agricultural areas compared to forest or mature grassland. Colony food accumulation was also negatively correlated with urban land cover in sites dominated by urban and agricultural land use, which does not support the popular opinion that the urban environment is more favorable to honey bees than cropland.

  2. Honey bee success predicted by landscape composition in Ohio, USA.

    Science.gov (United States)

    Sponsler, D B; Johnson, R M

    2015-01-01

    Foraging honey bees (Apis mellifera L.) can routinely travel as far as several kilometers from their hive in the process of collecting nectar and pollen from floral patches within the surrounding landscape. Since the availability of floral resources at the landscape scale is a function of landscape composition, apiculturists have long recognized that landscape composition is a critical determinant of honey bee colony success. Nevertheless, very few studies present quantitative data relating colony success metrics to local landscape composition. We employed a beekeeper survey in conjunction with GIS-based landscape analysis to model colony success as a function of landscape composition in the State of Ohio, USA, a region characterized by intensive cropland, urban development, deciduous forest, and grassland. We found that colony food accumulation and wax production were positively related to cropland and negatively related to forest and grassland, a pattern that may be driven by the abundance of dandelion and clovers in agricultural areas compared to forest or mature grassland. Colony food accumulation was also negatively correlated with urban land cover in sites dominated by urban and agricultural land use, which does not support the popular opinion that the urban environment is more favorable to honey bees than cropland.

  3. The Brand's PREACH Model: Predicting Readiness to Engage African American Churches in Health.

    Science.gov (United States)

    Brand, Dorine J; Alston, Reginald J

    2017-09-01

    Despite many attempts to reduce health disparities, health professionals face obstacles in improving poor health outcomes within the African American (AA) community. To promote change for improved health measures, it is important to implement culturally tailored programming through a trusted institution, such as the AA church. While churches have the potential to play an important role in positively impacting health among AAs, it is unclear what attributes are necessary to predict success or failure for health promotion within these institutions. The purpose of this study was to create a model, the Brand's PREACH ( Predicting Readiness to Engage African American Churches in Health) Model, to predict the readiness of AA churches to engage in health promotion programming. Thirty-six semistructured key informant interviews were conducted with 12 pastors, 12 health leaders, and 12 congregants to gain information on the relationship between church infrastructure (physical structure, personnel, funding, and social/cultural support), readiness, and health promotion programming. The findings revealed that church infrastructure has an association with and will predict the readiness of a church to engage in health promotion programming. The ability to identify readiness early on will be useful for developing, implementing, and evaluating faith-based interventions, in partnership with churches, which is a key factor for sustainable and effective programs.

  4. Building Models to Predict Hint-or-Attempt Actions of Students

    Science.gov (United States)

    Castro, Francisco Enrique Vicente; Adjei, Seth; Colombo, Tyler; Heffernan, Neil

    2015-01-01

    A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…

  5. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-01-01

    Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

  6. Alcator C-Mod predictive modeling

    International Nuclear Information System (INIS)

    Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

    2001-01-01

    Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles

  7. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    Science.gov (United States)

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

  8. Building a Model of Successful Collaborative Learning for Company Innovativeness

    Directory of Open Access Journals (Sweden)

    Agata Sudolska

    2014-01-01

    Full Text Available The aim of the paper is to develop a model of successful collaborative learning for company innovativeness. First of all, the paper explores the issue of inter-firm learning, focusing its attention on collaborative learning. Secondly, inter-firm learning relationships are considered. Thirdly, the ex ante conditions of collaborative learning and the intra-organizational enhancers of inter-firm learning processes are studied. Finally, a model of the critical success factors for collaborative learning is developed.

  9. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

    Science.gov (United States)

    Pradhan, Biswajeet

    2013-02-01

    The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e

  10. Using Patient Demographics and Statistical Modeling to Predict Knee Tibia Component Sizing in Total Knee Arthroplasty.

    Science.gov (United States)

    Ren, Anna N; Neher, Robert E; Bell, Tyler; Grimm, James

    2018-06-01

    Preoperative planning is important to achieve successful implantation in primary total knee arthroplasty (TKA). However, traditional TKA templating techniques are not accurate enough to predict the component size to a very close range. With the goal of developing a general predictive statistical model using patient demographic information, ordinal logistic regression was applied to build a proportional odds model to predict the tibia component size. The study retrospectively collected the data of 1992 primary Persona Knee System TKA procedures. Of them, 199 procedures were randomly selected as testing data and the rest of the data were randomly partitioned between model training data and model evaluation data with a ratio of 7:3. Different models were trained and evaluated on the training and validation data sets after data exploration. The final model had patient gender, age, weight, and height as independent variables and predicted the tibia size within 1 size difference 96% of the time on the validation data, 94% of the time on the testing data, and 92% on a prospective cadaver data set. The study results indicated the statistical model built by ordinal logistic regression can increase the accuracy of tibia sizing information for Persona Knee preoperative templating. This research shows statistical modeling may be used with radiographs to dramatically enhance the templating accuracy, efficiency, and quality. In general, this methodology can be applied to other TKA products when the data are applicable. Copyright © 2018 Elsevier Inc. All rights reserved.

  11. Predictive Modelling of Heavy Metals in Urban Lakes

    OpenAIRE

    Lindström, Martin

    2000-01-01

    Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes. Sediment and water data for this study were collected from ten small lakes in the ...

  12. Exploring nursing e-learning systems success based on information system success model.

    Science.gov (United States)

    Chang, Hui-Chuan; Liu, Chung-Feng; Hwang, Hsin-Ginn

    2011-12-01

    E-learning is thought of as an innovative approach to enhance nurses' care service knowledge. Extensive research has provided rich information toward system development, courses design, and nurses' satisfaction with an e-learning system. However, a comprehensive view in understanding nursing e-learning system success is an important but less focused-on topic. The purpose of this research was to explore net benefits of nursing e-learning systems based on the updated DeLone and McLean's Information System Success Model. The study used a self-administered questionnaire to collected 208 valid nurses' responses from 21 of Taiwan's medium- and large-scale hospitals that have implemented nursing e-learning systems. The result confirms that the model is sufficient to explore the nurses' use of e-learning systems in terms of intention to use, user satisfaction, and net benefits. However, while the three exogenous quality factors (system quality, information quality, and service quality) were all found to be critical factors affecting user satisfaction, only information quality showed a direct effect on the intention to use. This study provides useful insights for evaluating nursing e-learning system qualities as well as an understanding of nurses' intentions and satisfaction related to performance benefits.

  13. Experimental prediction of severe droughts on seasonal to intra-annual time scales with GFDL High-Resolution Atmosphere Model

    Science.gov (United States)

    Yu, Z.; Lin, S.

    2011-12-01

    Regional heat waves and drought have major economic and societal impacts on regional and even global scales. For example, during and following the 2010-2011 La Nina period, severe droughts have been reported in many places around the world including China, the southern US, and the east Africa, causing severe hardship in China and famine in east Africa. In this study, we investigate the feasibility and predictability of severe spring-summer draught events, 3 to 6 months in advance with the 25-km resolution Geophysical Fluid Dynamics Laboratory High-Resolution Atmosphere Model (HiRAM), which is built as a seamless weather-climate model, capable of long-term climate simulations as well as skillful seasonal predictions (e.g., Chen and Lin 2011, GRL). We adopted a similar methodology and the same (HiRAM) model as in Chen and Lin (2011), which is used successfully for seasonal hurricane predictions. A series of initialized 7-month forecasts starting from Dec 1 are performed each year (5 members each) during the past decade (2000-2010). We will then evaluate the predictability of the severe drought events during this period by comparing model predictions vs. available observations. To evaluate the predictive skill, in this preliminary report, we will focus on the anomalies of precipitation, sea-level-pressure, and 500-mb height. These anomalies will be computed as the individual model prediction minus the mean climatology obtained by an independent AMIP-type "simulation" using observed SSTs (rather than using predictive SSTs in the forecasts) from the same model.

  14. Stage-specific predictive models for breast cancer survivability.

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

    Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  15. Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between?

    Science.gov (United States)

    Niederer, Steven A; Smith, Nic P

    2016-12-01

    Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

  16. Impact of modellers' decisions on hydrological a priori predictions

    Science.gov (United States)

    Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

    2014-06-01

    In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of

  17. A multivariate model for predicting segmental body composition.

    Science.gov (United States)

    Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

    2013-12-01

    The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

  18. Molecular constraints on synaptic tagging and maintenance of long-term potentiation: a predictive model.

    Directory of Open Access Journals (Sweden)

    Paul Smolen

    Full Text Available Protein synthesis-dependent, late long-term potentiation (LTP and depression (LTD at glutamatergic hippocampal synapses are well characterized examples of long-term synaptic plasticity. Persistent increased activity of protein kinase M ζ (PKMζ is thought essential for maintaining LTP. Additional spatial and temporal features that govern LTP and LTD induction are embodied in the synaptic tagging and capture (STC and cross capture hypotheses. Only synapses that have been "tagged" by a stimulus sufficient for LTP and learning can "capture" PKMζ. A model was developed to simulate the dynamics of key molecules required for LTP and LTD. The model concisely represents relationships between tagging, capture, LTD, and LTP maintenance. The model successfully simulated LTP maintained by persistent synaptic PKMζ, STC, LTD, and cross capture, and makes testable predictions concerning the dynamics of PKMζ. The maintenance of LTP, and consequently of at least some forms of long-term memory, is predicted to require continual positive feedback in which PKMζ enhances its own synthesis only at potentiated synapses. This feedback underlies bistability in the activity of PKMζ. Second, cross capture requires the induction of LTD to induce dendritic PKMζ synthesis, although this may require tagging of a nearby synapse for LTP. The model also simulates the effects of PKMζ inhibition, and makes additional predictions for the dynamics of CaM kinases. Experiments testing the above predictions would significantly advance the understanding of memory maintenance.

  19. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Hybrid Corporate Performance Prediction Model Considering Technical Capability

    Directory of Open Access Journals (Sweden)

    Joonhyuck Lee

    2016-07-01

    Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.

  1. Dynamic Simulation of Human Gait Model With Predictive Capability.

    Science.gov (United States)

    Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

    2018-03-01

    In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

  2. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

  3. TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

    Science.gov (United States)

    Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

    2016-05-01

    Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

  4. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  5. Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

    Science.gov (United States)

    Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

    2013-10-01

    Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  6. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

    2005-07-01

    Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.

  7. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  8. Prediction of resource volumes at untested locations using simple local prediction models

    Science.gov (United States)

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2006-01-01

    This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

  9. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

    2006-05-15

    Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.

  10. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  11. Establishing a Cloud Computing Success Model for Hospitals in Taiwan

    OpenAIRE

    Lian, Jiunn-Woei

    2017-01-01

    The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality...

  12. A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

    Science.gov (United States)

    Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

    2014-01-01

    Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

  13. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Directory of Open Access Journals (Sweden)

    A. Elshorbagy

    2010-10-01

    Full Text Available In this second part of the two-part paper, the data driven modeling (DDM experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs, genetic programming (GP, evolutionary polynomial regression (EPR, Support vector machines (SVM, M5 model trees (M5, K-nearest neighbors (K-nn, and multiple linear regression (MLR techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it

  14. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Science.gov (United States)

    Elshorbagy, A.; Corzo, G.; Srinivasulu, S.; Solomatine, D. P.

    2010-10-01

    In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should

  15. Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients

    International Nuclear Information System (INIS)

    Dehing-Oberije, Cary; De Ruysscher, Dirk; Petit, Steven; Van Meerbeeck, Jan; Vandecasteele, Katrien; De Neve, Wilfried; Dingemans, Anne Marie C.; El Naqa, Issam; Deasy, Joseph; Bradley, Jeff; Huang, Ellen; Lambin, Philippe

    2010-01-01

    Introduction: Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods: Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results: The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia ≥ grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions: The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.

  16. Risk predictive modelling for diabetes and cardiovascular disease.

    Science.gov (United States)

    Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

    2014-02-01

    Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

  17. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...

  18. A predictive model of suitability for minimally invasive parathyroid surgery in the treatment of primary hyperparathyroidism [corrected].

    LENUS (Irish Health Repository)

    Kavanagh, Dara O

    2012-05-01

    Improved preoperative localizing studies have facilitated minimally invasive approaches in the treatment of primary hyperparathyroidism (PHPT). Success depends on the ability to reliably select patients who have PHPT due to single-gland disease. We propose a model encompassing preoperative clinical, biochemical, and imaging studies to predict a patient\\'s suitability for minimally invasive surgery.

  19. Cytological Sampling Versus Forceps Biopsy During Percutaneous Transhepatic Biliary Drainage and Analysis of Factors Predicting Success

    Energy Technology Data Exchange (ETDEWEB)

    Tapping, C. R.; Byass, O. R.; Cast, J. E. I., E-mail: james.cast@hey.nhs.uk [Hull Royal Infirmary, Department of Radiology (United Kingdom)

    2012-08-15

    Purpose: To assess the accuracy of cytological sampling and forceps biopsy in obstructing biliary lesions and to identify factors predictive of success. Methods: Consecutive patients (n = 119) with suspected malignant inoperable obstructive jaundice treated with percutaneous transhepatic biliary drainage during 7 years were included (60 male; mean age 72.5 years). All patients underwent forceps biopsy plus cytological sampling by washing the forceps device in cytological solution. Patient history, procedural and pathological records, and clinical follow-up were reviewed. Statistical analysis included chi-square test and multivariate regression analysis. Results: Histological diagnosis after forceps biopsy was more successful than cytology: Sensitivity was 78 versus 61%, and negative predictive value was 30 versus 19%. Cytology results were never positive when the forceps biopsy was negative. The cytological sample was negative and forceps sample positive in 2 cases of cholangiocarcinoma, 16 cases of pancreatic carcinoma, and 1 case of benign disease. Diagnostic accuracy was predicted by low bilirubin (p < 0.001), aspartate transaminase (p < 0.05), and white cell count (p {<=} 0.05). Conclusions: This technique is safe and effective and is recommended for histological diagnosis during PTBD in patients with inoperable malignant biliary strictures. Diagnostic yield is greater when bilirubin levels are low and there is no sepsis; histological diagnosis by way of forceps biopsy renders cytological sampling unnecessary.

  20. Survival prediction model for postoperative hepatocellular carcinoma patients.

    Science.gov (United States)

    Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

    2017-09-01

    This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

  1. Predicting future conflict between team-members with parameter-free models of social networks

    Science.gov (United States)

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-06-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  2. [Application of predictive model to estimate concentrations of chemical substances in the work environment].

    Science.gov (United States)

    Kupczewska-Dobecka, Małgorzata; Czerczak, Sławomir; Jakubowski, Marek; Maciaszek, Piotr; Janasik, Beata

    2010-01-01

    Based on the Estimation and Assessment of Substance Exposure (EASE) predictive model implemented into the European Union System for the Evaluation of Substances (EUSES 2.1.), the exposure to three chosen organic solvents: toluene, ethyl acetate and acetone was estimated and compared with the results of measurements in workplaces. Prior to validation, the EASE model was pretested using three exposure scenarios. The scenarios differed in the decision tree of pattern of use. Five substances were chosen for the test: 1,4-dioxane tert-methyl-butyl ether, diethylamine, 1,1,1-trichloroethane and bisphenol A. After testing the EASE model, the next step was the validation by estimating the exposure level and comparing it with the results of measurements in the workplace. We used the results of measurements of toluene, ethyl acetate and acetone concentrations in the work environment of a paint and lacquer factory, a shoe factory and a refinery. Three types of exposure scenarios, adaptable to the description of working conditions were chosen to estimate inhalation exposure. Comparison of calculated exposure to toluene, ethyl acetate and acetone with measurements in workplaces showed that model predictions are comparable with the measurement results. Only for low concentration ranges, the measured concentrations were higher than those predicted. EASE is a clear, consistent system, which can be successfully used as an additional component of inhalation exposure estimation. If the measurement data are available, they should be preferred to values estimated from models. In addition to inhalation exposure estimation, the EASE model makes it possible not only to assess exposure-related risk but also to predict workers' dermal exposure.

  3. Assessing the reliability of predictive activity coefficient models for molecules consisting of several functional groups

    Directory of Open Access Journals (Sweden)

    R. P. Gerber

    2013-03-01

    Full Text Available Currently, the most successful predictive models for activity coefficients are those based on functional groups such as UNIFAC. In contrast, these models require a large amount of experimental data for the determination of their parameter matrix. A more recent alternative is the models based on COSMO, for which only a small set of universal parameters must be calibrated. In this work, a recalibrated COSMO-SAC model was compared with the UNIFAC (Do model employing experimental infinite dilution activity coefficient data for 2236 non-hydrogen-bonding binary mixtures at different temperatures. As expected, UNIFAC (Do presented better overall performance, with a mean absolute error of 0.12 ln-units against 0.22 for our COSMO-SAC implementation. However, in cases involving molecules with several functional groups or when functional groups appear in an unusual way, the deviation for UNIFAC was 0.44 as opposed to 0.20 for COSMO-SAC. These results show that COSMO-SAC provides more reliable predictions for multi-functional or more complex molecules, reaffirming its future prospects.

  4. Hidden Semi-Markov Models for Predictive Maintenance

    Directory of Open Access Journals (Sweden)

    Francesco Cartella

    2015-01-01

    Full Text Available Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs with (i no constraints on the state duration density function and (ii being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL of the machine is calculated.

  5. Predicting start-up success with machine learning

    OpenAIRE

    Bento, Francisco Ramadas da Silva Ribeiro

    2018-01-01

    Start-ups are becoming the motor that moves our economy. Google, Apple, or more recently Airbnb and Uber are companies with tremendous impact in worldwide economy, social interactions and government. Over the past decade, both in the US and Europe, there has been an exponential growth in start-up formation. Thus, it seems a relevant challenge understanding what makes this type of high-risk ventures successful and as such, attractive to investors and entrepreneurs. Success for a start-up is de...

  6. National culture and business model change: a framework for successful expansions

    DEFF Research Database (Denmark)

    Dalby, J.; Nielsen, L.S.; Lueg, Rainer

    2014-01-01

    Dalby, J., Nielsen, Lueg, R., L. S., Pedersen, L., Tomoni, A. C. 2014. National culture and business model change: a framework for successful expansions. Journal of Enterprising Culture, 22(4): 379-498.......Dalby, J., Nielsen, Lueg, R., L. S., Pedersen, L., Tomoni, A. C. 2014. National culture and business model change: a framework for successful expansions. Journal of Enterprising Culture, 22(4): 379-498....

  7. Climate-based models for pulsed resources improve predictability of consumer population dynamics: outbreaks of house mice in forest ecosystems.

    Directory of Open Access Journals (Sweden)

    E Penelope Holland

    Full Text Available Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer-resource dynamics to predict invasive house mouse (Mus musculus outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year's advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer-resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.

  8. Modeling and Control of CSTR using Model based Neural Network Predictive Control

    OpenAIRE

    Shrivastava, Piyush

    2012-01-01

    This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

  9. Consensus models to predict endocrine disruption for all ...

    Science.gov (United States)

    Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

  10. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  11. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  12. Preclinical models used for immunogenicity prediction of therapeutic proteins.

    Science.gov (United States)

    Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

    2013-07-01

    All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

  13. A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

    Science.gov (United States)

    Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

    2014-01-01

    Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174

  14. Evaluating predictive models for solar energy growth in the US states and identifying the key drivers

    Science.gov (United States)

    Chakraborty, Joheen; Banerji, Sugata

    2018-03-01

    Driven by a desire to control climate change and reduce the dependence on fossil fuels, governments around the world are increasing the adoption of renewable energy sources. However, among the US states, we observe a wide disparity in renewable penetration. In this study, we have identified and cleaned over a dozen datasets representing solar energy penetration in each US state, and the potentially relevant socioeconomic and other factors that may be driving the growth in solar. We have applied a number of predictive modeling approaches - including machine learning and regression - on these datasets over a 17-year period and evaluated the relative performance of the models. Our goals were: (1) identify the most important factors that are driving the growth in solar, (2) choose the most effective predictive modeling technique for solar growth, and (3) develop a model for predicting next year’s solar growth using this year’s data. We obtained very promising results with random forests (about 90% efficacy) and varying degrees of success with support vector machines and regression techniques (linear, polynomial, ridge). We also identified states with solar growth slower than expected and representing a potential for stronger growth in future.

  15. Hard-Wired Dopant Networks and the Prediction of High Transition Temperatures in Ceramic Superconductors

    International Nuclear Information System (INIS)

    Phillips, J.C.

    2010-01-01

    The review multiple successes of the discrete hard-wired dopant network model ZZIP, and comment on the equally numerous failures of continuum models, in describing and predicting the properties of ceramic superconductors. The prediction of transition temperatures can be regarded in several ways, either as an exacting test of theory, or as a tool for identifying theoretical rules for defining new homology models. Popular first principle methods for predicting transition temperatures in conventional crystalline superconductors have failed for cuprate HTSC, as have parameterized models based on CuO2 planes (with or without apical oxygen). Following a path suggested by Bayesian probability, it was found that the glassy, self-organized dopant network percolative model is so successful that it defines a new homology class appropriate to ceramic superconductors. The reasons for this success in an exponentially complex (non-polynomial complete, NPC) problem are discussed, and a critical comparison is made with previous polynomial (PC) theories. The predictions are successful for the superfamily of all ceramics, including new non-cuprates based on FeAs in place of CuO2.

  16. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....

  17. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  18. Design and analysis of a model predictive controller for active queue management.

    Science.gov (United States)

    Wang, Ping; Chen, Hong; Yang, Xiaoping; Ma, Yan

    2012-01-01

    Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  20. Revised predictive equations for salt intrusion modelling in estuaries

    NARCIS (Netherlands)

    Gisen, J.I.A.; Savenije, H.H.G.; Nijzink, R.C.

    2015-01-01

    For one-dimensional salt intrusion models to be predictive, we need predictive equations to link model parameters to observable hydraulic and geometric variables. The one-dimensional model of Savenije (1993b) made use of predictive equations for the Van der Burgh coefficient $K$ and the dispersion

  1. Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.

    Science.gov (United States)

    Yin, Wen-Jun; Yi, Yi-Hu; Guan, Xiao-Feng; Zhou, Ling-Yun; Wang, Jiang-Lin; Li, Dai-Yang; Zuo, Xiao-Cong

    2017-02-03

    Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media. A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level. The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  2. Time dependent patient no-show predictive modelling development.

    Science.gov (United States)

    Huang, Yu-Li; Hanauer, David A

    2016-05-09

    Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

  3. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  4. Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

  5. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  6. Species distribution modeling for the invasive raccoon dog (Nyctereutes procyonoides) in Austria and first range predictions for alpine environments

    OpenAIRE

    Duscher Tanja; Nopp-Mayr Ursula

    2017-01-01

    Species distribution models are important tools for wildlife management planning, particularly in the case of invasive species. We employed a recent framework for niche-based invasive species distribution modeling to predict the probability of presence for the invasive raccoon dog (Nyctereutes procyonoides) in Austria. The raccoon dog is an adaptive, mobile and highly reproductive Asiatic canid that has successfully invaded many parts of Europe. It is known...

  7. Model-predictive control and real-time optimization of a cat cracker unit

    Directory of Open Access Journals (Sweden)

    Stig Strand

    1997-04-01

    Full Text Available A project for control and optimization of the Residual Catalytic Cracking Process at the Mongstad refinery is near completion. Four model-predictive control applications have been successfully implemented, using the IDCOM control software from Setpoint Inc. The most attractive feature of the controller is the well-defined control prioritizing hierarchy, and the linear impulse-response models have proved to give satisfactory performance on this process. Excitation and identification of the dynamic models proved to be a difficult task, and careful design and monitoring of the tests was mandatory in order to produce good results. Multi-variable Pseudo Random Binary Test Sequences were used for the excitation. Technical performance and operator acceptance of the new control functions have been good, but it is realized that a continuing effort is needed to fine-tune and maintain such functions.

  8. Predicting speech intelligibility in adverse conditions: evaluation of the speech-based envelope power spectrum model

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2011-01-01

    conditions by comparing predictions to measured data from [Kjems et al. (2009). J. Acoust. Soc. Am. 126 (3), 1415-1426] where speech is mixed with four different interferers, including speech-shaped noise, bottle noise, car noise, and cafe noise. The model accounts well for the differences in intelligibility......The speech-based envelope power spectrum model (sEPSM) [Jørgensen and Dau (2011). J. Acoust. Soc. Am., 130 (3), 1475–1487] estimates the envelope signal-to-noise ratio (SNRenv) of distorted speech and accurately describes the speech recognition thresholds (SRT) for normal-hearing listeners...... observed for the different interferers. None of the standardized models successfully describe these data....

  9. Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

    Science.gov (United States)

    Wang, Quanchao; Yu, Yang; Li, Fuhua; Zhang, Xiaojun; Xiang, Jianhai

    2017-09-01

    Genomic selection (GS) can be used to accelerate genetic improvement by shortening the selection interval. The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value (GEBV). This study is a first attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits. The performance of GS models in L. vannamei was evaluated in a population consisting of 205 individuals, which were genotyped for 6 359 single nucleotide polymorphism (SNP) markers by specific length amplified fragment sequencing (SLAF-seq) and phenotyped for body length and body weight. Three GS models (RR-BLUP, BayesA, and Bayesian LASSO) were used to obtain the GEBV, and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes. The mean reliability of the GEBVs for body length and body weight predicted by the different models was 0.296 and 0.411, respectively. For each trait, the performances of the three models were very similar to each other with respect to predictability. The regression coefficients estimated by the three models were close to one, suggesting near to zero bias for the predictions. Therefore, when GS was applied in a L. vannamei population for the studied scenarios, all three models appeared practicable. Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

  10. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  11. Prediction error, ketamine and psychosis: An updated model.

    Science.gov (United States)

    Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

    2016-11-01

    In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

  12. An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

    Directory of Open Access Journals (Sweden)

    Thomas R O'Brien

    Full Text Available Genetic variation in IL28B and other factors are associated with sustained virological response (SVR after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC. Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables.HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC.Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p10% (43.3% of subjects had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study.A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC.

  13. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  14. Open Source Software Success Model for Iran: End-User Satisfaction Viewpoint

    Directory of Open Access Journals (Sweden)

    Ali Niknafs

    2012-03-01

    Full Text Available The open source software development is notable option for software companies. Recent years, many advantages of this software type are cause of move to that in Iran. National security and international restrictions problems and also software and services costs and more other problems intensified importance of use of this software. Users and their viewpoints are the critical success factor in the software plans. But there is not an appropriate model for open source software case in Iran. This research tried to develop a measuring open source software success model for Iran. By use of data gathered from open source users and online survey the model was tested. The results showed that components by positive effect on open source success were user satisfaction, open source community services quality, open source quality, copyright and security.

  15. A Bayesian approach for parameter estimation and prediction using a computationally intensive model

    International Nuclear Information System (INIS)

    Higdon, Dave; McDonnell, Jordan D; Schunck, Nicolas; Sarich, Jason; Wild, Stefan M

    2015-01-01

    Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model η(θ), where θ denotes the uncertain, best input setting. Hence the statistical model is of the form y=η(θ)+ϵ, where ϵ accounts for measurement, and possibly other, error sources. When nonlinearity is present in η(⋅), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model η(⋅). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory. (paper)

  16. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  17. Improved cyberinfrastructure for integrated hydrometeorological predictions within the fully-coupled WRF-Hydro modeling system

    Science.gov (United States)

    gochis, David; hooper, Rick; parodi, Antonio; Jha, Shantenu; Yu, Wei; Zaslavsky, Ilya; Ganapati, Dinesh

    2014-05-01

    The community WRF-Hydro system is currently being used in a variety of flood prediction and regional hydroclimate impacts assessment applications around the world. Despite its increasingly wide use certain cyberinfrastructure bottlenecks exist in the setup, execution and post-processing of WRF-Hydro model runs. These bottlenecks result in wasted time, labor, data transfer bandwidth and computational resource use. Appropriate development and use of cyberinfrastructure to setup and manage WRF-Hydro modeling applications will streamline the entire workflow of hydrologic model predictions. This talk will present recent advances in the development and use of new open-source cyberinfrastructure tools for the WRF-Hydro architecture. These tools include new web-accessible pre-processing applications, supercomputer job management applications and automated verification and visualization applications. The tools will be described successively and then demonstrated in a set of flash flood use cases for recent destructive flood events in the U.S. and in Europe. Throughout, an emphasis on the implementation and use of community data standards for data exchange is made.

  18. Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model

    Science.gov (United States)

    Sharma, Kuldeep; Ashrit, Raghavendra; Bhatla, R.; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.

    2017-11-01

    The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.

  19. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  20. Using Pareto points for model identification in predictive toxicology

    Science.gov (United States)

    2013-01-01

    Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

  1. Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

    Science.gov (United States)

    Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

    Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

  2. SU-E-T-479: Development and Validation of Analytical Models Predicting Secondary Neutron Radiation in Proton Therapy Applications

    International Nuclear Information System (INIS)

    Farah, J; Bonfrate, A; Donadille, L; Martinetti, F; Trompier, F; Clairand, I; De Olivera, A; Delacroix, S; Herault, J; Piau, S; Vabre, I

    2014-01-01

    Purpose: Test and validation of analytical models predicting leakage neutron exposure in passively scattered proton therapy. Methods: Taking inspiration from the literature, this work attempts to build an analytical model predicting neutron ambient dose equivalents, H*(10), within the local 75 MeV ocular proton therapy facility. MC simulations were first used to model H*(10) in the beam axis plane while considering a closed final collimator and pristine Bragg peak delivery. Next, MC-based analytical model was tested against simulation results and experimental measurements. The model was also expended in the vertical direction to enable a full 3D mapping of H*(10) inside the treatment room. Finally, the work focused on upgrading the literature model to clinically relevant configurations considering modulated beams, open collimators, patient-induced neutron fluctuations, etc. Results: The MC-based analytical model efficiently reproduced simulated H*(10) values with a maximum difference below 10%. In addition, it succeeded in predicting measured H*(10) values with differences <40%. The highest differences were registered at the closest and farthest positions from isocenter where the analytical model failed to faithfully reproduce the high neutron fluence and energy variations. The differences remains however acceptable taking into account the high measurement/simulation uncertainties and the end use of this model, i.e. radiation protection. Moreover, the model was successfully (differences < 20% on simulations and < 45% on measurements) extended to predict neutrons in the vertical direction with respect to the beam line as patients are in the upright seated position during ocular treatments. Accounting for the impact of beam modulation, collimation and the present of a patient in the beam path is far more challenging and conversion coefficients are currently being defined to predict stray neutrons in clinically representative treatment configurations. Conclusion

  3. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

    KAUST Repository

    Lensink, Marc F.; Velankar, Sameer; Kryshtafovych, Andriy; Huang, Shen-You; Schneidman-Duhovny, Dina; Sali, Andrej; Segura, Joan; Fernandez-Fuentes, Narcis; Viswanath, Shruthi; Elber, Ron; Grudinin, Sergei; Popov, Petr; Neveu, Emilie; Lee, Hasup; Baek, Minkyung; Park, Sangwoo; Heo, Lim; Rie Lee, Gyu; Seok, Chaok; Qin, Sanbo; Zhou, Huan-Xiang; Ritchie, David W.; Maigret, Bernard; Devignes, Marie-Dominique; Ghoorah, Anisah; Torchala, Mieczyslaw; Chaleil, Raphaë l A.G.; Bates, Paul A.; Ben-Zeev, Efrat; Eisenstein, Miriam; Negi, Surendra S.; Weng, Zhiping; Vreven, Thom; Pierce, Brian G.; Borrman, Tyler M.; Yu, Jinchao; Ochsenbein, Franç oise; Guerois, Raphaë l; Vangone, Anna; Rodrigues, Joã o P.G.L.M.; van Zundert, Gydo; Nellen, Mehdi; Xue, Li; Karaca, Ezgi; Melquiond, Adrien S.J.; Visscher, Koen; Kastritis, Panagiotis L.; Bonvin, Alexandre M.J.J.; Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Li, Jilong; Ma, Zhiwei; Cheng, Jianlin; Zou, Xiaoqin; Shen, Yang; Peterson, Lenna X.; Kim, Hyung-Rae; Roy, Amit; Han, Xusi; Esquivel-Rodriguez, Juan; Kihara, Daisuke; Yu, Xiaofeng; Bruce, Neil J.; Fuller, Jonathan C.; Wade, Rebecca C.; Anishchenko, Ivan; Kundrotas, Petras J.; Vakser, Ilya A.; Imai, Kenichiro; Yamada, Kazunori; Oda, Toshiyuki; Nakamura, Tsukasa; Tomii, Kentaro; Pallara, Chiara; Romero-Durana, Miguel; Jimé nez-Garcí a, Brian; Moal, Iain H.; Fé rnandez-Recio, Juan; Joung, Jong Young; Kim, Jong Yun; Joo, Keehyoung; Lee, Jooyoung; Kozakov, Dima; Vajda, Sandor; Mottarella, Scott; Hall, David R.; Beglov, Dmitri; Mamonov, Artem; Xia, Bing; Bohnuud, Tanggis; Del Carpio, Carlos A.; Ichiishi, Eichiro; Marze, Nicholas; Kuroda, Daisuke; Roy Burman, Shourya S.; Gray, Jeffrey J.; Chermak, Edrisse; Cavallo, Luigi; Oliva, Romina; Tovchigrechko, Andrey; Wodak, Shoshana J.

    2016-01-01

    We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. © 2016 Wiley Periodicals, Inc.

  4. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

    KAUST Repository

    Lensink, Marc F.

    2016-04-28

    We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. © 2016 Wiley Periodicals, Inc.

  5. Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

    Science.gov (United States)

    Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

    2017-12-04

    Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

  6. Predicting the ungauged basin: Model validation and realism assessment

    Directory of Open Access Journals (Sweden)

    Tim evan Emmerik

    2015-10-01

    Full Text Available The hydrological decade on Predictions in Ungauged Basins (PUB led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of model outcome has not been discussed to a great extent. With this paper we aim to contribute to the discussion on how one can determine the value and validity of a hydrological model developed for an ungauged basin. As in many cases no local, or even regional, data are available, alternative methods should be applied. Using a PUB case study in a genuinely ungauged basin in southern Cambodia, we give several examples of how one can use different types of soft data to improve model design, calibrate and validate the model, and assess the realism of the model output. A rainfall-runoff model was coupled to an irrigation reservoir, allowing the use of additional and unconventional data. The model was mainly forced with remote sensing data, and local knowledge was used to constrain the parameters. Model realism assessment was done using data from surveys. This resulted in a successful reconstruction of the reservoir dynamics, and revealed the different hydrological characteristics of the two topographical classes. This paper does not present a generic approach that can be transferred to other ungauged catchments, but it aims to show how clever model design and alternative data acquisition can result in a valuable hydrological model for an ungauged catchment.

  7. Human Factors Predicting Failure and Success in Hospital Information System Implementations in Sub-Saharan Africa.

    Science.gov (United States)

    Verbeke, Frank; Karara, Gustave; Nyssen, Marc

    2015-01-01

    From 2007 through 2014, the authors participated in the implementation of open source hospital information systems (HIS) in 19 hospitals in Rwanda, Burundi, DR Congo, Congo-Brazzaville, Gabon, and Mali. Most of these implementations were successful, but some failed. At the end of a seven-year implementation effort, a number of risk factors, facilitators, and pragmatic approaches related to the deployment of HIS in Sub-Saharan health facilities have been identified. Many of the problems encountered during the HIS implementation process were not related to technical issues but human, cultural, and environmental factors. This study retrospectively evaluates the predictive value of 14 project failure factors and 15 success factors in HIS implementation in the Sub-Saharan region. Nine of the failure factors were strongly correlated with project failure, three were moderately correlated, and one weakly correlated. Regression analysis also confirms that eight factors were strongly correlated with project success, four moderately correlated, and two weakly correlated. The study results may help estimate the expedience of future HIS projects.

  8. Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

    Directory of Open Access Journals (Sweden)

    Nataša Šarlija

    2017-01-01

    Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

  9. Regression Models for Predicting Force Coefficients of Aerofoils

    Directory of Open Access Journals (Sweden)

    Mohammed ABDUL AKBAR

    2015-09-01

    Full Text Available Renewable sources of energy are attractive and advantageous in a lot of different ways. Among the renewable energy sources, wind energy is the fastest growing type. Among wind energy converters, Vertical axis wind turbines (VAWTs have received renewed interest in the past decade due to some of the advantages they possess over their horizontal axis counterparts. VAWTs have evolved into complex 3-D shapes. A key component in predicting the output of VAWTs through analytical studies is obtaining the values of lift and drag coefficients which is a function of shape of the aerofoil, ‘angle of attack’ of wind and Reynolds’s number of flow. Sandia National Laboratories have carried out extensive experiments on aerofoils for the Reynolds number in the range of those experienced by VAWTs. The volume of experimental data thus obtained is huge. The current paper discusses three Regression analysis models developed wherein lift and drag coefficients can be found out using simple formula without having to deal with the bulk of the data. Drag coefficients and Lift coefficients were being successfully estimated by regression models with R2 values as high as 0.98.

  10. Predicting Success in College Mathematics from High School Mathematics Preparation

    OpenAIRE

    Shepley, Richard A.

    1983-01-01

    The purpose of this study was to develop a model to predict the college mathematics courses a freshman could expect to pass by considering their high school mathematics preparation. The high school information that was used consisted of the student's sex, the student's grade point average in mathematics, the highest level of high school mathematics courses taken, and the number of mathematics courses taken in high school. The high school sample was drawn from graduated Seniors in the State...

  11. Model output statistics applied to wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

    1999-03-01

    Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

  12. Minimum requirements for predictive pore-network modeling of solute transport in micromodels

    Science.gov (United States)

    Mehmani, Yashar; Tchelepi, Hamdi A.

    2017-10-01

    Pore-scale models are now an integral part of analyzing fluid dynamics in porous materials (e.g., rocks, soils, fuel cells). Pore network models (PNM) are particularly attractive due to their computational efficiency. However, quantitative predictions with PNM have not always been successful. We focus on single-phase transport of a passive tracer under advection-dominated regimes and compare PNM with high-fidelity direct numerical simulations (DNS) for a range of micromodel heterogeneities. We identify the minimum requirements for predictive PNM of transport. They are: (a) flow-based network extraction, i.e., discretizing the pore space based on the underlying velocity field, (b) a Lagrangian (particle tracking) simulation framework, and (c) accurate transfer of particles from one pore throat to the next. We develop novel network extraction and particle tracking PNM methods that meet these requirements. Moreover, we show that certain established PNM practices in the literature can result in first-order errors in modeling advection-dominated transport. They include: all Eulerian PNMs, networks extracted based on geometric metrics only, and flux-based nodal transfer probabilities. Preliminary results for a 3D sphere pack are also presented. The simulation inputs for this work are made public to serve as a benchmark for the research community.

  13. Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR.

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    2009-12-01

    Full Text Available Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses. The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5alpha-androstan-3beta-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

  14. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  15. Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

    Directory of Open Access Journals (Sweden)

    Aiqing Kang

    2017-01-01

    Full Text Available Hybrid Ensemble Empirical Mode Decomposition (EEMD and Least Square Support Vector Machine (LSSVM is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP, Auto-Regressive Integrated Moving Average (ARIMA, combination of Empirical Mode Decomposition (EMD with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

  16. PREDICTED PERCENTAGE DISSATISFIED (PPD) MODEL ...

    African Journals Online (AJOL)

    HOD

    their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

  17. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  18. The Influence of Quality on E-Commerce Success: An Empirical Application of the Delone and Mclean IS Success Model

    OpenAIRE

    Ultan Sharkey; Murray Scott; Thomas Acton

    2010-01-01

    This research addresses difficulties in measuring e-commerce success by implementing the DeLone and McLean (D&M) model of IS success (1992, 2003) in an e-commerce environment. This research considers the influence of quality on e-commerce success by measuring the information quality and system quality attributes of an e-commerce system and the intention to use, user satisfaction and intention to transact from a sample of respondents. This research provides an empirical e-commerce application ...

  19. The viscoelastic standard nonlinear solid model: predicting the response of the lumbar intervertebral disk to low-frequency vibrations.

    Science.gov (United States)

    Groth, Kevin M; Granata, Kevin P

    2008-06-01

    Due to the mathematical complexity of current musculoskeletal spine models, there is a need for computationally efficient models of the intervertebral disk (IVD). The aim of this study is to develop a mathematical model that will adequately describe the motion of the IVD under axial cyclic loading as well as maintain computational efficiency for use in future musculoskeletal spine models. Several studies have successfully modeled the creep characteristics of the IVD using the three-parameter viscoelastic standard linear solid (SLS) model. However, when the SLS model is subjected to cyclic loading, it underestimates the load relaxation, the cyclic modulus, and the hysteresis of the human lumbar IVD. A viscoelastic standard nonlinear solid (SNS) model was used to predict the response of the human lumbar IVD subjected to low-frequency vibration. Nonlinear behavior of the SNS model was simulated by a strain-dependent elastic modulus on the SLS model. Parameters of the SNS model were estimated from experimental load deformation and stress-relaxation curves obtained from the literature. The SNS model was able to predict the cyclic modulus of the IVD at frequencies of 0.01 Hz, 0.1 Hz, and 1 Hz. Furthermore, the SNS model was able to quantitatively predict the load relaxation at a frequency of 0.01 Hz. However, model performance was unsatisfactory when predicting load relaxation and hysteresis at higher frequencies (0.1 Hz and 1 Hz). The SLS model of the lumbar IVD may require strain-dependent elastic and viscous behavior to represent the dynamic response to compressive strain.

  20. Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

    Science.gov (United States)

    Lim, Chun Shen; Brown, Chris M.

    2018-01-01

    Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community. PMID:29354101

  1. [Application of ARIMA model on prediction of malaria incidence].

    Science.gov (United States)

    Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

    2016-01-29

    To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

  2. Model of succession in degraded areas based on carabid beetles (Coleoptera, Carabidae).

    Science.gov (United States)

    Schwerk, Axel; Szyszko, Jan

    2011-01-01

    Degraded areas constitute challenging tasks with respect to sustainable management of natural resources. Maintaining or even establishing certain successional stages seems to be particularly important. This paper presents a model of the succession in five different types of degraded areas in Poland based on changes in the carabid fauna. Mean Individual Biomass of Carabidae (MIB) was used as a numerical measure for the stage of succession. The run of succession differed clearly among the different types of degraded areas. Initial conditions (origin of soil and origin of vegetation) and landscape related aspects seem to be important with respect to these differences. As characteristic phases, a 'delay phase', an 'increase phase' and a 'stagnation phase' were identified. In general, the runs of succession could be described by four different parameters: (1) 'Initial degradation level', (2) 'delay', (3) 'increase rate' and (4) 'recovery level'. Applying the analytic solution of the logistic equation, characteristic values for the parameters were identified for each of the five area types. The model is of practical use, because it provides a possibility to compare the values of the parameters elaborated in different areas, to give hints for intervention and to provide prognoses about future succession in the areas. Furthermore, it is possible to transfer the model to other indicators of succession.

  3. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  4. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Science.gov (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  5. Prediction of lithium-ion battery capacity with metabolic grey model

    International Nuclear Information System (INIS)

    Chen, Lin; Lin, Weilong; Li, Junzi; Tian, Binbin; Pan, Haihong

    2016-01-01

    Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures. - Highlights: • The metabolic mechanism is introduced in a grey system for capacity prediction. • Three metabolic grey models are presented and studied. • The universality of these models under different conditions is assessed. • A few data points are required for predicting the capacity with these models.

  6. Comparison of Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model in a successive temporal bisection task.

    Science.gov (United States)

    Arantes, Joana

    2008-06-01

    The present research tested the generality of the "context effect" previously reported in experiments using temporal double bisection tasks [e.g., Arantes, J., Machado, A. Context effects in a temporal discrimination task: Further tests of the Scalar Expectancy Theory and Learning-to-Time models. J. Exp. Anal. Behav., in press]. Pigeons learned two temporal discriminations in which all the stimuli appear successively: 1s (red) vs. 4s (green) and 4s (blue) vs. 16s (yellow). Then, two tests were conducted to compare predictions of two timing models, Scalar Expectancy Theory (SET) and the Learning-to-Time (LeT) model. In one test, two psychometric functions were obtained by presenting pigeons with intermediate signal durations (1-4s and 4-16s). Results were mixed. In the critical test, pigeons were exposed to signals ranging from 1 to 16s and followed by the green or the blue key. Whereas SET predicted that the relative response rate to each of these keys should be independent of the signal duration, LeT predicted that the relative response rate to the green key (compared with the blue key) should increase with the signal duration. Results were consistent with LeT's predictions, showing that the context effect is obtained even when subjects do not need to make a choice between two keys presented simultaneously.

  7. Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model.

    Science.gov (United States)

    Suresh, Krithika; Taylor, Jeremy M G; Spratt, Daniel E; Daignault, Stephanie; Tsodikov, Alexander

    2017-11-01

    Dynamic prediction incorporates time-dependent marker information accrued during follow-up to improve personalized survival prediction probabilities. At any follow-up, or "landmark", time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. To circumvent the assumptions and computational burden associated with a joint model, approximate methods for dynamic prediction have been proposed. One such method is landmarking, which fits a Cox model at a sequence of landmark times, and thus is not a comprehensive probability model of the marker process and the event time. Considering an illness-death model, we derive the residual time distribution and demonstrate that the structure of the Cox model baseline hazard and covariate effects under the landmarking approach do not have simple form. We suggest some extensions of the landmark Cox model that should provide a better approximation. We compare the performance of the landmark models with joint models using simulation studies and cognitive aging data from the PAQUID study. We examine the predicted probabilities produced under both methods using data from a prostate cancer study, where metastatic clinical failure is a time-dependent covariate for predicting death following radiation therapy. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. A Grey NGM(1,1,k Self-Memory Coupling Prediction Model for Energy Consumption Prediction

    Directory of Open Access Journals (Sweden)

    Xiaojun Guo

    2014-01-01

    Full Text Available Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1,k self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1,k model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1,k self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

  9. Prediction of autosomal STR typing success in ancient and Second World War bone samples.

    Science.gov (United States)

    Zupanič Pajnič, Irena; Zupanc, Tomaž; Balažic, Jože; Geršak, Živa Miriam; Stojković, Oliver; Skadrić, Ivan; Črešnar, Matija

    2017-03-01

    Human-specific quantitative PCR (qPCR) has been developed for forensic use in the last 10 years and is the preferred DNA quantification technique since it is very accurate, sensitive, objective, time-effective and automatable. The amount of information that can be gleaned from a single quantification reaction using commercially available quantification kits has increased from the quantity of nuclear DNA to the amount of male DNA, presence of inhibitors and, most recently, to the degree of DNA degradation. In skeletal remains samples from disaster victims, missing persons and war conflict victims, the DNA is usually degraded. Therefore the new commercial qPCR kits able to assess the degree of degradation are potentially able to predict the success of downstream short tandem repeat (STR) typing. The goal of this study was to verify the quantification step using the PowerQuant kit with regard to its suitability as a screening method for autosomal STR typing success on ancient and Second World War (WWII) skeletal remains. We analysed 60 skeletons excavated from five archaeological sites and four WWII mass graves from Slovenia. The bones were cleaned, surface contamination was removed and the bones ground to a powder. Genomic DNA was obtained from 0.5g of bone powder after total demineralization. The DNA was purified using a Biorobot EZ1 device. Following PowerQuant quantification, DNA samples were subjected to autosomal STR amplification using the NGM kit. Up to 2.51ng DNA/g of powder were extracted. No inhibition was detected in any of bones analysed. 82% of the WWII bones gave full profiles while 73% of the ancient bones gave profiles not suitable for interpretation. Four bone extracts yielded no detectable amplification or zero quantification results and no profiles were obtained from any of them. Full or useful partial profiles were produced only from bone extracts where short autosomal (Auto) and long degradation (Deg) PowerQuant targets were detected. It is

  10. Improving Predictive Modeling in Pediatric Drug Development: Pharmacokinetics, Pharmacodynamics, and Mechanistic Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Slikker, William; Young, John F.; Corley, Rick A.; Dorman, David C.; Conolly, Rory B.; Knudsen, Thomas; Erstad, Brian L.; Luecke, Richard H.; Faustman, Elaine M.; Timchalk, Chuck; Mattison, Donald R.

    2005-07-26

    A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

  11. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  12. Towards single embryo transfer? Modelling clinical outcomes of potential treatment choices using multiple data sources: predictive models and patient perspectives.

    Science.gov (United States)

    Roberts, Sa; McGowan, L; Hirst, Wm; Brison, Dr; Vail, A; Lieberman, Ba

    2010-07-01

    In vitro fertilisation (IVF) treatments involve an egg retrieval process, fertilisation and culture of the resultant embryos in the laboratory, and the transfer of embryos back to the mother over one or more transfer cycles. The first transfer is usually of fresh embryos and the remainder may be cryopreserved for future frozen cycles. Most commonly in UK practice two embryos are transferred (double embryo transfer, DET). IVF techniques have led to an increase in the number of multiple births, carrying an increased risk of maternal and infant morbidity. The UK Human Fertilisation and Embryology Authority (HFEA) has adopted a multiple birth minimisation strategy. One way of achieving this would be by increased use of single embryo transfer (SET). To collate cohort data from treatment centres and the HFEA; to develop predictive models for live birth and twinning probabilities from fresh and frozen embryo transfers and predict outcomes from treatment scenarios; to understand patients' perspectives and use the modelling results to investigate the acceptability of twin reduction policies. A multidisciplinary approach was adopted, combining statistical modelling with qualitative exploration of patients' perspectives: interviews were conducted with 27 couples at various stages of IVF treatment at both UK NHS and private clinics; datasets were collated of over 90,000 patients from the HFEA registry and nearly 9000 patients from five clinics, both over the period 2000-5; models were developed to determine live birth and twin outcomes and predict the outcomes of policies for selecting patients for SET or DET in the fresh cycle following egg retrieval and fertilisation, and the predictions were used in simulations of treatments; two focus groups were convened, one NHS and one web based on a patient organisation's website, to present the results of the statistical analyses and explore potential treatment policies. The statistical analysis revealed no characteristics that

  13. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

    Science.gov (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy

    2016-01-01

    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  14. Robust predictions of the interacting boson model

    International Nuclear Information System (INIS)

    Casten, R.F.; Koeln Univ.

    1994-01-01

    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  15. Developing a Successful Open Source Training Model

    Directory of Open Access Journals (Sweden)

    Belinda Lopez

    2010-01-01

    Full Text Available Training programs for open source software provide a tangible, and sellable, product. A successful training program not only builds revenue, it also adds to the overall body of knowledge available for the open source project. By gathering best practices and taking advantage of the collective expertise within a community, it may be possible for a business to partner with an open source project to build a curriculum that promotes the project and supports the needs of the company's training customers. This article describes the initial approach used by Canonical, the commercial sponsor of the Ubuntu Linux operating system, to engage the community in the creation of its training offerings. We then discuss alternate curriculum creation models and some of the conditions that are necessary for successful collaboration between creators of existing documentation and commercial training providers.

  16. MicroRNA prediction using a fixed-order Markov model based on the secondary structure pattern.

    Directory of Open Access Journals (Sweden)

    Wei Shen

    Full Text Available Predicting miRNAs is an arduous task, due to the diversity of the precursors and complexity of enzyme processes. Although several prediction approaches have reached impressive performances, few of them could achieve a full-function recognition of mature miRNA directly from the candidate hairpins across species. Therefore, researchers continue to seek a more powerful model close to biological recognition to miRNA structure. In this report, we describe a novel miRNA prediction algorithm, known as FOMmiR, using a fixed-order Markov model based on the secondary structural pattern. For a training dataset containing 809 human pre-miRNAs and 6441 human pseudo-miRNA hairpins, the model's parameters were defined and evaluated. The results showed that FOMmiR reached 91% accuracy on the human dataset through 5-fold cross-validation. Moreover, for the independent test datasets, the FOMmiR presented an outstanding prediction in human and other species including vertebrates, Drosophila, worms and viruses, even plants, in contrast to the well-known algorithms and models. Especially, the FOMmiR was not only able to distinguish the miRNA precursors from the hairpins, but also locate the position and strand of the mature miRNA. Therefore, this study provides a new generation of miRNA prediction algorithm, which successfully realizes a full-function recognition of the mature miRNAs directly from the hairpin sequences. And it presents a new understanding of the biological recognition based on the strongest signal's location detected by FOMmiR, which might be closely associated with the enzyme cleavage mechanism during the miRNA maturation.

  17. An approach to model validation and model-based prediction -- polyurethane foam case study.

    Energy Technology Data Exchange (ETDEWEB)

    Dowding, Kevin J.; Rutherford, Brian Milne

    2003-07-01

    Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

  18. Development and evaluation of mathematical model to predict disintegration time of fast disintegrating tablets using powder characteristics.

    Science.gov (United States)

    Goel, H; Arora, A; Tiwary, A K; Rana, V

    2011-02-01

    The objective of the study was to develop a mathematical model for predicting the disintegration time of fast disintegrating tablets (FDTs) by estimating the powder characteristics of powder blend prior to compression. A combination of chitosan-alginate complex and glycine in the ratio of 50:50 was used for preparing FDTs. The developed mathematical model allowed water sorption time (WST), effective pore radius (R(eff.p)) and swelling Index (SI) of powder mixture as well as tablet crushing strength to be successfully correlated with disintegration time (DT) of FDTs. The predicted model showed that disintegration time of FDTs to be directly correlated with powder characteristics and inversely correlated with tablet crushing strength. Furthermore, a correlation of 0.97 was obtained when DT of FDTs was compared with SI/(WST * R(eff.p)). This correlation was not affected by inclusion of water soluble (ondansetron hydrochloride or metaclopramide hydrochloride) or water insoluble (domperidone) drugs in the powder blend or FDTs. These observations indicated the versatility of the mathematical model in predicting the disintegration time of FDTs by evaluating the selected characteristics of the powder blends without actually preparing the FDTs.

  19. Predictive modeling of liquid-sodium thermal–hydraulics experiments and computations

    International Nuclear Information System (INIS)

    Arslan, Erkan; Cacuci, Dan G.

    2014-01-01

    Highlights: • We applied the predictive modeling method of Cacuci and Ionescu-Bujor (2010). • We assimilated data from sodium flow experiments. • We used computational fluid dynamics simulations of sodium experiments. • The predictive modeling method greatly reduced uncertainties in predicted results. - Abstract: This work applies the predictive modeling procedure formulated by Cacuci and Ionescu-Bujor (2010) to assimilate data from liquid-sodium thermal–hydraulics experiments in order to reduce systematically the uncertainties in the predictions of computational fluid dynamics (CFD) simulations. The predicted CFD-results for the best-estimate model parameters and results describing sodium-flow velocities and temperature distributions are shown to be significantly more precise than the original computations and experiments, in that the predicted uncertainties for the best-estimate results and model parameters are significantly smaller than both the originally computed and the experimental uncertainties

  20. Interpreting Disruption Prediction Models to Improve Plasma Control

    Science.gov (United States)

    Parsons, Matthew

    2017-10-01

    In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.