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  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

    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.

  3. 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.

  4. 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

  5. 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.

  6. 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

  7. 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 ...

  8. 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…

  9. 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.

  10. 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

  11. 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.

  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. 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…

  14. 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.

  15. 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.

  16. 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

  17. 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…

  18. 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

  19. 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.

  20. 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.

  1. 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…

  2. 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

  3. 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

  4. 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

  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. 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.

  7. 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…

  8. 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

  9. 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,

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Predicting academic success among deaf college students.

    Science.gov (United States)

    Convertino, Carol M; Marschark, Marc; Sapere, Patricia; Sarchet, Thomastine; Zupan, Megan

    2009-01-01

    For both practical and theoretical reasons, educators and educational researchers seek to determine predictors of academic success for students at different levels and from different populations. Studies involving hearing students at the postsecondary level have documented significant predictors of success relating to various demographic factors, school experience, and prior academic attainment. Studies involving deaf and hard-of-hearing students have focused primarily on younger students and variables such as degree of hearing loss, use of cochlear implants, educational placement, and communication factors-although these typically are considered only one or two at a time. The present investigation utilizes data from 10 previous experiments, all using the same paradigm, in an attempt to discern significant predictors of readiness for college (utilizing college entrance examination scores) and classroom learning at the college level (utilizing scores from tests in simulated classrooms). Academic preparation was a clear and consistent predictor in both domains, but the audiological and communication variables examined were not. Communication variables that were significant reflected benefits of language flexibility over skills in either spoken language or American Sign Language.

  17. 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

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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...

  3. 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…

  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. 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. 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…

  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. 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.

  9. 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

  10. 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...

  11. 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…

  12. 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

  13. 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…

  14. 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;…

  15. 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…

  16. 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)

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. MODEL OF TRAINING OF SUCCESS IN LIFE

    Directory of Open Access Journals (Sweden)

    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

  2. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    CR cultural resource CRM cultural resource management CRPM Cultural Resource Predictive Modeling DoD Department of Defense ESTCP Environmental...resource management ( CRM ) legal obligations under NEPA and the NHPA, military installations need to demonstrate that CRM decisions are based on objective...maxim “one size does not fit all,” and demonstrate that DoD installations have many different CRM needs that can and should be met through a variety

  3. 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....

  4. 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.

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

    DEFF Research Database (Denmark)

    Rein, Arno; Adam, Iris K.U.; Miltner, Anja

    2016-01-01

    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...

  6. 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.

  7. 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.

  8. 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 ...

  9. 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.

  10. 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

  11. 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. ...

  12. 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.

  13. 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.

  14. 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…

  15. 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.

  16. 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

  17. 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

  18. 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…

  19. 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...

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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,...

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. The success of cardiotocography in predicting perinatal outcome

    Directory of Open Access Journals (Sweden)

    Alpaslan Kaban

    2012-06-01

    Full Text Available Objectives: The determination of the fetal condition duringlabor is important to minimize fetal death due to asphyxiaand the neurological sequelae of fetal hypoxia.This study evaluated the success of fetal cardiotocographyin predicting perinatal consequences.Materials and methods: This study enrolled 101 full-termpregnant women admitted for delivery to Vakif GurebaTraining and Research Hospital between October 2009and February 2010. Women were included if they wereaged 18-45 years and within 36-41 weeks of gestation.During a 20-min period of fetal monitoring, a change inFHR (fetal heart rate lasting for 15 s or two elevated runsof 15 beats was evaluated as a reactive NST (non-stresstest. The umbilical artery pH was used as the “gold standard”for assessing fetal asphyxia.Results: The mean age of the women included in thestudy was 27.82 ± 5.29 years, the average parity was1.09± 0.96. The pH was normal in 85 neonates, while 13 hadfetal asphyxia. No significant difference in umbilical cordblood pH, pO2, or pCO2 was observed between these twogroups (p = 0.497, p = 0.722, and p = 0.053, respectively.No significant difference in maternal age, parity, or birthweight was found between the group with fetal distressbased on CTG (cardiotocography and the normal group.Conclusion: Cardiotocography is an important test duringlabor for labor management, it is insufficient for predictingthe perinatal outcome. Therefore, labor should beevaluated on an individualized basis. J Clin Exp Invest2012; 3(2: 168-171

  16. 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...

  17. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.

  18. 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.

  19. 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

  20. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. 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.

  2. A Course Specific Perspective in the Prediction of Academic Success.

    Science.gov (United States)

    Beaulieu, R. P.

    1990-01-01

    Students (N=94) enrolled in a senior-level management course over six semesters were used to investigate the ability of four measures from two industrial tests to predict course performance. The resulting multiple regression equation with four predictors could accurately predict achievement of males, but not of females. (Author/TE)

  3. 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...

  4. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  5. 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.

  6. 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

  7. 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...

  8. Predictive Variables of Success for Latino Enrollment in Higher Education

    Science.gov (United States)

    Sanchez, Jafeth E.; Usinger, Janet; Thornton, Bill W.

    2015-01-01

    It is necessary to better understand the unique variables that serve as predictors of Latino students' postsecondary enrollment and success. Impacts of various variables were examined among 850 Latino and Caucasian students (76% and 24% of the sample, respectively). Gender, ethnicity, perceived affordability, high school grade point average, and…

  9. Modeling Student Success in Engineering Education

    Science.gov (United States)

    Jin, Qu

    2013-01-01

    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.…

  10. 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.

  11. Can brain responses to movie trailers predict success?

    NARCIS (Netherlands)

    M.A.S. 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

  12. 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).

  13. 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

  14. 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

  15. FORMATIVE ASSESSMENT MODEL OF LEARNING SUCCESS ACHIEVEMENTS

    Directory of Open Access Journals (Sweden)

    Mikhailova Elena Konstantinovna

    2013-05-01

    Full Text Available The paper is devoted to the problem of assessment of the school students’ learning success achievements. The problem is investigated from the viewpoint of assessing the students’ learning outcomes that is aimed to ensure the teachers and students with the means and conditions to improve the educational process and results.

  16. Predicting successful introduction of novel fruit to preschool children.

    Science.gov (United States)

    Blissett, Jacqueline; Bennett, Carmel; Donohoe, Jessica; Rogers, Samantha; Higgs, Suzanne

    2012-12-01

    Few children eat sufficient fruits and vegetables despite their established health benefits. The feeding practices used by parents when introducing novel foods to their children, and their efficacy, require further investigation. We aimed to establish which feeding strategies parents commonly use when introducing a novel fruit to their preschool-aged children and assess the effectiveness of these feeding strategies on children's willingness to try a novel fruit. Correlational design. Twenty-five parents and their children aged 2 to 4 years attended our laboratory and consumed a standardized lunch, including a novel fruit. Interactions between parent and child were recorded and coded. Pearson's correlations and multiple linear regression analyses. The frequency with which children swallowed and enjoyed the novel fruit, and the frequency of taste exposures to the novel fruit during the meal, were positively correlated with parental use of physical prompting and rewarding/bargaining. Earlier introduction of solids was related to higher frequency of child acceptance behaviors. The child's age at introduction of solids and the number of physical prompts displayed by parents significantly predicted the frequency of swallowing and enjoying the novel fruit. Age of introduction to solids and parental use of rewards/bargaining significantly predicted the frequency of taste exposures. Prompting a child to eat and using rewards or bargains during a positive mealtime interaction can help to overcome barriers to novel fruit consumption. Early introduction of solids is also associated with greater willingness to consume a novel fruit. Copyright © 2012 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

  17. 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.

  18. Predicting STEM Career Success by STI Knowledge Utilization Patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bozeman, B.; Youtie, J.; Bretschneider, S.

    2016-07-01

    As a part of discussion on knowledge utilization on science and technology, the mixed of papers presented in the panel discussion is designed to illustrate the patterns of collaboration, mobility, and diffusion of knowledge as well as those of labor force. In particular, the first two papers presented in the panel explore the potential of STEM career success through cosmopolitan collaboration and international community collaboration (focused on the relationships between China and Russia) in nanotechnology, which would provide implications on national and international benchmarking of innovation. For policy implications on graduate education and innovation, mobility pattern of non-U.S. Ph.D. degree holders is examined, and impact of a policy report on the target academic communities is investigated through development of credibility map. This panel is designed to highlight a recent effort of understanding geographical, cognitive or social spaces that are present in the scientific and technological activity as well as in doctoral education. The papers presented in this panel, therefore, will provide a rich set of significant and relevant insights drawn from examining STI knowledge utilization patterns to the STI-ENID community. The anticipated length of the event may be 90 minutes and there is no preferred number of attendees in particular although it is expected to be in between 35 and 60 at the minimum. (Author)

  19. Surveying the elements of successful infrared predictive maintenance programs

    Science.gov (United States)

    Snell, John R., Jr.; Spring, Robert W.

    1991-03-01

    This paper summarizes the results of a survey of over three hundred maintenance personnel who use imaging equipment within their company or organization. All had previously participated in one or more of our training programs. The companies took in a broad range of industry, including, among other, power generation, pulp and paper, metals, mining, petrochemical, automotive and general manufacturing. The organizations were mainly quite large, either commercial or public, and included governmental agencies, military, colleges and universities, municipalities, and utilities. Although we had a very tight time line for the survey, we were pleased to have a 15% response rate. The results show that some of the causes of success and failure in infrared programs are not unlike those associated with any type of program in an organizational structure, i.e. the need for accurate and timely communications; justification requirements; etc. Another set of problems was shared more closely with other startup maintenance technologies (for example, vibration monitoring), such as the need for trending data; providing appropriate technical training; achieving reproducible results; etc. Finally, some of the driving mechanisms are more specific to this technology, such as re-designing equipment so that it can be thermally inspected; establishing effective documentation strategies; etc.

  20. 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.

  1. 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.

  2. 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.

  3. "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.

  4. Expanding on Successful Concepts, Models, and Organization

    Science.gov (United States)

    If the goal of the AEP framework was to replace existing exposure models or databases for organizing exposure data with a concept, we would share Dr. von Göetz concerns. Instead, the outcome we promote is broader use of an organizational framework for exposure science. The f...

  5. 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

  6. 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

  7. 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.

  8. 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...

  9. 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

  10. 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.

  11. 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.

  12. 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…

  13. 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…

  14. 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)…

  15. 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…

  16. 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 ...

  17. 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.

  18. 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.

  19. 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…

  20. 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

  1. 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…

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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...

  7. 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…

  8. 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...

  9. 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...

  10. 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.

  11. 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.

  12. 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.

  13. [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.

  14. Modelling the effect of food availability on recruitment success of ...

    African Journals Online (AJOL)

    Modelling the effect of food availability on recruitment success of Cape anchovy ichthyoplankton in ... To characterise the recruitment dynamics of Cape anchovy ichthyoplankton, we used an individual-based ... AJOL African Journals Online.

  15. Empirical evaluation of a forecasting model for successful facilitation ...

    African Journals Online (AJOL)

    During 2000 the annual Facilitator Customer Satisfaction Survey was ... the forecasting model is successful concerning the CSI value and a high positive linear ... namely that of human behaviour to incorporate other influences than just the ...

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  6. 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…

  7. 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-…

  8. 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…

  9. 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…

  10. 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…

  11. 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…

  12. 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.

  13. 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.

  14. 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

  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. 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.

  17. 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

  18. 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

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. 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...

  4. 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.

  5. 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.

  6. 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)

  7. 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

  8. 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...

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. Modeling disturbance and succession in forest landscapes using LANDIS: introduction

    Science.gov (United States)

    Brian R. Sturtevant; Eric J. Gustafson; Hong S. He

    2004-01-01

    Modeling forest landscape change is challenging because it involves the interaction of a variety of factors and processes, such as climate, succession, disturbance, and management. These processes occur at various spatial and temporal scales, and the interactions can be complex on heterogeneous landscapes. Because controlled field experiments designed to investigate...

  14. The Five-Factor Model of Personality and Career Success.

    Science.gov (United States)

    Seibert, Scott E.; Kraimer, Maria L.

    2001-01-01

    Measures of career success and an inventory of the Five-Factor Model of Personality were completed by 496 workers. Extraversion was related positively to salary, promotion, and career satisfaction; neuroticism was related negatively to satisfaction. A significant negative relationship between agreeableness and salary was found for workers in…

  15. A Lotka-Volterra competition model with seasonal succession.

    Science.gov (United States)

    Hsu, Sze-Bi; Zhao, Xiao-Qiang

    2012-01-01

    A complete classification for the global dynamics of a Lotka-Volterra two species competition model with seasonal succession is obtained via the stability analysis of equilibria and the theory of monotone dynamical systems. The effects of two death rates in the bad season and the proportion of the good season on the competition outcomes are also discussed. © Springer-Verlag 2011

  16. The prediction of epidemics through mathematical modeling.

    Science.gov (United States)

    Schaus, Catherine

    2014-01-01

    Mathematical models may be resorted to in an endeavor to predict the development of epidemics. The SIR model is one of the applications. Still too approximate, the use of statistics awaits more data in order to come closer to reality.

  17. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  18. 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

  19. The General Education Collaboration Model: A Model for Successful Mainstreaming.

    Science.gov (United States)

    Simpson, Richard L.; Myles, Brenda Smith

    1990-01-01

    The General Education Collaboration Model is designed to support general educators teaching mainstreamed disabled students, through collaboration with special educators. The model is based on flexible departmentalization, program ownership, identification and development of supportive attitudes, student assessment as a measure of program…

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. A nurse manager succession planning model with associated empirical outcomes.

    Science.gov (United States)

    Titzer, Jennifer L; Shirey, Maria R; Hauck, Sheila

    2014-01-01

    Perceptions of leadership and management competency after a formal nurse manager succession planning program were evaluated. A lack of strategic workforce planning and development of a leadership pipeline contributes to a predicted nurse manager shortage. To meet the anticipated needs for future leadership, evidence-based action is critical. A quasi-experimental mixed-methods, 1-group pretest/posttest research design was used. Nurses working in an acute care hospital were recruited for the study and selected using an objective evaluative process. Participant perceptions regarding their leadership and management competencies significantly increased after the leadership program. Program evaluations confirmed that participants found the program beneficial. One year after program completion, 100% of the program participants have been retained at the organization and 73% had transitioned to leadership roles. Succession planning and leadership development serve as beneficial and strategic mechanisms for identifying and developing high-potential individuals for leadership positions, contributing toward the future nursing leadership pipeline.

  6. 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...

  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. 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

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Concepts, challenges, and successes in modeling thermodynamics of metabolism.

    Science.gov (United States)

    Cannon, William R

    2014-01-01

    The modeling of the chemical reactions involved in metabolism is a daunting task. Ideally, the modeling of metabolism would use kinetic simulations, but these simulations require knowledge of the thousands of rate constants involved in the reactions. The measurement of rate constants is very labor intensive, and hence rate constants for most enzymatic reactions are not available. Consequently, constraint-based flux modeling has been the method of choice because it does not require the use of the rate constants of the law of mass action. However, this convenience also limits the predictive power of constraint-based approaches in that the law of mass action is used only as a constraint, making it difficult to predict metabolite levels or energy requirements of pathways. An alternative to both of these approaches is to model metabolism using simulations of states rather than simulations of reactions, in which the state is defined as the set of all metabolite counts or concentrations. While kinetic simulations model reactions based on the likelihood of the reaction derived from the law of mass action, states are modeled based on likelihood ratios of mass action. Both approaches provide information on the energy requirements of metabolic reactions and pathways. However, modeling states rather than reactions has the advantage that the parameters needed to model states (chemical potentials) are much easier to determine than the parameters needed to model reactions (rate constants). Herein, we discuss recent results, assumptions, and issues in using simulations of state to model metabolism.

  16. 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...

  17. 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.

  18. 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…

  19. 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.

  20. 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...

  1. 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

  2. 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.

  3. 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...

  4. 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.

  5. 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...

  6. 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....

  7. 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...

  8. 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.

  9. 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...

  10. 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

  11. 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.

  12. 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.

  13. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  19. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  20. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  1. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  2. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. 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...

  5. 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 ...

  6. 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...

  7. Improving Student Success Using Predictive Models and Data Visualisations

    Science.gov (United States)

    Essa, Alfred; Ayad, Hanan

    2012-01-01

    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…

  8. Spent fuel: prediction model development

    International Nuclear Information System (INIS)

    Almassy, M.Y.; Bosi, D.M.; Cantley, D.A.

    1979-07-01

    The need for spent fuel disposal performance modeling stems from a requirement to assess the risks involved with deep geologic disposal of spent fuel, and to support licensing and public acceptance of spent fuel repositories. Through the balanced program of analysis, diagnostic testing, and disposal demonstration tests, highlighted in this presentation, the goal of defining risks and of quantifying fuel performance during long-term disposal can be attained

  9. Navy Recruit Attrition Prediction Modeling

    Science.gov (United States)

    2014-09-01

    have high correlation with attrition, such as age, job characteristics, command climate, marital status, behavior issues prior to recruitment, and the...the additive model. glm(formula = Outcome ~ Age + Gender + Marital + AFQTCat + Pay + Ed + Dep, family = binomial, data = ltraining) Deviance ...0.1 ‘ ‘ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance : 105441 on 85221 degrees of freedom Residual deviance

  10. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  11. 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.

  12. 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.

  13. Predictive Models and Computational Toxicology (II IBAMTOX)

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  14. Finding furfural hydrogenation catalysts via predictive modelling

    NARCIS (Netherlands)

    Strassberger, Z.; Mooijman, M.; Ruijter, E.; Alberts, A.H.; Maldonado, A.G.; Orru, R.V.A.; Rothenberg, G.

    2010-01-01

    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

  15. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL STRESSES IN ... the transverse residual stress in the x-direction (σx) had a maximum value of 375MPa ... the finite element method are in fair agreement with the experimental results.

  16. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico; Kryshtafovych, Andriy; Tramontano, Anna

    2009-01-01

    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

  17. 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.

  18. 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.

  19. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  20. 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...

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. A model for successful use of student response systems.

    Science.gov (United States)

    Klein, Kathleen; Kientz, Mary

    2013-01-01

    This article presents a model developed to assist teachers in selecting, implementing, and assessing student response system (SRS) use in the classroom. Research indicates that SRS technology is effective in achieving desired outcomes in higher education settings. Studies indicate that effective SRS use promotes greater achievement of learning outcomes, increased student attention, improved class participation, and active engagement. The model offered in this article is based on best practices described in the literature and several years of SRS use in a traditional higher education classroom setting. Student feedback indicates increased class participation and engagement with SRS technology. Teacher feedback indicates opportunities for contingent teaching. The model described in this article provides a process to assist teachers in the successful selection, implementation, and assessment of SRS technology in the classroom.

  6. Successes and failures of the constituent quark model

    International Nuclear Information System (INIS)

    Lipkin, H.J.

    1982-01-01

    Our approach considers the model as a possible bridge between QCD and the experimental data and examines its predictions to see where these succeed and where they fail. We also attempt to improve the model by looking for additional simple assumptions which give better fits to the experimental data. But we avoid complicated models with too many ad hoc assumptions and too many free parameters; these can fit everything but teach us nothing. We define our constituent quark model by analogy with the constituent electron model of the atom and the constituent nucleon model of the nucleus. In the same way that an atom is assumed to consist only of constituent electrons and a central Coulomb field and a nucleus is assumed to consist only of constituent nucleons hadrons are assumed to consist only of their constituent valence quarks with no bag, no glue, no ocean, nor other constituents. Although these constituent models are oversimplified and neglect other constituents we push them as far as we can. Atomic physics has photons and vacuum polarization as well as constituent electrons, but the constituent model is adequate for calculating most features of the spectrum when finer details like the Lamb shift are neglected. 54 references

  7. 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

  8. 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

  9. Entrepreneurial Women in Radiology: Role Models of Success.

    Science.gov (United States)

    Anzai, Yoshimi; Meltzer, Carolyn C; DeStigter, Kristen K; Destounis, Stamatia; Pawley, Barbara K; Oates, M Elizabeth

    2016-11-01

    Radiology is undeniably male dominated. Alongside surgery and orthopedic surgery, academic radiology ranks near the bottom in having the lowest proportion of full-time female faculty members. Despite many efforts to recruit talented women, the pipeline entering the radiologic disciplines continues to flow at a trickle. One factor is the relative lack of role models for female medical students. Entrepreneurial women in radiology can lead the field with their innovation and creativity, courage, and commitment. In this article, the authors highlight two entrepreneurial female radiologists who shared their success stories at the American Association for Women Radiologists' session at the 2015 ACR annual meeting. Their successes underscore the potential for such women to serve as role models to female medical students and even college undergraduates. Despite the gender gap in radiology, the field has yielded some exceptional women who can take on challenges, overcome barriers and assume risks, create strategies and processes to operationalize their visions, secure funding, and expand their enterprises to make sustainable impacts both at home and abroad. As we move toward more patient- and family-centered care models and become increasingly visible to diverse populations, there is no better time for female leaders in radiology to inspire the next generation to join our essential and rewarding specialty. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  10. 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

  11. Successful model of suicide prevention in the Ukraine military environment.

    Science.gov (United States)

    Rozanov, Vsevolod A; Mokhovikov, Alexander N; Stiliha, Richard

    2002-01-01

    The article deals with the problem of suicidal behavior in the Ukraine military environment and gives an example of the successful prevention approach. The model of prevention is based on (1) education of the responsible officers, (2) training of the representatives of the most vulnerable risk groups, and (3) follow-up procedures based on distribution of pocket books for soldiers, educational booklets, and sets of helpful materials for officers. One of the main conclusions is that the prevention activity must be organized as a continuum of actions, seminars, consultations, and materials distribution.

  12. 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...

  13. 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.

  14. 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.

  15. 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

  16. Developing entrepreneurial competencies for successful business model canvas

    Science.gov (United States)

    Sundah, D. I. E.; Langi, C.; Maramis, D. R. S.; Tawalujan, L. dan

    2018-01-01

    We explore the competencies of entrepreneurship that contribute to business model canvas. This research conducted at smoked fish industries in Province of North Sulawesi, Indonesia. This research used a mixed method which integrating both quantitative and qualitative approaches in a sequential design. The technique of snowball sampling and questionnaire has been used in collecting data from 44 entrepreneurs. Structural equation modeling with SmartPLS application program has been used in analyzing this data to determine the effect of entrepreneurial competencies on business model canvas. We also investigate 3 entrepreneurs who conducted smoked fish business and analyzed their business by using business model canvas. Focus Group Discussion is used in collecting data from 2 groups of entrepreneurs from 2 different locations. The empirical results show that entrepreneurial competencies which consists of managerial competencies, technical competencies, marketing competencies, financial competencies, human relations competencies, and the specific working attitude of entrepreneur has a positive and significantly effect on business model canvas. Additionally, the empirical cases and discussion with 2 groups of entrepreneurs support the quantitative result and it found that human relations competencies have greater influence in achieving successful business model canvas.

  17. Preparing for success: Readiness models for rural telehealth

    Directory of Open Access Journals (Sweden)

    Jennett P

    2005-01-01

    Full Text Available Background: Readiness is an integral and preliminary step in the successful implementation of telehealth services into existing health systems within rural communities. Methods and Materials: This paper details and critiques published international peer-reviewed studies that have focused on assessing telehealth readiness for rural and remote health. Background specific to readiness and change theories is provided, followed by a critique of identified telehealth readiness models, including a commentary on their readiness assessment tools. Results: Four current readiness models resulted from the search process. The four models varied across settings, such as rural outpatient practices, hospice programs, rural communities, as well as government agencies, national associations, and organizations. All models provided frameworks for readiness tools. Two specifically provided a mechanism by which communities could be categorized by their level of telehealth readiness. Discussion: Common themes across models included: an appreciation of practice context, strong leadership, and a perceived need to improve practice. Broad dissemination of these telehealth readiness models and tools is necessary to promote awareness and assessment of readiness. This will significantly aid organizations to facilitate the implementation of telehealth.

  18. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  19. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  20. 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....

  1. 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%

  2. 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.

  3. 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%.

  4. 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

  5. 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

  6. Value of routine investigations to predict loop diuretic down-titration success in stable heart failure.

    Science.gov (United States)

    Martens, Pieter; Verbrugge, Frederik H; Boonen, Levinia; Nijst, Petra; Dupont, Matthias; Mullens, Wilfried

    2018-01-01

    Guidelines advocate down-titration of loop diuretics in chronic heart failure (CHF) when patients have no signs of volume overload. Limited data are available on the expected success rate of this practice or how routine diagnostic tests might help steering this process. Fifty ambulatory CHF-patients on stable neurohumoral blocker/diuretic therapy for at least 3months without any clinical sign of volume overload were prospectively included to undergo loop diuretic down-titration. All patients underwent a similar pre-down-titration evaluation consisting of a dyspnea scoring, physical examination, transthoracic echocardiography (diastolic function, right ventricular function, cardiac filling pressures and valvular disease), blood sample (serum creatinine, plasma NT-pro-BNP and neurohormones). Loop diuretic maintenance dose was subsequently reduced by 50% or stopped if dose was ≤40mg furosemide equivalents. Successful down-titration was defined as a persistent dose reduction after 30days without weight increase >1.5kg or new-onset symptoms of worsening heart failure. At 30-day follow-up, down-titration was successful in 62% (n=31). In 12/19 patients exhibiting down-titration failure, this occurred within the first week. Physical examination, transthoracic echocardiography and laboratory analysis had limited predictive capability to detect patients with down-titration success/failure (positive likelihood-ratios below 1.5, or area under the curve [AUC] non-statically different from AUC=0.5). Loop diuretic down-titration is feasible in a majority of stable CHF patients in which the treating clinician felt continuation of loops was unnecessary to sustain euvolemia. Importantly, routine diagnostics which suggest euvolemia, have limited diagnostic impact on the post-test probability. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Why achievement motivation predicts success in business but failure in politics: the importance of personal control.

    Science.gov (United States)

    Winter, David G

    2010-12-01

    Several decades of research have established that implicit achievement motivation (n Achievement) is associated with success in business, particularly in entrepreneurial or sales roles. However, several political psychology studies have shown that achievement motivation is not associated with success in politics; rather, implicit power motivation often predicts political success. Having versus lacking control may be a key difference between business and politics. Case studies suggest that achievement-motivated U.S. presidents and other world leaders often become frustrated and thereby fail because of lack of control, whereas power-motivated presidents develop ways to work with this inherent feature of politics. A reevaluation of previous research suggests that, in fact, relationships between achievement motivation and business success only occur when control is high. The theme of control is also prominent in the development of achievement motivation. Cross-national data are also consistent with this analysis: In democratic industrialized countries, national levels of achievement motivation are associated with strong executive control. In countries with low opportunity for education (thus fewer opportunities to develop a sense of personal control), achievement motivation is associated with internal violence. Many of these manifestations of frustrated achievement motivation in politics resemble authoritarianism. This conclusion is tested by data from a longitudinal study of 113 male college students, showing that high initial achievement motivation combined with frustrated desires for control is related to increases in authoritarianism (F-scale scores) during the college years. Implications for the psychology of leadership and practical politics are discussed. © 2010 The Author. Journal of Personality © 2010, Wiley Periodicals, Inc.

  8. 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.

  9. 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

  10. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)

    2002-06-01

    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  11. 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

  12. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2011-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  13. 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…

  14. 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.

  15. Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

    Science.gov (United States)

    Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

    2012-01-01

    Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…

  16. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. Sperm quality but not relatedness predicts sperm competition success in threespine sticklebacks (Gasterosteus aculeatus).

    Science.gov (United States)

    Mehlis, Marion; Rahn, Anna K; Bakker, Theo C M

    2015-04-26

    Mating between close relatives often leads to a reduction of an individual's fitness, due to an increased expression of deleterious alleles. Thus, in many animal taxa pre- as well as postcopulatory inbreeding avoidance mechanisms have evolved. An increased risk of inbreeding and hence a loss of genetic variation may occur during founder events as in most cases only few individuals establish a new population. The threespine stickleback (Gasterosteus aculeatus) is a small externally fertilizing fish species subject to strong sperm competition. Sticklebacks inhabit both marine and freshwater environments and anadromous populations have repeatedly established new genetically less diverse freshwater populations. Previous studies showed that anadromous sticklebacks strongly suffer from inbreeding depression and when given the choice females prefer to mate with unrelated males. The present study aimed to address whether there exists a postcopulatory inbreeding avoidance mechanism solely based on sperm-egg interactions in sperm competition experiments. We used F1 individuals that originated either from a large, genetically heterogeneous anadromous population or from a small, genetically less diverse freshwater population. For each population, eggs of two different females were in vitro fertilized by the same two males' sperm in a paired study design. In the main experiment one male was the female's full-sib brother and in the control experiment all individuals were unrelated. The results revealed that fertilization success was independent of relatedness in both populations suggesting a general lack of a postcopulatory inbreeding avoidance mechanism. Instead, male quality (i.e. sperm morphology) predicted paternity success during competitive fertilization trials. In sticklebacks, there is no evidence for postcopulatory inbreeding avoidance. Sperm morphology predicted paternity instead, thus sperm quality traits are under strong sexual selection, presumably driven by the

  2. Recent Successes and Remaining Challenges in Predicting Phosphorus Loading to Surface Waters at Large Scales

    Science.gov (United States)

    Harrison, J.; Metson, G.; Beusen, A.

    2017-12-01

    Over the past century humans have greatly accelerated phosphorus (P) flows from land to aquatic ecosystems, causing eutrophication and associated effects such as harmful algal blooms and hypoxia. Effectively addressing this challenge requires understanding geographic and temporal distribution of aquatic P loading, knowledge of major controls on P loading, and the relative importance of various potential P sources. The Global (N)utrient (E)xport from (W)ater(S)heds) NEWS model and recent improvements and extensions of this modeling system can be used to generate this understanding. This presentation will focus on insights global NEWS models grant into past, present, and potential future P sources and sinks, with a focus on the world's large rivers. Early results suggest: 1) that while aquatic P loading is globally dominated by particulate forms, dissolved P can be locally dominant; 2) that P loading has increased substantially at the global scale, but unevenly between world regions, with hotspots in South and East Asia; 3) that P loading is likely to continue to increase globally, but decrease in certain regions that are actively pursuing proactive P management; and 4) that point sources, especially in urban centers, play an important (even dominant) role in determining loads of dissolved inorganic P. Despite these insights, substantial unexplained variance remains when model predictions and measurements are compared at global and regional scales, for example within the U.S. Disagreements between model predictions and measurements suggest opportunities for model improvement. In particular, explicit inclusion of soil characteristics and the concept of temporal P legacies in future iterations of NEWS (and other) models may help improve correspondence between models and measurements.

  3. Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction.

    Science.gov (United States)

    López, Yosvany; Sharma, Alok; Dehzangi, Abdollah; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Sattar, Abdul; Tsunoda, Tatsuhiko

    2018-01-19

    Post-translational modification is considered an important biological mechanism with critical impact on the diversification of the proteome. Although a long list of such modifications has been studied, succinylation of lysine residues has recently attracted the interest of the scientific community. The experimental detection of succinylation sites is an expensive process, which consumes a lot of time and resources. Therefore, computational predictors of this covalent modification have emerged as a last resort to tackling lysine succinylation. In this paper, we propose a novel computational predictor called 'Success', which efficiently uses the structural and evolutionary information of amino acids for predicting succinylation sites. To do this, each lysine was described as a vector that combined the above information of surrounding amino acids. We then designed a support vector machine with a radial basis function kernel for discriminating between succinylated and non-succinylated residues. We finally compared the Success predictor with three state-of-the-art predictors in the literature. As a result, our proposed predictor showed a significant improvement over the compared predictors in statistical metrics, such as sensitivity (0.866), accuracy (0.838) and Matthews correlation coefficient (0.677) on a benchmark dataset. The proposed predictor effectively uses the structural and evolutionary information of the amino acids surrounding a lysine. The bigram feature extraction approach, while retaining the same number of features, facilitates a better description of lysines. A support vector machine with a radial basis function kernel was used to discriminate between modified and unmodified lysines. The aforementioned aspects make the Success predictor outperform three state-of-the-art predictors in succinylation detection.

  4. 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.

  5. New models for success emerge for US natural gas industry

    International Nuclear Information System (INIS)

    Addy, W.M.; Hutchinson, R.A.

    1994-01-01

    Very few companies in the US natural gas industry are confident in their ability to compete effectively in the brave new world of deregulation. Boston Consulting Group recently conducted an internal study to help the industry think about its future and identify models for success in this new environment. The authors examined the historical performance of 800 companies using several shareholder-value indicators, including cash-flow returns on investment, a measure of cash returns on cash invested that correlates closely to share price. Based on that review and discussions with investment managers and industry analysts, the authors were able to focus on a handful of companies that actually have thrived and created value against the difficult landscape of the past decade. Interviews with their senior executives provided important strategic and operational insights

  6. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  7. Logistic regression analysis to predict Medical Licensing Examination of Thailand (MLET) Step1 success or failure.

    Science.gov (United States)

    Wanvarie, Samkaew; Sathapatayavongs, Boonmee

    2007-09-01

    The aim of this paper was to assess factors that predict students' performance in the Medical Licensing Examination of Thailand (MLET) Step1 examination. The hypothesis was that demographic factors and academic records would predict the students' performance in the Step1 Licensing Examination. A logistic regression analysis of demographic factors (age, sex and residence) and academic records [high school grade point average (GPA), National University Entrance Examination Score and GPAs of the pre-clinical years] with the MLET Step1 outcome was accomplished using the data of 117 third-year Ramathibodi medical students. Twenty-three (19.7%) students failed the MLET Step1 examination. Stepwise logistic regression analysis showed that the significant predictors of MLET Step1 success/failure were residence background and GPAs of the second and third preclinical years. For students whose sophomore and third-year GPAs increased by an average of 1 point, the odds of passing the MLET Step1 examination increased by a factor of 16.3 and 12.8 respectively. The minimum GPAs for students from urban and rural backgrounds to pass the examination were estimated from the equation (2.35 vs 2.65 from 4.00 scale). Students from rural backgrounds and/or low-grade point averages in their second and third preclinical years of medical school are at risk of failing the MLET Step1 examination. They should be given intensive tutorials during the second and third pre-clinical years.

  8. 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...

  9. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  10. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  11. Predictive performance models and multiple task performance

    Science.gov (United States)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  12. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  13. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  14. 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,...

  15. Modeling Forest Succession among Ecological Land Units in Northern Minnesota

    Directory of Open Access Journals (Sweden)

    George Host

    1998-12-01

    Full Text Available Field and modeling studies were used to quantify potential successional pathways among fine-scale ecological classification units within two geomorphic regions of north-central Minnesota. Soil and overstory data were collected on plots stratified across low-relief ground moraines and undulating sand dunes. Each geomorphic feature was sampled across gradients of topography or soil texture. Overstory conditions were sampled using five variable-radius point samples per plot; soil samples were analyzed for carbon and nitrogen content. Climatic, forest composition, and soil data were used to parameterize the sample plots for use with LINKAGES, a forest growth model that simulates changes in composition and soil characteristics over time. Forest composition and soil properties varied within and among geomorphic features. LINKAGES simulations were using "bare ground" and the current overstory as starting conditions. Northern hardwoods or pines dominated the late-successional communities of morainal and dune landforms, respectively. The morainal landforms were dominated by yellow birch and sugar maple; yellow birch reached its maximum abundance in intermediate landscape positions. On the dune sites, pine was most abundant in drier landscape positions, with white spruce increasing in abundance with increasing soil moisture and N content. The differences in measured soil properties and predicted late-successional composition indicate that ecological land units incorporate some of the key variables that govern forest composition and structure. They further show the value of ecological classification and modeling for developing forest management strategies that incorporate the spatial and temporal dynamics of forest ecosystems.

  16. A stepwise model to predict monthly streamflow

    Science.gov (United States)

    Mahmood Al-Juboori, Anas; Guven, Aytac

    2016-12-01

    In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

  17. Business Models for Successfully Maintaining Games for Health.

    Science.gov (United States)

    Baranowski, Moderator Tom; Isaac, Participants Fikry; Ashford, Chris; Goldman, Ron; Lenihan, David J; Poole, Brent; Buday, Richard; van Rijswijk, Jurriaan

    2013-04-01

    Videogames for health provide innovative, exciting, and possibly highly effective new media for helping players change their behaviors or otherwise benefit their health. Getting the right videogames into the hands of players who can benefit most in a way that pays for the continued innovation and creation of such games is a current challenge. Entertainment videogame companies, which create games primarily to enhance players' enjoyment, have used the general business marketplace (e.g., online stores, walk-in stores, app stores) to deliver their products directly to consumers and earn enough capital to invest in making new products. No one believes, however, that enough kids or adults would use the general business marketplace to purchase games for health in sufficient volume to provide the down payment for the innovation and creation of new games for health. A successful business model is critical to the financial future of games for health. We asked members of our Editorial Board who are in health-related companies (Fikry Isaac, MD, MPH), in several game development companies (Chris Ashford, Ron Goldman, David J. Lenihan, Brent Poole, and Richard Buday, FAIA), and the head of the Games for Health Europe Foundation (Jurriaan van Rijswijk, MSc) to address questions in a roundtable about the current and possible future business models for games for health.

  18. 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.

  19. [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

  20. 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.

  1. 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.

  2. 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.)

  3. An Intelligent Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    IbrahimM. Hamed

    2012-08-01

    Full Text Available This paper presents an intelligent model for stock market signal prediction using Multi-Layer Perceptron (MLP Artificial Neural Networks (ANN. Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue. Finally, statistical significance was examined using ANOVA test.

  4. Predictive Models, How good are they?

    DEFF Research Database (Denmark)

    Kasch, Helge

    The WAD grading system has been used for more than 20 years by now. It has shown long-term viability, but with strengths and limitations. New bio-psychosocial assessment of the acute whiplash injured subject may provide better prediction of long-term disability and pain. Furthermore, the emerging......-up. It is important to obtain prospective identification of the relevant risk underreported disability could, if we were able to expose these hidden “risk-factors” during our consultations, provide us with better predictive models. New data from large clinical studies will present exciting new genetic risk markers...

  5. 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.

  6. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    SILVA R. G.

    1999-01-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  7. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  8. 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.

  9. 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)

  10. Immobilized metal-affinity chromatography protein-recovery screening is predictive of crystallographic structure success

    International Nuclear Information System (INIS)

    Choi, Ryan; Kelley, Angela; Leibly, David; Nakazawa Hewitt, Stephen; Napuli, Alberto; Van Voorhis, Wesley

    2011-01-01

    An overview of the methods used for high-throughput cloning and protein-expression screening of SSGCID hexahistidine recombinant proteins is provided. It is demonstrated that screening for recombinant proteins that are highly recoverable from immobilized metal-affinity chromatography improves the likelihood that a protein will produce a structure. The recombinant expression of soluble proteins in Escherichia coli continues to be a major bottleneck in structural genomics. The establishment of reliable protocols for the performance of small-scale expression and solubility testing is an essential component of structural genomic pipelines. The SSGCID Protein Production Group at the University of Washington (UW-PPG) has developed a high-throughput screening (HTS) protocol for the measurement of protein recovery from immobilized metal-affinity chromatography (IMAC) which predicts successful purification of hexahistidine-tagged proteins. The protocol is based on manual transfer of samples using multichannel pipettors and 96-well plates and does not depend on the use of robotic platforms. This protocol has been applied to evaluate the expression and solubility of more than 4000 proteins expressed in E. coli. The UW-PPG also screens large-scale preparations for recovery from IMAC prior to purification. Analysis of these results show that our low-cost non-automated approach is a reliable method for the HTS demands typical of large structural genomic projects. This paper provides a detailed description of these protocols and statistical analysis of the SSGCID screening results. The results demonstrate that screening for proteins that yield high recovery after IMAC, both after small-scale and large-scale expression, improves the selection of proteins that can be successfully purified and will yield a crystal structure

  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. 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.

  13. 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 ...

  14. 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…

  15. 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

  16. Novelty-Sensitive Dopaminergic Neurons in the Human Substantia Nigra Predict Success of Declarative Memory Formation.

    Science.gov (United States)

    Kamiński, Jan; Mamelak, Adam N; Birch, Kurtis; Mosher, Clayton P; Tagliati, Michele; Rutishauser, Ueli

    2018-04-12

    The encoding of information into long-term declarative memory is facilitated by dopamine. This process depends on hippocampal novelty signals, but it remains unknown how midbrain dopaminergic neurons are modulated by declarative-memory-based information. We recorded individual substantia nigra (SN) neurons and cortical field potentials in human patients performing a recognition memory task. We found that 25% of SN neurons were modulated by stimulus novelty. Extracellular waveform shape and anatomical location indicated that these memory-selective neurons were putatively dopaminergic. The responses of memory-selective neurons appeared 527 ms after stimulus onset, changed after a single trial, and were indicative of recognition accuracy. SN neurons phase locked to frontal cortical theta-frequency oscillations, and the extent of this coordination predicted successful memory formation. These data reveal that dopaminergic neurons in the human SN are modulated by memory signals and demonstrate a progression of information flow in the hippocampal-basal ganglia-frontal cortex loop for memory encoding. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  17. Validated predictive modelling of the environmental resistome.

    Science.gov (United States)

    Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

    2015-06-01

    Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

  18. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  19. Baryogenesis model predicting antimatter in the Universe

    International Nuclear Information System (INIS)

    Kirilova, D.

    2003-01-01

    Cosmic ray and gamma-ray data do not rule out antimatter domains in the Universe, separated at distances bigger than 10 Mpc from us. Hence, it is interesting to analyze the possible generation of vast antimatter structures during the early Universe evolution. We discuss a SUSY-condensate baryogenesis model, predicting large separated regions of matter and antimatter. The model provides generation of the small locally observed baryon asymmetry for a natural initial conditions, it predicts vast antimatter domains, separated from the matter ones by baryonically empty voids. The characteristic scale of antimatter regions and their distance from the matter ones is in accordance with observational constraints from cosmic ray, gamma-ray and cosmic microwave background anisotropy data

  20. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    OpenAIRE

    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 t...

  1. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  2. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  3. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  4. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  5. 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.

  6. Predicting extinction rates in stochastic epidemic models

    International Nuclear Information System (INIS)

    Schwartz, Ira B; Billings, Lora; Dykman, Mark; Landsman, Alexandra

    2009-01-01

    We investigate the stochastic extinction processes in a class of epidemic models. Motivated by the process of natural disease extinction in epidemics, we examine the rate of extinction as a function of disease spread. We show that the effective entropic barrier for extinction in a susceptible–infected–susceptible epidemic model displays scaling with the distance to the bifurcation point, with an unusual critical exponent. We make a direct comparison between predictions and numerical simulations. We also consider the effect of non-Gaussian vaccine schedules, and show numerically how the extinction process may be enhanced when the vaccine schedules are Poisson distributed

  7. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  8. 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

  9. Data Driven Economic Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Masoud Kheradmandi

    2018-04-01

    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  10. 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.

  11. 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.

  12. Plant control using embedded predictive models

    International Nuclear Information System (INIS)

    Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.

    1990-01-01

    B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available

  13. 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...

  14. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  15. Modeling and Prediction of Krueger Device Noise

    Science.gov (United States)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

    2016-01-01

    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  16. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  17. Evaluating Predictive Models of Software Quality

    Science.gov (United States)

    Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

    2014-06-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  18. Predicting FLDs Using a Multiscale Modeling Scheme

    Science.gov (United States)

    Wu, Z.; Loy, C.; Wang, E.; Hegadekatte, V.

    2017-09-01

    The measurement of a single forming limit diagram (FLD) requires significant resources and is time consuming. We have developed a multiscale modeling scheme to predict FLDs using a combination of limited laboratory testing, crystal plasticity (VPSC) modeling, and dual sequential-stage finite element (ABAQUS/Explicit) modeling with the Marciniak-Kuczynski (M-K) criterion to determine the limit strain. We have established a means to work around existing limitations in ABAQUS/Explicit by using an anisotropic yield locus (e.g., BBC2008) in combination with the M-K criterion. We further apply a VPSC model to reduce the number of laboratory tests required to characterize the anisotropic yield locus. In the present work, we show that the predicted FLD is in excellent agreement with the measured FLD for AA5182 in the O temper. Instead of 13 different tests as for a traditional FLD determination within Novelis, our technique uses just four measurements: tensile properties in three orientations; plane strain tension; biaxial bulge; and the sheet crystallographic texture. The turnaround time is consequently far less than for the traditional laboratory measurement of the FLD.

  19. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  20. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  1. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  2. 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.

  3. ORIENTEERING SITUATION TESTS IN THE FUNCTION OF PREDICTING SUCCESS OF POLICE OFFICERS IN TOPOGRAPHY FIELD TRAINING

    Directory of Open Access Journals (Sweden)

    Boban Milojković

    2006-06-01

    Full Text Available The sample of 45 students (15 students of the I year of Police Academy – the members of orienteering section – Group 1, 15 students of the I year of Police Academy – Group 2 and 15 students of Advanced School of Interior Affairs – Group 3 has been chosen to test the degree of success in topography field training through several stages, and orienteering situation tests were used in one of these stages. The research was carried out following the completed theoretical and practical training in topography by the same teacher but according to various models. During the research, three batteries of tests were used, the tests of capability of fast and accurate reading of topographic maps in the form of perforated sections (T-1, T-2 and T-3. With regard to measuring success in solving orienteering situation tests of three tested groups based on which the educational efficiency of police members in topography field training should have been evaluated, the obtained results have shown that at a general level there were statistically important differences of total variance of the observed set of variables of tested groups at the level p = 0.000 (Willks Lambda, 0.056, F = 225.598. The results have shown that there were statistically important differences between the success in test solving with reference to groups at the level p = 0.002 and p = 0.000, respectively. Thedifferences between groups in the function of an individual test were as follows: T-1, there was a cross difference between all three groups; T-2, there was no difference between the first and second groups, but the third group differed in relation to the first and second ones; T-3, there was a cross difference between all three groups. The results of tested population by means of the stated instruments describe the level of competency of police members in topography respectively in order to individualize training, but primarily prove statistically considerable difference of the level

  4. 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 ...

  5. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  6. An Anisotropic Hardening Model for Springback Prediction

    Science.gov (United States)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  7. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  8. 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...

  9. 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.

  10. 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.

  11. 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

  12. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  13. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...

  14. 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.

  15. 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.

  16. Effective modelling for predictive analytics in data science ...

    African Journals Online (AJOL)

    Effective modelling for predictive analytics in data science. ... the nearabsence of empirical or factual predictive analytics in the mainstream research going on ... Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning.

  17. The business of emergency medicine: a model for success.

    Science.gov (United States)

    Proctor, John; Hall, Peter; Carr, Janet

    2004-02-01

    Today's EPOs and their physicians face an array of daunting challenges. Falling reimbursement, rising malpractice costs. ED and hospital crowding,and demands for improving ED efficiency and patient satisfaction all contribute to the challenging and sometimes threatening environment of EM practice. The EP involved in a busy and often hectic ED shift may feel unduly and unnecessarily distracted when required to continuously acknowledge and address the business aspect of the practice. Nevertheless,regardless of the size and structure, fiscal viability ultimately determines the EPO's ability to continue to offer access to care. This article contends that a comprehensive business strategy drives superior financial performance and supports the organization's mission. The business strategy must identify financial and non-financial determinants of the EPO's success and provide a mechanism for understanding how the organization's resources are converted to value for customers. The section offers a framework for developing this strategy, for identifying possible gaps or deficiencies, and for measuring and monitoring progress in achieving strategic objectives and ultimately, the EPO's mission. The importance of the mission and the dynamic EM environment require that the strategy development process be more than an annual exercise for the leadership of the organization. Though key leaders in any size EPO--set the course for the organization, the entire organization must be aware and understand the strategy before they commit themselves and adopt actions and behaviors that promote it. The model presented here provides a graphic display that lends itself well to consistent communication of a comprehensive strategy in a concise way throughout the organization.Furthermore, the balance of the model, across four perspectives, recognizes the value of balanced organizational objectives and lends itself well to the creation of a measurement system that supports cause and effect

  18. Motivational patterns as an instrument for predicting success in promising young football players.

    Science.gov (United States)

    Zuber, Claudia; Zibung, Marc; Conzelmann, Achim

    2015-01-01

    Psychological characteristics are crucial to identifying talents, which is why these are being incorporated in today's multidimensional talent models. In addition to multidimensionality, talent studies are increasingly drawing on holistic theories of development, leading to the use of person-oriented approaches. The present study adopts such an approach by looking at the influence that motivational characteristics have on the development of performance, in a person-oriented way. For this purpose, it looks at how the constructs achievement motive, achievement goal orientation and self-determination interact with one another, what patterns they form and how these patterns are linked to subsequent sports success. Ninety-seven top young football players were questioned twice. Another year later, it was enquired which of these players had been selected for the U15 national team. At both measuring points, four patterns were identified, which displayed a high degree of structural and individual stability. As expected, the highly intrinsically achievement-oriented players were significantly more likely to move up into the U15 national team. The results point to the importance of favourable patterns of motivational variables in the form of specific types, for medium-term performance development among promising football talents, and thus provide valuable clues for the selection and promotion of those.

  19. 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.)

  20. Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers' Academic Success upon Entering Graduate Education

    Science.gov (United States)

    Bahadir, Elif

    2016-01-01

    The ability to predict the success of students when they enter a graduate program is critical for educational institutions because it allows them to develop strategic programs that will help improve students' performances during their stay at an institution. In this study, we present the results of an experimental comparison study of Logistic…

  1. Predicting College Math Success: Do High School Performance and Gender Matter? Evidence from Sultan Qaboos University in Oman

    Science.gov (United States)

    Islam, M. Mazharul; Al-Ghassani, Asma

    2015-01-01

    The objective of this study was to evaluate the performance of students of college of Science of Sultan Qaboos University (SQU) in Calculus I course, and examine the predictive validity of student's high school performance and gender for Calculus I success. The data for the study was extracted from students' database maintained by the Deanship of…

  2. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  3. 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.

  4. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  5. Towards predictive models for transitionally rough surfaces

    Science.gov (United States)

    Abderrahaman-Elena, Nabil; Garcia-Mayoral, Ricardo

    2017-11-01

    We analyze and model the previously presented decomposition for flow variables in DNS of turbulence over transitionally rough surfaces. The flow is decomposed into two contributions: one produced by the overlying turbulence, which has no footprint of the surface texture, and one induced by the roughness, which is essentially the time-averaged flow around the surface obstacles, but modulated in amplitude by the first component. The roughness-induced component closely resembles the laminar steady flow around the roughness elements at the same non-dimensional roughness size. For small - yet transitionally rough - textures, the roughness-free component is essentially the same as over a smooth wall. Based on these findings, we propose predictive models for the onset of the transitionally rough regime. Project supported by the Engineering and Physical Sciences Research Council (EPSRC).

  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. Pathways, mechanisms and predictability of vegetation change during tropical dry forest succession

    NARCIS (Netherlands)

    Lebrija Trejos, E.E.; Meave, J.; Poorter, L.; Pérez- García, E.A.; Bongers, F.

    2010-01-01

    The development of forest succession theory has been based on studies in temperate and tropical wet forests. As rates and pathways of succession vary with the environment, advances in successional theory and study approaches are challenged by controversies derived from such variation and by the

  8. Prediction of successful induction of labour wıth dinoprostone in a ...

    African Journals Online (AJOL)

    No significant association was found between transvaginal measurement of cervical length and the success of labour induction (p=0.201). We found no statistically significant difference between failure of labour induction and successful labour induction in terms of transvaginal measurement of cervical length (area under ...

  9. 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.

  10. Resource-estimation models and predicted discovery

    International Nuclear Information System (INIS)

    Hill, G.W.

    1982-01-01

    Resources have been estimated by predictive extrapolation from past discovery experience, by analogy with better explored regions, or by inference from evidence of depletion of targets for exploration. Changes in technology and new insights into geological mechanisms have occurred sufficiently often in the long run to form part of the pattern of mature discovery experience. The criterion, that a meaningful resource estimate needs an objective measure of its precision or degree of uncertainty, excludes 'estimates' based solely on expert opinion. This is illustrated by development of error measures for several persuasive models of discovery and production of oil and gas in USA, both annually and in terms of increasing exploration effort. Appropriate generalizations of the models resolve many points of controversy. This is illustrated using two USA data sets describing discovery of oil and of U 3 O 8 ; the latter set highlights an inadequacy of available official data. Review of the oil-discovery data set provides a warrant for adjusting the time-series prediction to a higher resource figure for USA petroleum. (author)

  11. Predicting Successful Aging in a Population-Based Sample of Georgia Centenarians

    Science.gov (United States)

    Arnold, Jonathan; Dai, Jianliang; Nahapetyan, Lusine; Arte, Ankit; Johnson, Mary Ann; Hausman, Dorothy; Rodgers, Willard L.; Hensley, Robert; Martin, Peter; MacDonald, Maurice; Davey, Adam; Siegler, Ilene C.; Jazwinski, S. Michal; Poon, Leonard W.

    2010-01-01

    Used a population-based sample (Georgia Centenarian Study, GCS), to determine proportions of centenarians reaching 100 years as (1) survivors (43%) of chronic diseases first experienced between 0–80 years of age, (2) delayers (36%) with chronic diseases first experienced between 80–98 years of age, or (3) escapers (17%) with chronic diseases only at 98 years of age or older. Diseases fall into two morbidity profiles of 11 chronic diseases; one including cardiovascular disease, cancer, anemia, and osteoporosis, and another including dementia. Centenarians at risk for cancer in their lifetime tended to be escapers (73%), while those at risk for cardiovascular disease tended to be survivors (24%), delayers (39%), or escapers (32%). Approximately half (43%) of the centenarians did not experience dementia. Psychiatric disorders were positively associated with dementia, but prevalence of depression, anxiety, and psychoses did not differ significantly between centenarians and an octogenarian control group. However, centenarians were higher on the Geriatric Depression Scale (GDS) than octogenarians. Consistent with our model of developmental adaptation in aging, distal life events contribute to predicting survivorship outcome in which health status as survivor, delayer, or escaper appears as adaptation variables late in life. PMID:20885919

  12. Predicting Successful Aging in a Population-Based Sample of Georgia Centenarians

    Directory of Open Access Journals (Sweden)

    Jonathan Arnold

    2010-01-01

    Full Text Available Used a population-based sample (Georgia Centenarian Study, GCS, to determine proportions of centenarians reaching 100 years as (1 survivors (43% of chronic diseases first experienced between 0–80 years of age, (2 delayers (36% with chronic diseases first experienced between 80–98 years of age, or (3 escapers (17% with chronic diseases only at 98 years of age or older. Diseases fall into two morbidity profiles of 11 chronic diseases; one including cardiovascular disease, cancer, anemia, and osteoporosis, and another including dementia. Centenarians at risk for cancer in their lifetime tended to be escapers (73%, while those at risk for cardiovascular disease tended to be survivors (24%, delayers (39%, or escapers (32%. Approximately half (43% of the centenarians did not experience dementia. Psychiatric disorders were positively associated with dementia, but prevalence of depression, anxiety, and psychoses did not differ significantly between centenarians and an octogenarian control group. However, centenarians were higher on the Geriatric Depression Scale (GDS than octogenarians. Consistent with our model of developmental adaptation in aging, distal life events contribute to predicting survivorship outcome in which health status as survivor, delayer, or escaper appears as adaptation variables late in life.

  13. 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.

  14. 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)

  15. 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.

  16. 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

  17. An Operational Model for the Prediction of Jet Blast

    Science.gov (United States)

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  18. 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

  19. Determinants of Business Success – Theoretical Model and Empirical Verification

    Directory of Open Access Journals (Sweden)

    Kozielski Robert

    2016-12-01

    Full Text Available Market knowledge, market orientation, learning competencies, and a business performance were the key issues of the research project conducted in the 2006 study. The main findings identified significant relationships between the independent variables (market knowledge, market orientation, learning competencies and the dependent variables (business success. A partial correlation analysis indicated that a business success primarily relies on organisational learning competencies. Organisational learning competencies, to a large extent (almost 60%, may be explained by the level of corporate market knowledge and market orientation. The aim of the paper is to evaluate to what extent the relationships between the variables are still valid. The research was based on primary and secondary data sources. The major field of the research was carried out in the form of quantitative studies. The results of the 2014 study are consistent with the previous (2006 results.

  20. Data driven propulsion system weight prediction model

    Science.gov (United States)

    Gerth, Richard J.

    1994-10-01

    The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

  1. Predictive modeling of emergency cesarean delivery.

    Directory of Open Access Journals (Sweden)

    Carlos Campillo-Artero

    Full Text Available To increase discriminatory accuracy (DA for emergency cesarean sections (ECSs.We prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE, and analyzed a random forest model (RFM. We used the areas under the receiver-operating-characteristic (ROC curves (AUCs to assess their DA.The magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93-0.95.Putative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.

  2. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  3. 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

  4. Description of Success: A Four-Teacher Instructional Model.

    Science.gov (United States)

    Reed, Dianne

    This study described a four-teacher instructional model in operation at an elementary school, noting the perceptions of fourth grade students, parents, and teachers regarding the model. The model encompassed teaming, block scheduling, departmentalization of subjects, integrated/interdisciplinary instruction, and in-depth instruction in each…

  5. Scaling Student Success with Predictive Analytics: Reflections after Four Years in the Data Trenches

    Science.gov (United States)

    Wagner, Ellen; Longanecker, David

    2016-01-01

    The metrics used in the US to track students do not include adults and part-time students. This has led to the development of a massive data initiative--the Predictive Analytics Reporting (PAR) framework--that uses predictive analytics to trace the progress of all types of students in the system. This development has allowed actionable,…

  6. Leveraging Non-Cognitive Testing to Predict Success at USMC Scout Sniper Course

    Science.gov (United States)

    2017-03-01

    whether aptitude requirements translate to job performance. Mayberry (1990) determines the ASVAB a valid predictor of success for infantrymen...Reduction Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE March 2017 3. REPORT TYPE AND DATES COVERED...success at scout sniper school. We use data from 2012 through 2016 containing more than 700 Marines from every infantry military occupational specialty

  7. PREDICTING SUCCESS INDICATORS OF AN INTERVENTION PROGRAMME FOR CONVICTED INTIMATE-PARTNER VIOLENCE OFFENDERS: THE CONTEXTO PROGRAMME

    Directory of Open Access Journals (Sweden)

    Enrique Gracia

    2013-01-01

    Full Text Available Recent legal changes in Spain have led to an important increase in the number of men court-mandated to community-based partner violence offender intervention programmes. However, just a few of those interventions have been systematically examined. This study aims to predict success indicators of an intervention programme for convicted intimate-partner violence offenders. The sample consisted of 212 convicted intimate-partner violence offenders who participated in the Contexto Programme. Three “intervention gains” or target criteria were established (increasing the perceived severity of violence, increasing the responsibility assumption for one’s actions, and reducing the risk of recidivism. A structural equations model was tested, fitting data appropriately. Participants with major gain in recidivism risk were those who presented lower levels of alcohol consumption, shorter sentences, lower impulsivity, and a higher degree of life satisfaction. The largest gain in perceived severity was found in younger participants, participants with shorter sentences, lower alcohol consumption, higher life satisfaction, higher participation in their community, and higher self-esteem. And, finally, participants with the highest gains in responsibility assumption were older participants, participants who presented higher intimate support, higher anxiety, higher sexism, lower anger control, higher depression, higher impulsivity and higher self-esteem.

  8. Finite Unification: Theory, Models and Predictions

    CERN Document Server

    Heinemeyer, S; Zoupanos, G

    2011-01-01

    All-loop Finite Unified Theories (FUTs) are very interesting N=1 supersymmetric Grand Unified Theories (GUTs) realising an old field theory dream, and moreover have a remarkable predictive power due to the required reduction of couplings. The reduction of the dimensionless couplings in N=1 GUTs is achieved by searching for renormalization group invariant (RGI) relations among them holding beyond the unification scale. Finiteness results from the fact that there exist RGI relations among dimensional couplings that guarantee the vanishing of all beta-functions in certain N=1 GUTs even to all orders. Furthermore developments in the soft supersymmetry breaking sector of N=1 GUTs and FUTs lead to exact RGI relations, i.e. reduction of couplings, in this dimensionful sector of the theory, too. Based on the above theoretical framework phenomenologically consistent FUTs have been constructed. Here we review FUT models based on the SU(5) and SU(3)^3 gauge groups and their predictions. Of particular interest is the Hig...

  9. 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

  10. 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.

  11. Neutrino nucleosynthesis in supernovae: Shell model predictions

    International Nuclear Information System (INIS)

    Haxton, W.C.

    1989-01-01

    Almost all of the 3 · 10 53 ergs liberated in a core collapse supernova is radiated as neutrinos by the cooling neutron star. I will argue that these neutrinos interact with nuclei in the ejected shells of the supernovae to produce new elements. It appears that this nucleosynthesis mechanism is responsible for the galactic abundances of 7 Li, 11 B, 19 F, 138 La, and 180 Ta, and contributes significantly to the abundances of about 15 other light nuclei. I discuss shell model predictions for the charged and neutral current allowed and first-forbidden responses of the parent nuclei, as well as the spallation processes that produce the new elements. 18 refs., 1 fig., 1 tab

  12. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...

  13. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  14. Model predictive control of a wind turbine modelled in Simpack

    International Nuclear Information System (INIS)

    Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G

    2014-01-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

  15. Model predictive control of a wind turbine modelled in Simpack

    Science.gov (United States)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  16. 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.

  17. 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.

  18. 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

  19. 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,...

  20. 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.

  1. 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…

  2. Predictive integrated modelling for ITER scenarios

    International Nuclear Information System (INIS)

    Artaud, J.F.; Imbeaux, F.; Aniel, T.; Basiuk, V.; Eriksson, L.G.; Giruzzi, G.; Hoang, G.T.; Huysmans, G.; Joffrin, E.; Peysson, Y.; Schneider, M.; Thomas, P.

    2005-01-01

    The uncertainty on the prediction of ITER scenarios is evaluated. 2 transport models which have been extensively validated against the multi-machine database are used for the computation of the transport coefficients. The first model is GLF23, the second called Kiauto is a model in which the profile of dilution coefficient is a gyro Bohm-like analytical function, renormalized in order to get profiles consistent with a given global energy confinement scaling. The package of codes CRONOS is used, it gives access to the dynamics of the discharge and allows the study of interplay between heat transport, current diffusion and sources. The main motivation of this work is to study the influence of parameters such plasma current, heat, density, impurities and toroidal moment transport. We can draw the following conclusions: 1) the target Q = 10 can be obtained in ITER hybrid scenario at I p = 13 MA, using either the DS03 two terms scaling or the GLF23 model based on the same pedestal; 2) I p = 11.3 MA, Q = 10 can be reached only assuming a very peaked pressure profile and a low pedestal; 3) at fixed Greenwald fraction, Q increases with density peaking; 4) achieving a stationary q-profile with q > 1 requires a large non-inductive current fraction (80%) that could be provided by 20 to 40 MW of LHCD; and 5) owing to the high temperature the q-profile penetration is delayed and q = 1 is reached about 600 s in ITER hybrid scenario at I p = 13 MA, in the absence of active q-profile control. (A.C.)

  3. A Model for Physician Leadership Development and Succession Planning.

    Science.gov (United States)

    Dubinsky, Isser; Feerasta, Nadia; Lash, Rick

    2015-01-01

    Although the presence of physicians in formal leadership positions has often been limited to roles of department chiefs, MAC chairs, etc., a growing number of organizations are recruiting physicians to other leadership positions (e.g., VP, CEO) where their involvement is being genuinely sought and valued. While physicians have traditionally risen to leadership positions based on clinical excellence or on a rotational basis, truly effective physician leadership that includes competencies such as strategic planning, budgeting, mentoring, network development, etc., is essential to support organizational goals, improve performance and overall efficiency as well as ensuring the quality of care. In this context, the authors have developed a physician leader development and succession planning matrix and supporting toolkit to assist hospitals in identifying and nurturing the next generation of physician leaders.

  4. Successful public-private partnerships: The NYPD shield model.

    Science.gov (United States)

    Amadeo, Vincent; Iannone, Stephen

    2017-12-01

    This article will identify the challenges that post 9/11 law enforcement faces regarding privatepublic partnerships and describe in detail the NYPD Shield programme, created to combat those challenges. Recommendations made by the 911 Commission included the incorporation of the private sector into future homeland security strategies. One such strategy is NYPD Shield. This programme is a nationally recognized award-winning public-private partnership dedicated to providing counterterrorism training and information sharing with government agencies, non-government organizations, private businesses, and the community. Information is shared through several platforms that include a dedicated website, instruction of counterterrorism training curricula, e-mail alerts, intelligence assessments and the hosting of quarterly conferences. This article also details how the NYPD Shield is providing its successful template to other law enforcement agencies enabling them to initiate similar programmes in their respective jurisdictions, and in doing so joining a National Shield Network.

  5. Analysis of syntax and word use to predict successful participation in guided self-help for anxiety and depression

    DEFF Research Database (Denmark)

    Zinken, Jörg; Zinken, Katarzyna; Wilson, J. Clare

    2010-01-01

    This study investigated whether an analysis of narrative style (word use and cross-clausal syntax) of patients with symptoms of generalised anxiety and depression disorders can help predict the likelihood of successful participation in guided self-help. Texts by 97 people who had made contact...... with a primary care mental health service were analysed. Outcome measures were completion of the guided self-help programme, and change in symptoms assessed by a standardised scale (CORE-OM). Regression analyses indicated that some aspects of participants' syntax helped to predict completion of the programme...

  6. Can magnetic resonance imaging predict the success of parturition in oxytocin-induced pregnant women?

    International Nuclear Information System (INIS)

    Sabir, N.; Akkemik, B.; Dicle, O.; Yurdakul, B.

    2000-01-01

    The aim of this study was to assess whether magnetic resonance imaging could predict the outcome of attempted vaginal delivery in a group of pregnant women whose parturition had to be induced by oxytocin. The signal intensity and morphology alterations in the cervix of 21 full-term pregnant women were analyzed before the induction of parturition. T2-weighted gradient echo sequences were utilized and signal intensity in the cervix was measured from the anterior and posterior lips of the cervix. An index indicating the brightness range of the cervix was formulated to overcome the effects of the individual intensity changes. Imaging features including the signal intensity and the evidence of effacement were correlated with the actual type of delivery performed. Images were also assessed visually by two independent radiologists. Statistical analysis of brightness indexes that were considered to have a predictive value as an indicator for possible delivery was not significant. However, visually assessed signal intensity of the cervix correlated strongly with the type of delivery. Effacement itself was the most reliable parameter in predicting the progress of the delivery. In conclusion, MR imaging seems to be useful for predicting normal parturition in full-term pregnant women who need oxytocin induction. However, the presence of effacement seems to be a more reliable and practical parameter that will be preferred in that prediction. (orig.)

  7. Prediction of successful induction of labour wıth dinoprostone in a ...

    African Journals Online (AJOL)

    Eighty-four patients induced with prostaglandin E2 (dinoprostone) for medical indications were included in the study. Results. No significant association was found between transvaginal measurement of cervical length and the success of labour induction. (p=0.201). We found no statistically significant difference between ...

  8. Predicting Academic Success and Psychological Wellness in a Sample of Canadian Undergraduate Students

    Science.gov (United States)

    Chow, Henry P. H.

    2010-01-01

    Introduction: University students need to cope with a complex new life role and to achieve academic success. This article explores the academic performance and psychological well-being among university students in a western Canadian city. Method: Using a convenience sample, a total of 501 undergraduate students in Regina, Saskatchewan took part in…

  9. Cognitive ability is heritable and predicts the success of an alternative mating tactic.

    Science.gov (United States)

    Smith, Carl; Philips, André; Reichard, Martin

    2015-06-22

    The ability to attract mates, acquire resources for reproduction, and successfully outcompete rivals for fertilizations may make demands on cognitive traits--the mechanisms by which an animal acquires, processes, stores and acts upon information from its environment. Consequently, cognitive traits potentially undergo sexual selection in some mating systems. We investigated the role of cognitive traits on the reproductive performance of male rose bitterling (Rhodeus ocellatus), a freshwater fish with a complex mating system and alternative mating tactics. We quantified the learning accuracy of males and females in a spatial learning task and scored them for learning accuracy. Males were subsequently allowed to play the roles of a guarder and a sneaker in competitive mating trials, with reproductive success measured using paternity analysis. We detected a significant interaction between male mating role and learning accuracy on reproductive success, with the best-performing males in maze trials showing greater reproductive success in a sneaker role than as a guarder. Using a cross-classified breeding design, learning accuracy was demonstrated to be heritable, with significant additive maternal and paternal effects. Our results imply that male cognitive traits may undergo intra-sexual selection. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  10. Individual differences in self-reported self-control predict successful emotion regulation.

    Science.gov (United States)

    Paschke, Lena M; Dörfel, Denise; Steimke, Rosa; Trempler, Ima; Magrabi, Amadeus; Ludwig, Vera U; Schubert, Torsten; Stelzel, Christine; Walter, Henrik

    2016-08-01

    Both self-control and emotion regulation enable individuals to adapt to external circumstances and social contexts, and both are assumed to rely on the overlapping neural resources. Here, we tested whether high self-reported self-control is related to successful emotion regulation on the behavioral and neural level. One hundred eight participants completed three self-control questionnaires and regulated their negative emotions during functional magnetic resonance imaging using reappraisal (distancing). Trait self-control correlated positively with successful emotion regulation both subjectively and neurally, as indicated by online ratings of negative emotions and functional connectivity strength between the amygdala and prefrontal areas, respectively. This stronger overall connectivity of the left amygdala was related to more successful subjective emotion regulation. Comparing amygdala activity over time showed that high self-controllers successfully maintained down-regulation of the left amygdala over time, while low self-controllers failed to down-regulate towards the end of the experiment. This indicates that high self-controllers are better at maintaining a motivated state supporting emotion regulation over time. Our results support assumptions concerning a close relation of self-control and emotion regulation as two domains of behavioral control. They further indicate that individual differences in functional connectivity between task-related brain areas directly relate to differences in trait self-control. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  11. Sperm swimming velocity predicts competitive fertilization success in the green swordtail Xiphophorus helleri.

    Directory of Open Access Journals (Sweden)

    Clelia Gasparini

    Full Text Available Sperm competition is expected to favour the evolution of traits that influence the performance of sperm when they compete to fertilize a female's eggs. While there is considerable evidence that selection favours increases in sperm numbers, much less is known about how sperm quality contributes towards competitive fertilization success. Here, we determine whether variation in sperm quality influences competitive fertilization success in the green swordtail Xiphophorus helleri, a highly promiscuous livebearing fish. We use artificial insemination as a method of controlled sperm delivery and show that sperm swimming velocity is the primary determinant of fertilization success when ejaculates from two males compete to fertilize a female's eggs. By contrast, we found no evidence that sperm length had any effect on siring success. We also found no evidence that pre- and postcopulatory sexual traits were phenotypically integrated in this species, suggesting that the previous observation that reproductive skew favours males with high mating rates is unlikely to be due to any direct association between sperm quality and male sexual ornamentation.

  12. Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective

    Science.gov (United States)

    Dietz-Uhler, Beth; Hurn, Janet E.

    2013-01-01

    Learning analytics is receiving increased attention, in part because it offers to assist educational institutions in increasing student retention, improving student success, and easing the burden of accountability. Although these large-scale issues are worthy of consideration, faculty might also be interested in how they can use learning analytics…

  13. Use of Standardized Test Scores to Predict Success in a Computer Applications Course

    Science.gov (United States)

    Harris, Robert V.; King, Stephanie B.

    2016-01-01

    The purpose of this study was to see if a relationship existed between American College Testing (ACT) scores (i.e., English, reading, mathematics, science reasoning, and composite) and student success in a computer applications course at a Mississippi community college. The study showed that while the ACT scores were excellent predictors of…

  14. Predicting Online Learning Success: Applying the Situational Theory of Publics to the Virtual Classroom

    Science.gov (United States)

    Kruger-Ross, Matthew J.; Waters, Richard D.

    2013-01-01

    Following the trend of increased interest by students to take online courses and by institutions to offer them, scholars have taken many different approaches to understand what makes one student successful in online learning while another may fail. This study proposes that using the situational theory of publics will provide a better understanding…

  15. 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.

  16. 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.

  17. MODEL OF SUCCESSFUL STRATEGY EXECUTION: REVISING THE CONCEPT

    Directory of Open Access Journals (Sweden)

    Joanna Radomska

    2014-12-01

    Full Text Available The purpose of this research is to examine the relationship between the elements of the Eight "S" model that affect strategic implementation and results achieved by companies. The main research question, to which the author sought an answer, was whether there was a relationship between individual elements that affect strategy implementation and the effects it brings in revenue growth. The survey covered 200 of the best-ranked Polish companies (where revenues constituted one of the ranking criteria where the level of strategic implementation was considered satisfactory. Testing of the research hypotheses has shown that the factors defined as Resources and Shared Values have a minor impact on the strategy implementation. The research also has shown that there is an additional element that could be incorporated into the model - the system of informal communication. In addition, the paper describes the interrelations between elements of the model.

  18. Our sun. I. The standard model: Successes and failures

    International Nuclear Information System (INIS)

    Sackmann, I.J.; Boothroyd, A.I.; Fowler, W.A.

    1990-01-01

    The results of computing a number of standard solar models are reported. A presolar helium content of Y = 0.278 is obtained, and a Cl-37 capture rate of 7.7 SNUs, consistently several times the observed rate of 2.1 SNUs, is determined. Thus, the solar neutrino problem remains. The solar Z value is determined primarily by the observed Z/X ratio and is affected very little by differences in solar models. Even large changes in the low-temperature molecular opacities have no effect on Y, nor even on conditions at the base of the convective envelope. Large molecular opacities do cause a large increase in the mixing-length parameter alpha but do not cause the convective envelope to reach deeper. The temperature remains too low for lithium burning, and there is no surface lithium depletion; thus, the lithium problem of the standard solar model remains. 103 refs

  19. 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.

  20. Landsat analysis of tropical forest succession employing a terrain model

    Science.gov (United States)

    Barringer, T. H.; Robinson, V. B.; Coiner, J. C.; Bruce, R. C.

    1980-01-01

    Landsat multispectral scanner (MSS) data have yielded a dual classification of rain forest and shadow in an analysis of a semi-deciduous forest on Mindonoro Island, Philippines. Both a spatial terrain model, using a fifth side polynomial trend surface analysis for quantitatively estimating the general spatial variation in the data set, and a spectral terrain model, based on the MSS data, have been set up. A discriminant analysis, using both sets of data, has suggested that shadowing effects may be due primarily to local variations in the spectral regions and can therefore be compensated for through the decomposition of the spatial variation in both elevation and MSS data.

  1. 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

  2. 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

  3. 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...

  4. Teaching Modeling with Partial Differential Equations: Several Successful Approaches

    Science.gov (United States)

    Myers, Joseph; Trubatch, David; Winkel, Brian

    2008-01-01

    We discuss the introduction and teaching of partial differential equations (heat and wave equations) via modeling physical phenomena, using a new approach that encompasses constructing difference equations and implementing these in a spreadsheet, numerically solving the partial differential equations using the numerical differential equation…

  5. 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.

  6. 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.

  7. 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...

  8. Predicting Academic Success and Technological Literacy in Secondary Education: A Learning Styles Perspective

    Science.gov (United States)

    Avsec, Stanislav; Szewczyk-Zakrzewska, Agnieszka

    2017-01-01

    This paper aims to investigate the predictive validity of learning styles on academic achievement and technological literacy (TL). For this purpose, secondary school students were recruited (n = 150). An empirical research design was followed where the TL test was used with a learning style inventory measuring learning orientation, processing…

  9. Predicting success in an online parenting intervention: the role of child, parent, and family factors.

    Science.gov (United States)

    Dittman, Cassandra K; Farruggia, Susan P; Palmer, Melanie L; Sanders, Matthew R; Keown, Louise J

    2014-04-01

    The present study involved an examination of the extent to which a wide range of child, parent, family, and program-related factors predicted child behavior and parenting outcomes after participation in an 8-session online version of the Triple P-Positive Parenting Program. Participants were mothers and fathers of 97 children aged between 3 and 8 years displaying elevated levels of disruptive behavior problems. For both mothers and fathers, poorer child behavior outcomes at postintervention were predicted by the number of sessions of the intervention completed by the family. For mothers, postintervention child behavior was also predicted by the quality of the mother-child relationship at baseline; for fathers, baseline child behavior severity was an additional predictor. Mothers' postintervention ineffective parenting was predicted by session completion and preintervention levels of ineffective parenting, whereas the only predictor of fathers' ineffective parenting at postintervention was preintervention levels of ineffective parenting. Socioeconomic risk, parental adjustment, and father participation in the intervention were not significant predictors of mother- or father-reported treatment outcomes. The implications of the findings for the provision of online parenting support are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  10. Predicting academic success in higher education: what’s more important than being smart?

    NARCIS (Netherlands)

    Kappe, F.R.; van der Flier, H.

    2012-01-01

    This study investigated the combined predictive validity of intelligence and personality factors on multiple measures of academic achievement. Students in a college of higher education in the Netherlands (N0137) completed a survey that measured intelligence, the Big Five personality traits,

  11. Predicting Academic Success in Higher Education: What's More Important than Being Smart?

    Science.gov (United States)

    Kappe, Rutger; van der Flier, Henk

    2012-01-01

    This study investigated the combined predictive validity of intelligence and personality factors on multiple measures of academic achievement. Students in a college of higher education in the Netherlands (N = 137) completed a survey that measured intelligence, the Big Five personality traits, motivation, and four specific personality traits.…

  12. A combination of dopamine genes predicts success by professional Wall Street traders.

    Science.gov (United States)

    Sapra, Steve; Beavin, Laura E; Zak, Paul J

    2012-01-01

    What determines success on Wall Street? This study examined if genes affecting dopamine levels of professional traders were associated with their career tenure. Sixty professional Wall Street traders were genotyped and compared to a control group who did not trade stocks. We found that distinct alleles of the dopamine receptor 4 promoter (DRD4P) and catecholamine-O-methyltransferase (COMT) that affect synaptic dopamine were predominant in traders. These alleles are associated with moderate, rather than very high or very low, levels of synaptic dopamine. The activity of these alleles correlated positively with years spent trading stocks on Wall Street. Differences in personality and trading behavior were also correlated with allelic variants. This evidence suggests there may be a genetic basis for the traits that make one a successful trader.

  13. 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.

  14. Initial species composition predicts the progress in the spontaneous succession on post-mining sites

    Czech Academy of Sciences Publication Activity Database

    Mudrák, Ondřej; Doležal, Jiří; Frouz, J.

    2016-01-01

    Roč. 95, č. 11 (2016), s. 665-670 ISSN 0925-8574 R&D Projects: GA ČR GA13-13368S; GA ČR GA13-10377S; GA ČR(CZ) GAP505/11/0256 Institutional support: RVO:67985939 Keywords : Spontaneous succession * Ecological restoration * Calamagrostis epigejos Subject RIV: EH - Ecology, Behaviour Impact factor: 2.914, year: 2016

  15. A Model of Social Selection and Successful Altruism

    Science.gov (United States)

    1989-10-07

    D., The evolution of social behavior. Annual Reviews of Ecological Systems, 5:325-383 (1974). 2. Dawkins , R., The selfish gene . Oxford: Oxford...alive and well. it will be important to re- examine this striking historical experience,-not in terms o, oversimplified models of the " selfish gene ," but...Darwinian Analysis The acceptance by many modern geneticists of the axiom that the basic unit of selection Is the " selfish gene " quickly led to the

  16. A model for successful research partnerships: a New Brunswick experience.

    Science.gov (United States)

    Tamlyn, Karen; Creelman, Helen; Fisher, Garfield

    2002-01-01

    The purpose of this paper is to present an overview of a partnership model used to conduct a research study entitled "Needs of patients with cancer and their family members in New Brunswick Health Region 3 (NBHR3)" (Tamlyn-Leaman, Creelman, & Fisher, 1997). This partial replication study carried out by the three authors between 1995 and 1997 was a needs assessment, adapted with permission from previous work by Fitch, Vachon, Greenberg, Saltmarche, and Franssen (1993). In order to conduct a comprehensive needs assessment with limited resources, a partnership between academic, public, and private sectors was established. An illustration of this partnership is presented in the model entitled "A Client-Centred Partnership Model." The operations of this partnership, including the strengths, the perceived benefits, lessons learned by each partner, the barriers, and the process for conflict resolution, are described. A summary of the cancer care initiatives undertaken by NBHR3, which were influenced directly or indirectly by the recommendations from this study, is included.

  17. The secret to successful solute-transport modeling

    Science.gov (United States)

    Konikow, Leonard F.

    2011-01-01

    Modeling subsurface solute transport is difficult—more so than modeling heads and flows. The classical governing equation does not always adequately represent what we see at the field scale. In such cases, commonly used numerical models are solving the wrong equation. Also, the transport equation is hyperbolic where advection is dominant, and parabolic where hydrodynamic dispersion is dominant. No single numerical method works well for all conditions, and for any given complex field problem, where seepage velocity is highly variable, no one method will be optimal everywhere. Although we normally expect a numerically accurate solution to the governing groundwater-flow equation, errors in concentrations from numerical dispersion and/or oscillations may be large in some cases. The accuracy and efficiency of the numerical solution to the solute-transport equation are more sensitive to the numerical method chosen than for typical groundwater-flow problems. However, numerical errors can be kept within acceptable limits if sufficient computational effort is expended. But impractically long

  18. Observational constraints on successful model of quintessential Inflation

    Energy Technology Data Exchange (ETDEWEB)

    Geng, Chao-Qiang [Chongqing University of Posts and Telecommunications, Chongqing, 400065 (China); Lee, Chung-Chi [DAMTP, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA (United Kingdom); Sami, M. [Centre for Theoretical Physics, Jamia Millia Islamia, New Delhi 110025 (India); Saridakis, Emmanuel N. [Physics Division, National Technical University of Athens, 15780 Zografou Campus, Athens (Greece); Starobinsky, Alexei A., E-mail: geng@phys.nthu.edu.tw, E-mail: lee.chungchi16@gmail.com, E-mail: sami@iucaa.ernet.in, E-mail: Emmanuel_Saridakis@baylor.edu, E-mail: alstar@landau.ac.ru [L. D. Landau Institute for Theoretical Physics RAS, Moscow 119334 (Russian Federation)

    2017-06-01

    We study quintessential inflation using a generalized exponential potential V (φ)∝ exp(−λ φ {sup n} / M {sub Pl} {sup n} ), n >1, the model admits slow-roll inflation at early times and leads to close-to-scaling behaviour in the post inflationary era with an exit to dark energy at late times. We present detailed investigations of the inflationary stage in the light of the Planck 2015 results, study post-inflationary dynamics and analytically confirm the existence of an approximately scaling solution. Additionally, assuming that standard massive neutrinos are non-minimally coupled, makes the field φ dominant once again at late times giving rise to present accelerated expansion of the Universe. We derive observational constraints on the field and time-dependent neutrino masses. In particular, for n =6 (8), the parameter λ is constrained to be, log λ > −7.29 (−11.7); the model produces the spectral index of the power spectrum of primordial scalar (matter density) perturbations as n {sub s} = 0.959 ± 0.001 (0.961 ± 0.001) and tiny tensor-to-scalar ratio, r <1.72 × 10{sup −2} (2.32 × 10{sup −2}) respectively. Consequently, the upper bound on possible values of the sum of neutrino masses Σ m {sub ν} ∼< 2.5 eV significantly enhances compared to that in the standard ΛCDM model.

  19. Observational constraints on successful model of quintessential Inflation

    International Nuclear Information System (INIS)

    Geng, Chao-Qiang; Lee, Chung-Chi; Sami, M.; Saridakis, Emmanuel N.; Starobinsky, Alexei A.

    2017-01-01

    We study quintessential inflation using a generalized exponential potential V (φ)∝ exp(−λ φ n / M Pl n ), n >1, the model admits slow-roll inflation at early times and leads to close-to-scaling behaviour in the post inflationary era with an exit to dark energy at late times. We present detailed investigations of the inflationary stage in the light of the Planck 2015 results, study post-inflationary dynamics and analytically confirm the existence of an approximately scaling solution. Additionally, assuming that standard massive neutrinos are non-minimally coupled, makes the field φ dominant once again at late times giving rise to present accelerated expansion of the Universe. We derive observational constraints on the field and time-dependent neutrino masses. In particular, for n =6 (8), the parameter λ is constrained to be, log λ > −7.29 (−11.7); the model produces the spectral index of the power spectrum of primordial scalar (matter density) perturbations as n s = 0.959 ± 0.001 (0.961 ± 0.001) and tiny tensor-to-scalar ratio, r <1.72 × 10 −2 (2.32 × 10 −2 ) respectively. Consequently, the upper bound on possible values of the sum of neutrino masses Σ m ν ∼< 2.5 eV significantly enhances compared to that in the standard ΛCDM model.

  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. Nonconvex model predictive control for commercial refrigeration

    Science.gov (United States)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  2. 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

  3. 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 ...

  4. The social networking application success model : An empirical study of Facebook and Twitter

    NARCIS (Netherlands)

    Ou, Carol; Davison, R.M.; Huang, Q.

    2016-01-01

    Social networking applications (SNAs) are among the fastest growing web applications of recent years. In this paper, we propose a causal model to assess the success of SNAs, grounded on DeLone and McLean’s updated information systems (IS) success model. In addition to their original three dimensions

  5. Predictive value of lidocaine for treatment success of oxcarbazepine in patients with neuropathic pain syndrome.

    Science.gov (United States)

    Schipper, Sivan; Gantenbein, Andreas R; Maurer, Konrad; Alon, Eli; Sándor, Peter S

    2013-06-01

    Pharmacotherapy in patients with neuropathic pain syndromes (NPS) can be associated with long periods of trial and error before reaching satisfactory analgesia. The aim of this study was to investigate whether a short intravenous (i.v.) infusion of lidocaine may have a predictive value for the efficacy of oxcarbazepine. In total, 16 consecutive patients with NPS were studied in a prospective, uncontrolled, open-label study design. Each patient received i.v. lidocaine (5 mg/kg) within 30 min followed by a long-term oral oxcarbazepine treatment (900-1,500 mg/day). During an observation period of 28 days, treatment response was documented by a questionnaire including the average daily pain score documented on a numeric rating scale (NRS). A total of 6 out of 16 patients (38%) were lidocaine responders (defined as pain reduction >50% during the infusion), and 4 of 16 (25%) were oxcarbazepine responders. In total, 6 out of 16 participants (38%) discontinued oxcarbazepine treatment due to side effects. In an interim analysis predictive value of the lidocaine infusion was low with a Kendall's tau correlation coefficient of 0.29 and coefficient of determination R(2) of 0.119 (95% confidence interval -0.29 to 0.72). As a consequence of this low correlation, the study was discontinued for ethical reasons. In conclusion, lidocaine infusion has a low predictive value for effectiveness of oxcarbazepine-if at all.

  6. 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.

  7. Predicting Manual Therapy Treatment Success in Patients With Chronic Ankle Instability: Improving Self-Reported Function.

    Science.gov (United States)

    Wikstrom, Erik A; McKeon, Patrick O

    2017-04-01

      Therapeutic modalities that stimulate sensory receptors around the foot-ankle complex improve chronic ankle instability (CAI)-associated impairments. However, not all patients have equal responses to these modalities. Identifying predictors of treatment success could improve clinician efficiency when treating patients with CAI.   To conduct a response analysis on existing data to identify predictors of improved self-reported function in patients with CAI.   Secondary analysis of a randomized controlled clinical trial.   Sports medicine research laboratories.   Fifty-nine patients with CAI, which was defined in accordance with the International Ankle Consortium recommendations.   Participants were randomized into 3 treatment groups (plantar massage [PM], ankle-joint mobilization [AJM], or calf stretching [CS]) that received six 5-minute treatments over 2 weeks.   Treatment success, defined as a patient exceeding the minimally clinically important difference of the Foot and Ankle Ability Measure-Sport (FAAM-S).   Patients with ≤5 recurrent sprains and ≤82.73% on the Foot and Ankle Ability Measure had a 98% probability of having a meaningful FAAM-S improvement after AJM. As well, ≥5 balance errors demonstrated 98% probability of meaningful FAAM-S improvements from AJM. Patients <22 years old and with ≤9.9 cm of dorsiflexion had a 99% probability of a meaningful FAAM-S improvement after PM. Also, those who made ≥2 single-limb-stance errors had a 98% probability of a meaningful FAAM-S improvement from PM. Patients with ≤53.1% on the FAAM-S had an 83% probability of a meaningful FAAM-S improvement after CS.   Each sensory-targeted ankle-rehabilitation strategy resulted in a unique combination of predictors of success for patients with CAI. Specific indicators of success with AJM were deficits in self-reported function, single-limb balance, and <5 previous sprains. Age, weight-bearing-dorsiflexion restrictions, and single-limb balance

  8. 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.

  9. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  10. 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.

  11. Prediction of pregnancy success rate through in vitro fertilization based on maternal age

    Directory of Open Access Journals (Sweden)

    Soegiharto Soebijanto

    2009-12-01

    Full Text Available Aim To evaluate the correlation between the success of pregnancy through in vitro fertilization and maternal age. Methods Assessment of pregnancy was performed in eight in vitro fertilization centers in Indonesia: Harapan Kita Pediatric and Obstetric Hospital from 1997 to 2001, and seven in vitro fertilization centers in Indonesia. Follicular induction was performed through the long protocol, short protocol and natural cycle. Insemination was performed through ICSI (intra cytoplasmic sperm injection on petri dish. Spermatozoa were obtained through masturbation, testicular biopsy and epididimical biopsy. A successful pregnancy was indicated chemically, with the presence of fetal heart beat and the birth of a baby (take home baby. Results There was a 34% pregnancy rate for the age group below 30 years, 33.75% for those between 31 and 35 years olds, and 26% for the age group 36 to 40 years old, and 8% for the age group above 40 years. Conclusion The higher the maternal age, the lower pregnancy rate. In other words, the higher the maternal age, the higher the rate of miscarriage. (Med J Indones 2009; 18: 244-8Keywords: pregnancy, in vitro fertilization

  12. Is the Bishop-score significant in predicting the success of labor induction in multiparous women?

    Science.gov (United States)

    Navve, D; Orenstein, N; Ribak, R; Daykan, Y; Shechter-Maor, G; Biron-Shental, T

    2017-05-01

    To determine whether the Bishop-score upon admission effects mode of delivery, maternal or neonatal outcomes of labor induction in multiparous women. A retrospective study including 600 multiparous women with a singleton pregnancy, 34 gestational weeks and above who underwent labor induction for maternal, fetal or combined indications. Induction was performed with one of three methods- oxytocin, a slow release vaginal prostaglandin E2 insert (10 mg dinoprostone) or a transcervical double balloon catheter. The women were divided into two groups-Bishop-score manual lysis, uterine revision, perineal tear grade 3-4, need for blood transfusions, relaparotomy, prolonged hospitalization) and neonatal outcomes (Apgar score, cord pH, hospitalization in the neonatal intensive care unit, prolonged hospitalization). Both groups had a high rate of vaginal deliveries-93.7% and 94.9%, respectively. There was no difference between the two groups in terms of maternal or neonatal outcomes. Labor induction in multiparous women is safe and successful regardless of the initial Bishop-score. In multiparous women the Bishop-score is not a good predictor for the success of labor induction, nor is it a predictor for maternal of neonatal adverse outcomes and complications.

  13. Observing preschoolers' social-emotional behavior: structure, foundations, and prediction of early school success.

    Science.gov (United States)

    Denham, Susanne A; Bassett, Hideko Hamada; Thayer, Sara K; Mincic, Melissa S; Sirotkin, Yana S; Zinsser, Katherine

    2012-01-01

    Social-emotional behavior of 352 3- and 4-year-olds attending private child-care and Head Start programs was observed using the Minnesota Preschool Affect Checklist, Revised (MPAC-R). Goals of the investigation included (a) using MPAC-R data to extract a shortened version, MPAC-R/S, comparing structure, internal consistency, test-retest reliability, and stability of both versions; and, using the shortened measure, to examine (b) age, gender, and risk status differences in social-emotional behaviors; (c) contributions of emotion knowledge and executive function to social-emotional behaviors; and (d) contributions of social-emotional behaviors to early school adjustment and kindergarten academic success. Results show that reliability of MPAC-R/S was as good, or better, than the MPAC-R. MPAC-R/S structure, at both times of observation, included emotionally negative/aggressive, emotionally regulated/prosocial, and emotionally positive/productive behaviors; MPAC-R structure was similar but less replicable over time. Age, gender, and risk differences were found. Children's emotion knowledge contributed to later emotionally regulated/prosocial behavior. Finally, preschool emotionally negative/aggressive behaviors were associated with concurrent and kindergarten school success, and there was evidence of social-emotional behavior mediating relations between emotion knowledge or executive function, and school outcomes. The importance of portable, empirically supported observation measures of social-emotional behaviors is discussed along with possible applications, teacher utilization, and implementation barriers.

  14. 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.

  15. Climate modelling, uncertainty and responses to predictions of change

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    1996-01-01

    Article 4.1(F) of the Framework Convention on Climate Change commits all parties to take climate change considerations into account, to the extent feasible, in relevant social, economic and environmental policies and actions and to employ methods such as impact assessments to minimize adverse effects of climate change. This could be achieved by, inter alia, incorporating climate change risk assessment into development planning processes, i.e. relating climatic change to issues of habitability and sustainability. Adaptation is an ubiquitous and beneficial natural and human strategy. Future adaptation (adjustment) to climate is inevitable at the least to decrease the vulnerability to current climatic impacts. An urgent issue is the mismatch between the predictions of global climatic change and the need for information on local to regional change in order to develop adaptation strategies. Mitigation efforts are essential since the more successful mitigation activities are, the less need there will be for adaptation responses. And, mitigation responses can be global (e.g. a uniform percentage reduction in greenhouse gas emissions) while adaptation responses will be local to regional in character and therefore depend upon confident predictions of regional climatic change. The dilemma facing policymakers is that scientists have considerable confidence in likely global climatic changes but virtually zero confidence in regional changes. Mitigation and adaptation strategies relevant to climatic change can most usefully be developed in the context of sound understanding of climate, especially the near-surface continental climate, permitting discussion of societally relevant issues. But, climate models can't yet deliver this type of regionally and locationally specific prediction and some aspects of current research even seem to indicate increased uncertainty. These topics are explored in this paper using the specific example of the prediction of land-surface climate changes

  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. Cognitive function predicts 24-month weight loss success after bariatric surgery.

    Science.gov (United States)

    Spitznagel, Mary Beth; Alosco, Michael; Strain, Gladys; Devlin, Michael; Cohen, Ronald; Paul, Robert; Crosby, Ross D; Mitchell, James E; Gunstad, John

    2013-01-01

    Clinically significant cognitive impairment, particularly in attention/executive and memory function, is found in many patients undergoing bariatric surgery. These difficulties have previously been linked to decreased weight loss 12 months after surgery, but more protracted examination of this relationship has not yet been conducted. The present study prospectively examined the independent contribution of cognitive function to weight loss 24 months after bariatric surgery. Given the rapid rate of cognitive improvement observed after surgery, postoperative cognitive function (i.e., cognition 12 weeks after surgery, controlling for baseline cognition) was expected to predict lower body mass index (BMI) and higher percent total weight loss (%WL) at 24-month follow-up. Data were collected by 3 sites of the Longitudinal Assessment of Bariatric Surgery (LABS) parent project. Fifty-seven individuals enrolled in the LABS project who were undergoing bariatric surgery completed cognitive evaluation at baseline, 12 weeks, and 24 months. BMI and %WL were calculated for 24-month postoperative follow-up. Better cognitive function 12 weeks after surgery predicted higher %WL and lower BMI at 24 months, and specific domains of attention/executive and memory function were robustly related to decreased BMI and greater %WL at 24 months. Results show that cognitive performance shortly after bariatric surgery predicts greater long-term %WL and lower BMI 24 months after bariatric surgery. Further work is needed to clarify the degree to which this relationship is mediated by adherence to postoperative guidelines. Copyright © 2013 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

  18. Predicting success of oligomerized pool engineering (OPEN for zinc finger target site sequences

    Directory of Open Access Journals (Sweden)

    Goodwin Mathew J

    2010-11-01

    Full Text Available Abstract Background Precise and efficient methods for gene targeting are critical for detailed functional analysis of genomes and regulatory networks and for potentially improving the efficacy and safety of gene therapies. Oligomerized Pool ENgineering (OPEN is a recently developed method for engineering C2H2 zinc finger proteins (ZFPs designed to bind specific DNA sequences with high affinity and specificity in vivo. Because generation of ZFPs using OPEN requires considerable effort, a computational method for identifying the sites in any given gene that are most likely to be successfully targeted by this method is desirable. Results Analysis of the base composition of experimentally validated ZFP target sites identified important constraints on the DNA sequence space that can be effectively targeted using OPEN. Using alternate encodings to represent ZFP target sites, we implemented Naïve Bayes and Support Vector Machine classifiers capable of distinguishing "active" targets, i.e., ZFP binding sites that can be targeted with a high rate of success, from those that are "inactive" or poor targets for ZFPs generated using current OPEN technologies. When evaluated using leave-one-out cross-validation on a dataset of 135 experimentally validated ZFP target sites, the best Naïve Bayes classifier, designated ZiFOpT, achieved overall accuracy of 87% and specificity+ of 90%, with an ROC AUC of 0.89. When challenged with a completely independent test set of 140 newly validated ZFP target sites, ZiFOpT performance was comparable in terms of overall accuracy (88% and specificity+ (92%, but with reduced ROC AUC (0.77. Users can rank potentially active ZFP target sites using a confidence score derived from the posterior probability returned by ZiFOpT. Conclusion ZiFOpT, a machine learning classifier trained to identify DNA sequences amenable for targeting by OPEN-generated zinc finger arrays, can guide users to target sites that are most likely to function

  19. Predictive value of early serum beta-human chorionic gonadotrophin for the successful outcome in women undergoing in vitro fertilization

    Directory of Open Access Journals (Sweden)

    Neeta Singh

    2013-01-01

    Full Text Available Aims: Pregnancies achieved by in vitro fertilization (IVF are at increased risk of adverse outcome. The main objective of this study was to evaluate the predictive value of β-human chorionic gonadotrophin (β-HCG and age of the patient for the successful outcome in IVF. Materials and Methods: A retrospective study was done in 139 pregnancies after IVF at single IVF center from June 2007 to July 2012. The age of the patient and initial serum values of β-HCG on day 14 of embryo transfer were correlated with ongoing pregnancy (>12 weeks gestation. Results: The β-HCG level on day 14 of more than 347 mIU/ml has a sensitivity of 72.2% and specificity of 73.6% in prediction of pregnancy beyond 12 weeks period of gestation. Positive likelihood ratio (LR is 2.74 and negative LR is 0.37, (receiver operating characteristic area = 0.79. Discussion: In IVF cycles, there is a lot of stress on the couples while the cycle is going on. There was a positive correlation between the higher values of early serum β-HCG levels and ongoing pregnancy. Hence, it can be used as an independent predictor of a successful outcome of IVF cycle. Conclusion: We concluded from our study that early serum β-HCG can be used as a predictor of a successful outcome in IVF.

  20. The prediction of success in kickboxing based on the analysis of morphofunctional, physiological, biomechanical and psychophysiological indicators

    Directory of Open Access Journals (Sweden)

    L.V. Podrigalo

    2018-02-01

    Full Text Available Purpose: To substantiate and develop a methodology for predicting success in kickboxing on the basis of analysis of morphofunctional, physiological, biomechanical and psychophysiological indicators. Material: it was examined athletes of kickboxing (n=185, age 18.58 +- 0.46 years. It was studied the features of physical development (n = 18. The main biomechanical parameters (n=45 were determined. Goniometric indices of limb joints (n=29 were studied. It was studied the features of psychophysiological reactions (n =76. The adaptive capabilities of the cardiovascular system were studied (n=17. The prognostication is carried out by means of a sequential Wald procedure. Prognostic coefficients and their informativity were calculated. Results: a prognostic table was developed containing the indicators of the functional state of kickboxing athletes. The table includes 31 criteria, the informativeness of which varied within 115,45 - 2,23. The content of the forecast consists in: an evaluation of the results; determination of the corresponding prognostic coefficient; summation of coefficients. The threshold was set at the level +- 13, which corresponds to a probability of 95% (p<0,05. Exceeding the positive threshold means a high level of success for the athlete. When the negative threshold is reached, the probability of success is low. Conclusions: The proposed methodology is based on a sequential analysis of Wald. The technique is a simple, informative and objective tool for monitoring and predicting the status of athletes.

  1. Butterfly, Recurrence, and Predictability in Lorenz Models

    Science.gov (United States)

    Shen, B. W.

    2017-12-01

    Over the span of 50 years, the original three-dimensional Lorenz model (3DLM; Lorenz,1963) and its high-dimensional versions (e.g., Shen 2014a and references therein) have been used for improving our understanding of the predictability of weather and climate with a focus on chaotic responses. Although the Lorenz studies focus on nonlinear processes and chaotic dynamics, people often apply a "linear" conceptual model to understand the nonlinear processes in the 3DLM. In this talk, we present examples to illustrate the common misunderstandings regarding butterfly effect and discuss the importance of solutions' recurrence and boundedness in the 3DLM and high-dimensional LMs. The first example is discussed with the following folklore that has been widely used as an analogy of the butterfly effect: "For want of a nail, the shoe was lost.For want of a shoe, the horse was lost.For want of a horse, the rider was lost.For want of a rider, the battle was lost.For want of a battle, the kingdom was lost.And all for the want of a horseshoe nail."However, in 2008, Prof. Lorenz stated that he did not feel that this verse described true chaos but that it better illustrated the simpler phenomenon of instability; and that the verse implicitly suggests that subsequent small events will not reverse the outcome (Lorenz, 2008). Lorenz's comments suggest that the verse neither describes negative (nonlinear) feedback nor indicates recurrence, the latter of which is required for the appearance of a butterfly pattern. The second example is to illustrate that the divergence of two nearby trajectories should be bounded and recurrent, as shown in Figure 1. Furthermore, we will discuss how high-dimensional LMs were derived to illustrate (1) negative nonlinear feedback that stabilizes the system within the five- and seven-dimensional LMs (5D and 7D LMs; Shen 2014a; 2015a; 2016); (2) positive nonlinear feedback that destabilizes the system within the 6D and 8D LMs (Shen 2015b; 2017); and (3

  2. Initial Sleep Time Predicts Success in Manual-Guided Cognitive Behavioral Therapy for Insomnia.

    Science.gov (United States)

    Bothelius, Kristoffer; Kyhle, Kicki; Broman, Jan-Erik; Gordh, Torsten; Fredrikson, Mats

    2016-01-01

    Cognitive behavioral therapy produces significant and long-lasting improvement for individuals with insomnia, but treatment resources are scarce. A "stepped care" approach has therefore been proposed, but knowledge is limited on how to best allocate patients to different treatment steps. In this study, 66 primary-care patients with insomnia attended a low-end treatment step: manual-guided cognitive behavioral therapy (CBT) for insomnia delivered by ordinary primary-care personnel. Based on clinically significant treatment effects, subjects were grouped into treatment responders or nonresponders. Baseline data were analyzed to identify predictors for treatment success. Long total sleep time at baseline assessment was the only statistically significant predictor for becoming a responder, and sleep time may thus be important to consider before enrolling patients in low-end treatments.

  3. Auditing predictive models : a case study in crop growth

    NARCIS (Netherlands)

    Metselaar, K.

    1999-01-01

    Methods were developed to assess and quantify the predictive quality of simulation models, with the intent to contribute to evaluation of model studies by non-scientists. In a case study, two models of different complexity, LINTUL and SUCROS87, were used to predict yield of forage maize

  4. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  5. A model for estimating the minimum number of offspring to sample in studies of reproductive success.

    Science.gov (United States)

    Anderson, Joseph H; Ward, Eric J; Carlson, Stephanie M

    2011-01-01

    Molecular parentage permits studies of selection and evolution in fecund species with cryptic mating systems, such as fish, amphibians, and insects. However, there exists no method for estimating the number of offspring that must be assigned parentage to achieve robust estimates of reproductive success when only a fraction of offspring can be sampled. We constructed a 2-stage model that first estimated the mean (μ) and variance (v) in reproductive success from published studies on salmonid fishes and then sampled offspring from reproductive success distributions simulated from the μ and v estimates. Results provided strong support for modeling salmonid reproductive success via the negative binomial distribution and suggested that few offspring samples are needed to reject the null hypothesis of uniform offspring production. However, the sampled reproductive success distributions deviated significantly (χ(2) goodness-of-fit test p value reproductive success distribution at rates often >0.05 and as high as 0.24, even when hundreds of offspring were assigned parentage. In general, reproductive success patterns were less accurate when offspring were sampled from cohorts with larger numbers of parents and greater variance in reproductive success. Our model can be reparameterized with data from other species and will aid researchers in planning reproductive success studies by providing explicit sampling targets required to accurately assess reproductive success.

  6. Greater hunger and less restraint predict weight loss success with phentermine treatment.

    Science.gov (United States)

    Thomas, Elizabeth A; Mcnair, Bryan; Bechtell, Jamie L; Ferland, Annie; Cornier, Marc-Andre; Eckel, Robert H

    2016-01-01

    Phentermine is thought to cause weight loss through a reduction in hunger. It was hypothesized that higher hunger ratings would predict greater weight loss with phentermine. This is an observational pilot study in which all subjects were treated with phentermine for 8 weeks and appetite and eating behaviors were measured at baseline and week 8. Outcomes were compared in subjects with ≥5% vs. hunger (P = 0.017), desire to eat (P =0.003), and prospective food consumption (0.006) and lower baseline cognitive restraint (P = 0.01). In addition, higher baseline home prospective food consumption (P = 0.002) and lower baseline cognitive restraint (P hunger and less restraint are more likely to achieve significant weight loss with phentermine. This information can be used clinically to determine who might benefit most from phentermine treatment. © 2015 The Obesity Society.

  7. 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.

  8. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...

  9. 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

  10. From Predictive Models to Instructional Policies

    Science.gov (United States)

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  11. Breeding phenology and winter activity predict subsequent breeding success in a trans-global migratory seabird.

    Science.gov (United States)

    Shoji, A; Aris-Brosou, S; Culina, A; Fayet, A; Kirk, H; Padget, O; Juarez-Martinez, I; Boyle, D; Nakata, T; Perrins, C M; Guilford, T

    2015-10-01

    Inter-seasonal events are believed to connect and affect reproductive performance (RP) in animals. However, much remains unknown about such carry-over effects (COEs), in particular how behaviour patterns during highly mobile life-history stages, such as migration, affect RP. To address this question, we measured at-sea behaviour in a long-lived migratory seabird, the Manx shearwater (Puffinus puffinus) and obtained data for individual migration cycles over 5 years, by tracking with geolocator/immersion loggers, along with 6 years of RP data. We found that individual breeding and non-breeding phenology correlated with subsequent RP, with birds hyperactive during winter more likely to fail to reproduce. Furthermore, parental investment during one year influenced breeding success during the next, a COE reflecting the trade-off between current and future RP. Our results suggest that different life-history stages interact to influence RP in the next breeding season, so that behaviour patterns during winter may be important determinants of variation in subsequent fitness among individuals. © 2015 The Authors.

  12. Can a virtual reality assessment of fine motor skill predict successful central line insertion?

    Science.gov (United States)

    Mohamadipanah, Hossein; Parthiban, Chembian; Nathwani, Jay; Rutherford, Drew; DiMarco, Shannon; Pugh, Carla

    2016-10-01

    Due to the increased use of peripherally inserted central catheter lines, central lines are not performed as frequently. The aim of this study is to evaluate whether a virtual reality (VR)-based assessment of fine motor skills can be used as a valid and objective assessment of central line skills. Surgical residents (N = 43) from 7 general surgery programs performed a subclavian central line in a simulated setting. Then, they participated in a force discrimination task in a VR environment. Hand movements from the subclavian central line simulation were tracked by electromagnetic sensors. Gross movements as monitored by the electromagnetic sensors were compared with the fine motor metrics calculated from the force discrimination tasks in the VR environment. Long periods of inactivity (idle time) during needle insertion and lack of smooth movements, as detected by the electromagnetic sensors, showed a significant correlation with poor force discrimination in the VR environment. Also, long periods of needle insertion time correlated to the poor performance in force discrimination in the VR environment. This study shows that force discrimination in a defined VR environment correlates to needle insertion time, idle time, and hand smoothness when performing subclavian central line placement. Fine motor force discrimination may serve as a valid and objective assessment of the skills required for successful needle insertion when placing central lines. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. 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.

  14. The Complexity of Developmental Predictions from Dual Process Models

    Science.gov (United States)

    Stanovich, Keith E.; West, Richard F.; Toplak, Maggie E.

    2011-01-01

    Drawing developmental predictions from dual-process theories is more complex than is commonly realized. Overly simplified predictions drawn from such models may lead to premature rejection of the dual process approach as one of many tools for understanding cognitive development. Misleading predictions can be avoided by paying attention to several…

  15. Measuring online learning systems success: applying the updated DeLone and McLean model.

    Science.gov (United States)

    Lin, Hsiu-Fen

    2007-12-01

    Based on a survey of 232 undergraduate students, this study used the updated DeLone and McLean information systems success model to examine the determinants for successful use of online learning systems (OLS). The results provided an expanded understanding of the factors that measure OLS success. The results also showed that system quality, information quality, and service quality had a significant effect on actual OLS use through user satisfaction and behavioral intention to use OLS.

  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. Self-responsibility predicts the successful outcome of coronary artery bypass surgery

    Directory of Open Access Journals (Sweden)

    C. J. Eales

    2004-01-01

    and their spouses/care-givers had a greater knowledge about the disease and the risk factor modification (p=0.01; p<0.01, and twelve months after the operation the patients are satisfied with the outcome of the operation (p<0.01. Conclusions: A stepwise logistic regression established that the acceptance of self-responsibility was the strongest  factor predicting an improved quality of life after CABG surgery. Patients who did not accept responsibility did not have an improved quality of life irrespective of the impact of all other parameters. Patients' satisfaction with the outcome of the operative procedure is an important predictor of the acceptance of self-responsibility. Realistic expectations of the outcome of CABG surgery will improve patients' satisfaction with the outcome. The knowledge of the spouse is a significant factor in the patients' acceptance of self-responsibility. Knowledge of the chronic nature of their disease as well as risk factor modification and realistic expectations of the outcome of CABG surgery influences patientsacceptance of self-responsibility.

  18. PREDICTING A FAST-TRACK MARITIME CAREER: CHARACTERISTICS OF SUCCESSFUL OFFICERS DURING TEENAGE YEARS

    Directory of Open Access Journals (Sweden)

    Manuel Joaquín Fernández González

    2017-12-01

    Full Text Available Fast-track maritime career is a topical question worldwide due to the shortage of seafarers in maritime industry. Assuming that the fast-track career officers’ relevant common characteristics in adolescence could predict future maritime career speed, the research questions of this research are: What were the common characteristics of fast-track career officers when they were 16-18? Were there any statistically significant differences between the fast-track career groups and the officers with a slower career at that age? A questionnaire survey involving 175 maritime officers was conducted in Latvia in January – October 2016, regarding officers’ family context, school achievement, involvement in sports, and personality traits when they were 16-18. Fast-track career officers perceived themselves as more conscientious, calm and more leadership oriented than the whole group in adolescence. Statistically significant differences among career-speed groups were found regarding family socioeconomic status, family atmosphere and family career support at that age. Based on those communalities among maritime officers with a fast-track carrier when they were 16-18, maritime education and training institutions could better find and give appropriate career guidance to prospective maritime officers. Even if maritime career speed is a very individualized phenomenon, family characteristics could be studied further as a potential good predictor of fast-track maritime career.

  19. 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.

  20. Evaluating Methods for Isolating Total RNA and Predicting the Success of Sequencing Phylogenetically Diverse Plant Transcriptomes

    Science.gov (United States)

    Bruskiewich, Richard; Burris, Jason N.; Carrigan, Charlotte T.; Chase, Mark W.; Clarke, Neil D.; Covshoff, Sarah; dePamphilis, Claude W.; Edger, Patrick P.; Goh, Falicia; Graham, Sean; Greiner, Stephan; Hibberd, Julian M.; Jordon-Thaden, Ingrid; Kutchan, Toni M.; Leebens-Mack, James; Melkonian, Michael; Miles, Nicholas; Myburg, Henrietta; Patterson, Jordan; Pires, J. Chris; Ralph, Paula; Rolf, Megan; Sage, Rowan F.; Soltis, Douglas; Soltis, Pamela; Stevenson, Dennis; Stewart, C. Neal; Surek, Barbara; Thomsen, Christina J. M.; Villarreal, Juan Carlos; Wu, Xiaolei; Zhang, Yong; Deyholos, Michael K.; Wong, Gane Ka-Shu

    2012-01-01

    Next-generation sequencing plays a central role in the characterization and quantification of transcriptomes. Although numerous metrics are purported to quantify the quality of RNA, there have been no large-scale empirical evaluations of the major determinants of sequencing success. We used a combination of existing and newly developed methods to isolate total RNA from 1115 samples from 695 plant species in 324 families, which represents >900 million years of phylogenetic diversity from green algae through flowering plants, including many plants of economic importance. We then sequenced 629 of these samples on Illumina GAIIx and HiSeq platforms and performed a large comparative analysis to identify predictors of RNA quality and the diversity of putative genes (scaffolds) expressed within samples. Tissue types (e.g., leaf vs. flower) varied in RNA quality, sequencing depth and the number of scaffolds. Tissue age also influenced RNA quality but not the number of scaffolds ≥1000 bp. Overall, 36% of the variation in the number of scaffolds was explained by metrics of RNA integrity (RIN score), RNA purity (OD 260/230), sequencing platform (GAIIx vs HiSeq) and the amount of total RNA used for sequencing. However, our results show that the most commonly used measures of RNA quality (e.g., RIN) are weak predictors of the number of scaffolds because Illumina sequencing is robust to variation in RNA quality. These results provide novel insight into the methods that are most important in isolating high quality RNA for sequencing and assembling plant transcriptomes. The methods and recommendations provided here could increase the efficiency and decrease the cost of RNA sequencing for individual labs and genome centers. PMID:23185583