WorldWideScience

Sample records for model predicted values

  1. Predictability of extreme values in geophysical models

    Directory of Open Access Journals (Sweden)

    A. E. Sterk

    2012-09-01

    Full Text Available Extreme value theory in deterministic systems is concerned with unlikely large (or small values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical models. We study whether finite-time Lyapunov exponents are larger or smaller for initial conditions leading to extremes. General statements on whether extreme values are better or less predictable are not possible: the predictability of extreme values depends on the observable, the attractor of the system, and the prediction lead time.

  2. Predictability of extreme values in geophysical models

    NARCIS (Netherlands)

    Sterk, A.E.; Holland, M.P.; Rabassa, P.; Broer, H.W.; Vitolo, R.

    2012-01-01

    Extreme value theory in deterministic systems is concerned with unlikely large (or small) values of an observable evaluated along evolutions of the system. In this paper we study the finite-time predictability of extreme values, such as convection, energy, and wind speeds, in three geophysical model

  3. Prediction of survival with alternative modeling techniques using pseudo values.

    Directory of Open Access Journals (Sweden)

    Tjeerd van der Ploeg

    Full Text Available BACKGROUND: The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo values enable statistically appropriate analyses of survival outcomes when used in seven alternative modeling techniques. METHODS: In this case study, we analyzed survival of 1282 Dutch patients with newly diagnosed Head and Neck Squamous Cell Carcinoma (HNSCC with conventional Kaplan-Meier and Cox regression analysis. We subsequently calculated pseudo values to reflect the individual survival patterns. We used these pseudo values to compare recursive partitioning (RPART, neural nets (NNET, logistic regression (LR general linear models (GLM and three variants of support vector machines (SVM with respect to dichotomous 60-month survival, and continuous pseudo values at 60 months or estimated survival time. We used the area under the ROC curve (AUC and the root of the mean squared error (RMSE to compare the performance of these models using bootstrap validation. RESULTS: Of a total of 1282 patients, 986 patients died during a median follow-up of 66 months (60-month survival: 52% [95% CI: 50%-55%]. The LR model had the highest optimism corrected AUC (0.791 to predict 60-month survival, followed by the SVM model with a linear kernel (AUC 0.787. The GLM model had the smallest optimism corrected RMSE when continuous pseudo values were considered for 60-month survival or the estimated survival time followed by SVM models with a linear kernel. The estimated importance of predictors varied substantially by the specific aspect of survival studied and modeling technique used. CONCLUSIONS: The use of pseudo values makes it readily possible to apply alternative modeling techniques to survival problems, to compare their performance and to search further for promising

  4. Values and uncertainties in the predictions of global climate models.

    Science.gov (United States)

    Winsberg, Eric

    2012-06-01

    Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science-that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But even as these technical challenges are being met, a number of persistent conceptual difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policy makers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in a great deal of climate modeling, because the roles of values in creating the models cannot be discerned after the fact-the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work well here.

  5. An infinitesimal model for quantitative trait genomic value prediction.

    Directory of Open Access Journals (Sweden)

    Zhiqiu Hu

    Full Text Available We developed a marker based infinitesimal model for quantitative trait analysis. In contrast to the classical infinitesimal model, we now have new information about the segregation of every individual locus of the entire genome. Under this new model, we propose that the genetic effect of an individual locus is a function of the genome location (a continuous quantity. The overall genetic value of an individual is the weighted integral of the genetic effect function along the genome. Numerical integration is performed to find the integral, which requires partitioning the entire genome into a finite number of bins. Each bin may contain many markers. The integral is approximated by the weighted sum of all the bin effects. We now turn the problem of marker analysis into bin analysis so that the model dimension has decreased from a virtual infinity to a finite number of bins. This new approach can efficiently handle virtually unlimited number of markers without marker selection. The marker based infinitesimal model requires high linkage disequilibrium of all markers within a bin. For populations with low or no linkage disequilibrium, we develop an adaptive infinitesimal model. Both the original and the adaptive models are tested using simulated data as well as beef cattle data. The simulated data analysis shows that there is always an optimal number of bins at which the predictability of the bin model is much greater than the original marker analysis. Result of the beef cattle data analysis indicates that the bin model can increase the predictability from 10% (multiple marker analysis to 33% (multiple bin analysis. The marker based infinitesimal model paves a way towards the solution of genetic mapping and genomic selection using the whole genome sequence data.

  6. Prediction of survival with alternative modeling techniques using pseudo values

    NARCIS (Netherlands)

    T. van der Ploeg (Tjeerd); F.R. Datema (Frank); R.J. Baatenburg de Jong (Robert Jan); E.W. Steyerberg (Ewout)

    2014-01-01

    textabstractBackground: The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo

  7. Predictive Models of Alcohol Use Based on Attitudes and Individual Values

    Science.gov (United States)

    Del Castillo Rodríguez, José A. García; López-Sánchez, Carmen; Soler, M. Carmen Quiles; Del Castillo-López, Álvaro García; Pertusa, Mónica Gázquez; Campos, Juan Carlos Marzo; Inglés, Cándido J.

    2013-01-01

    Two predictive models are developed in this article: the first is designed to predict people' attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The…

  8. Predictive Models of Alcohol Use Based on Attitudes and Individual Values

    Science.gov (United States)

    Del Castillo Rodríguez, José A. García; López-Sánchez, Carmen; Soler, M. Carmen Quiles; Del Castillo-López, Álvaro García; Pertusa, Mónica Gázquez; Campos, Juan Carlos Marzo; Inglés, Cándido J.

    2013-01-01

    Two predictive models are developed in this article: the first is designed to predict people' attitudes to alcoholic drinks, while the second sets out to predict the use of alcohol in relation to selected individual values. University students (N = 1,500) were recruited through stratified sampling based on sex and academic discipline. The…

  9. Oxygen and hydrogen isotope ratios in tree rings: how well do models predict observed values?

    CSIR Research Space (South Africa)

    Waterhouse, JS

    2002-07-30

    Full Text Available the trunk, it is proficient to model the observed annual values of oxygen isotope ratios of alpha-cellulose to a significant level (r = 0.77, P < 0.01). When the same model is applied to hydrogen isotope ratios, results are found, and predictions can be made...

  10. The Prediction Value

    NARCIS (Netherlands)

    Koster, M.; Kurz, S.; Lindner, I.; Napel, S.

    2013-01-01

    We introduce the prediction value (PV) as a measure of players’ informational importance in probabilistic TU games. The latter combine a standard TU game and a probability distribution over the set of coalitions. Player i’s prediction value equals the difference between the conditional expectations

  11. The Predictive Value of Subjective Labour Supply Data: A Dynamic Panel Data Model with Measurement Error

    OpenAIRE

    Euwals, Rob

    2002-01-01

    This paper tests the predictive value of subjective labour supply data for adjustments in working hours over time. The idea is that if subjective labour supply data help to predict next year?s working hours, such data must contain at least some information on individual labour supply preferences. This informational content can be crucial to identify models of labour supply. Furthermore, it can be crucial to investigate the need for, or, alternatively, the support for laws and collective agree...

  12. Predicted value of $0 \\, \

    CERN Document Server

    Maedan, Shinji

    2016-01-01

    Assuming that the lightest neutrino mass $ m_0 $ is measured, we study the influence of error of the measured $ m_0 $ on the uncertainty of the predicted value of the neutrinoless double beta decay ($0 \\, \

  13. Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

    DEFF Research Database (Denmark)

    Christensen, Steen; Doherty, John

    2008-01-01

    over the model area. Singular value decomposition (SVD) of the (possibly weighted) sensitivity matrix of the pilot point based model produces eigenvectors of which we pick a small number corresponding to significant eigenvalues. Super parameters are defined as factors through which parameter...... conditions near an inflow boundary where data is lacking and which exhibit apparent significant nonlinear behavior. It is shown that inclusion of Tikhonov regularization can stabilize and speed up the parameter estimation process. A method of linearized model analysis of predictive uncertainty...... nonlinear functions. Recommendations concerning the use of pilot points and singular value decomposition in real-world groundwater model calibration are finally given. (c) 2008 Elsevier Ltd. All rights reserved....

  14. Semiparametric models of time-dependent predictive values of prognostic biomarkers.

    Science.gov (United States)

    Zheng, Yingye; Cai, Tianxi; Stanford, Janet L; Feng, Ziding

    2010-03-01

    Rigorous statistical evaluation of the predictive values of novel biomarkers is critical prior to applying novel biomarkers into routine standard care. It is important to identify factors that influence the performance of a biomarker in order to determine the optimal conditions for test performance. We propose a covariate-specific time-dependent positive predictive values curve to quantify the predictive accuracy of a prognostic marker measured on a continuous scale and with censored failure time outcome. The covariate effect is accommodated with a semiparametric regression model framework. In particular, we adopt a smoothed survival time regression technique (Dabrowska, 1997, The Annals of Statistics 25, 1510-1540) to account for the situation where risk for the disease occurrence and progression is likely to change over time. In addition, we provide asymptotic distribution theory and resampling-based procedures for making statistical inference on the covariate-specific positive predictive values. We illustrate our approach with numerical studies and a dataset from a prostate cancer study.

  15. Models for measuring and predicting shareholder value: A study of third party software service providers

    Indian Academy of Sciences (India)

    N Viswanadham; Poornima Luthra

    2005-04-01

    In this study, we use the strategic profit model (SPM) and the economic value-added (EVA to measure shareholder value). SPM measures the return on net worth (RONW) which is defined as the return on assets (ROA) multiplied by the financial leverage. EVA is defined as the firm’s net operating profit after taxes (NOPAT) minus the capital charge. Both, RONW and EVA provide an indication of how much shareholder value a firm creates for its shareholders, year on year. With the increasing focus on creation of shareholder value and core competencies, many companies are outsourcing their information technology (IT) related activities to third party software companies. Indian software companies have become leaders in providing these services. Companies from several other countries are also competing for the top slot. We use the SPM and EVA models to analyse the four listed players of the software industry using the publicly available published data. We compare the financial data obtained from the models, and use peer average data to provide customized recommendations for each company to improve their shareholder value. Assuming that the companies follow these rules, we also predict future RONW and EVA for the companies for the financial year 2005. Finally, we make several recommendations to software providers for effectively competing in the global arena.

  16. The value of "black-box" neural network modeling in subsurface flow prediction

    Science.gov (United States)

    Paleologos, E.; Skitzi, I.; Katsifarakis, K.

    2012-04-01

    In several hydrologic cases the complexity of the processes involved tied in with the uncertainty in the subsurface geologic environment, geometries, and boundary conditions cannot be addressed by constitutive relationships, either in a deterministic or a stochastic framework. "Black-box" models are used routinely in surface hydrologic predictions, but in subsurface hydrology there is still a tendency to rely on physical descriptions, even in problems where the geometry, the medium, the processes, the boundary conditions are largely unknown. Subsurface flow in karstic environments exemplifies all the above complexities and uncertainties rendering the use of physical models impractical. The current study uses neural networks to exemplify that "black-box" models can provide useful predictions even in the absence of physical process descriptions. Daily discharges of two springs lying in a karstic environment were simulated for a period of two and a half years with the use of a multi-layer perceptron back-propagation neural network. Missing discharge values were supplemented by assuming linear relationships during base flow conditions, thus extending the length of the data record during the network's training phase and improving its performance. The time lag between precipitation and spring discharge differed significantly for the two springs indicating that in karstic environments hydraulic behavior is dominated, even within a few hundred meters, by local conditions. Optimum training results were attained with a Levenberg-Marquardt algorithm resulting in a network architecture consisting of two input layer neurons, four hidden layer neurons, and one output layer neuron, the spring's discharge. The neural network's predictions captured the behavior for both springs and followed very closely the discontinuities in the discharge time series. Under/over-estimation of observed discharges for the two springs remained below 3%, with the exception of a few local maxima where

  17. A CBR-Based and MAHP-Based Customer Value Prediction Model for New Product Development

    Directory of Open Access Journals (Sweden)

    Yu-Jie Zhao

    2014-01-01

    Full Text Available In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM. CBR (case based reasoning can reduce experts’ workload and evaluation time, while MAHP (multiplicative analytic hierarchy process can use actual but average influencing factor’s effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers’ transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM’s three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment.

  18. A CBR-Based and MAHP-Based Customer Value Prediction Model for New Product Development

    Science.gov (United States)

    Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

    2014-01-01

    In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment. PMID:25162050

  19. A CBR-based and MAHP-based customer value prediction model for new product development.

    Science.gov (United States)

    Zhao, Yu-Jie; Luo, Xin-xing; Deng, Li

    2014-01-01

    In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts' workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor's effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers' transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM's three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment.

  20. From 'solution shop' model to 'focused factory' in hospital surgery: increasing care value and predictability.

    Science.gov (United States)

    Cook, David; Thompson, Jeffrey E; Habermann, Elizabeth B; Visscher, Sue L; Dearani, Joseph A; Roger, Veronique L; Borah, Bijan J

    2014-05-01

    The full-service US hospital has been described organizationally as a "solution shop," in which medical problems are assumed to be unstructured and to require expert physicians to determine each course of care. If universally applied, this model contributes to unwarranted variation in care, which leads to lower quality and higher costs. We purposely disrupted the adult cardiac surgical practice that we led at Mayo Clinic, in Rochester, Minnesota, by creating a "focused factory" model (characterized by a uniform approach to delivering a limited set of high-quality products) within the practice's solution shop. Key elements of implementing the new model were mapping the care process, segmenting the patient population, using information technology to communicate clearly defined expectations, and empowering nonphysician providers at the bedside. Using a set of criteria, we determined that the focused-factory model was appropriate for 67 percent of cardiac surgical patients. We found that implementation of the model reduced resource use, length-of-stay, and cost. Variation was markedly reduced, and outcomes were improved. Assigning patients to different care models increases care value and the predictability of care process, outcomes, and costs while preserving (in a lesser clinical footprint) the strengths of the solution shop. We conclude that creating a focused-factory model within a solution shop, by applying industrial engineering principles and health information technology tools and changing the model of work, is very effective in both improving quality and reducing costs.

  1. Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value

    Energy Technology Data Exchange (ETDEWEB)

    Callejon-Ferre, A.J.; Lopez-Martinez, J.A.; Manzano-Agugliaro, F. [Departamento de Ingenieria Rural, Universidad de Almeria, Ctra. Sacramento s/n, La Canada de San Urbano, 04120 Almeria (Spain); Velazquez-Marti, B. [Departamento de Ingenieria Rural y Agroalimentaria, Universidad Politecnica de Valencia, Camino de Vera s/n, 46022 Valencia (Spain)

    2011-02-15

    Almeria, in southeastern Spain, generates some 1,086,261 t year{sup -1} (fresh weight) of greenhouse crop (Cucurbita pepo L., Cucumis sativus L., Solanum melongena L., Solanum lycopersicum L., Phaseoulus vulgaris L., Capsicum annuum L., Citrillus vulgaris Schrad. and Cucumis melo L.) residues. The energy potential of this biomass is unclear. The aim of the present work was to accurately quantify this variable, differentiating between crop species while taking into consideration the area they each occupy. This, however, required the direct analysis of the higher heating value (HHV) of these residues, involving very expensive and therefore not commonly available equipment. Thus, a further aim was to develop models for predicting the HHV of these residues, taking into account variables measured by elemental and/or proximate analysis, thus providing an economically attractive alternative to direct analysis. All the analyses in this work involved the use of worldwide-recognised standards and methods. The total energy potential for these plant residues, as determined by direct analysis, was 1,003,497.49 MW h year{sup -1}. Twenty univariate and multivariate equations were developed to predict the HHV. The R{sup 2} and adjusted R{sup 2} values obtained for the univariate and multivariate models were 0.909 and 0.946 or above respectively. In all cases, the mean absolute percentage error varied between 0.344 and 2.533. These results show that any of these 20 equations could be used to accurately predict the HHV of crop residues. The residues produced by the Almeria greenhouse industry would appear to be an interesting source of renewable energy. (author)

  2. Slip Model Used for Prediction of r Value of BCC Metal Sheets from ODF Coefficients

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Different slip models were used for prediction of rvalue of BCC metal sheets from ODF coefficients. According to the maximum plastic work theory developed by Bishop and Hill, it is expected that the higher of Taylor factors given by a slip model, the better predictio nobtained based on the model. From this point of view, a composed slip model of BCC metals was presented. Based on the model, the agreement of predicted rvalues for deep drawing steels with experimental ones is excellent.

  3. The value of using feasibility models in systematic conservation planning to predict landholder management uptake.

    Science.gov (United States)

    Tulloch, Ayesha I T; Tulloch, Vivitskaia J D; Evans, Megan C; Mills, Morena

    2014-12-01

    Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple-use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders' willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders' decisions to engage in management. To test the utility of our best-supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost-effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost-effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost-efficient and effective manner. © 2014 Society for Conservation Biology.

  4. Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

    Directory of Open Access Journals (Sweden)

    G Khalili-Zadeh-Mahani

    2016-07-01

    Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

  5. Modeling and prediction of monetary and non-monetary business values

    NARCIS (Netherlands)

    Välja, Margus; Österlind, Magnus; Iacob, Maria-Eugenia; Sinderen, van Marten; Johnson, Pontus; Gasevic, D; Hatala, M.; Motahari Nezhad, H.R.; Reichert, M.U.

    2013-01-01

    In existing business model frameworks little attention is paid to a thorough understanding of the perceived customer value of a business’ offering as compared to competing offers. In this paper, we propose to use utility theory in combination with e3value models to address this issue. An actor's joi

  6. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

    2015-01-01

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare w

  7. Financial crisis early-warning model of listed companies based on predicted value

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without...

  8. Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

    Science.gov (United States)

    Wang, Quanchao; Yu, Yang; Li, Fuhua; Zhang, Xiaojun; Xiang, Jianhai

    2017-09-01

    Genomic selection (GS) can be used to accelerate genetic improvement by shortening the selection interval. The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value (GEBV). This study is a first attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits. The performance of GS models in L. vannamei was evaluated in a population consisting of 205 individuals, which were genotyped for 6 359 single nucleotide polymorphism (SNP) markers by specific length amplified fragment sequencing (SLAF-seq) and phenotyped for body length and body weight. Three GS models (RR-BLUP, BayesA, and Bayesian LASSO) were used to obtain the GEBV, and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes. The mean reliability of the GEBVs for body length and body weight predicted by the different models was 0.296 and 0.411, respectively. For each trait, the performances of the three models were very similar to each other with respect to predictability. The regression coefficients estimated by the three models were close to one, suggesting near to zero bias for the predictions. Therefore, when GS was applied in a L. vannamei population for the studied scenarios, all three models appeared practicable. Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

  9. Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

    Science.gov (United States)

    Wang, Quanchao; Yu, Yang; Li, Fuhua; Zhang, Xiaojun; Xiang, Jianhai

    2016-10-01

    Genomic selection (GS) can be used to accelerate genetic improvement by shortening the selection interval. The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value (GEBV). This study is a first attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits. The performance of GS models in L. vannamei was evaluated in a population consisting of 205 individuals, which were genotyped for 6 359 single nucleotide polymorphism (SNP) markers by specific length amplified fragment sequencing (SLAF-seq) and phenotyped for body length and body weight. Three GS models (RR-BLUP, BayesA, and Bayesian LASSO) were used to obtain the GEBV, and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes. The mean reliability of the GEBVs for body length and body weight predicted by the different models was 0.296 and 0.411, respectively. For each trait, the performances of the three models were very similar to each other with respect to predictability. The regression coefficients estimated by the three models were close to one, suggesting near to zero bias for the predictions. Therefore, when GS was applied in a L. vannamei population for the studied scenarios, all three models appeared practicable. Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

  10. Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

    Institute of Scientific and Technical Information of China (English)

    Yuhong Wang; Qin Liu; Jianrong Tang; Wenbin Cao; Xiaozhong Li

    2014-01-01

    A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM(1,1) model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM(1,1) model relatively.

  11. Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis.

    Science.gov (United States)

    Uzun, Harun; Yıldız, Zeynep; Goldfarb, Jillian L; Ceylan, Selim

    2017-06-01

    As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. What Is the Predictive Value of Animal Models for Vaccine Efficacy in Humans? Consideration of Strategies to Improve the Value of Animal Models.

    Science.gov (United States)

    Herati, Ramin Sedaghat; Wherry, E John

    2017-03-27

    Animal models are an essential feature of the vaccine design toolkit. Although animal models have been invaluable in delineating the mechanisms of immune function, their precision in predicting how well specific vaccines work in humans is often suboptimal. There are, of course, many obvious species differences that may limit animal models from predicting all details of how a vaccine works in humans. However, careful consideration of which animal models may have limitations should also allow more accurate interpretations of animal model data and more accurate predictions of what is to be expected in clinical trials. In this article, we examine some of the considerations that might be relevant to cross-species extrapolation of vaccine-related immune responses for the prediction of how vaccines will perform in humans.

  13. Comparison of breeding value prediction for two traits in a Nellore-Angus crossbred population using different Bayesian modeling methodologies

    Directory of Open Access Journals (Sweden)

    Lauren L. Hulsman Hanna

    2014-12-01

    Full Text Available The objectives of this study were to 1 compare four models for breeding value prediction using genomic or pedigree information and 2 evaluate the impact of fixed effects that account for family structure. Comparisons were made in a Nellore-Angus population comprising F2, F3 and half-siblings to embryo transfer F2 calves with records for overall temperament at weaning (TEMP; n = 769 and Warner-Bratzler shear force (WBSF; n = 387. After quality control, there were 34,913 whole genome SNP markers remaining. Bayesian methods employed were BayesB ( π = 0.995 or 0.997 for WBSF or TEMP, respectively and BayesC (π = 0 and π, where π is the ideal proportion of markers not included. Direct genomic values (DGV from single trait Bayesian analyses were compared to conventional pedigree-based animal model breeding values. Numerically, BayesC procedures (using π had the highest accuracy of all models for WBSF and TEMP ( ρgg = 0.843 and 0.923, respectively, but BayesB had the least bias (regression of performance on prediction closest to 1, βy,x = 2.886 and 1.755, respectively. Accounting for family structure decreased accuracy and increased bias in prediction of DGV indicating a detrimental impact when used in these prediction methods that simultaneously fit many markers.

  14. Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats.

    Science.gov (United States)

    Andonov, S; Ødegård, J; Svendsen, M; Ådnøy, T; Vegara, M; Klemetsdal, G

    2013-03-01

    One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.

  15. The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

    Science.gov (United States)

    Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

    2013-10-01

    Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

  16. A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

    Science.gov (United States)

    Græsbøll, Kaare; Kirkeby, Carsten; Nielsen, Søren Saxmose; Halasa, Tariq; Toft, Nils; Christiansen, Lasse Engbo

    2017-01-01

    The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction of the future value of a dairy cow requires further detailed knowledge of the costs associated with feed, management practices, production systems, and disease. Here, we present a method to predict the future value of the milk production of a dairy cow based on herd recording data only. The method consists of several steps to evaluate lifetime milk production and individual cow somatic cell counts and to finally predict the average production for each day that the cow is alive. Herd recording data from 610 Danish Holstein herds were used to train and test a model predicting milk production (including factors associated with milk yield, somatic cell count, and the survival of individual cows). All estimated parameters were either herd- or cow-specific. The model prediction deviated, on average, less than 0.5 kg from the future average milk production of dairy cows in multiple herds after adjusting for the effect of somatic cell count. We conclude that estimates of future average production can be used on a day-to-day basis to rank cows for culling, or can be implemented in simulation models of within-herd disease spread to make operational decisions, such as culling versus treatment. An advantage of the approach presented in this paper is that it requires no specific knowledge of disease status or any other information beyond herd recorded milk yields, somatic cell counts, and reproductive status. PMID:28261585

  17. Linear and regressive stochastic models for prediction of daily maximum ozone values at Mexico City atmosphere

    Energy Technology Data Exchange (ETDEWEB)

    Bravo, J. L [Instituto de Geofisica, UNAM, Mexico, D.F. (Mexico); Nava, M. M [Instituto Mexicano del Petroleo, Mexico, D.F. (Mexico); Gay, C [Centro de Ciencias de la Atmosfera, UNAM, Mexico, D.F. (Mexico)

    2001-07-01

    We developed a procedure to forecast, with 2 or 3 hours, the daily maximum of surface ozone concentrations. It involves the adjustment of Autoregressive Integrated and Moving Average (ARIMA) models to daily ozone maximum concentrations at 10 monitoring atmospheric stations in Mexico City during one-year period. A one-day forecast is made and it is adjusted with the meteorological and solar radiation information acquired during the first 3 hours before the occurrence of the maximum value. The relative importance for forecasting of the history of the process and of meteorological conditions is evaluated. Finally an estimate of the daily probability of exceeding a given ozone level is made. [Spanish] Se aplica un procedimiento basado en la metodologia conocida como ARIMA, para predecir, con 2 o 3 horas de anticipacion, el valor maximo de la concentracion diaria de ozono. Esta basado en el calculo de autorregresiones y promedios moviles aplicados a los valores maximos de ozono superficial provenientes de 10 estaciones de monitoreo atmosferico en la Ciudad de Mexico y obtenidos durante un ano de muestreo. El pronostico para un dia se ajusta con la informacion meteorologica y de radiacion solar correspondiente a un periodo que antecede con al menos tres horas la ocurrencia esperada del valor maximo. Se compara la importancia relativa de la historia del proceso y de las condiciones meteorologicas previas para el pronostico. Finalmente se estima la probabilidad diaria de que un nivel normativo o preestablecido para contingencias de ozono sea rebasado.

  18. Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration

    DEFF Research Database (Denmark)

    Christensen, Steen; Doherty, John

    2008-01-01

    A significant practical problem with the pilot point method is to choose the location of the pilot points. We present a method that is intended to relieve the modeler from much of this responsibility. The basic idea is that a very large number of pilot points are distributed more or less uniforml...

  19. The Nature and Predictive Value of Mothers' Beliefs Regarding Infants' and Toddlers' TV/Video Viewing: Applying the Integrative Model of Behavioral Prediction.

    Science.gov (United States)

    Vaala, Sarah E

    2014-01-01

    Viewing television and video programming has become a normative behavior among US infants and toddlers. Little is understood about parents' decision-making about the extent of their young children's viewing, though numerous organizations are interested in reducing time spent viewing among infants and toddlers. Prior research has examined parents' belief in the educational value of TV/videos for young children and the predictive value of this belief for understanding infant/toddler viewing rates, though other possible salient beliefs remain largely unexplored. This study employs the integrative model of behavioral prediction (Fishbein & Ajzen, 2010) to examine 30 maternal beliefs about infants' and toddlers' TV/video viewing which were elicited from a prior sample of mothers. Results indicate that mothers tend to hold more positive than negative beliefs about the outcomes associated with young children's TV/video viewing, and that the nature of the aggregate set of beliefs is predictive of their general attitudes and intentions to allow their children to view, as well as children's estimated viewing rates. Analyses also uncover multiple dimensions within the full set of beliefs, which explain more variance in mothers' attitudes and intentions and children's viewing than the uni-dimensional index. The theoretical and practical implications of the findings are discussed.

  20. Modelo de previsão de value at risk utilizando volatilidade de longo prazo = Value at Risk prediction model using long term volatility

    Directory of Open Access Journals (Sweden)

    Vinicius Mothé Maia

    2016-07-01

    Full Text Available Tendo em vista a importância do Value at Risk (VaR como medida de risco para instituições financeiras e agências de risco, o presente estudo avaliou se o modelo ARLS é mais preciso no cálculo do VaR de longo prazo que os modelos tradicionais, dada sua maior adequação para a previsão da volatilidade. Considerando a utilização do VaR pelos agentes de mercado como medida de risco para o gerenciamento de portfólios é importante sua adequada mensuração. A partir de dados diários dos mercados de ações e cambial dos BRICS (Brasil, Rússia, Índia, China e África do Sul foram calculadas as volatilidades futuras para 15 dias, 1 mês e 3 meses. Em seguida, calculou-se as medidas tradicionais de avaliação da precisão do VaR. Os resultados sugerem a superioridade do modelo ARLS para a previsão da volatilidade cambial, capaz de prever corretamente o número de violações em 33% dos casos, enquanto os modelos tradicionais não obtiveram um bom desempenho. Com relação ao mercado acionário, os modelos GARCH e ARLS apresentaram desempenho similar. O modelo GARCH é superior considerando a perda média quadrática. Esses resultados apontam para a escolha do modelo ARLS no cálculo do VaR de portfólios cambiais devido a maior precisão alcançada. Ajuda assim os agentes de mercado a melhor gerirem o risco de suas carteiras. Em relação ao mercado acionário, em função do desempenho similar dos modelos GARCH e ARLS, o modelo GARCH é o mais indicado devido a sua maior simplicidade e fácil implementação computacional. Having in mind the importance of Value at Risk (VaR as a risk measure for financial institutions and rating agencies, this study evaluated whether the ARLS model is more accurate in the calculation of the long term VaR than the traditional models, considering it is more appropriate for predicting the long-term volatility. Due to the fact that VaR s being used for market players as a measure of risk for the portfolio

  1. A comprehensive subaxial cervical spine injury severity assessment model using numeric scores and its predictive value for surgical intervention.

    Science.gov (United States)

    Tsou, Paul M; Daffner, Scott D; Holly, Langston T; Shamie, A Nick; Wang, Jeffrey C

    2012-02-10

    Multiple factors contribute to the determination for surgical intervention in the setting of cervical spinal injury, yet to date no unified classification system exists that predicts this need. The goals of this study were twofold: to create a comprehensive subaxial cervical spine injury severity numeric scoring model, and to determine the predictive value of this model for the probability of surgical intervention. In a retrospective cohort study of 333 patients, neural impairment, patho-morphology, and available spinal canal sagittal diameter post-injury were selected as injury severity determinants. A common numeric scoring trend was created; smaller values indicated less favorable clinical conditions. Neural impairment was graded from 2-10, patho-morphology scoring ranged from 2-15, and post-injury available canal sagittal diameter (SD) was measured in millimeters at the narrowest point of injury. Logistic regression analysis was performed using the numeric scores to predict the probability for surgical intervention. Complete neurologic deficit was found in 39 patients, partial deficits in 108, root injuries in 19, and 167 were neurologically intact. The pre-injury mean canal SD was 14.6 mm; the post-injury measurement mean was 12.3 mm. The mean patho-morphology score for all patients was 10.9 and the mean neurologic function score was 7.6. There was a statistically significant difference in mean scores for neural impairment, canal SD, and patho-morphology for surgical compared to nonsurgical patients. At the lowest clinical score for each determinant, the probability for surgery was 0.949 for neural impairment, 0.989 for post-injury available canal SD, and 0.971 for patho-morphology. The unit odds ratio for each determinant was 1.73, 1.61, and 1.45, for neural impairment, patho-morphology, and canal SD scores, respectively. The subaxial cervical spine injury severity determinants of neural impairment, patho-morphology, and post-injury available canal SD have

  2. Predictive Value of the Model of End-Stage Liver Disease in Cirrhotic Patients with and without Spontaneous Bacterial Peritonitis

    Directory of Open Access Journals (Sweden)

    Bledar Kraja

    2012-01-01

    Full Text Available Objective. We aimed to assess the predictive value of the model of end-stage liver disease (MELD in hospitalized cirrhotic patients with and without spontaneous bacterial peritonitis (SBP and fatal outcome. Methods. A cross-sectional study included 256 consecutive patients (199 men and 57 women diagnosed with cirrhosis and ascites who were hospitalized at the University Hospital Center in Tirana from January 2008 to December 2009. SBP was defined as a neutrophil count of ≥250 cells/mm3 in ascitic fluid. MELD score was based on laboratory parameters determined by UNOS Internet site MELD calculator. Results. In multivariable-adjusted logistic regression models controlling for age, sex, diabetes, and etiology, there was evidence of a positive association of SBP with MELD score: the odds ratio (OR for SBP for one unit increment of MELD score was 1.06 (95% Cl = 1.02–1.09. MELD score was significantly higher in fatal cases than nonfatal patients (mean age-adjusted score was 32.7 versus 18.4 overall; 34.8 versus 18.0 in SBP patients, and 32.0 versus 18.5 in non-SBP patients; all P<0.001. Conclusions. In this Albanian sample of hospitalized cirrhotic patients, MELD score was confirmed as a significant predictor of both SBP and fatal outcome.

  3. Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model.

    Science.gov (United States)

    Wolc, Anna; Stricker, Chris; Arango, Jesus; Settar, Petek; Fulton, Janet E; O'Sullivan, Neil P; Preisinger, Rudolf; Habier, David; Fernando, Rohan; Garrick, Dorian J; Lamont, Susan J; Dekkers, Jack C M

    2011-01-21

    Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line. The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV. Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.

  4. Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones

    DEFF Research Database (Denmark)

    Hüsing, Anika; Fortner, Renée T; Kühn, Tilman

    2017-01-01

    PURPOSE: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models. EXPERIMENTAL DESIGN: We evaluated improvements in discrimination...

  5. A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

    DEFF Research Database (Denmark)

    Græsbøll, Kaare; Kirkeby, Carsten Thure; Nielsen, Søren Saxmose

    2017-01-01

    The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction of the fut...

  6. A non-parametric mixture model for genome-enabled prediction of genetic value for a quantitative trait.

    Science.gov (United States)

    Gianola, Daniel; Wu, Xiao-Lin; Manfredi, Eduardo; Simianer, Henner

    2010-10-01

    A Bayesian nonparametric form of regression based on Dirichlet process priors is adapted to the analysis of quantitative traits possibly affected by cryptic forms of gene action, and to the context of SNP-assisted genomic selection, where the main objective is to predict a genomic signal on phenotype. The procedure clusters unknown genotypes into groups with distinct genetic values, but in a setting in which the number of clusters is unknown a priori, so that standard methods for finite mixture analysis do not work. The central assumption is that genetic effects follow an unknown distribution with some "baseline" family, which is a normal process in the cases considered here. A Bayesian analysis based on the Gibbs sampler produces estimates of the number of clusters, posterior means of genetic effects, a measure of credibility in the baseline distribution, as well as estimates of parameters of the latter. The procedure is illustrated with a simulation representing two populations. In the first one, there are 3 unknown QTL, with additive, dominance and epistatic effects; in the second, there are 10 QTL with additive, dominance and additive × additive epistatic effects. In the two populations, baseline parameters are inferred correctly. The Dirichlet process model infers the number of unique genetic values correctly in the first population, but it produces an understatement in the second one; here, the true number of clusters is over 900, and the model gives a posterior mean estimate of about 140, probably because more replication of genotypes is needed for correct inference. The impact on inferences of the prior distribution of a key parameter (M), and of the extent of replication, was examined via an analysis of mean body weight in 192 paternal half-sib families of broiler chickens, where each sire was genotyped for nearly 7,000 SNPs. In this small sample, it was found that inference about the number of clusters was affected by the prior distribution of M. For a

  7. A Robust Statistical Model to Predict the Future Value of the Milk Production of Dairy Cows Using Herd Recording Data

    DEFF Research Database (Denmark)

    Græsbøll, Kaare; Kirkeby, Carsten Thure; Nielsen, Søren Saxmose

    2017-01-01

    The future value of an individual dairy cow depends greatly on its projected milk yield. In developed countries with developed dairy industry infrastructures, facilities exist to record individual cow production and reproduction outcomes consistently and accurately. Accurate prediction...... of the future value of a dairy cow requires further detailed knowledge of the costs associated with feed, management practices, production systems, and disease. Here, we present a method to predict the future value of the milk production of a dairy cow based on herd recording data only. The method consists...... presented in this paper is that it requires no specific knowledge of disease status or any other information beyond herd recorded milk yields, somatic cell counts, and reproductive status....

  8. Sound propagation in forests: A comparison of experimental results and values predicted by the Nord 2000 model

    DEFF Research Database (Denmark)

    Tarrero, A.I.; Martín, M.A.; González, J.

    2008-01-01

    to predict scattering effects when sound propagates in outdoor spaces with obstacles. The comparison of experimental results and predictions shows that the Nord 2000 model predicts the ground effect dip in forests with acceptable accuracy in about 60% of the cases if the flow resistivity of the ground......The purpose of the work described in this paper is twofold: (i) to present the results of an experimental investigation of the sound attenuation in different types of forest, and (ii) to validate a part of the Nord 2000 model. A number of measurements have been carried out in regular and irregular...... forests with trees with deciduous and evergreen leaves, different tree density, different trunk diameter, etc. The experimental results indicate that trees have a noticeable effect on sound propagation at medium and high frequencies at distances longer than 40m. The Nord 2000 model uses a simple algorithm...

  9. Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture.

    Science.gov (United States)

    Vallejo, Roger L; Leeds, Timothy D; Gao, Guangtu; Parsons, James E; Martin, Kyle E; Evenhuis, Jason P; Fragomeni, Breno O; Wiens, Gregory D; Palti, Yniv

    2017-02-01

    Previously, we have shown that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative that enables exploitation of within-family genetic variation. We compared three GS models [single-step genomic best linear unbiased prediction (ssGBLUP), weighted ssGBLUP (wssGBLUP), and BayesB] to predict genomic-enabled breeding values (GEBV) for BCWD resistance in a commercial rainbow trout population, and compared the accuracy of GEBV to traditional estimates of breeding values (EBV) from a pedigree-based BLUP (P-BLUP) model. We also assessed the impact of sampling design on the accuracy of GEBV predictions. For these comparisons, we used BCWD survival phenotypes recorded on 7893 fish from 102 families, of which 1473 fish from 50 families had genotypes [57 K single nucleotide polymorphism (SNP) array]. Naïve siblings of the training fish (n = 930 testing fish) were genotyped to predict their GEBV and mated to produce 138 progeny testing families. In the following generation, 9968 progeny were phenotyped to empirically assess the accuracy of GEBV predictions made on their non-phenotyped parents. The accuracy of GEBV from all tested GS models were substantially higher than the P-BLUP model EBV. The highest increase in accuracy relative to the P-BLUP model was achieved with BayesB (97.2 to 108.8%), followed by wssGBLUP at iteration 2 (94.4 to 97.1%) and 3 (88.9 to 91.2%) and ssGBLUP (83.3 to 85.3%). Reducing the training sample size to n = ~1000 had no negative impact on the accuracy (0.67 to 0.72), but with n = ~500 the accuracy dropped to 0.53 to 0.61 if the training and testing fish were full-sibs, and even substantially lower, to 0.22 to 0.25, when they were not full-sibs. Using progeny performance data, we showed that the accuracy of genomic predictions is substantially higher

  10. Strategic integration of in vivo cardiovascular models during lead optimization: predictive value of 4 models independent of species, route of administration, and influence of anesthesia.

    Science.gov (United States)

    Fryer, Ryan M; Harrison, Paul C; Muthukumarana, Akalushi; Nodop Mazurek, Suzanne G; Ng, Khing Jow; Chen, Rong Rhonda; Harrington, Kyle E; Dinallo, Roger M; Chi, Liguo; Reinhart, Glenn A

    2012-04-01

    The strategic integration of in vivo cardiovascular models is important during lead optimization to enable a wide therapeutic index for cardiovascular safety. However, under what conditions (eg, species, route of administration, anesthesia) studies should be performed to drive go/no-go is open to interpretation. Two compounds, torcetrapib and a novel steroid hormone mimetic (SHM-1121X), both with off-target cardiovascular liabilities, were profiled in 4 in vivo cardiovascular models. Overlapping plasma concentrations of torcetrapib were achieved in all models tested; values ranged from therapeutic to supratherapeutic. In anesthetized rats, intravenous torcetrapib elicited dose-dependent increases in mean arterial pressure (MAP; 2-18 mm Hg above vehicle during the low- and high-dose infusion), and in anesthetized dogs, torcetrapib increased MAP from 4 to 22 mm Hg. In conscious rats, a single oral dose of torcetrapib increased MAP from 10 to 18 mm Hg in the low-dose and high-dose groups, respectively, whereas in conscious dogs, MAP increased from 3 to 12 mm Hg. SHM-1121X produced marked hypotension in the same models. Pharmacokinetic-pharmacodynamic analysis demonstrated strong correlation across the models tested for both compounds. Results suggest that equivalency across models allows for flexibility to address key issues and enable go/no-go during lead optimization without concern for discordant results. The predictive value of each model was validated with torcetrapib and, when put into practice, led to a decisive no-go for SHM-1121X.

  11. Introduction of the conditional correlated Bernoulli model of similarity value distributions and its application to the prospective prediction of fingerprint search performance.

    Science.gov (United States)

    Vogt, Martin; Bajorath, Jürgen

    2011-10-24

    A statistical approach named the conditional correlated Bernoulli model is introduced for modeling of similarity scores and predicting the potential of fingerprint search calculations to identify active compounds. Fingerprint features are rationalized as dependent Bernoulli variables and conditional distributions of Tanimoto similarity values of database compounds given a reference molecule are assessed. The conditional correlated Bernoulli model is utilized in the context of virtual screening to estimate the position of a compound obtaining a certain similarity value in a database ranking. Through the generation of receiver operating characteristic curves from cumulative distribution functions of conditional similarity values for known active and random database compounds, one can predict how successful a fingerprint search might be. The comparison of curves for different fingerprints makes it possible to identify fingerprints that are most likely to identify new active molecules in a database search given a set of known reference molecules.

  12. QSPR Models for Predicting Log Pliver Values for Volatile Organic Compounds Combining Statistical Methods and Domain Knowledge

    Directory of Open Access Journals (Sweden)

    Mónica F. Díaz

    2012-12-01

    Full Text Available Volatile organic compounds (VOCs are contained in a variety of chemicals that can be found in household products and may have undesirable effects on health. Thereby, it is important to model blood-to-liver partition coefficients (log Pliver for VOCs in a fast and inexpensive way. In this paper, we present two new quantitative structure-property relationship (QSPR models for the prediction of log Pliver, where we also propose a hybrid approach for the selection of the descriptors. This hybrid methodology combines a machine learning method with a manual selection based on expert knowledge. This allows obtaining a set of descriptors that is interpretable in physicochemical terms. Our regression models were trained using decision trees and neural networks and validated using an external test set. Results show high prediction accuracy compared to previous log Pliver models, and the descriptor selection approach provides a means to get a small set of descriptors that is in agreement with theoretical understanding of the target property.

  13. 基于灰色GA-LM-BP模型的CODMn预测%Prediction of CODMn value based on the grey GA-LM-BP model

    Institute of Scientific and Technical Information of China (English)

    崔雪梅

    2013-01-01

    Due to the large fitting-errors of grey GM(1,1) model and the weak generalization ability of LM-BP neural network, a model of grey GA-LM-BP network was proposed in this paper. The grey GM(1,1) model was used to predict data and obtain residual errors. After the residual errors were fitted, tested and forecasted with LM-BP neural network, more reasonable predicted values can be obtained by correcting the predicted values of the GM(1,1) model. In the meantime, the initialized weights and threshold of LM-BP neural network were optimized with the genetic algorithm ( GA) . The grey GA-LM-BP model was then used to predict the CODMn values at the Xiaogan segment of Lunhe River. Since the prediction errors were found to be less than 2.33%, the accuracy of the model is considered to be reasonable. The model can be used to predict the CODMn values and the water quality early warning.%针对灰色GM(1,1)模型拟合误差较大和LM-BP神经网络泛化能力不强的问题,提出了灰色GA-LM-BP模型,该模型采用灰色GM(1,1)模型预测数据并得到其残差,利用LM-BP神经网络对残差进行拟合、测试、预测后,对灰色GM(1,1)模型的数据预测值进行修正从而得到较合理的预测值,并运用遗传算法对LM-BP神经网络的初始权值和阈值进行优化。运用该模型对伦河孝感段的CODMn进行了预测,预测误差不超过2.33%,表明模型的预测数据是合理的,可用于CODMn的预测和水质预警预报。

  14. Probabilistic maximum-value wind prediction for offshore environments

    DEFF Research Database (Denmark)

    Staid, Andrea; Pinson, Pierre; Guikema, Seth D.

    2015-01-01

    , and probabilistic forecasts result in greater value to the end-user. The models outperform traditional baseline forecast methods and achieve low predictive errors on the order of 1–2 m s−1. We show the results of their predictive accuracy for different lead times and different training methodologies....... statistical models to predict the full distribution of the maximum-value wind speeds in a 3 h interval. We take a detailed look at the performance of linear models, generalized additive models and multivariate adaptive regression splines models using meteorological covariates such as gust speed, wind speed......, convective available potential energy, Charnock, mean sea-level pressure and temperature, as given by the European Center for Medium-Range Weather Forecasts forecasts. The models are trained to predict the mean value of maximum wind speed, and the residuals from training the models are used to develop...

  15. A Model for Predicting Chlorophyll Content in Cassava Based on SPAD Values%基于SPAD值的木薯叶绿素含量预测模型

    Institute of Scientific and Technical Information of China (English)

    杨生红; 杨重法; 辛阳; 刘传军; 陈慧娟

    2012-01-01

    In order to investigate a non-destructive and quick measuring method for chlorophyll content in cassava, a model for predicting total chlorophyll content that was based on SPAD values,specific leaf area, chlorophyll content was established. The results indicated that the influence of chlorophyll A to SPAD values was larger than chlorophyll B; specific leaf area and leaf position and SPAD values and chlorophyll content showed significant correlation; added specific leaf area and leaf position, the accuracy of the model for predicting total chlorophyll content was improved; based on SPAD values,specific leaf area and leaf position, the model for predicting total chlorophyll content was established as C=-4.51+0.092 5 Sp+0.039 9 Sa+0.145 0 (Lp).%为了探讨木薯叶绿素含量的非破坏性快速测定方法,在测量叶片SPAD值、比叶面积和叶绿素含量的基础上构建通过SPAD值预测总叶绿素含量的数学模型.结果表明,叶绿素a对于SPAD值的贡献率大于叶绿素b:比叶面积和叶位显著影响SPAD值与总叶绿素含量的关系,在SPAD值预测总叶绿素含量的模型中导入比叶面积和叶位2个自变量可提高预测总叶绿素含量的精度;基于SPAD值、比叶面积、叶位预测总叶绿素含量的模型为C=-4.51+0.0925 Spv+0.0399 Sa+0.145 0(Lp).

  16. Inactivation model equations and their associated parameter values obtained under static acid stress conditions cannot be used directly for predicting inactivation under dynamic conditions.

    Science.gov (United States)

    Janssen, M; Verhulst, A; Valdramidis, V; Devlieghere, F; Van Impe, J F; Geeraerd, A H

    2008-11-30

    Organic acids (e.g., lactic acid, acetic acid and citric acid) are popular preservatives. In this study, the Listeria innocua inactivation is investigated under dynamic conditions of pH and undissociated lactic acid ([LaH]). A combined primary (Weibull-type) and secondary model developed for the L. innocua inactivation under static conditions [Janssen, M., Geeraerd, A.H., Cappuyns, A., Garcia-Gonzalez, L., Schockaert, G., Van Houteghem, N., Vereecken, K.M., Debevere, J., Devlieghere, F., Van Impe, J.F., 2007. Individual and combined effects of pH and lactic acid concentration on L. innocua inactivation: development of a predictive model and assessment of experimental variability. Applied and Environmental Microbiology 73(5), 1601-1611] was applied to predict the microbial inactivation under dynamic conditions. Because of its non-autonomous character, two approaches were proposed for the application of the Weibull-type model to dynamic conditions. The results quantitatively indicated that the L. innocua cell population was able to develop an induced acid stress resistance under dynamic conditions of pH and [LaH]. From a modeling point of view, it needs to be stressed that (i) inactivation model equations and associated parameter values, derived under static conditions, may not be suitable for use as such under dynamic conditions, and (ii) non-autonomous dynamic models reveal additional technical intricacies in comparison with autonomous models.

  17. QSPR models for prediction of the soil sorption coefficient (log KOC) values of 209 polychlorinated trans-azobenzenes (PCt-ABs).

    Science.gov (United States)

    Wilczyńska-Piliszek, Agata J; Piliszek, Sławomir; Falandysz, Jerzy

    2012-01-01

    The values of the soil sorption coefficient (K(OC)) have been computed for 209 environmentally relevant trans polychlorinated azobenzenes (PCABs) lacking experimental partitioning data. The quantitative structure-property relationship (QSPR) approach and artificial neural networks (ANN) predictive ability used in models based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6-311++G** basis set and of the semi-empirical quantum chemistry method for property parameterization (PM6) of the molecular orbital package (MOPAC). An experimentally available data on physical and chemical properties of PCDD/Fs and PCBs were used as reference data for the QSPR models and ANNs predictions in this study. Both calculation methods gave similar results in term of absolute log K(OC) values, while the PM6 model generated in the MOPAC was a much more efficient compared to the DFT model in GAUSSIAN. The estimated values of log K(OC) varied between 4.93 and 5.62 for mono-, 5.27 and 7.46 for di-, 6.46 and 8.09 for tri-, 6.65 and 9.11 for tetra-, 6.75 and 9.68 for penta-, 6.44 and 10.24 for hexa-, 7.00 and 10.36 for hepta-, 7.09 and 9.82 octa-, 8.94 and 9.71 for nona-Ct-ABs, and 9.26 and 9.34 for deca-Ct-AB. Because of high log K(OC) values PCt-ABs could be classified as compounds with high affinity to the particles of soil, sediments and organic matter.

  18. Predicting Customer Lifetime Value in Multi-Service Industries

    NARCIS (Netherlands)

    A.C.D. Donkers (Bas); P.C. Verhoef (Peter); M.G. de Jong (Martijn)

    2003-01-01

    textabstractCustomer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in

  19. Predicting Customer Lifetime Value in Multi-Service Industries

    NARCIS (Netherlands)

    A.C.D. Donkers (Bas); P.C. Verhoef (Peter); M.G. de Jong (Martijn)

    2003-01-01

    textabstractCustomer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-

  20. Predicting Customer Lifetime Value in Multi-Service Industries

    NARCIS (Netherlands)

    A.C.D. Donkers (Bas); P.C. Verhoef (Peter); M.G. de Jong (Martijn)

    2003-01-01

    textabstractCustomer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-

  1. The use of predicted values for item parameters in item response theory models: An application in intelligence tests

    NARCIS (Netherlands)

    Matteucci, M.; S. Mignani, Prof.; Veldkamp, Bernard P.

    2012-01-01

    In testing, item response theory models are widely used in order to estimate item parameters and individual abilities. However, even unidimensional models require a considerable sample size so that all parameters can be estimated precisely. The introduction of empirical prior information about candi

  2. Modeling and predicting pKa values of mono-hydroxylated polychlorinated biphenyls (HO-PCBs) and polybrominated diphenyl ethers (HO-PBDEs) by local molecular descriptors.

    Science.gov (United States)

    Yu, Haiying; Wondrousch, Dominik; Yuan, Quan; Lin, Hongjun; Chen, Jianrong; Hong, Huachang; Schüürmann, Gerrit

    2015-11-01

    Hydroxylated polychlorinated biphenyls (HO-PCBs) and polybrominated diphenyl ethers (HO-PBDEs) are attracting considerable concerns because of their multiple endocrine-disrupting effects and wide existence in environment and organisms. The hydroxyl groups enable these chemicals to be ionizable, and dissociation constant, pKa, becomes an important parameter for investigating their environmental behavior and biological activities. In this study, a new pKa prediction model was developed using local molecular descriptors. The dataset contains 21 experimental pKa values of HO-PCBs and HO-PBDEs. The optimized geometries by ab initio HF/6-31G(∗∗) algorithm were used to calculate the site-specific molecular readiness to accept or donate electron charges. The developed model obtained good statistical performance, which significantly outperformed commercial software ACD and SPARC. Mechanism analysis indicates that pKa values increase with the charge-limited donor energy EQocc on hydroxyl oxygen atom and decrease with the energy-limited acceptor charge QEvac on hydroxyl hydrogen atom. The regression model was also applied to calculate pKa values for all 837 mono-hydroxylated PCBs and PBDEs in each class, aiming to provide basic data for the ecological risk assessment of these chemicals.

  3. Simulation model estimates of test accuracy and predictive values for the Danish Salmonella surveillance program in dairy herds

    DEFF Research Database (Denmark)

    Warnick, L.D.; Nielsen, L.R.; Nielsen, Jens

    2006-01-01

    antibody measurements for infected and noninfected herds were determined from field study data. Herd infection was defined as having either >= 1 Salmonella culture-positive fecal sample or >= 5% within-herd prevalence based on antibody measurements in serum or milk from individual animals. No distinction......The Danish government and cattle industry instituted a Salmonella surveillance program in October 2002 to help reduce Salmonella enterica subsp. enterica serotype Dublin (S. Dublin) infections. All dairy herds are tested by measuring antibodies in bulk tank milk at 3-month intervals. The program...... is based on a well-established ELISA, but the overall test program accuracy and misclassification was not previously investigated. We developed a model to simulate repeated bulk tank milk antibody measurements for dairy herds conditional on true infection status. The distributions of bulk tank milk...

  4. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...... to standard ATSM estimations. The model's excess return predictions are unbiased, produce regression R2s beyond those reported in the literature, exhibit high forecast accuracy, and allow to generate positive bond portfolio excess returns in- and out-of-sample. Nevertheless, these models cannot beat...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia....

  5. The value of intravoxel incoherent motion model-based diffusion-weighted imaging for outcome prediction in resin-based radioembolization of breast cancer liver metastases

    Directory of Open Access Journals (Sweden)

    Pieper CC

    2016-07-01

    remission or stable disease (responders according to RECIST survived longer than nonresponders (7.2 [range 2.6–54.9] vs 2.6 [range 1.5–17.4] months, P<0.0001. An ECOG status ≤1 resulted in longer median OS than >1 (7.6 [range 2.6–54.9] vs 1.7 [range 1.5–4.5] months, P<0.0001. Pretreatment IVIM parameters and the other clinical characteristics were not associated with OS. Classification by f’-value changes and ECOG status remained as independent predictors of OS on multivariate analysis, while RECIST response and D’-value changes did not predict survival.Conclusion: Following radioembolization of breast cancer liver metastases, early changes in the IVIM model-derived perfusion fraction f’ and baseline ECOG score were predictive of patient outcome, and may thus help to guide treatment strategy. Keywords: MRI, DWI, IVIM, breast cancer, selective internal radiation therapy, radioem­bolization

  6. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    2016-01-01

    This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information...... otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast...... evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states....

  7. Value of Bulk Heat Flux Parameterizations for Ocean SST Prediction

    Science.gov (United States)

    2008-03-01

    Value of bulk heat flux parameterizations for ocean SST prediction Alan J. Wallcraft a,⁎, A. Birol Kara a, Harley E. Hurlburt a, Eric P. Chassignet b...G., Doney, S.C., McWilliams , J.C., 1997. Sensitivity to surface forcing and boundary layer mixing in a global ocean model: annual-mean climatology. J

  8. The Economic Value of Predicting Stock Index Returns and Volatility

    NARCIS (Netherlands)

    W.A. Marquering (Wessel); M.J.C.M. Verbeek (Marno)

    2001-01-01

    textabstractIn this paper, we analyze the economic value of predicting stock index returns as well as volatility. On the basis of simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from

  9. The Economic Value of Predicting Stock Index Returns and Volatility

    NARCIS (Netherlands)

    Marquering, W.; Verbeek, M.J.C.M.

    2000-01-01

    In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954-1998, we

  10. Value Preferences Predicting Narcissistic Personality Traits in Young Adults

    Science.gov (United States)

    Gungor, Ibrahim Halil; Eksi, Halil; Aricak, Osman Tolga

    2012-01-01

    This study aimed at showing how the value preferences of young adults could predict the narcissistic characteristics of young adults according to structural equation modeling. 133 female (59.6%) and 90 male (40.4%), total 223 young adults participated the study (average age: 25.66, ranging from 20 to 38). Ratio group sampling method was used while…

  11. 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.

  12. Initial value predictability of intrinsic oceanic modes and implications for decadal prediction over North America

    Energy Technology Data Exchange (ETDEWEB)

    Branstator, Grant [National Center for Atmospheric Research, Boulder, CO (United States)

    2014-12-09

    The overall aim of our project was to quantify and characterize predictability of the climate as it pertains to decadal time scale predictions. By predictability we mean the degree to which a climate forecast can be distinguished from the climate that exists at initial forecast time, taking into consideration the growth of uncertainty that occurs as a result of the climate system being chaotic. In our project we were especially interested in predictability that arises from initializing forecasts from some specific state though we also contrast this predictability with predictability arising from forecasting the reaction of the system to external forcing – for example changes in greenhouse gas concentration. Also, we put special emphasis on the predictability of prominent intrinsic patterns of the system because they often dominate system behavior. Highlights from this work include: • Development of novel methods for estimating the predictability of climate forecast models. • Quantification of the initial value predictability limits of ocean heat content and the overturning circulation in the Atlantic as they are represented in various state of the art climate models. These limits varied substantially from model to model but on average were about a decade with North Atlantic heat content tending to be more predictable than North Pacific heat content. • Comparison of predictability resulting from knowledge of the current state of the climate system with predictability resulting from estimates of how the climate system will react to changes in greenhouse gas concentrations. It turned out that knowledge of the initial state produces a larger impact on forecasts for the first 5 to 10 years of projections. • Estimation of the predictability of dominant patterns of ocean variability including well-known patterns of variability in the North Pacific and North Atlantic. For the most part these patterns were predictable for 5 to 10 years. • Determination of

  13. The value of multivariate model sophistication

    DEFF Research Database (Denmark)

    Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco

    2014-01-01

    We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances....

  14. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...... the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... to standard ATSM estimations. The model's excess return predictions are unbiased, produce regression R2s beyond those reported in the literature, exhibit high forecast accuracy, and allow to generate positive bond portfolio excess returns in- and out-of-sample. Nevertheless, these models cannot beat...

  15. Predicting breeding values in animals by kalman filter

    DEFF Research Database (Denmark)

    Karacaören, Burak; Janss, Luc; Kadarmideen, Haja

    2012-01-01

    The aim of this study was to investigate usefulness of Kalman Filter (KF) Random Walk methodology (KF-RW) for prediction of breeding values in animals. We used body condition score (BCS) from dairy cattle for illustrating use of KF-RW. BCS was measured by Swiss Holstein Breeding Association during...... May 2004-March 2005 for 7 times approximately at monthly intervals from dairy cows (n=80) stationed at the Chamau research farm of Eidgenössische Technische Hochschule (ETH), Switzerland. Benefits of KF were demonstrated using random walk models via simulations. Breeding values were predicted over...... for variance components were found (with standard errors) 0.03 (0.006) for animal genetic variance 0.04 (0.007) for permanent environmental variance and 0.21 (0.02) for error variance. Since KF gives online estimation of breeding values and does not need to store or invert matrices, this methodology could...

  16. The relative value of operon predictions

    NARCIS (Netherlands)

    Brouwer, Rutger W. W.; Kuipers, Oscar P.; van Hijum, Sacha A. F. T.

    2008-01-01

    For most organisms, computational operon predictions are the only source of genome-wide operon information. Operon prediction methods described in literature are based on (a combination of) the following five criteria: (i) intergenic distance, (ii) conserved gene clusters, (iii) functional relation,

  17. Predicting community sensitivity to ozone, using Ellenberg Indicator values

    Energy Technology Data Exchange (ETDEWEB)

    Jones, M. Laurence M. [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: lj@ceh.ac.uk; Hayes, Felicity [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: fhay@ceh.ac.uk; Mills, Gina [Centre for Ecology and Hydrology Bangor, Orton Building, Deiniol Road, Bangor, Gwynedd LL57 2UP (United Kingdom)]. E-mail: gmi@ceh.ac.uk; Sparks, Tim H. [Centre for Ecology and Hydrology Monks Wood, Abbots Ripton, Huntingdon, Cambridgeshire PE28 2LS (United Kingdom)]. E-mail: ths@ceh.ac.uk; Fuhrer, Juerg [Swiss Federal Research Station for Agroecology and Agriculture (FAL), Air Pollution/Climate Group, Reckenholzstrasse 191, CH-8046 Zurich (Switzerland)]. E-mail: juerg.fuhrer@fal.admin.ch

    2007-04-15

    This paper develops a regression-based model for predicting changes in biomass of individual species exposed to ozone (RS{sub p}), based on their Ellenberg Indicator values. The equation (RS{sub p}=1.805-0.118Light-0.135Salinity) underpredicts observed sensitivity but has the advantage of widespread applicability to almost 3000 European species. The model was applied to grassland communities to develop two further predictive tools. The first tool, percentage change in biomass (ORI%) was tested on data from a field-based ozone exposure experiment and predicted a 27% decrease in biomass over 5 years compared with an observed decrease of 23%. The second tool, an index of community sensitivity to ozone (CORI), was applied to 48 grassland communities and suggests that community sensitivity to ozone is primarily species-driven. A repeat-sampling routine showed that nine species were the minimum requirement to estimate CORI within 5%.

  18. Predicting total solar irradiation values using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mubiru, J. [Department of Physics, Makerere University, P.O. Box 7062, Kampala (Uganda)

    2008-10-15

    This study explores the possibility of developing an artificial neural networks model that could be used to predict monthly average daily total solar irradiation on a horizontal surface for locations in Uganda based on geographical and meteorological data: latitude, longitude, altitude, sunshine duration, relative humidity and maximum temperature. Results have shown good agreement between the predicted and measured values of total solar irradiation. A correlation coefficient of 0.997 was obtained with mean bias error of 0.018 MJ/m{sup 2} and root mean square error of 0.131 MJ/m{sup 2}. Overall, the artificial neural networks model predicted with an accuracy of 0.1% of the mean absolute percentage error. (author)

  19. Predictive models in urology.

    Science.gov (United States)

    Cestari, Andrea

    2013-01-01

    Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

  20. Use of simulation modeling to estimate herd-level sensitivity, specificity, and predictive values of diagnostic tests for detection of tuberculosis in cattle.

    Science.gov (United States)

    Norby, Bo; Bartlett, Paul C; Grooms, Daniel L; Kaneene, John B; Bruning-Fann, Colleen S

    2005-07-01

    To estimate herd-level sensitivity (HSe), specificity (HSp), and predictive values for a positive (HPVP) and negative (HPVN) test result for several testing scenarios for detection of tuberculosis in cattle by use of simulation modeling. Empirical distributions of all herds (15,468) and herds in a 10-county area (1,016) in Michigan. 5 test scenarios were simulated: scenario 1, serial interpretation of the caudal fold tuberculin (CFT) test and comparative cervical test (CCT); scenario 2, serial interpretation of the CFT test and CCT, microbial culture for mycobacteria, and polymerase chain reaction assay; scenario 3, same as scenario 2 but specificity was fixed at 1.0; and scenario 4, sensitivity was 0.9 (scenario 4a) or 0.95 (scenario 4b), and specificity was fixed at 1.0. Estimates for HSe were reasonably high, ranging between 0.712 and 0.840. Estimates for HSp were low when specificity was not fixed at 1.0. Estimates of HPVP were low for scenarios 1 and 2 (0.042 and 0.143, respectively) but increased to 1.0 when specificity was fixed at 1.0. The HPVN remained high for all 5 scenarios, ranging between 0.995 and 0.997. As herd size increased, HSe increased and HSp and HPVP decreased. However, fixing specificity at 1.0 had only minor effects on HSp and HPVN, but HSe was low when the herd size was small. Tests used for detecting cattle herds infected with tuberculosis work well on a herd basis. Herds with < approximately 100 cattle should be tested more frequently or for a longer duration than larger herds to ensure that these small herds are free of tuberculosis.

  1. Neural networks for predicting breeding values and genetic gains

    Directory of Open Access Journals (Sweden)

    Gabi Nunes Silva

    2014-12-01

    Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

  2. Coal Calorific Value Prediction Based on Projection Pursuit Principle

    Directory of Open Access Journals (Sweden)

    QI Minfang

    2012-10-01

    Full Text Available The calorific value of coal is an important factor for the economic operation of coal-fired power plant. However, calorific value is tremendous difference between the different coal, and even if coal is from the same mine. Restricted by the coal market, most of coal fired power plants can not burn the designed-coal by now in China. The properties of coal as received are changing so frequently that pulverized coal firing is always with the unexpected condition. Therefore, the researches on the prediction of calorific value of coal have a profound significance for the economic operation of power plants. Aiming at the problem of uncertainty of coal calorific value, establish a soft measurement model for calorific value of coal based on projection pursuit principle combined with genetic algorithm to optimize parameters, and support vector machine algorithm. It is shown by an example that the model has a stronger objectivity, effective and feasible for avoiding the disadvantage of the artificially decided weights of feature indexes. The model could provide a good guidance for the calculation of the coal calorific value and optimization operation of coal-fired power plants.  

  3. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... paper, we will present an introduction to the theory and application of MPC with Matlab codes written to ... model predictive control, linear systems, discrete-time systems, ... and then compute very rapidly for this open-loop con-.

  4. Brand Value - Proposed Model Danrise

    Directory of Open Access Journals (Sweden)

    Daniel Nascimento Pereira da Silva

    2011-12-01

    Full Text Available Brands have taken dominance in the strategies of enterprises once they are able to generate feelings, sensations and emotions in their clients. These values, value for the enterprises and for the brands themselves, are not measurable. A strong brand configures itself as the highest representative of an enterprise and the brand is regarded as an asset of the enterprise. The evolution of a brand, as an intangible and strategic asset, becomes more vitally important for the enterprises, as a way of maximizing the results. This need, whether of the market or the enterprises, justifies the direction of the research for this vector – the value of the brand. A main objective of the research is to present a new model of brand evaluation. This model is supported by a tangible and intangible aspects and the intangible aspect, evaluates the knowledge and capacity of their managers and workers to build a brand with value through the correct ordering of the priorities of the dimensions of the proposed model. The model was tested on the brand ‗Blue Rise.‘ 

  5. Predicting hospital cost in CKD patients through blood chemistry values

    Directory of Open Access Journals (Sweden)

    Bessette Russell W

    2011-12-01

    Full Text Available Abstract Background Controversy exists in predicting costly hospitalization in patients with chronic kidney disease and co-morbid conditions. We therefore tested associations between serum chemistry values and the occurrence of in-patient hospital costs over a thirteen month study period. Secondarily, we derived a linear combination of variables to estimate probability of such occurrences in any patient. Method We calculated parsimonious values for select variables associated with in-patient hospitalization and compared sensitivity and specificity of these models to ordinal staging of renal disease. Data from 1104 de-identified patients which included 18 blood chemistry observations along with complete claims data for all medical expenses. We employed multivariable logistic regression for serum chemistry values significantly associated with in-patient hospital costs exceeding $3,000 in any single month and contrasted those results to other models by ROC area curves. Results The linear combination of weighted Z scores for parathyroid hormone, phosphorus, and albumin correlated with in-patient hospital care at p Conclusion Further study is justified to explore indices that predict costly hospitalization. Such metrics could assist Accountable Care Organizations in evaluating risk adjusted compensation for providers.

  6. Nominal model predictive control

    OpenAIRE

    Grüne, Lars

    2013-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  7. Nominal Model Predictive Control

    OpenAIRE

    Grüne, Lars

    2014-01-01

    5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...

  8. Numerical weather prediction model tuning via ensemble prediction system

    Science.gov (United States)

    Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

    2011-12-01

    This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

  9. Multifractal Value at Risk model

    Science.gov (United States)

    Lee, Hojin; Song, Jae Wook; Chang, Woojin

    2016-06-01

    In this paper new Value at Risk (VaR) model is proposed and investigated. We consider the multifractal property of financial time series and develop a multifractal Value at Risk (MFVaR). MFVaR introduced in this paper is analytically tractable and not based on simulation. Empirical study showed that MFVaR can provide the more stable and accurate forecasting performance in volatile financial markets where large loss can be incurred. This implies that our multifractal VaR works well for the risk measurement of extreme credit events.

  10. 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....

  11. 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.

  12. One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

    OpenAIRE

    Jin Xiao; Bing Zhu; Geer Teng; Changzheng He; Dunhu Liu

    2014-01-01

    Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classif...

  13. PV panel model based on datasheet values

    DEFF Research Database (Denmark)

    Sera, Dezso; Teodorescu, Remus; Rodriguez, Pedro

    2007-01-01

    This work presents the construction of a model for a PV panel using the single-diode five-parameters model, based exclusively on data-sheet parameters. The model takes into account the series and parallel (shunt) resistance of the panel. The equivalent circuit and the basic equations of the PV cell....../panel in Standard Test Conditions (STC) are shown, as well as the parameters extraction from the data-sheet values. The temperature dependence of the cell dark saturation current is expressed with an alternative formula, which gives better correlation with the datasheet values of the power temperature dependence....... Based on these equations, a PV panel model, which is able to predict the panel behavior in different temperature and irradiance conditions, is built and tested....

  14. Achieving Value in Primary Care: The Primary Care Value Model.

    Science.gov (United States)

    Rollow, William; Cucchiara, Peter

    2016-03-01

    The patient-centered medical home (PCMH) model provides a compelling vision for primary care transformation, but studies of its impact have used insufficiently patient-centered metrics with inconsistent results. We propose a framework for defining patient-centered value and a new model for value-based primary care transformation: the primary care value model (PCVM). We advocate for use of patient-centered value when measuring the impact of primary care transformation, recognition, and performance-based payment; for financial support and research and development to better define primary care value-creating activities and their implementation; and for use of the model to support primary care organizations in transformation.

  15. Performance Evaluation of Different Data Value Prediction Schemes

    Institute of Scientific and Technical Information of China (English)

    Yong Xiao; Xing-Ming Zhou

    2005-01-01

    Data value prediction has been widely accepted as an effective mechanism to break data hazards for high performance processor design. Several works have reported promising performance potential. However, there is hardly enough information that is presented in a clear way about performance comparison of these prediction mechanisms. This paper investigates the performance impact of four previously proposed value predictors, namely last value predictor, stride value predictor, two-level value predictor and hybrid (stride+two-level) predictor. The impact of misprediction penalty,which has been frequently ignored, is discussed in detail. Several other implementation issues, including instruction window size, issue width and branch predictor are also addressed and simulated. Simulation results indicate that data value predictors act differently under different configurations. In some cases, simpler schemes may be more beneficial than complicated ones.In some particular cases, value prediction may have negative impact on performance.

  16. Prediction of cereal feed value using spectroscopy and chemometrics

    DEFF Research Database (Denmark)

    Jørgensen, Johannes Ravn; Gislum, René

    2009-01-01

    value, the prediction error has to be compared with the error in the chemical analysis. Prediction error by NIRS prediction of feed value is above the error of the chemical measurement. The conclusion is that it is possible to predict the feed value in cereals with NIRS quickly and cheaply......, but prediction error with this method is relatively high in relation to a chemical determination of the feed value. A further improvement of the NIRS method will probably be possible with the addition of further references (several years, varieties and sites), which is therefore recommended. Likewise...... in-situ. Near infra-red reflection spectroscopy (NIRS) is appropriate as a standard analysis of dry matter, total N and starch in grains, since it is rapid (approximately 1 minute per measurement of a ground test) and cheap. NIRS is therefore appropriate as a quick method for the determination...

  17. Macrophage inflammatory protein-1α shows predictive value as a risk marker for subjects and sites vulnerable to bone loss in a longitudinal model of aggressive periodontitis.

    Directory of Open Access Journals (Sweden)

    Daniel H Fine

    Full Text Available Improved diagnostics remains a fundamental goal of biomedical research. This study was designed to assess cytokine biomarkers that could predict bone loss (BL in localized aggressive periodontitis. 2,058 adolescents were screened. Two groups of 50 periodontally healthy adolescents were enrolled in the longitudinal study. One group had Aggregatibacter actinomycetemcomitans (Aa, the putative pathogen, while the matched cohort did not. Cytokine levels were assessed in saliva and gingival crevicular fluid (GCF. Participants were sampled, examined, and radiographed every 6 months for 2-3 years. Disease was defined as radiographic evidence of BL. Saliva and GCF was collected at each visit, frozen, and then tested retrospectively after detection of BL. Sixteen subjects with Aa developed BL. Saliva from Aa-positive and Aa-negative healthy subjects was compared to subjects who developed BL. GCF was collected from 16 subjects with BL and from another 38 subjects who remained healthy. GCF from BL sites in the 16 subjects was compared to healthy sites in these same subjects and to healthy sites in subjects who remained healthy. Results showed that cytokines in saliva associated with acute inflammation were elevated in subjects who developed BL (i.e., MIP-1α MIP-1β IL-α, IL-1β and IL-8; p<0.01. MIP-1α was elevated 13-fold, 6 months prior to BL. When MIP-1α levels were set at 40 pg/ml, 98% of healthy sites were below that level (Specificity; whereas, 93% of sites with BL were higher (Sensitivity, with comparable Predictive Values of 98%; p<0.0001; 95% C.I. = 42.5-52.7. MIP-1α consistently showed elevated levels as a biomarker for BL in both saliva and GCF, 6 months prior to BL. MIP-1α continues to demonstrate its strong candidacy as a diagnostic biomarker for both subject and site vulnerability to BL.

  18. 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...

  19. Predicting Proenvironmental Behavior Cross-Nationally: Values, the Theory of Planned Behavior, and Value-Belief-Norm Theory

    Science.gov (United States)

    Oreg, Shaul; Katz-Gerro, Tally

    2006-01-01

    This article builds on Ajzen's theory of planned behavior and on Stern et al.'s value-belief-norm theory to propose and test a model that predicts proenvironmental behavior. In addition to relationships between beliefs, attitudes, and behaviors, we incorporate Inglehart's postmaterialist and Schwartz's harmony value dimensions as contextual…

  20. Hyperbolic value addition and general models of animal choice.

    Science.gov (United States)

    Mazur, J E

    2001-01-01

    Three mathematical models of choice--the contextual-choice model (R. Grace, 1994), delay-reduction theory (N. Squires & E. Fantino, 1971), and a new model called the hyperbolic value-added model--were compared in their ability to predict the results from a wide variety of experiments with animal subjects. When supplied with 2 or 3 free parameters, all 3 models made fairly accurate predictions for a large set of experiments that used concurrent-chain procedures. One advantage of the hyperbolic value-added model is that it is derived from a simpler model that makes accurate predictions for many experiments using discrete-trial adjusting-delay procedures. Some results favor the hyperbolic value-added model and delay-reduction theory over the contextual-choice model, but more data are needed from choice situations for which the models make distinctly different predictions.

  1. Predictive value of prostate-specific antigen for prostate cancer

    DEFF Research Database (Denmark)

    Shepherd, Leah; Borges, Alvaro Humberto; Ravn, Lene

    2014-01-01

    and predictive value of PSA in HIV+ men. METHODS: Men with PCa (n=21) and up to two matched controls (n=40) with prospectively stored plasma samples before PCa (or matched date in controls) were selected. Cases and controls were matched on date of first and last sample, age, region of residence and CD4 count...... at first sample date. Total PSA (tPSA), free PSA (fPSA), testosterone and sex hormone binding globulin (SHBG) were measured. Conditional logistic regression models investigated associations between markers and PCa. Sensitivity and specificity of using tPSA >4 µg/L to predict PCa was calculated. Mixed....../L, respectively, but were stable in controls, with a median at last sample of 0.8 (0.5-1.4) and 0.2 (0.2-0.4) µg/L (Figure). Higher levels of tPSA and fPSA were associated with higher odds of PCa at first sample [OR for 2-fold higher 4.7 (CI: 1.7-12.9) and 5.4 (1.7-17.4)]. Elevated tPSA values in cases were...

  2. [Predictive value of Hodgkin's lymphoma tumor burden in present].

    Science.gov (United States)

    Kulyova, S A; Karitsky, A P

    2014-01-01

    Today approximately 70% of patients with Hodgkin lymphoma can be cured with the combined-modality therapy. Tumor burden, the importance of which was demonstrated 15 years ago for the first time, is a powerful prognostic factor. Data of literature of representations on predictive value of Hodgkin's lymphoma tumor burden are shown in the article. The difficult immunological relations between tumor cells and reactive ones lead to development of the main symptoms. Nevertheless, the collective sign of tumor burden shows the greatest influence on survival and on probability of resistance, which relative risk can be predicted on this variable and treatment program. Patients with bulky disease need escalated therapy with high-dose chemotherapy. Integration into predictive models of the variable will change an expected contribution of clinical and laboratory parameters in the regression analyses constructed on patients with Hodgkin's lymphoma. Today the role of diagnostic functional methods, in particular a positron emission tomography, for metabolic active measurement is conducted which allows excluding a reactive component.

  3. 利用Smiths公式预测有剖宫产史孕妇再次妊娠分娩方式的初步探索%Value of Smith's Prediction Model Used in Probability of Vaginal Birth after Caesarean

    Institute of Scientific and Technical Information of China (English)

    沈敏红; 叶蕾; 丁燕琴

    2011-01-01

    目的:探讨Smiths公式用于有剖宫产(CS)史的孕妇再次妊娠时预测CS风险的意义.方法:对我院63例有CS史并再次妊娠的经产妇利用Smiths公式对不同分娩方式预测其风险系数并探讨Smiths公式的预测值.结果:63例患者中,按公式计算出CS风险预测值40%者有37.5%阴道试产失败.结论:利用Smith公式,取40%为临界点,对CS风险预测值>40%的既往有cS史的孕妇行阴道分娩时需慎重.%Objective:To measure the value of Smith's prediction model in the prediction of vaginal birth after caesarean (VBAC).Methods: Smith's prediction model was used to measure the risk of caesarean section (CS) in 63 multiparas who had a CS in their preceding pregnancy.Results: In 63 multiparas, the successful vaginal birth rate of patients with low predictive risk ( <40% ) according to the formula was 100%.In patients with high predictive risk( >40% ), 37.5% had an emergency CS.Conclusions: Using 40% as cutoff value by the Smith's prediction model, gravida has VBAC should be carefully monitored with high predictive risk( >40% ).

  4. Melanoma risk prediction models

    Directory of Open Access Journals (Sweden)

    Nikolić Jelena

    2014-01-01

    Full Text Available Background/Aim. The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. Methods. This case-control study included 697 participants (341 patients and 356 controls that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR and alternating decision trees (ADT prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS based on the outcome of the LR model was presented. Results. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724- 9.366 for those that sometimes used sunbeds, solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage, hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair, the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931, the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119, Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were

  5. Theoretical modeling of iodine value and saponification value of biodiesel fuels from their fatty acid composition

    Energy Technology Data Exchange (ETDEWEB)

    Gopinath, A.; Puhan, Sukumar; Nagarajan, G. [Internal Combustion Engineering Division, Department of Mechanical Engineering, Anna University, Chennai 600 025, Tamil Nadu (India)

    2009-07-15

    Biodiesel is an alternative fuel consisting of alkyl esters of fatty acids from vegetable oils or animal fats. The properties of biodiesel depend on the type of vegetable oil used for the transesterification process. The objective of the present work is to theoretically predict the iodine value and the saponification value of different biodiesels from their fatty acid methyl ester composition. The fatty acid ester compositions and the above values of different biodiesels were taken from the available published data. A multiple linear regression model was developed to predict the iodine value and saponification value of different biodiesels. The predicted results showed that the prediction errors were less than 3.4% compared to the available published data. The predicted values were also verified by substituting in the available published model which was developed to predict the higher heating values of biodiesel fuels from their iodine value and the saponification value. The resulting heating values of biodiesels were then compared with the published heating values and reported. (author)

  6. Prediction of cereal feed value by near infrared spectroscopy

    DEFF Research Database (Denmark)

    Jørgensen, Johannes Ravn

    with the error in the chemical analysis. Prediction error by NIRS prediction of feed value has been shown to be above the error of the chemical measurement. The conclusion is that it has proved possible to predict the feed value in cereals with NIRS quickly and cheaply, but prediction error with this method....... NIRS is therefore appropriate as a quick method for the determination of FEsv and FEso, since it is rapid (approximately 1 minute per measurement of a ground test) and cheap. The aim is to develop a rapid method to analyse grain feed value. This will contribute to highlight the opportunities...... and problems that crop variety choices and cropping practices have on feeding value of winter wheat, triticale and spring barley. A successful development of an EDOM, EDOMi, FEso and FEsv calibration to NIRS will be a relatively cheap tool to monitor, diversify and evaluate the quality of cereals for animal...

  7. Prediction Model and Algorithm of Industrial Gross Output Value Based on Electric Power Consumption%基于电力消耗的行业总产值预测模型及算法研究

    Institute of Scientific and Technical Information of China (English)

    陈晔; 王国瑞; 方彦军

    2014-01-01

    在“大数据”技术背景下,获取广东省规模以上工业企业电力消耗及总产值月度数据,基于人工神经网络结构建立行业总产值预测模型,并提出一种新的带抱团行为的粒子群优化算法完成对神经网络预测模型的参数优化,进而实现各行业基于电力消耗的总产值有效预测。仿真分析表明,新的改进型带抱团行为的粒子群优化算法具有更快的收敛速度和更高的寻优精度,能够有效地优化神经网络模型参数,实现基于电力消耗的行业总产值的有效、可靠预测。%In the context of big data technology,a prediction model for industrial gross output value based on artificial neural network was built by achieving monthly data of electric power consumption and output value of industrial enterprises above Guangdong provincial designated size. Meanwhile,a kind of new particle swarm optimization with gathering behavior was proposed to finish optimization on parameters of ANN prediction model and realize effective prediction on gross value based on electric consumption of all industries. Simulation analysis indicated that the new improved PSO was provided with faster convergence speed and higher optimizing precision which was able effectively optimize parameters of ANN model and realize effective and reliable prediction on gross output value of industries based on electric power consumption.

  8. Predictive and prognostic value of FDG-PET.

    NARCIS (Netherlands)

    Geus-Oei, L.F. de; Oyen, W.J.G.

    2008-01-01

    The predictive and prognostic value of fluorodeoxyglucose (FDG)-positron emission tomography (PET) in non-small-cell lung carcinoma, colorectal carcinoma and lymphoma is discussed. The degree of FDG uptake is of prognostic value at initial presentation, after induction treatment prior to resection a

  9. Prediction of 3-hydroxypyridin-4-one (HPO) log K1 values for Fe(III).

    Science.gov (United States)

    Chen, Yu-Lin; Barlow, Dave J; Kong, Xiao-Le; Ma, Yong-Min; Hider, Robert C

    2012-09-21

    As a means to aid in the design of 3-hydroxypyridin-4-ones (HPOs) intended for use as therapeutic Fe(3+) chelating agents, a novel methodology has been developed using quantum mechanical (QM) calculations for predicting the iron binding affinities of the compounds (more specifically, their log K(1) values). The reported/measured HPO log K(1) values were verified through their correlation with the corresponding sum of the compounds' ligating group pK(a) values. Using a training set of eleven HPOs with known log K(1) values, reliable predictions are shown to be obtained with QM calculations using the B3LYP/6-31+G(d)/CPCM model chemistry (with Bondi radii, and water as solvent). With this methodology, the observed log K(1) values for the training set compounds are closely matched by the predicted values, with the correlation between the observed and predicted values giving r(2) = 0.9. Predictions subsequently made by this method for a test set of 42 HPOs of known log K(1) values gave predicted values accurate to within ±0.32 log units. In order to further investigate the predictive power of the method, four novel HPOs were synthesised and their log K(1) values were determined experimentally. Comparison of these predicted log K(1) values against the measured values gave absolute deviations of 0.22 (13.87 vs. 14.09), 0.02 (14.31 vs. 14.29), 0.12 (14.62 vs. 14.50), and 0.13 (15.04 vs. 15.17). The prediction methodology reported here is the first to be provided for predicting the absolute log K(1) values of iron-chelating agents in the absence of pK(a) values.

  10. One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

    Directory of Open Access Journals (Sweden)

    Jin Xiao

    2014-01-01

    Full Text Available Scientific customer value segmentation (CVS is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

  11. Evaluation of random forest regression for prediction of breeding value from genomewide SNPs

    Indian Academy of Sciences (India)

    Rupam Kumar Sarkar; A. R. Rao; Prabina Kumar Meher; T. Nepolean; T. Mohapatra

    2015-06-01

    Genomic prediction is meant for estimating the breeding value using molecular marker data which has turned out to be a powerful tool for efficient utilization of germplasm resources and rapid improvement of cultivars. Model-based techniques have been widely used for prediction of breeding values of genotypes from genomewide association studies. However, application of the random forest (RF), a model-free ensemble learning method, is not widely used for prediction. In this study, the optimum values of tuning parameters of RF have been identified and applied to predict the breeding value of genotypes based on genomewide single-nucleotide polymorphisms (SNPs), where the number of SNPs ($P$ variables) is much higher than the number of genotypes ($n$ observations) ($P >> n$). Further, a comparison was made with the model-based genomic prediction methods, namely, least absolute shrinkage and selection operator (LASSO), ridge regression (RR) and elastic net (EN) under $P >> n$. It was found that the correlations between the predicted and observed trait response were 0.591, 0.539, 0.431 and 0.587 for RF, LASSO, RR and EN, respectively, which implies superiority of the RF over the model-based techniques in genomic prediction. Hence, we suggest that the RF methodology can be used as an alternative to the model-based techniques for the prediction of breeding value at genome level with higher accuracy.

  12. Predicting Customer Potential Value: an application in the insurance industry

    NARCIS (Netherlands)

    P.C. Verhoef (Peter); A.C.D. Donkers (Bas)

    2001-01-01

    textabstractFor effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for pre

  13. Predicting Customer Potential Value: an application in the insurance industry

    NARCIS (Netherlands)

    P.C. Verhoef (Peter); A.C.D. Donkers (Bas)

    2001-01-01

    textabstractFor effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for pre

  14. Assessing the Incremental Value of KABC-II Luria Model Scores in Predicting Achievement: What Do They Tell Us beyond the MPI?

    Science.gov (United States)

    McGill, Ryan J.; Spurgin, Angelia R.

    2016-01-01

    The current study examined the incremental validity of the Luria interpretive scheme for the Kaufman Assessment Battery for Children-Second Edition (KABC-II) for predicting scores on the Kaufman Test of Educational Achievement-Second Edition (KTEA-II). All participants were children and adolescents (N = 2,025) drawn from the nationally…

  15. Assessing the Incremental Value of KABC-II Luria Model Scores in Predicting Achievement: What Do They Tell Us beyond the MPI?

    Science.gov (United States)

    McGill, Ryan J.; Spurgin, Angelia R.

    2016-01-01

    The current study examined the incremental validity of the Luria interpretive scheme for the Kaufman Assessment Battery for Children-Second Edition (KABC-II) for predicting scores on the Kaufman Test of Educational Achievement-Second Edition (KTEA-II). All participants were children and adolescents (N = 2,025) drawn from the nationally…

  16. Predictive Models for Music

    OpenAIRE

    Paiement, Jean-François; Grandvalet, Yves; Bengio, Samy

    2008-01-01

    Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce generative models for melodies. We decompose melodic modeling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modeling sequences of Narmour featur...

  17. 一种基于混合神经网络的浮选pH值预测模型%Prediction Model for pH Value in Flotation Process Based on Hybrid Neural Network

    Institute of Scientific and Technical Information of China (English)

    唐朝晖; 杜金芳; 陈青

    2012-01-01

    矿物浮选过程中,矿浆pH值作为影响浮选效果的一个重要因素,是实现浮选过程监视及优化控制的一个重要参量.目前的pH值测定仪存在交叉污染、测量滞后等问题,难以获得实时准确的pH值.为使浮选运行在最优状态,在泡沫图像特征提取的基础上,提出一种基于自适应遗传混合神经网络的预测模型,该模型首先利用主元分析(PCA)方法对提取的多个图像特征进行降维,然后采用自适应遗传混合神经网络(AGA-HNN)建立PH值预测模型.最后将该模型应用于浮选现场,预测结果能够实时跟踪实际值,根据预测值实时调整工况条件,改善了浮选效果,提高了浮选效率.%In mineral flotation process, pH value is one of the flotation elements which affect the flotation performance significantly. It is very important for flotation process monitoring and optimized controtl. At present, pH determinator has the problem of cross contamination , measurement lag, and so on. So it is difficult to obtain real lime and accurate pH value. To make flotation limning in an optimal slate, a novel prediction model is proposed in this paper based on adaptive genetic hybrid neural network after extracting several image features. Firstly, feature dimension reduction is done by principal component analysia( PCA). Then prediction model is built through a-daptive genetic hybrid neural network( AGA-HNN). Finally, the model is applied to flotation field. Predicted value can well trace the actual value. At the same time, working condition is adjusted according to the predicted value. As a result, the flotation performance and efficiency are improved obviously.

  18. What's the Value of VAM (Value-Added Modeling)?

    Science.gov (United States)

    Scherrer, Jimmy

    2012-01-01

    The use of value-added modeling (VAM) in school accountability is expanding, but deciding how to embrace VAM is difficult. Various experts say it's too unreliable, causes more harm than good, and has a big margin for error. Others assert VAM is imperfect but useful, and provides valuable feedback. A closer look at the models, and their use,…

  19. The value of oxygen-isotope data and multiple discharge records in calibrating a fully-distributed, physically-based rainfall-runoff model (CRUM3) to improve predictive capability

    Science.gov (United States)

    Neill, Aaron; Reaney, Sim

    2015-04-01

    those derived from the oxygen-18 data to see how well the model captured catchment dynamics. The value of incorporating the oxygen-18 data set, as well as discharge data sets from multiple as opposed to single gauging stations in each catchment, in the calibration process to improve the predictive capability of the model was then investigated. This was achieved by assessing by how much the identifiability of the model parameters and the ability of the model to represent the runoff processes operating in each catchment improved with the inclusion of the additional data sets with respect to the likely costs that would be incurred in obtaining the data sets themselves.

  20. Predicting quantitative and qualitative values of recreation participation

    Science.gov (United States)

    Elwood L., Jr. Shafer; George Moeller

    1971-01-01

    If future recreation consumption and associated intangible values can be predicted, the problem of rapid decision making in recreation-resource management can be reduced, and the problems of implementing those decisions can be anticipated. Management and research responsibilities for meeting recreation demand are discussed, and proved methods for forecasting recreation...

  1. Predictive Value and Psychological Meaning of Vocational Aspirations

    Science.gov (United States)

    Holland, John L.; Gottfredson, Gary D.

    1975-01-01

    The psychological meaning and predictive value of a person's vocational aspirations were examined by applying Holland's typology to the vocational aspirations of high school juniors (N=140), and a second sample of college students studied over a one-year interval (N-624). (Author)

  2. Determinants of work ability and its predictive value for disability

    NARCIS (Netherlands)

    S.M. Alavinia; A.G.E.M. de Boer; J.C. van Duivenbooden; M.H.W. Frings-Dresen; A. Burdorf

    2009-01-01

    Background Maintaining the ability of workers to cope with physical and psychosocial demands at work becomes increasingly important in prolonging working life. Aims To analyse the effects of work-related factors and individual characteristics on work ability and to determine the predictive value of

  3. Zephyr - the prediction models

    DEFF Research Database (Denmark)

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

    2001-01-01

    This paper briefly describes new models and methods for predicationg the wind power output from wind farms. The system is being developed in a project which has the research organization Risø and the department of Informatics and Mathematical Modelling (IMM) as the modelling team and all the Dani...

  4. Predictive value of exfoliative cytology in pigmented conjunctival lesions.

    Science.gov (United States)

    Keijser, Sander; van Luijk, Chantal M; Missotten, Guy S; Veselic-Charvat, Maud; de Wolff-Rouendaal, Didi; de Keizer, Rob J W

    2006-04-01

    Pigmented lesions of the conjunctiva are often difficult to classify clinically. Exfoliative cytology may be helpful, but reliable data regarding the sensitivity and specificity of this test are currently lacking. We determined the value of exfoliative cytology with regard to pigmented conjunctival lesions. A total of 294 smears from 182 patients were screened for malignancy within 6 months of exfoliative cytology. Smears were classified according to the following categories: grade 0 = insufficient material for diagnosis; grade 1 = normal conjunctival cells; grade 2 = melanocytes with mild atypia; grade 3 = melanocytes with moderate atypia, and grade 4 = melanocytes with severe atypia. The sensitivity, specificity, positive predictive value and negative predictive value of exfoliative cytology were 85%, 78%, 59% and 93%, respectively. Exfoliative cytology is a fast, easy and non-invasive technique that may be used in the evaluation of patients with a pigmented conjunctival lesion.

  5. Evaluation of the efficiency of artificial neural networks for genetic value prediction.

    Science.gov (United States)

    Silva, G N; Tomaz, R S; Sant'Anna, I C; Carneiro, V Q; Cruz, C D; Nascimento, M

    2016-03-28

    Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency.

  6. Value of transient elastography for the prediction of variceal bleeding

    Institute of Scientific and Technical Information of China (English)

    Ioan Sporea; Iulia Ra(t)iu; Roxana (S)irli; Alina Popescu; Simona Bota

    2011-01-01

    AIM: To determine if liver stiffness (LS) measurements by means of transient elastography (TE) correlate with the presence of significant esophageal varices (EV) and if they can predict the occurrence of variceal bleeding. METHODS: We studied 1000 cases of liver cirrhosis divided into 2 groups: patients without EV or with grade 1 varices (647 cases) and patients with significant varices (grade 2 and 3 EV) (353 cases). We divided the group of 540 cases with EV into another 2 subgroups: without variceal hemorrhage (375 patients) and patients with a history of variceal bleeding (165 cases). We compared the LS values between the groups using the unpaired t-test and we established cut-off LS values for the presence of significant EV and for the risk of bleeding by using the ROC curve. RESULTS: The mean LS values in the 647 patients without or with grade 1 EV was statistically significantly lower than in the 353 patients with significant EV (26.29 ± 0.60 kPa vs 45.21 ± 1.07 kPa,P < 0.0001). Using the ROC curve we established a cut-off value of 31 kPa for the presence of EV,with 83% sensitivity (95% CI: 79.73%-85.93%) and 62% specificity (95% CI: 57.15%-66.81%),with 76.2% positive predictive value (PPV) (95% CI: 72.72%-79.43%) and 71.3% negative predictive value (NPV) (95% CI: 66.37%-76.05%) (AUROC 0.7807,P < 0.0001). The mean LS values in the group with a history of variceal bleeding (165 patients) was statistically significantly higher than in the group with no bleeding history (375 patients): 51.92 ± 1.56 kPa vs 35.20 ± 0.91 kPa,P < 0.0001). For a cut-off value of 50.7 kPa,LS had 53.33% sensitivity (95% CI: 45.42%-61.13%) and 82.67% specificity (95% CI: 78.45%-86.36%),with 82.71% PPV (95% CI: 78.5%-86.4%) and 53.66% NPV (95% CI: 45.72%-61.47%) (AUROC 0.7300,P < 0.0001) for the prediction of esophageal bleeding. CONCLUSION: LS measurement by means of TE is a reliable noninvasive method for the detection of EV and for the prediction of variceal bleeding.

  7. Gap Model for Dual Customer Values

    Institute of Scientific and Technical Information of China (English)

    HOU Lun; TANG Xiaowo

    2008-01-01

    The customer value, the key problem in customer relationship management (CRM), was studied to construct a gap model for dual customer values. A basic description of customer values is given, and then the gaps between products and services in different periods for the customers and companies are analyzed based on the product or service life-cycle. The main factors that influence the perceived customer value were analyzed to define the "recognized value gap" and a gap model for the dual customer values was constructed to supply companies with a tool to analyze existing customer value gaps and improve customer relationship management.

  8. The Predictive Value of Germline Polymorphisms in Patients with NSCLC

    DEFF Research Database (Denmark)

    Nygaard, Anneli Dowler; Spindler, Karen-Lise Garm; Andersen, Rikke Fredslund;

    2010-01-01

    . Haplotype analysis of the VEGF+405 and VEGF- 460 supported our findings. These results were, however, not confirmed in the validation cohort. Although significant results regarding VEGF related SNPs, in the primary analysis, no predictive value of a broad panel of SNPs in NSCLC was found in the validation......Lung cancer is one of the most common cancers in the western world, and closely related to smoking. The majority of the patients can not be offered treatment with curative intent. Palliative chemotherapy has limited effect but a considerable level of toxicity. Predictive markers are therefore...... cohort, underlining the importance of independent validation of biomarker analysis....

  9. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    modelling strategy is applied to different training sets. For each modelling strategy we estimate a confidence score based on the same repeated bootstraps. A new decomposition of the expected Brier score is obtained, as well as the estimates of population average confidence scores. The latter can be used...... to distinguish rival prediction models with similar prediction performances. Furthermore, on the subject level a confidence score may provide useful supplementary information for new patients who want to base a medical decision on predicted risk. The ideas are illustrated and discussed using data from cancer...

  10. Mean Value Modelling of Turbocharged SI Engines

    DEFF Research Database (Denmark)

    Müller, Martin; Hendricks, Elbert; Sorenson, Spencer C.

    1998-01-01

    The development of a computer simulation to predict the performance of a turbocharged spark ignition engine during transient operation. New models have been developed for the turbocharged and the intercooling system. An adiabatic model for the intake manifold is presented....

  11. Predictive values for cardiopulmonary exercise testing in sedentary Chinese adults.

    Science.gov (United States)

    Ong, Kian Chung; Loo, Chian Min; Ong, Yong Yau; Chan, Siew Pang; Earnest, Arul; Saw, Seang Mei

    2002-09-01

    Normative data for cardiopulmonary exercise testing (CPET) may vary among subjects of different races. The objectives of the present study were to: (i) establish normal standards for cardiopulmonary responses during incremental cycle ergometer testing in order to derive predictive equations for clinically useful variables during CPET of Chinese subjects; and (ii) determine the validity of existing prediction equations of maximal exercise performance for use in our local Chinese population. The maximal and submaximal cardiopulmonary responses were analysed for 95 healthy sedentary adult Chinese subjects (48 men and 47 women; aged 20-70 years) who underwent CPET using a cycle ergometer and an incremental work-rate protocol until symptom limitation. Measurements, at maximal exercise, of oxygen uptake (VO2(max)), power output and heart rate were regressed on age, height, weight and gender. The predictive equations for these exercise parameters performed better than those published previously in out-sample predictive accuracy. Comparison with previous studies also showed that prediction equations of VO2(max) derived from studies based predominantly or exclusively on Caucasian populations overestimated the actual values for our subjects. Previously established prediction equations for maximal exercise performance during CPET based on non-Chinese populations may not be applicable to Chinese subjects in our population.

  12. Modelling, controlling, predicting blackouts

    CERN Document Server

    Wang, Chengwei; Baptista, Murilo S

    2016-01-01

    The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids, and another one for smart grids. The control strategie...

  13. 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.

  14. PREDICTIVE VALUE OF CTG IN POST-DATED PREGNANCY

    Directory of Open Access Journals (Sweden)

    Suganthi

    2016-05-01

    Full Text Available AIM This study evaluates the usefulness of intrapartum cardiotocography in patients with post-dated pregnancy compared to intermittent auscultation. MATERIALS AND METHODS 100 patients with pregnancies beyond EDD and with no other risk factors were included in the study; 50 patients who underwent CTG on admission into labour ward formed the study group and 50 patients who underwent intermittent auscultation formed the control group. Antenatal foetal monitoring namely daily foetal movement count, twice-weekly non-stress test with amniotic fluid assessment and Doppler velocimetry using ultrasound were done in all patients until the onset of labour. Labour was induced whenever NST was non-reassuring or ultrasound showed oligohydramnios. Partogram was used to monitor the course of labour. RESULTS The foetal outcome was better in the study group than in the control group with fewer depressed babies. Cardiotocography had a positive predictive value of 36 36% and a negative predictive value of 94.04% with a P value of 0.010. CONCLUSION Cardiotocography is definitely superior to intermittent auscultation in intrapartum foetal monitoring. Despite the high number of false positives, CTG predicts the outcome of labour in every patient and especially in cases with prolonged pregnancy it serves as a valuable screening tool to pick up those cases that may be compromised by the events of labour.

  15. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    are calculated using on-line measurements of power production as well as HIRLAM predictions as input thus taking advantage of the auto-correlation, which is present in the power production for shorter pediction horizons. Statistical models are used to discribe the relationship between observed energy production......The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...... and HIRLAM predictions. The statistical models belong to the class of conditional parametric models. The models are estimated using local polynomial regression, but the estimation method is here extended to be adaptive in order to allow for slow changes in the system e.g. caused by the annual variations...

  16. Value Modeling for Enterprise Resilience

    Energy Technology Data Exchange (ETDEWEB)

    Henderson, Dale L.; Lancaster, Mary J.

    2015-10-20

    Abstract. The idea that resilience is a tangible, measureable, and desirable system attribute has grown rapidly over the last decade beyond is origins in explaining ecological, physiological, psychological, and social systems. Operational enterprise resilience requires two types of measurement. First, the system must monitor various operational conditions in order to respond to disruptions. These measurements are part of one or more observation, orientation, decision, and action (OODA) loops The OODA control processes that implement a resilience strategy use these measurements to provide robustness, rapid recovery and reconstitution. In order to assess the effectiveness of the resilience strategy, a different class of measurements is necessary. This second type consists of measurements about how well the OODA processes cover critical enterprise functions and the hazards to which the enterprise is exposed. They allow assessment of how well enterprise management processes anticipate, mitigate, and adapt to a changing environment and the degree to which the system is fault tolerant. This paper nominates a theoretical framework, in the form of definitions, a model, and a syntax, that accounts for this important distinction, and in so doing provides a mechanism for bridging resilience management process models and the many proposed cyber-defense metric enumerations.

  17. MODELING A VALUE CHAIN IN PUBLIC SECTOR

    Directory of Open Access Journals (Sweden)

    Daiva Rapcevičienė

    2014-08-01

    Full Text Available Purpose – Over the past three decades comprehensive insights were made in order to design and manage the value chain. A lot of scholars discuss differences between private sector value chain – creation profit for the business and public sector value chain, the approach that public sector creates value through the services that it provides. However, there is a lack of a common understanding of what public sector value chain is in general. This paper reviews the literature on how the private value chain was transformed into public value chain and reviews a determination and architecture of a value chain in public sector which gives a structural approach to greater picture of how all structure works. It reviews an approach that the value chain for the public sector shows how the public sector organizes itself to ensure it is of value to the citizens. Design/methodology/approach – descriptive method, analysis of scientific literature. Findings – The public sector value chain is an adaptation of the private sector value chain. The difference between the two is that the customer is the focus of the public sector context, versus the profit focus in the private sector context. There are significant similarities between the two chain models. Each of the chain models are founded on a series of core components. For the public sector context, the core components are people, service and trust. Research limitations/implications – this paper based on presenting value chain for both private and public sectors and giving deeper knowledge for public sector value chain model. Practical implications – comprehension of general value chain model concept and public sector value chain model helps to see multiple connections throughout the entire process: from the beginning to the end. The paper presents the theoretical framework for further study of the value chain model for waste management creation. Originality/Value – The paper reveals the systematic

  18. Can Hounsfield Unit Value Predict Type of Urinary Stones?

    Directory of Open Access Journals (Sweden)

    Alper Gok

    2014-03-01

    Full Text Available Aim: Aim of this study is to determine the role of Hounsfield unit (HU in predicting results of stone analysis. Material and Method: This study included 199 patients to whom percutaneous nephrolithotomy (PNL procedures were applied between January 2008 and May 2011 in our clinic. Before the procedure HU values of kidney stones were measured using non-contrast computed tomography. After the operation, obtained stone samples were analysed using X-ray diffraction technique. HU values were compared with stone analysis results. Results: Stone analysis revealed eight different stone types. Distribution of stone types and HU value ranges were as follows: 85% calcium oxalate monohydrate, 730-1130 HU; 38% calcium oxalate dihydrate, 510-810 HU; 21% uric acid, 320-550 HU; 23% struvite, 614-870 HU; 7% calcium hydrogene phosphate, 1100-1365 HU; 3% cystine, 630-674 HU; 15% mixed uric acid plus calcium oxalate, 499-840 HU; and 7% mixed cystine plus calcium phosphate, 430-520 HU. HU values of all stone types ranged between 320 and 1365. There was a statistically significant relation between HU values of uric acid and non uric acid stones (p

  19. Predictive values of symptoms in relation to cancer diagnosis

    DEFF Research Database (Denmark)

    Krasnik, Ivan; Andersen, John Sahl

    a manual describing the symptoms that should engender reasonable suspicion of malignancy (“alarm symptoms”) to the general practitioner. Objectives: To investigate the evidence in the literature of the predictive value (PPV) placed on the”alarm symptoms” for colon cancer, breast cancer, prostate cancer...... years (6,6%-21,2%), but much lower in younger age groups. ”Change in bowel habits” and ”Significant general symptoms” are more uncertain (3,5%-8,5%). Breast cancer: ”Palpable suspect tumor” is well supported (8,1%-24%). The predictive value of ”Pitting of the skin”, ”Papil-areola eczema....../ulceration” and ”Clinically suspect axillary lymph nodes” was not found in the literature. Prostate cancer: One study shows a high PPV for rectal examination (12%). The value of “Lower urinary tract symptoms” is more uncertain (1,0%-3,0%). PPV of ”Perianal pain” and ”Haemospermia” are not described in the literature. Lung...

  20. Prediction models in complex terrain

    DEFF Research Database (Denmark)

    Marti, I.; Nielsen, Torben Skov; Madsen, Henrik

    2001-01-01

    The objective of the work is to investigatethe performance of HIRLAM in complex terrain when used as input to energy production forecasting models, and to develop a statistical model to adapt HIRLAM prediction to the wind farm. The features of the terrain, specially the topography, influence...

  1. Modeling Business Strategy: A Consumer Value Perspective

    OpenAIRE

    Svee, Eric-Oluf; Giannoulis, Constantinos; Zdravkovic, Jelena

    2011-01-01

    Part 3: Business Modeling; International audience; Business strategy lays out the plan of an enterprise to achieve its vision by providing value to its customers. Typically, business strategy focuses on economic value and its relevant exchanges with customers and does not directly address consumer values. However, consumer values drive customers’ choices and decisions to use a product or service, and therefore should have a direct impact on business strategy. This paper explores whether and h...

  2. Reducing dimensionality for prediction of genome-wide breeding values

    Directory of Open Access Journals (Sweden)

    Woolliams John A

    2009-03-01

    Full Text Available Abstract Partial least square regression (PLSR and principal component regression (PCR are methods designed for situations where the number of predictors is larger than the number of records. The aim was to compare the accuracy of genome-wide breeding values (EBV produced using PLSR and PCR with a Bayesian method, 'BayesB'. Marker densities of 1, 2, 4 and 8 Ne markers/Morgan were evaluated when the effective population size (Ne was 100. The correlation between true breeding value and estimated breeding value increased with density from 0.611 to 0.681 and 0.604 to 0.658 using PLSR and PCR respectively, with an overall advantage to PLSR of 0.016 (s.e = 0.008. Both methods gave a lower accuracy compared to the 'BayesB', for which accuracy increased from 0.690 to 0.860. PLSR and PCR appeared less responsive to increased marker density with the advantage of 'BayesB' increasing by 17% from a marker density of 1 to 8Ne/M. PCR and PLSR showed greater bias than 'BayesB' in predicting breeding values at all densities. Although, the PLSR and PCR were computationally faster and simpler, these advantages do not outweigh the reduction in accuracy, and there is a benefit in obtaining relevant prior information from the distribution of gene effects.

  3. A Bayesian model of context-sensitive value attribution.

    Science.gov (United States)

    Rigoli, Francesco; Friston, Karl J; Martinelli, Cristina; Selaković, Mirjana; Shergill, Sukhwinder S; Dolan, Raymond J

    2016-06-22

    Substantial evidence indicates that incentive value depends on an anticipation of rewards within a given context. However, the computations underlying this context sensitivity remain unknown. To address this question, we introduce a normative (Bayesian) account of how rewards map to incentive values. This assumes that the brain inverts a model of how rewards are generated. Key features of our account include (i) an influence of prior beliefs about the context in which rewards are delivered (weighted by their reliability in a Bayes-optimal fashion), (ii) the notion that incentive values correspond to precision-weighted prediction errors, (iii) and contextual information unfolding at different hierarchical levels. This formulation implies that incentive value is intrinsically context-dependent. We provide empirical support for this model by showing that incentive value is influenced by context variability and by hierarchically nested contexts. The perspective we introduce generates new empirical predictions that might help explaining psychopathologies, such as addiction.

  4. A Binomial Integer-Valued ARCH Model.

    Science.gov (United States)

    Ristić, Miroslav M; Weiß, Christian H; Janjić, Ana D

    2016-11-01

    We present an integer-valued ARCH model which can be used for modeling time series of counts with under-, equi-, or overdispersion. The introduced model has a conditional binomial distribution, and it is shown to be strictly stationary and ergodic. The unknown parameters are estimated by three methods: conditional maximum likelihood, conditional least squares and maximum likelihood type penalty function estimation. The asymptotic distributions of the estimators are derived. A real application of the novel model to epidemic surveillance is briefly discussed. Finally, a generalization of the introduced model is considered by introducing an integer-valued GARCH model.

  5. Predictive models of forest dynamics.

    Science.gov (United States)

    Purves, Drew; Pacala, Stephen

    2008-06-13

    Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

  6. Interpretation of Spirometry: Selection of Predicted Values and Defining Abnormality.

    Science.gov (United States)

    Chhabra, S K

    2015-01-01

    Spirometry is the most frequently performed investigation to evaluate pulmonary function. It provides clinically useful information on the mechanical properties of the lung and the thoracic cage and aids in taking management-related decisions in a wide spectrum of diseases and disorders. Few measurements in medicine are so dependent on factors related to equipment, operator and the patient. Good spirometry requires quality assured measurements and a systematic approach to interpretation. Standard guidelines on the technical aspects of equipment and their calibration as well as the test procedure have been developed and revised from time-to-time. Strict compliance with standardisation guidelines ensures quality control. Interpretation of spirometry data is based only on two basic measurements--the forced vital capacity (FVC) and the forced expiratory volume in 1 second (FEV1) and their ratio, FEV1/FVC. A meaningful and clinically useful interpretation of the measured data requires a systematic approach and consideration of several important issues. Central to interpretation is the understanding of the development and application of prediction equations. Selection of prediction equations that are appropriate for the ethnic origin of the patient is vital to avoid erroneous interpretation. Defining abnormal values is a debatable but critical aspect of spirometry. A statistically valid definition of the lower limits of normal has been advocated as the better method over the more commonly used approach of defining abnormality as a fixed percentage of the predicted value. Spirometry rarely provides a specific diagnosis. Examination of the flow-volume curve and the measured data provides information to define patterns of ventilatory impairment. Spirometry must be interpreted in conjunction with clinical information including results of other investigations.

  7. Predictive value of hormonal parameters for live birth in women with unexplained infertility and male infertility

    OpenAIRE

    Murto, Tiina; Bjuresten, Kerstin; Landgren, Britt-Marie; Stavreus-Evers, Anneli

    2013-01-01

    Background: Infertile women might get pregnant sometime after fertility treatment, but today, there is no prediction model on who will eventually have children. The objective of the present study was to characterize hormone levels in an arbitrary menstrual cycle in women with unexplained infertility and male infertility, and to determine the predictive value for long-term possibility of live birth. Methods: In this cross-sectional study, with 71 infertile women with diagnosis unexplained infe...

  8. [Prediction of SPAD value in oilseed rape leaves using hyperspectral imaging technique].

    Science.gov (United States)

    Ding, Xi-bin; Liu, Fei; Zhang, Chu; He, Yong

    2015-02-01

    In the present work, prediction models of SPAD value (Soil and Plant Analyzer Development, often used as a parameter to indicate chlorophyll content) in oilseed rape leaves were successfully built using hyperspectral imaging technique. The hy perspectral images of 160 oilseed rape leaf samples in the spectral range of 380-1030 nm were acquired. Average spectrum was extracted from the region of interest (ROI) of each sample. We chose spectral data in the spectral range of 500-900 nm for analysis. Using Monte Carlo partial least squares(MC-PLS) algorithm, 13 samples were identified as outliers and eliminated. Based on the spectral information and measured SPAD values of the rest 147 samples, several estimation models have been built based on different parameters using different algorithms for comparison, including: (1) a SPAD value estimation model based on partial least squares(PLS) in the whole wavelength region of 500-900 nm; (2) a SPAD value estimation model based on successive projections algorithmcombined with PLS(SPA-PLS); (3) 4 kind of simple experience SPAD value estimation models in which red edge position was used as an argument; (4) 4 kind of simple experience SPAD value estimation models in which three vegetation indexes R710/R760, (R750-R705)/(R750-R705) and R860/(R550 x R708), which all have been proved to have a good relevance with chlorophyll content, were used as an argument respectively; (5) a SPAD value estimation model based on PLS using the 3 vegetation indexes mentioned above. The results indicate that the optimal prediction performance is achieved by PLS model in the whole wavelength region of 500-900 nm, which has a correlation coefficient(r(p)) of 0.8339 and a root mean squares error of predicted (RMSEP) of 1.52. The SPA-PLS model can provide avery close prediction result while the calibration computation has been significantly reduced and the calibration speed has been accelerated sharply. For simple experience models based on red edge

  9. Tailoring the Implementation of New Biomarkers Based on Their Added Predictive Value in Subgroups of Individuals

    NARCIS (Netherlands)

    van Giessen, A.; Moons, K.G.M.; de Wit, G.A.; Verschuren, W.M.M.; Boer, J.M.A.; Koffijberg, Hendrik

    2015-01-01

    Background The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We

  10. Tailoring the Implementation of New Biomarkers Based on Their Added Predictive Value in Subgroups of Individuals

    NARCIS (Netherlands)

    Giessen, van A.; Moons, K.G.M.; Wit, de G.A.; Verschuren, W.M.M.; Boer, J.M.A.; Koffijberg, H.

    2015-01-01

    Background The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We

  11. Genomic breeding value estimation using nonparametric additive regression models

    Directory of Open Access Journals (Sweden)

    Solberg Trygve

    2009-01-01

    Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.

  12. A Study On Distributed Model Predictive Consensus

    CERN Document Server

    Keviczky, Tamas

    2008-01-01

    We investigate convergence properties of a proposed distributed model predictive control (DMPC) scheme, where agents negotiate to compute an optimal consensus point using an incremental subgradient method based on primal decomposition as described in Johansson et al. [2006, 2007]. The objective of the distributed control strategy is to agree upon and achieve an optimal common output value for a group of agents in the presence of constraints on the agent dynamics using local predictive controllers. Stability analysis using a receding horizon implementation of the distributed optimal consensus scheme is performed. Conditions are given under which convergence can be obtained even if the negotiations do not reach full consensus.

  13. A two step Bayesian approach for genomic prediction of breeding values

    DEFF Research Database (Denmark)

    Mahdi Shariati, Mohammad; Sørensen, Peter; Janss, Luc

    2012-01-01

    Background: In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter...... of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances. Conclusions: Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker...

  14. Theory of multivariate compound extreme value distribution and its application to extreme sea state prediction

    Institute of Scientific and Technical Information of China (English)

    LIU Defu; WANG Liping; PANG Liang

    2006-01-01

    In this paper, a new type of distribution,multivariate compound extreme value distribution(MCEVD), is introduced by compounding a discrete distribution with a multivariate continuous distribution of extreme sea events. In its engineering application the number over certain threshold level per year is fitting to Poisson distribution and the corresponding extreme sea events are fitting to Nested Logistic distribution, then the Poisson-Nested logistic trivariate compound extreme value distribution (PNLTCED) is proposed to predict extreme wave heights, periods and wind speeds in Yellow Sea. The new model gives more stable and reasonable predicted results.

  15. The predictive value of the height ratio and thyromental distance: four predictive tests for difficult laryngoscopy.

    Science.gov (United States)

    Krobbuaban, Banjong; Diregpoke, Siriwan; Kumkeaw, Sujarit; Tanomsat, Malin

    2005-11-01

    Preoperative evaluation of anatomical landmarks and clinical factors help identify potentially difficult laryngoscopies; however, predictive reliability is unclear. Because the ratio of height to thyromental distance (RHTMD) has a demonstrably better predictive value than the thyromental distance (TMD), we evaluated the predictive value and odds ratios of RHTMD versus mouth opening, TMD, neck movement, and oropharyngeal view (modified Mallampati). We collected data on 550 consecutive patients scheduled for elective-surgery general anesthesia requiring endotracheal intubation and then assessed all five factors before surgery. An experienced anesthesiologist, not apprised of the recorded preoperative airway assessment, performed the laryngoscopy and grading (as per Cormack and Lehane's classification). Difficult laryngoscopy (Grade 3 or 4) occurred in 69 patients (12.5%). RHTMD had a higher sensitivity, positive predictive value, and fewer false negatives than the other variables tested. In the multivariate analysis, three criteria were found independent for difficult laryngoscopy (neck movement or =23.5). The odds ratio (95% confidence interval) of the RHTMD, Mallampati class, and neck movement were 6.72 (3.29-13.72), 2.96 (1.63-5.35), and 2.73 (1.14-6.51), respectively. The odds ratio for RHTMD was the largest and thus may prove a useful screening test for difficult laryngoscopy.

  16. [Spirographic reference values. Mathematical models and practical use (author's transl)].

    Science.gov (United States)

    Drouet, D; Kauffmann, F; Brille, D; Lellouch, J

    1980-01-01

    Various models predicting VC and FEV1 from age and height have been compared by both theoretical and practical approaches on several subgroups of a working population examined in 1960 and 1972. The models in which spirographic values are proportional to the cube of the height give a significantly worse fit of the data. All the other models give similar predicted values in practical terms, but cutoff points depend on the distributions of VC and FEV1 given age and height. Results show that these distributions are closer to a normal than to a lognormal distribution. The use of reference values and classical cutoffs is then discussed. Rather than using a single cutoff point, a more quantitative way is proposed to describe the subjects' functional status, for example by situating him in the percentile of the reference population. In screening, cutoff points cannot be choosen without specifying first the decision considered and the population concerned.

  17. Evaluating Predictive Performance of Value-at-Risk Models Based on Generalized Spectrum and MCS Tests%基于广义谱和MCS检验的VaR模型预测绩效评估

    Institute of Scientific and Technical Information of China (English)

    张玉鹏; 洪永淼

    2015-01-01

    asymmetric loss functions proposed by Koenker and Bassett and the magnitude loss function proposed by Lopez.Comparing with SPA( Superior Predictive Ability) test, the main advantage of MCS test is that it does not require a benchmark model to be specified as is the case for SPA tests.It char-acterizes the entire set of models that are not significantly outperformed by other models, while a test for SPA only provides evi-dence about the relative performance of a single model ( the benchmark) . The empirical results imply the following three conclusions:①it would cause wrong results using the commonly applied backtest-ing techniques such as Kupiec likelihood ratio test, Christoffersen likelihood ratio test and Engle and Manganelli dynamic quantile test.However adopting generalized spectral test and MCS test with Lopez loss function simultaneously would give us more accu-rate and robust results.②Comparing with historical simulation models, extreme value theory models, CAViaR and CARE mod-els, the out-of-sample predictive performance of the GARCH family with student-t distribution is the best at 1%and 5%signifi-cant level during the financial crisis for these three stock indexes.This implies that the risk characteristics of mainland stock mar-ket is getting closer and closer to the mature stock markets of Hong Kong and Taiwan after more than 20 years development.③At 1%significant level, the optimal VaR predictive models of Hang Seng Index include one of the CARE models which can be used to measure extreme loss situation with small probability.This implies that price limit system implemented by Hong Kong yet not by mainland and Taiwan will make Hong Kong′s stock market face more risk during the financial crisis.

  18. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-02-01

    Full Text Available Orientation: The article discussed the importance of rigour in credit risk assessment.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.

  19. The predictive value of proteinuria in acute pancreatitis.

    Science.gov (United States)

    Zuidema, M J; van Santvoort, H C; Besselink, M G; van Ramshorst, B; Boerma, D; Timmer, R; Bollen, T L; Weusten, B L A M

    2014-01-01

    Acute pancreatitis has a highly variable clinical course. Early and reliable predictors for the severity of acute pancreatitis are lacking. Proteinuria appears to be a useful predictor of disease severity and outcome in a variety of clinical conditions. This study aims to investigate the predictive value of proteinuria on admission for the severity of acute pancreatitis compared with other commonly used predictors; the APACHE II score, Modified Glasgow score and C-reactive protein (CRP). This is a post-hoc analysis of 64 patients admitted with acute pancreatitis treated in one teaching hospital, who participated in a previous randomized trial. Proteinuria was defined as a Protein/Creatinine (P/C) ratio >23 mg/mmol. The primary endpoint was severe acute pancreatitis. Secondary endpoints included infectious complications, need for invasive intervention, ICU stay and in-hospital mortality. Proteinuria was present in 30/64 patients (47%). Eleven patients (17%) had severe acute pancreatitis. There was no difference in incidence of severe acute pancreatitis between patients with and without proteinuria: 6/30 patients (20%) versus 5/34 patients (15%) respectively (p = 0.58). Likewise, the occurrence of infectious complications, need for intervention and ICU stay and mortality did not differ significantly (p = 0.58, p = 0.99, p = 0.33 and p = 0.60 respectively). The diagnostic performance of the P/C ratio for the prediction of severe pancreatitis was inferior to the Modified Glasgow score (p = 0.04) and CRP (p = 0.03). Proteinuria on admission does not seem to be a reliable predictor for disease severity in acute pancreatitis. The diagnostic performance of the P/C ratio is inferior to the Modified Glasgow score and CRP. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

  20. Statistical assessment of predictive modeling uncertainty

    Science.gov (United States)

    Barzaghi, Riccardo; Marotta, Anna Maria

    2017-04-01

    When the results of geophysical models are compared with data, the uncertainties of the model are typically disregarded. We propose a method for defining the uncertainty of a geophysical model based on a numerical procedure that estimates the empirical auto and cross-covariances of model-estimated quantities. These empirical values are then fitted by proper covariance functions and used to compute the covariance matrix associated with the model predictions. The method is tested using a geophysical finite element model in the Mediterranean region. Using a novel χ2 analysis in which both data and model uncertainties are taken into account, the model's estimated tectonic strain pattern due to the Africa-Eurasia convergence in the area that extends from the Calabrian Arc to the Alpine domain is compared with that estimated from GPS velocities while taking into account the model uncertainty through its covariance structure and the covariance of the GPS estimates. The results indicate that including the estimated model covariance in the testing procedure leads to lower observed χ2 values that have better statistical significance and might help a sharper identification of the best-fitting geophysical models.

  1. Positive Predictive Values in Diagnosis of Incidental Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Caner Ediz

    2016-03-01

    Full Text Available Objective: Although the incidence of incidental prostate cancer (IPCa decreases in recent years; for patients who performed by transurethral resection of the prostate (TURP due to bladder outlet obstruction with or without prostatism symptoms (BPH, it is still can be seen. This article purposes to answer two questions a for urologist, which clinical parameters including obesity and smoking have positive predictive value. b for pathologists; which materials are wholly sampled for reducing the cancer ? Methods: We evaluated 1315 cases who were per­formed by TURP due to bladder outlet obstruction with or without prostatism symptoms the years 2006-2015. The ages of the patients, smoking, body mass index (BMI, digital rectal examination (DRE findings, preoperative prostate specific antigen (PSA levels, uroflow values, to­tal prostate volume determined by suprapubic ultrasound and Gleason score were recorded. We analyzed the re­lationship between these parameters and IPCa. These situation compared with benign prostate tissue materials. Results: Totally 31 cases (2.35% were found in the IPCa. While the cases of 24 were pT1a, 7 cases were pT1b. Age, body mass index, PSA, peak current speed and mean flow rate parameters respectively 8.887, 5.668, 9.660, 4.814 and 3.716 times as an incidental effect in detecting prostate cancer has been concluded. Conclusion: Older patient age, over the 25 kg/m2 of BMI, over the 4 ng/dl of PSA levels, the peak flow rate less than 10 ml/sec and the mean flow rate less than 5 ml/sec might be independent risk factors for detecting IPCa. More ex­ternal validation is needed for confirming our results.

  2. Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection.

    Science.gov (United States)

    Boligon, A A; Long, N; Albuquerque, L G; Weigel, K A; Gianola, D; Rosa, G J M

    2012-12-01

    Genomewide marker information can improve the reliability of breeding value predictions for young selection candidates in genomic selection. However, the cost of genotyping limits its use to elite animals, and how such selective genotyping affects predictive ability of genomic selection models is an open question. We performed a simulation study to evaluate the quality of breeding value predictions for selection candidates based on different selective genotyping strategies in a population undergoing selection. The genome consisted of 10 chromosomes of 100 cM each. After 5,000 generations of random mating with a population size of 100 (50 males and 50 females), generation G(0) (reference population) was produced via a full factorial mating between the 50 males and 50 females from generation 5,000. Different levels of selection intensities (animals with the largest yield deviation value) in G(0) or random sampling (no selection) were used to produce offspring of G(0) generation (G(1)). Five genotyping strategies were used to choose 500 animals in G(0) to be genotyped: 1) Random: randomly selected animals, 2) Top: animals with largest yield deviation values, 3) Bottom: animals with lowest yield deviations values, 4) Extreme: animals with the 250 largest and the 250 lowest yield deviations values, and 5) Less Related: less genetically related animals. The number of individuals in G(0) and G(1) was fixed at 2,500 each, and different levels of heritability were considered (0.10, 0.25, and 0.50). Additionally, all 5 selective genotyping strategies (Random, Top, Bottom, Extreme, and Less Related) were applied to an indicator trait in generation G(0,) and the results were evaluated for the target trait in generation G(1), with the genetic correlation between the 2 traits set to 0.50. The 5 genotyping strategies applied to individuals in G(0) (reference population) were compared in terms of their ability to predict the genetic values of the animals in G(1) (selection

  3. Early Diagnostic and Predictive Value of Capillaroscopy in Systemic Sclerosis.

    Science.gov (United States)

    Cutolo, Maurizio; Pizzorni, Carmen; Sulli, Alberto; Smith, Vanessa

    2013-01-01

    Nailfold microvascular impairment represents an early feature of systemic sclerosis (SSc) and its progression through different patterns of capillary damage and their validated scoring, is evaluable by nailfold videocapillaroscopy (NVC) in a safe and reliable manner. The presence of specific morphological microvascular alterations at the NVC (i.e., presence of giant capillaries) is fundamental and mandatory for the early diagnosis of SSc, together with the presence of the Raynaud's phenomenon. Furthermore, a recent longitudinal study showed a dynamic transition of microvascular damage through different NVC patterns of microangiopathy in almost 50% of SSc patients and clinical symptoms progressed in accordance with the NVC morphologic changes in 60% of the SSc patients. A pilot study was the first demonstrating an association between baseline NVC patterns and future severe, peripheral vascular and lung involvement with stronger odds according to worsening scleroderma patterns. Prognostic indexes for digital trophic lesions, especially for daily use in SSc clinics and simply limited to the mean score of capillary loss are now validated. Very recently, it has been described that efficacious potentially disease modifying therapies in SSc may interfere with progression of nailfold microvascular damage, as assessed by NVC, over long term at least in presence of digital ulcers. NVC is a safe and reliable tool for the early diagnosis of SSc and the different NVC scleroderma patterns have a predictive value for the clinical complications of the disease.

  4. Predictive Value of Auricular Diagnosis on Coronary Heart Disease

    Directory of Open Access Journals (Sweden)

    Lorna Kwai-Ping Suen

    2012-01-01

    Full Text Available The ear has a reflexive property; therefore, various physical attributes may appear on the auricle when disorders of the internal organs or other parts of the body exist. Auricular diagnostics is an objective, painless, and noninvasive method that provides rapid access to information. Thus, the association between auricular signals and coronary heart disease (CHD should be further investigated. A case control study was conducted to determine the predictive value of auricular signals on 100 cases of CHD (CHD+ve = 50; CHD−ve = 50 via visual inspection, electrical skin resistance measurement, and tenderness testing. The results showed that the presence of an ear lobe crease (ELC was significantly associated with coronary heart disease. The “heart” zone of the CHD+ve group significantly exhibited higher conductivity on both ears than that of the controls. The CHD+ve group experienced significant tenderness in the “heart” region compared with those in the CHD−ve group in both acute and chronic conditions. Further studies that take into consideration the impact of age, race, and earlobe shape on ELC prevalence in a larger sample should be done.

  5. Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems.

    Science.gov (United States)

    Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A

    2012-01-01

    Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

  6. Responsiveness and predictive value of EDSS and MSFC in primary progressive MS.

    Science.gov (United States)

    Kragt, J J; Thompson, A J; Montalban, X; Tintoré, M; Río, J; Polman, C H; Uitdehaag, B M J

    2008-03-25

    We studied the responsiveness and predictive value of two widely used clinical outcome measures that document multiple sclerosis (MS) disease progression-the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC)-in patients with primary progressive (PP) MS. Disease course in PPMS shows less fluctuation than in relapsing remitting (RR) MS. In a group of 161 patients with PPMS, EDSS and MSFC were performed at three timepoints. To assess responsiveness, mean change scores and variances were plotted against baseline scores and effect sizes were calculated. Predictive value was determined by calculating sensitivity, specificity, and likelihood ratios (LRs) of 1-year changes to predict changes over 2 years. Furthermore, multivariate logistic regression models were used to assess the predictive value of short-term worsening on EDSS and MSFC. Responsiveness of both EDSS and MSFC was shown to be limited and mean changes were highly dependent on the baseline scores. Effect sizes for EDSS and MSFC were small and inconclusive (0.239 and 0.161). The predictive value of a short-term worsening (baseline to year 1) to predict worsening in the long term (baseline to year 2) was expressed for EDSS by a sensitivity of 0.55 and a LR+ of 8.64. For MSFC, sensitivity was 0.68 and LR+ was 3.14. However, short-term worsening was a poor predictor of subsequent worsening (year 1 to year 2) for EDSS (LR+ 1.06) and this relationship was actually inverse for MSFC (LR+ 0.61). In this study over a period of 2 years in primary progressive multiple sclerosis, the Multiple Sclerosis Functional Composite (MSFC) was less responsive than the Expanded Disability Status Scale (EDSS). The predictive value of neither EDSS nor MSFC was very powerful.

  7. Suitability of faecal near-infrared reflectance spectroscopy (NIRS) predictions for estimating gross calorific value

    Energy Technology Data Exchange (ETDEWEB)

    De la Roza-Delgado, B.; Modroño, S.; Vicente, F.; Martínez-Fernández, A.; Soldado, A.

    2015-07-01

    A total of 220 faecal pig and poultry samples, collected from different experimental trials were employed with the aim to demonstrate the suitability of Near Infrared Reflectance Spectroscopy (NIRS) technology for estimation of gross calorific value on faeces as output products in energy balances studies. NIR spectra from dried and grounded faeces samples were analyzed using a Foss NIRSystem 6500 instrument, scanning over the wavelength range 400-2500 nm. Validation studies for quantitative analytical models were carried out to estimate the relevance of method performance associated to reference values to obtain an appropriate, accuracy and precision. The results for prediction of gross calorific value (GCV) of NIRS calibrations obtained for individual species showed high correlation coefficients comparing chemical analysis and NIRS predictions, ranged from 0.92 to 0.97 for poultry and pig. For external validation, the ratio between the standard error of cross validation (SECV) and the standard error of prediction (SEP) varied between 0.73 and 0.86 for poultry and pig respectively, indicating a sufficiently precision of calibrations. In addition a global model to estimate GCV in both species was developed and externally validated. It showed correlation coefficients of 0.99 for calibration, 0.98 for cross-validation and 0.97 for external validation. Finally, relative uncertainty was calculated for NIRS developed prediction models with the final value when applying individual NIRS species model of 1.3% and 1.5% for NIRS global prediction. This study suggests that NIRS is a suitable and accurate method for the determination of GCV in faeces, decreasing cost, timeless and for convenient handling of unpleasant samples.. (Author)

  8. An analytical high value target acquisition model

    OpenAIRE

    Becker, Kevin J.

    1986-01-01

    Approved for public release; distribution is unlimited An Analytical High Value Target (HVT) acquisition model is developed for a generic anti-ship cruise missile system. the target set is represented as a single HVT within a field of escorts. The HVT's location is described by a bivariate normal probability distribution. the escorts are represented by a spatially homogeneous Poisson random field surrounding the HVT. Model output consists of the probability that at least one missile of...

  9. Cultural Values Predicting Acculturation Orientations: Operationalizing a Quantitative Measure

    Science.gov (United States)

    Ehala, Martin

    2012-01-01

    This article proposes that acculturation orientations are related to two sets of cultural values: utilitarianism (Ut) and traditionalism (Tr). While utilitarian values enhance assimilation, traditional values support language and identity maintenance. It is proposed that the propensity to either end of this value opposition can be measured by an…

  10. Cultural Values Predicting Acculturation Orientations: Operationalizing a Quantitative Measure

    Science.gov (United States)

    Ehala, Martin

    2012-01-01

    This article proposes that acculturation orientations are related to two sets of cultural values: utilitarianism (Ut) and traditionalism (Tr). While utilitarian values enhance assimilation, traditional values support language and identity maintenance. It is proposed that the propensity to either end of this value opposition can be measured by an…

  11. Value-Added Modeling in Physical Education

    Science.gov (United States)

    Hushman, Glenn; Hushman, Carolyn

    2015-01-01

    The educational reform movement in the United States has resulted in a variety of states moving toward a system of value-added modeling (VAM) to measure a teacher's contribution to student achievement. Recently, many states have begun using VAM scores as part of a larger system to evaluate teacher performance. In the past decade, only "core…

  12. A Model for Valuing Military Talents

    Institute of Scientific and Technical Information of China (English)

    LIU Hong-sheng

    2002-01-01

    The method of collocating military talents is a difficult problem. It is different from other talents, for the characteristic of military talents. This paper presents a model for valuing military talents,which can assists the military leaders to collocate military talents properly.

  13. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  14. Values, attitudes, and frequency of meat consumption. Predicting meat-reduced diet in Australians.

    Science.gov (United States)

    Hayley, Alexa; Zinkiewicz, Lucy; Hardiman, Kate

    2015-01-01

    Reduced consumption of meat, particularly red meat, is associated with numerous health benefits. While past research has examined demographic and cognitive correlates of meat-related diet identity and meat consumption behaviour, the predictive influence of personal values on meat-consumption attitudes and behaviour, as well as gender differences therein, has not been explicitly examined, nor has past research focusing on 'meat' generally addressed 'white meat' and 'fish/seafood' as distinct categories of interest. Two hundred and two Australians (59.9% female, 39.1% male, 1% unknown), aged 18 to 91 years (M = 31.42, SD = 16.18), completed an online questionnaire including the Schwartz Values Survey, and measures of diet identity, attitude towards reduced consumption of each of red meat, white meat, and fish/seafood, as well as self-reported estimates of frequency of consumption of each meat type. Results showed that higher valuing of Universalism predicted more positive attitudes towards reducing, and less frequent consumption of, each of red meat, white meat, and fish/seafood, while higher Power predicted less positive attitudes towards reducing, and more frequent consumption of, these meats. Higher Security predicted less positive attitudes towards reducing, and more frequent consumption, of white meat and fish/seafood, while Conformity produced this latter effect for fish/seafood only. Despite men valuing Power more highly than women, women valuing Universalism more highly than men, and men eating red meat more frequently than women, gender was not a significant moderator of the value-attitude-behaviour mediations described, suggesting that gender's effects on meat consumption may not be robust once entered into a multivariate model of MRD attitudes and behaviour. Results support past findings associating Universalism, Power, and Security values with meat-eating preferences, and extend these findings by articulating how these values relate specifically

  15. 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.

  16. Positive predictive value for polyps detected at screening CT colonography

    Energy Technology Data Exchange (ETDEWEB)

    Pickhardt, Perry J.; Wise, Steven M.; Kim, David H. [University of Wisconsin School of Medicine and Public Health, Department of Radiology, Madison, WI (United States)

    2010-07-15

    To determine the positive predictive value (PPV) for polyps detected at CT colonography (CTC). Assessment of 739 colorectal lesions {>=}6 mm detected prospectively at CTC screening in 479 patients was performed. By-polyp PPV was analyzed according to small (6-9 mm) versus large ({>=}10 mm) size; morphology (sessile/pedunculated/flat); diagnostic confidence level (3 = most confident, 1 = least confident); and histology. By-patient PPV was analyzed at various polyp size thresholds. By-polyp PPV for CTC-detected lesions {>=}6 mm, 6-9 mm, and {>=}10 mm was 91.6% (677/739), 90.1% (410/451), and 92.7% (267/288), respectively (p = 0.4). By-polyp PPV according to sessile, pedunculated, flat, and mass-like morphology was 92.5% (441/477), 96.5% (139/144), 77.7% (73/94), and 97.6% (40/41), respectively (p < 0.0001 for flat versus polypoid morphology). By-polyp PPV according to diagnostic confidence level was 94.7% (554/585) for highest (= level 3), 83.5% (106/127) for intermediate (= level 2), and 63.0% (17/27) for lowest (= level 1) confidence (p < 0.0001 for levels-2/3 versus level-1). By-patient PPV at 6-mm, 8-mm, 10-mm, and 30-mm polyp size thresholds was 92.3% (442/479), 93.0% (306/329), 93.1% (228/245), and 97.4% (38/39), respectively. The overall per-polyp and per-patient PPV for lesions {>=}6 mm was 92% for CTC screening. Increased diagnostic confidence and polypoid (non-flat) morphology correlated with a higher PPV, whereas small versus large polyp size had very little effect. (orig.)

  17. Selecting Testlet Features With Predictive Value for the Testlet Effect

    Directory of Open Access Journals (Sweden)

    Muirne C. S. Paap

    2015-04-01

    Full Text Available High-stakes tests often consist of sets of questions (i.e., items grouped around a common stimulus. Such groupings of items are often called testlets. A basic assumption of item response theory (IRT, the mathematical model commonly used in the analysis of test data, is that individual items are independent of one another. The potential dependency among items within a testlet is often ignored in practice. In this study, a technique called tree-based regression (TBR was applied to identify key features of stimuli that could properly predict the dependence structure of testlet data for the Analytical Reasoning section of a high-stakes test. Relevant features identified included Percentage of “If” Clauses, Number of Entities, Theme/Topic, and Predicate Propositional Density; the testlet effect was smallest for stimuli that contained 31% or fewer “if” clauses, contained 9.8% or fewer verbs, and had Media or Animals as the main theme. This study illustrates the merits of TBR in the analysis of test data.

  18. PREDICT : model for prediction of survival in localized prostate cancer

    NARCIS (Netherlands)

    Kerkmeijer, Linda G W; Monninkhof, Evelyn M.; van Oort, Inge M.; van der Poel, Henk G.; de Meerleer, Gert; van Vulpen, Marco

    2016-01-01

    Purpose: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed.Methods: From 1989 to 2008, 3383 patients were treated with I

  19. Using Heuristic Value Prediction and Dynamic Task Granularity Resizing to Improve Software Speculation

    Directory of Open Access Journals (Sweden)

    Fan Xu

    2014-01-01

    Full Text Available Exploiting potential thread-level parallelism (TLP is becoming the key factor to improving performance of programs on multicore or many-core systems. Among various kinds of parallel execution models, the software-based speculative parallel model has become a research focus due to its low cost, high efficiency, flexibility, and scalability. The performance of the guest program under the software-based speculative parallel execution model is closely related to the speculation accuracy, the control overhead, and the rollback overhead of the model. In this paper, we first analyzed the conventional speculative parallel model and presented an analytic model of its expectation of the overall overhead, then optimized the conventional model based on the analytic model, and finally proposed a novel speculative parallel model named HEUSPEC. The HEUSPEC model includes three key techniques, namely, the heuristic value prediction, the value based correctness checking, and the dynamic task granularity resizing. We have implemented the runtime system of the model in ANSI C language. The experiment results show that when the speedup of the HEUSPEC model can reach 2.20 on the average (15% higher than conventional model when depth is equal to 3 and 4.51 on the average (12% higher than conventional model when speculative depth is equal to 7. Besides, it shows good scalability and lower memory cost.

  20. Skill and relative economic value of medium-range hydrological ensemble predictions

    Directory of Open Access Journals (Sweden)

    E. Roulin

    2007-01-01

    Full Text Available A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF Ensemble Prediction System (EPS, is evaluated for two Belgian catchments using verification methods borrowed from meteorology. The skill of the probability forecast that the streamflow exceeds a given level is measured with the Brier Skill Score. Then the value of the system is assessed using a cost-loss decision model. The verification results of the hydrological ensemble predictions are compared with the corresponding results obtained for simpler alternatives as the one obtained by using of the deterministic forecast of ECMWF which is characterized by a higher spatial resolution or by using of the EPS ensemble mean.

  1. The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer.

    Science.gov (United States)

    Hawken, Steven J; Greenwood, Celia M T; Hudson, Thomas J; Kustra, Rafal; McLaughlin, John; Yang, Quanhe; Zanke, Brent W; Little, Julian

    2010-07-01

    Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., approximately 140-160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual's CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci

  2. Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

    Science.gov (United States)

    Crossa, José; Campos, Gustavo de los; Pérez, Paulino; Gianola, Daniel; Burgueño, Juan; Araus, José Luis; Makumbi, Dan; Singh, Ravi P.; Dreisigacker, Susanne; Yan, Jianbing; Arief, Vivi; Banziger, Marianne; Braun, Hans-Joachim

    2010-01-01

    The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed. PMID:20813882

  3. Belief about nicotine selectively modulates value and reward prediction error signals in smokers

    Science.gov (United States)

    Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R.; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M.; Montague, P. Read

    2015-01-01

    Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers’ prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers’ beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of “no nicotine in cigarette” (compared with “nicotine in cigarette”) strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers’ choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems. PMID:25605923

  4. Belief about nicotine selectively modulates value and reward prediction error signals in smokers.

    Science.gov (United States)

    Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro; Baldwin, Philip R; Soltani, Alireza; Kirk, Ulrich; Cinciripini, Paul M; Montague, P Read

    2015-02-24

    Little is known about how prior beliefs impact biophysically described processes in the presence of neuroactive drugs, which presents a profound challenge to the understanding of the mechanisms and treatments of addiction. We engineered smokers' prior beliefs about the presence of nicotine in a cigarette smoked before a functional magnetic resonance imaging session where subjects carried out a sequential choice task. Using a model-based approach, we show that smokers' beliefs about nicotine specifically modulated learning signals (value and reward prediction error) defined by a computational model of mesolimbic dopamine systems. Belief of "no nicotine in cigarette" (compared with "nicotine in cigarette") strongly diminished neural responses in the striatum to value and reward prediction errors and reduced the impact of both on smokers' choices. These effects of belief could not be explained by global changes in visual attention and were specific to value and reward prediction errors. Thus, by modulating the expression of computationally explicit signals important for valuation and choice, beliefs can override the physical presence of a potent neuroactive compound like nicotine. These selective effects of belief demonstrate that belief can modulate model-based parameters important for learning. The implications of these findings may be far ranging because belief-dependent effects on learning signals could impact a host of other behaviors in addiction as well as in other mental health problems.

  5. Efficient Smoothing for Boundary Value Models

    Science.gov (United States)

    1989-12-29

    IEEE Transactions on Automatic Control , vol. 29, pp. 803-821, 1984. [2] A. Bagchi and H. Westdijk, "Smoothing...and likelihood ratio for Gaussian boundary value processes," IEEE Transactions on Automatic Control , vol. 34, pp. 954-962, 1989. [3] R. Nikoukhah et...77-96, 1988. [6] H. L. Weinert and U. B. Desai, "On complementary models and fixed- interval smoothing," IEEE Transactions on Automatic Control ,

  6. Value Concept and Economic Growth Model

    Directory of Open Access Journals (Sweden)

    Truong Hong Trinh

    2014-12-01

    Full Text Available This paper approaches the value added method for Gross Domestic Product (GDP measurement that explains the interrelationship between the expenditure approach and the income approach. The economic growth model is also proposed with three key elements of capital accumulation, technological innovation, and institutional reform. Although capital accumulation and technological innovation are two integrated elements in driving economic growth, institutional reforms play a key role in creating incentives that effect the transitional and steady state growth rate in the real world economy. The paper provides a theoretical insight on economic growth to understand incentives and driving forces in economic growth model.

  7. Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables

    DEFF Research Database (Denmark)

    Guo, Gang; Lund, Mogens Sandø; Zhang, Y;

    2010-01-01

    This study compared genomic predictions using conventional estimated breeding values (EBV) and daughter yield deviations (DYD) as response variables based on simulated data. Eight scenarios were simulated in regard to heritability (0.05 and 0.30), number of daughters per sire (30, 100, and unequal......), the EBV and DYD approaches provided similar genomic estimated breeding value (GEBV) reliabilities, except for scenarios with unequal numbers of daughters and half of sires without genotype, for which the EBV approach was superior to the DYD approach (by 1.2 and 2.4%). Using a Bayesian mixture prior model...

  8. Pressure prediction model for compression garment design.

    Science.gov (United States)

    Leung, W Y; Yuen, D W; Ng, Sun Pui; Shi, S Q

    2010-01-01

    Based on the application of Laplace's law to compression garments, an equation for predicting garment pressure, incorporating the body circumference, the cross-sectional area of fabric, applied strain (as a function of reduction factor), and its corresponding Young's modulus, is developed. Design procedures are presented to predict garment pressure using the aforementioned parameters for clinical applications. Compression garments have been widely used in treating burning scars. Fabricating a compression garment with a required pressure is important in the healing process. A systematic and scientific design method can enable the occupational therapist and compression garments' manufacturer to custom-make a compression garment with a specific pressure. The objectives of this study are 1) to develop a pressure prediction model incorporating different design factors to estimate the pressure exerted by the compression garments before fabrication; and 2) to propose more design procedures in clinical applications. Three kinds of fabrics cut at different bias angles were tested under uniaxial tension, as were samples made in a double-layered structure. Sets of nonlinear force-extension data were obtained for calculating the predicted pressure. Using the value at 0° bias angle as reference, the Young's modulus can vary by as much as 29% for fabric type P11117, 43% for fabric type PN2170, and even 360% for fabric type AP85120 at a reduction factor of 20%. When comparing the predicted pressure calculated from the single-layered and double-layered fabrics, the double-layered construction provides a larger range of target pressure at a particular strain. The anisotropic and nonlinear behaviors of the fabrics have thus been determined. Compression garments can be methodically designed by the proposed analytical pressure prediction model.

  9. Predictive value of hormonal parameters for live birth in women with unexplained infertility and male infertility.

    Science.gov (United States)

    Murto, Tiina; Bjuresten, Kerstin; Landgren, Britt-Marie; Stavreus-Evers, Anneli

    2013-07-11

    Infertile women might get pregnant sometime after fertility treatment, but today, there is no prediction model on who will eventually have children. The objective of the present study was to characterize hormone levels in an arbitrary menstrual cycle in women with unexplained infertility and male infertility, and to determine the predictive value for long-term possibility of live birth. In this cross-sectional study, with 71 infertile women with diagnosis unexplained infertility and male infertility, blood samples were obtained during the proliferative and secretory phases of an arbitrary menstrual cycle. Serum concentrations of FSH, LH, AMH, inhibin B, estradiol, progesterone, PRL and TSH were determined. The predictive value of ovulation and hormonal analysis was determined by identifying the proportion of women with at least one live birth. Mann Whitney U test, chi2 test and Spearman's correlation were used for statistical analysis. A value of p hormone values and live birth rates between women with unexplained infertility and male infertility. The best sole predictors of live birth were age of the women, followed by ovulatory cycle, defined as serum progesterone concentration of greater than or equal to 32 nmol/L, and a serum TSH concentration of less than or equal to 2.5 mIU/L. Combining the age with the ovulatory cycle and serum TSH less than or equal to 2.5 mIU/L or serum AMH greater than or equal to 10 pmol/L the predictive value was close to 90%. Age in combination with the presence of an ovulatory cycle and serum TSH or serum AMH is predictive for long-term live birth. The advantage of serum AMH compared with serum TSH is the very little variation throughout the menstrual cycle, which makes it a useful tool in infertility diagnosis.

  10. Predictive Modeling of Cardiac Ischemia

    Science.gov (United States)

    Anderson, Gary T.

    1996-01-01

    The goal of the Contextual Alarms Management System (CALMS) project is to develop sophisticated models to predict the onset of clinical cardiac ischemia before it occurs. The system will continuously monitor cardiac patients and set off an alarm when they appear about to suffer an ischemic episode. The models take as inputs information from patient history and combine it with continuously updated information extracted from blood pressure, oxygen saturation and ECG lines. Expert system, statistical, neural network and rough set methodologies are then used to forecast the onset of clinical ischemia before it transpires, thus allowing early intervention aimed at preventing morbid complications from occurring. The models will differ from previous attempts by including combinations of continuous and discrete inputs. A commercial medical instrumentation and software company has invested funds in the project with a goal of commercialization of the technology. The end product will be a system that analyzes physiologic parameters and produces an alarm when myocardial ischemia is present. If proven feasible, a CALMS-based system will be added to existing heart monitoring hardware.

  11. Incremental value of hormonal therapy for deep vein thrombosis prediction: an adjusted Wells score for women.

    Science.gov (United States)

    Barros, Márcio Vinícius Lins de; Arancibia, Ana Elisa Loyola; Costa, Ana Paula; Bueno, Fernando Brito; Martins, Marcela Aparecida Corrêa; Magalhães, Maria Cláudia; Silva, José Luiz Padilha; Bastos, Marcos de

    2016-04-01

    Deep venous thrombosis (DVT) management includes prediction rule evaluation to define standard pretest DVT probabilities in symptomatic patients. The aim of this study was to evaluate the incremental usefulness of hormonal therapy to the Wells prediction rules for DVT in women. We studied women undertaking compressive ultrasound scanning for suspected DVT. We adjusted the Wells score for DVT, taking into account the β-coefficients of the logistic regression model. Data discrimination was evaluated by the receiver operating characteristic (ROC) curve. The adjusted score calibration was assessed graphically and by the Hosmer-Lemeshow test. Reclassification tables and the net reclassification index were used for the adjusted score comparison with the Wells score for DVT. We observed 461 women including 103 DVT events. The mean age was 56 years (±21 years). The adjusted logistic regression model included hormonal therapy and six Wells prediction rules for DVT. The adjusted score weights ranged from -4 to 4. Hosmer-Lemeshow test showed a nonsignificant P value (0.69) and the calibration graph showed no differences between the expected and the observed values. The area under the ROC curve was 0.92 [95% confidence interval (CI) 0.90-0.95] for the adjusted model and 0.87 (95% CI 0.84-0.91) for the Wells score for DVT (Delong test, P value < 0.01). Net reclassification index for the adjusted score was 0.22 (95% CI 0.11-0.33, P value < 0.01). Our results suggest an incremental usefulness of hormonal therapy as an independent DVT prediction rule in women compared with the Wells score for DVT. The adjusted score must be evaluated in different populations before clinical use.

  12. Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.

    Science.gov (United States)

    Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W

    2014-11-26

    Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding.

  13. Predictive In Vivo Models for Oncology.

    Science.gov (United States)

    Behrens, Diana; Rolff, Jana; Hoffmann, Jens

    2016-01-01

    Experimental oncology research and preclinical drug development both substantially require specific, clinically relevant in vitro and in vivo tumor models. The increasing knowledge about the heterogeneity of cancer requested a substantial restructuring of the test systems for the different stages of development. To be able to cope with the complexity of the disease, larger panels of patient-derived tumor models have to be implemented and extensively characterized. Together with individual genetically engineered tumor models and supported by core functions for expression profiling and data analysis, an integrated discovery process has been generated for predictive and personalized drug development.Improved “humanized” mouse models should help to overcome current limitations given by xenogeneic barrier between humans and mice. Establishment of a functional human immune system and a corresponding human microenvironment in laboratory animals will strongly support further research.Drug discovery, systems biology, and translational research are moving closer together to address all the new hallmarks of cancer, increase the success rate of drug development, and increase the predictive value of preclinical models.

  14. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    Energy Technology Data Exchange (ETDEWEB)

    Jairam, Pushpa M. [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Jong, Pim A. de; Mali, Willem P.T.M. [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Isgum, Ivana [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); Graaf, Yolanda van der [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); Collaboration: PROVIDI study-group

    2015-06-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  15. Development of Interpretable Predictive Models for BPH and Prostate Cancer

    Science.gov (United States)

    Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, JA

    2015-01-01

    BACKGROUND Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. METHODS An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. RESULTS Statistical dependence with PC and BPH was found for prostate volume (P-value < 0.001), PSA (P-value < 0.001), international prostate symptom score (IPSS; P-value < 0.001), digital rectal examination (DRE; P-value < 0.001), age (P-value < 0.002), antecedents (P-value < 0.006), and meat consumption (P-value < 0.08). The two predictive models that were constructed selected a subset of these, namely, volume, PSA, DRE, and IPSS, obtaining an area under the ROC curve (AUC) between 72% and 80% for both PC and BPH prediction. CONCLUSION PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced. PMID:25780348

  16. From Business Value Model to Coordination Process Model

    Science.gov (United States)

    Fatemi, Hassan; van Sinderen, Marten; Wieringa, Roel

    The increased complexity of business webs calls for modeling the collaboration of enterprises from different perspectives, in particular the business and process perspectives, and for mutually aligning these perspectives. Business value modeling and coordination process modeling both are necessary for a good e-business design, but these activities have different goals and use different concepts. Nevertheless, the resulting models should be consistent with each other because they refer to the same system from different perspectives. Hence, checking the consistency between these models or producing one based on the other would be of high value. In this paper we discuss the issue of achieving consistency in multi-level e-business design and give guidelines to produce consistent coordination process models from business value models in a stepwise manner.

  17. Structural features that predict real-value fluctuations of globular proteins.

    Science.gov (United States)

    Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

    2012-05-01

    It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

  18. Intelligent predictive model of ventilating capacity of imperial smelt furnace

    Institute of Scientific and Technical Information of China (English)

    唐朝晖; 胡燕瑜; 桂卫华; 吴敏

    2003-01-01

    In order to know the ventilating capacity of imperial smelt furnace (ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.

  19. Prediction of cereal feed value using spectroscopy and chemometrics

    DEFF Research Database (Denmark)

    Jørgensen, Johannes Ravn; Gislum, René

    2009-01-01

    Feed value in form of FEsv (Feed unit / kg dry matter, for piglets) and FEso (Feed unit / kg dry matter, for sows), EDOM (Enzyme Degradable Organic Matter) and EDOMi (Enzyme Degradable Organic Matter, Ileum) is used in the feed evaluation system for pigs. Analysis of feed value have highlighted...... for cereal breeders. A collection of 1213 grain samples of wheat, triticale, barley and rye, and related chemical reference analyses to describe the feed value have been established. The samples originate from available field trials over a three-year period. The chemical reference analyses are dry matter...

  20. Predictive Model of Radiative Neutrino Masses

    CERN Document Server

    Babu, K S

    2013-01-01

    We present a simple and predictive model of radiative neutrino masses. It is a special case of the Zee model which introduces two Higgs doublets and a charged singlet. We impose a family-dependent Z_4 symmetry acting on the leptons, which reduces the number of parameters describing neutrino oscillations to four. A variety of predictions follow: The hierarchy of neutrino masses must be inverted; the lightest neutrino mass is extremely small and calculable; one of the neutrino mixing angles is determined in terms of the other two; the phase parameters take CP-conserving values with \\delta_{CP} = \\pi; and the effective mass in neutrinoless double beta decay lies in a narrow range, m_{\\beta \\beta} = (17.6 - 18.5) meV. The ratio of vacuum expectation values of the two Higgs doublets, tan\\beta, is determined to be either 1.9 or 0.19 from neutrino oscillation data. Flavor-conserving and flavor-changing couplings of the Higgs doublets are also determined from neutrino data. The non-standard neutral Higgs bosons, if t...

  1. Incremental predictive value of natriuretic peptides for prognosis in the chronic stable heart failure population: a systematic review.

    Science.gov (United States)

    Don-Wauchope, Andrew C; Santaguida, Pasqualina L; Oremus, Mark; McKelvie, Robert; Ali, Usman; Brown, Judy A; Bustamam, Amy; Sohel, Nazmul; Hill, Stephen A; Booth, Ronald A; Balion, Cynthia; Raina, Parminder

    2014-08-01

    The aim of this study was to determine whether measurement of natriuretic peptides independently adds incremental predictive value for mortality and morbidity in patients with chronic stable heart failure (CSHF). We electronically searched Medline®, Embase™, AMED, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and CINAHL from 1989 to June 2012. We also searched reference lists of included articles, systematic reviews, and the gray literature. Studies were screened for eligibility criteria and assessed for methodological quality. Data were extracted on study design, population demographics, assay cutpoints, prognostic risk prediction model covariates, statistical methods, outcomes, and results. One hundred and eighty-three studies were identified as prognostic in the systematic review. From these, 15 studies (all NT-proBNP) considered incremental predictive value in CSHF subjects. Follow-up varied from 12 to 37 months. All studies presented at least one estimate of incremental predictive value of NT-proBNP relative to the base prognostic model. Using discrimination or likelihood statistics, these studies consistently showed that NT-proBNP increased model performance. Three studies used re-classification and model validation computations to establish incremental predictive value; these studies showed less consistency with respect to added value. Although there were differences in the base risk prediction models, assay cutpoints, and lengths of follow-up, there was consistency in NT-proBNP adding incremental predictive value for prognostic models in chronic stable CSHF patients. The limitations in the literature suggest that studies designed to evaluate prognostic models should be undertaken to evaluate the incremental value of natriuretic peptide as a predictor of mortality and morbidity in CSHF.

  2. Proving the ecosystem value through hydrological modelling

    Science.gov (United States)

    Dorner, W.; Spachinger, K.; Porter, M.; Metzka, R.

    2008-11-01

    Ecosystems provide valuable functions. Also natural floodplains and river structures offer different types of ecosystem functions such as habitat function, recreational area and natural detention. From an economic stand point the loss (or rehabilitation) of these natural systems and their provided natural services can be valued as a damage (or benefit). Consequently these natural goods and services must be economically valued in project assessments e.g. cost-benefit-analysis or cost comparison. Especially in smaller catchments and river systems exists significant evidence that natural flood detention reduces flood risk and contributes to flood protection. Several research projects evaluated the mitigating effect of land use, river training and the loss of natural flood plains on development, peak and volume of floods. The presented project analysis the hypothesis that ignoring natural detention and hydrological ecosystem services could result in economically inefficient solutions for flood protection and mitigation. In test areas, subcatchments of the Danube in Germany, a combination of hydrological and hydrodynamic models with economic evaluation techniques was applied. Different forms of land use, river structure and flood protection measures were assed and compared from a hydrological and economic point of view. A hydrodynamic model was used to simulate flows to assess the extent of flood affected areas and damages to buildings and infrastructure as well as to investigate the impacts of levees and river structure on a local scale. These model results provided the basis for an economic assessment. Different economic valuation techniques, such as flood damage functions, cost comparison method and substation-approach were used to compare the outcomes of different hydrological scenarios from an economic point of view and value the ecosystem service. The results give significant evidence that natural detention must be evaluated as part of flood mitigation projects

  3. Prediction of Genomic Breeding Values for feed efficiency and related traits in pigs

    DEFF Research Database (Denmark)

    Do, Duy Ngoc; Janss, Luc; Strathe, Anders Bjerring

    Improvement of feed efficiency is essential in pig breeding and selection for reduced residual feed intake (RFI) is an option. Accuracy of genomic prediction (GP) relies on assumptions of genetic architecture of the traits. This study applied five different Bayesian Power LASSO (BPL) models...... with different power parameters to investigate genetic architecture of RFI, to predict genomic breeding values, and to partition genetic variances for different SNP groups. Data were 1272 Duroc pigs with both genotypic and phenotypic records for RFI as well as daily feed intake (DFI). The gene mapping confirmed...... and indicates their potentials for genomic prediction. Further work includes applying other GP methods for RFI and DFI as well as extending these methods to feed efficiency related traits such as feeding behaviour and body composition traits....

  4. 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...

  5. Episodic memories predict adaptive value-based decision-making.

    Science.gov (United States)

    Murty, Vishnu P; FeldmanHall, Oriel; Hunter, Lindsay E; Phelps, Elizabeth A; Davachi, Lila

    2016-05-01

    Prior research illustrates that memory can guide value-based decision-making. For example, previous work has implicated both working memory and procedural memory (i.e., reinforcement learning) in guiding choice. However, other types of memories, such as episodic memory, may also influence decision-making. Here we test the role for episodic memory-specifically item versus associative memory-in supporting value-based choice. Participants completed a task where they first learned the value associated with trial unique lotteries. After a short delay, they completed a decision-making task where they could choose to reengage with previously encountered lotteries, or new never before seen lotteries. Finally, participants completed a surprise memory test for the lotteries and their associated values. Results indicate that participants chose to reengage more often with lotteries that resulted in high versus low rewards. Critically, participants not only formed detailed, associative memories for the reward values coupled with individual lotteries, but also exhibited adaptive decision-making only when they had intact associative memory. We further found that the relationship between adaptive choice and associative memory generalized to more complex, ecologically valid choice behavior, such as social decision-making. However, individuals more strongly encode experiences of social violations-such as being treated unfairly, suggesting a bias for how individuals form associative memories within social contexts. Together, these findings provide an important integration of episodic memory and decision-making literatures to better understand key mechanisms supporting adaptive behavior.

  6. Modeling the value of strategic actions in the superior colliculus

    Directory of Open Access Journals (Sweden)

    Dhushan Thevarajah

    2010-02-01

    Full Text Available In learning models of strategic game play, an agent constructs a valuation (action value over possible future choices as a function of past actions and rewards. Choices are then stochastic functions of these action values. Our goal is to uncover a neural signal that correlates with the action value posited by behavioral learning models. We measured activity from neurons in the superior colliculus (SC, a midbrain region involved in planning saccadic eye movements, in monkeys while they performed two saccade tasks. In the strategic task, monkeys competed against a computer in a saccade version of the mixed-strategy game “matching-pennies”. In the instructed task, stochastic saccades were elicited through explicit instruction rather than free choices. In both tasks, neuronal activity and behavior were shaped by past actions and rewards with more recent events exerting a larger influence. Further, SC activity predicted upcoming choices during the strategic task and upcoming reaction times during the instructed task. Finally, we found that neuronal activity in both tasks correlated with an established learning model, the Experience Weighted Attraction model of action valuation (Ho, Camerer, and Chong, 2007. Collectively, our results provide evidence that action values hypothesized by learning models are represented in the motor planning regions of the brain in a manner that could be used to select strategic actions.

  7. Predictive Model Assessment for Count Data

    Science.gov (United States)

    2007-09-05

    critique count regression models for patent data, and assess the predictive performance of Bayesian age-period-cohort models for larynx cancer counts...the predictive performance of Bayesian age-period-cohort models for larynx cancer counts in Germany. We consider a recent suggestion by Baker and...Figure 5. Boxplots for various scores for patent data count regressions. 11 Table 1 Four predictive models for larynx cancer counts in Germany, 1998–2002

  8. Is genetic testing of value in predicting and treating obesity?

    Science.gov (United States)

    Ng, Maggie C Y; Bowden, Donald W

    2013-01-01

    Obesity is a multifactorial disease resulting from the interaction between genetic factors and lifestyle. Identification of rare genetic variations with strong effects on obesity has been useful in diagnosing and designing personalized therapy for early-onset or syndromic obesity. However, common variants identified in recent genome-wide association studies have limited clinical value.

  9. Is Genetic Testing of Value in Predicting and Treating Obesity?

    OpenAIRE

    Ng, Maggie C.Y.; Bowden, Donald W.

    2013-01-01

    Obesity is a multifactorial disease resulting from the interaction between genetic factors and lifestyle. Identification of rare genetic variations with strong effects on obesity has been useful in diagnosing and designing personalized therapy for early-onset or syndromic obesity. However, common variants identified in recent genome-wide association studies have limited clinical value.

  10. Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

    Energy Technology Data Exchange (ETDEWEB)

    Genders, Tessa S.S. [Erasmus University Medical Center, Department of Epidemiology, P.O. Box 2040, CA, Rotterdam (Netherlands); Erasmus University Medical Center, Department of Radiology, P.O. Box 2040, CA, Rotterdam (Netherlands); Pugliese, Francesca; Mollet, Nico R.; Meijboom, W. Bob; Weustink, Annick C.; Mieghem, Carlos A.G. van; Feyter, Pim J. de [Erasmus University Medical Center, Department of Radiology, P.O. Box 2040, CA, Rotterdam (Netherlands); Erasmus University Medical Center, Department of Cardiology, P.O. Box 2040, CA, Rotterdam (Netherlands); Hunink, M.G.M. [Erasmus University Medical Center, Department of Epidemiology, P.O. Box 2040, CA, Rotterdam (Netherlands); Erasmus University Medical Center, Department of Radiology, P.O. Box 2040, CA, Rotterdam (Netherlands); Harvard University, Department of Health Policy and Management, Harvard School of Public Health, Boston (United States)

    2010-10-15

    To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as {>=}50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Re-analysing the variables used by Diamond and Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. (orig.)

  11. PREDICTION OF THE VALUE OF IRREVERSIBLE DEFORMATION OF ROAD STRUCTURE FROM THE IMPACT OF TRAFFIC

    Directory of Open Access Journals (Sweden)

    F. V. Matvienko

    2011-07-01

    Full Text Available Problem statement. The study of irreversible strains in areas of non-rigid pavement with asphalt coating under the influence of traffic flow requires development of methodologies for assessment of the operational status of asphalt concrete pavement subjected to the formation of ruts. To pre-dict the magnitude of irreversible deformation of the pavement, that is rut, mathematical model, methodology and instruments to measure the parameters of road construction should be developed.Results and conclusions. Measurements of the deflection of road construction and rut parameters, including wear and plastic deformation, proved the adequacy of the proposed mathematical model. Obtained analytical dependences allow prediction of pavement wear, plastic deformation and subgrade deterioration. In contrast to the known ones, they take into account the impact of traffic on the formation of a rut. Proposed methods allow estimation of irreversible pavement deformations based on the values obtained with the help of instruments.

  12. Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

    Science.gov (United States)

    Osman, Marisol; Vera, C. S.

    2016-11-01

    This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

  13. Technical note: A linear model for predicting δ13 Cprotein.

    Science.gov (United States)

    Pestle, William J; Hubbe, Mark; Smith, Erin K; Stevenson, Joseph M

    2015-08-01

    Development of a model for the prediction of δ(13) Cprotein from δ(13) Ccollagen and Δ(13) Cap-co . Model-generated values could, in turn, serve as "consumer" inputs for multisource mixture modeling of paleodiet. Linear regression analysis of previously published controlled diet data facilitated the development of a mathematical model for predicting δ(13) Cprotein (and an experimentally generated error term) from isotopic data routinely generated during the analysis of osseous remains (δ(13) Cco and Δ(13) Cap-co ). Regression analysis resulted in a two-term linear model (δ(13) Cprotein (%) = (0.78 × δ(13) Cco ) - (0.58× Δ(13) Cap-co ) - 4.7), possessing a high R-value of 0.93 (r(2)  = 0.86, P < 0.01), and experimentally generated error terms of ±1.9% for any predicted individual value of δ(13) Cprotein . This model was tested using isotopic data from Formative Period individuals from northern Chile's Atacama Desert. The model presented here appears to hold significant potential for the prediction of the carbon isotope signature of dietary protein using only such data as is routinely generated in the course of stable isotope analysis of human osseous remains. These predicted values are ideal for use in multisource mixture modeling of dietary protein source contribution. © 2015 Wiley Periodicals, Inc.

  14. Simple predictions from multifield inflationary models.

    Science.gov (United States)

    Easther, Richard; Frazer, Jonathan; Peiris, Hiranya V; Price, Layne C

    2014-04-25

    We explore whether multifield inflationary models make unambiguous predictions for fundamental cosmological observables. Focusing on N-quadratic inflation, we numerically evaluate the full perturbation equations for models with 2, 3, and O(100) fields, using several distinct methods for specifying the initial values of the background fields. All scenarios are highly predictive, with the probability distribution functions of the cosmological observables becoming more sharply peaked as N increases. For N=100 fields, 95% of our Monte Carlo samples fall in the ranges ns∈(0.9455,0.9534), α∈(-9.741,-7.047)×10-4, r∈(0.1445,0.1449), and riso∈(0.02137,3.510)×10-3 for the spectral index, running, tensor-to-scalar ratio, and isocurvature-to-adiabatic ratio, respectively. The expected amplitude of isocurvature perturbations grows with N, raising the possibility that many-field models may be sensitive to postinflationary physics and suggesting new avenues for testing these scenarios.

  15. Objective calibration of numerical weather prediction models

    Science.gov (United States)

    Voudouri, A.; Khain, P.; Carmona, I.; Bellprat, O.; Grazzini, F.; Avgoustoglou, E.; Bettems, J. M.; Kaufmann, P.

    2017-07-01

    Numerical weather prediction (NWP) and climate models use parameterization schemes for physical processes, which often include free or poorly confined parameters. Model developers normally calibrate the values of these parameters subjectively to improve the agreement of forecasts with available observations, a procedure referred as expert tuning. A practicable objective multi-variate calibration method build on a quadratic meta-model (MM), that has been applied for a regional climate model (RCM) has shown to be at least as good as expert tuning. Based on these results, an approach to implement the methodology to an NWP model is presented in this study. Challenges in transferring the methodology from RCM to NWP are not only restricted to the use of higher resolution and different time scales. The sensitivity of the NWP model quality with respect to the model parameter space has to be clarified, as well as optimize the overall procedure, in terms of required amount of computing resources for the calibration of an NWP model. Three free model parameters affecting mainly turbulence parameterization schemes were originally selected with respect to their influence on the variables associated to daily forecasts such as daily minimum and maximum 2 m temperature as well as 24 h accumulated precipitation. Preliminary results indicate that it is both affordable in terms of computer resources and meaningful in terms of improved forecast quality. In addition, the proposed methodology has the advantage of being a replicable procedure that can be applied when an updated model version is launched and/or customize the same model implementation over different climatological areas.

  16. 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.

  17. Nonlinear chaotic model for predicting storm surges

    NARCIS (Netherlands)

    Siek, M.; Solomatine, D.P.

    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.

  18. Model predictive torque control with an extended prediction horizon for electrical drive systems

    Science.gov (United States)

    Wang, Fengxiang; Zhang, Zhenbin; Kennel, Ralph; Rodríguez, José

    2015-07-01

    This paper presents a model predictive torque control method for electrical drive systems. A two-step prediction horizon is achieved by considering the reduction of the torque ripples. The electromagnetic torque and the stator flux error between predicted values and the references, and an over-current protection are considered in the cost function design. The best voltage vector is selected by minimising the value of the cost function, which aims to achieve a low torque ripple in two intervals. The study is carried out experimentally. The results show that the proposed method achieves good performance in both steady and transient states.

  19. Predeliberation activity in prefrontal cortex and striatum and the prediction of subsequent value judgment.

    Science.gov (United States)

    Maoz, Uri; Rutishauser, Ueli; Kim, Soyoun; Cai, Xinying; Lee, Daeyeol; Koch, Christof

    2013-01-01

    Rational, value-based decision-making mandates selecting the option with highest subjective expected value after appropriate deliberation. We examined activity in the dorsolateral prefrontal cortex (DLPFC) and striatum of monkeys deciding between smaller, immediate rewards and larger, delayed ones. We previously found neurons that modulated their activity in this task according to the animal's choice, while it deliberated (choice neurons). Here we found neurons whose spiking activities were predictive of the spatial location of the selected target (spatial-bias neurons) or the size of the chosen reward (reward-bias neurons) before the onset of the cue presenting the decision-alternatives, and thus before rational deliberation could begin. Their predictive power increased as the values the animals associated with the two decision alternatives became more similar. The ventral striatum (VS) preferentially contained spatial-bias neurons; the caudate nucleus (CD) preferentially contained choice neurons. In contrast, the DLPFC contained significant numbers of all three neuron types, but choice neurons were not preferentially also bias neurons of either kind there, nor were spatial-bias neurons preferentially also choice neurons, and vice versa. We suggest a simple winner-take-all (WTA) circuit model to account for the dissociation of choice and bias neurons. The model reproduced our results and made additional predictions that were borne out empirically. Our data are compatible with the hypothesis that the DLPFC and striatum harbor dissociated neural populations that represent choices and predeliberation biases that are combined after cue onset; the bias neurons have a weaker effect on the ultimate decision than the choice neurons, so their influence is progressively apparent for trials where the values associated with the decision alternatives are increasingly similar.

  20. Predeliberation activity in prefrontal cortex and striatum and the prediction of subsequent value judgment

    Directory of Open Access Journals (Sweden)

    Uri eMaoz

    2013-11-01

    Full Text Available Rational, value-based decision-making mandates selecting the option with highest subjective expected value after appropriate deliberation. We examined activity in the dorsolateral prefrontal cortex (DLPFC and striatum of monkeys deciding between smaller, immediate rewards and larger, delayed ones. We previously found neurons that modulated their activity in this task according to the animal’s choice, while it deliberated (choice neurons. Here we found neurons whose spiking activities were predictive of the spatial location of the selected target (spatial-bias neurons or the size of the chosen reward (reward-bias neurons before the onset of the cue presenting the decision-alternatives, and thus before rational deliberation could begin. Their predictive power increased as the values the animals associated with the two decision alternatives became more similar. The ventral striatum (VS preferentially contained spatial-bias neurons; the caudate nucleus (CD preferentially contained choice neurons. In contrast, the DLPFC contained significant numbers of all three neuron types, but choice neurons were not preferentially also bias neurons of either kind there, nor were spatial-bias neurons preferentially also choice neurons, and vice versa. We suggest a simple winner-take-all circuit model to account for the dissociation of choice and bias neurons. The model reproduced our results and made additional predictions that were borne out empirically. Our data are compatible with the hypothesis that the DLPFC and striatum harbor dissociated neural populations that represent choices and predeliberation biases that are combined after cue onset; the bias neurons have a weaker effect on the ultimate decision than the choice neurons, so their influence is progressively apparent for trials where the values associated with the decision alternatives are increasingly similar.

  1. The Symmetric Solutions of Affiliated Value Model

    Institute of Scientific and Technical Information of China (English)

    Che Ka-jia; Li Zhi-chen

    2004-01-01

    In a symmetric affiliated value model, this paper analyses High-Technology industrial firms' competitive strategy in research and development (R&D). We obtain the symmetric Bayesian Nash Equilibrium functions with or without government's prize:b1(x)=v(x,x)Fn-1(x|x)-∫x0Fn-1(y|y)dv(y,y), b2(x)=∫x0[v(y,y)+v0]dFn-1(y|y), and b3(x)=∫x0v(y,y)(fn-1(y|y))/(1-Fn-1(y|y))dy. We find the firm's investment level will increase in prize, only when the constant prize v0≥v(y,y)(Fn-1(y|y))/(1-Fn-1(y|y)), does the firm invest more aggressively with constant prize than with variable prize.

  2. 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,...

  3. Issues in Value-at-Risk Modeling and Evaluation

    NARCIS (Netherlands)

    J. Danielsson; C.G. de Vries (Casper); B.N. Jorgensen (Bjørn); P.F. Christoffersen (Peter); F.X. Diebold (Francis); T. Schuermann (Til); J.A. Lopez (Jose); B. Hirtle (Beverly)

    1998-01-01

    textabstractDiscusses the issues in value-at-risk modeling and evaluation. Value of value at risk; Horizon problems and extreme events in financial risk management; Methods of evaluating value-at-risk estimates.

  4. How to Establish Clinical Prediction Models

    Directory of Open Access Journals (Sweden)

    Yong-ho Lee

    2016-03-01

    Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

  5. Predictive value of specific ultrasound findings when used as a screening test for abnormalities on VCUG.

    Science.gov (United States)

    Logvinenko, Tanya; Chow, Jeanne S; Nelson, Caleb P

    2015-08-01

    Renal and bladder ultrasound (RBUS) is often used as an initial screening test for children after urinary tract infection (UTI), and the 2011 AAP guidelines specifically recommend RBUS be performed first, with voiding cystourethrogram (VCUG) to be performed only if the ultrasound is abnormal. It is uncertain whether specific RBUS findings, alone or in combination, might make RBUS more useful as a predictor of VCUG abnormalities. To evaluate the association of specific RBUS with VCUG findings, and determine whether predictive models that accurately predict patients at high risk of VCUG abnormalities, based on RBUS findings, can be constructed. and study sample: A total of 3995 patients were identified with VCUG and RBUS performed on the same day. The RBUS and VCUG reports were reviewed and the findings were classified. Analysis was limited to patients aged 0-60 months with no prior postnatal genitourinary imaging and no history of prenatal hydronephrosis. The associations between large numbers of specific RBUS findings with abnormalities seen on VCUG were investigated. Both multivariate logistic models and a neural network machine learning algorithms were constructed to evaluate the predictive power of RBUS for VCUG abnormalities (including VUR or bladder/urethral findings). Sensitivity, specificity, predictive values and area under receiving operating curves (AUROC) of RBUS for VCUG abnormalities were determined. A total of 2259 patients with UTI as the indication for imaging were identified. The RBUS was reported as "normal" in 75.0%. On VCUG, any VUR was identified in 41.7%, VUR grade > II in 20.9%, and VUR grade > III in 2.8%. Many individual RBUS findings were significantly associated with VUR on VCUG. Despite these strong univariate associations, multivariate modeling didn't result in a predictive model that was highly accurate. Multivariate logistic regression built via stepwise selection had: AUROC = 0.57, sensitivity = 86% and specificity = 25% for any VUR

  6. Predictive value of nerve trunk size in the neonate.

    Science.gov (United States)

    Rassouli-Kirchmeier, Roxana; Lok, Maarten Janssen; Kusters, Benno; Nagtegaal, Iris; Köster, Nils; van der Steeg, Herjan; Wijnen, Marc; de Blaauw, Ivo

    2014-08-01

    The diagnosis of Hirschsprung's disease (HD) remains challenging. The identification of ganglion cells is difficult and acetycholine esterase (AChE) staining can be subject to a great variability, particularly in the neonatal period (trunks greater than 40 µm are considered to be predictive for HD. The aim of this study was to evaluate the usefulness of measuring nerve trunk size in the newborn with HD. Out of 292 biopsies 69 could be reanalyzed by three independent researchers. 40 µm was used as cutoff point for nerve trunk size. They were subdivided into three groups: (a) diagnosis of HD certain at the first biopsy, (b) no HD and (c) diagnosis of HD remains doubtful and re-biopsy taken. In 87 % of group A nerve trunk size was ≥ 40 µm (SD 13.8). In 84 % of group B trunk size was trunk size. Using 40 µm as the cutoff point gave 13 % false-negative and 16 % false-positive cases. Measurement of the nerve trunk in the neonatal period does not seem to be a reliable method for detecting HD.

  7. Predictive Value Of Ocular Trauma Score In Open Globe Combat Eye Injuries.

    Science.gov (United States)

    Islam, Qamar Ul; Ishaq, Mazhar; Yaqub, Muhammad Amer; Mehboob, Mohammad Asim

    2016-01-01

    Prediction of final visual outcome in ocular injuries is of paramount importance and various prognostic models have been proposed to predict final visual outcome. The objective of this study was to validate the predictive value of ocular trauma score (OTS) in patients with combat related open globe injuries and to evaluate the factors affecting the final visual outcome. Data of 93 patients admitted in AFIO Rawalpindi between Jan 2010 to June 2014 with combat related open globe ocular injuries was analysed. Initial and final best corrected visual acuity (BCVA) was categorized as No Light Perception (NLP), Light Perception (LP) to Hand Movement (HM), 1/200-19/200, 20/200-20/50, and ≥20/40. OTS was calculated for each eye by assigning numerical raw points to six variables and then scores were stratified into five OTS categories. Mean age of study population was 28.77±8.37 years. Presenting visual acuity was <20/200 (6/60) in 103 (96.23%) eyes. However, final BCVA of ≥20/40 (6/12) was achieved in 18 (16.82%) eyes, while 72 (67.28%) eyes had final BCVA of <20/200 (6/60). Final visual outcome in our study were similar to those in OTS study, except for NLP in category 1 (81% vs. 74%) and ≥20/40 in category 3 (30% vs. 41%). The OTS model predicted visual survival (LP or better) with a sensitivity of 94.80% and predicted no vision (NLP) with a specificity of 100%. OTS is a reliable tool for assessment of ocular injuries and predicting final visual outcome at the outset.

  8. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    is a realization of a continuous-discrete multivariate stochastic transfer function model. The proposed prediction error-methods are demonstrated for a SISO system parameterized by the transfer functions with time delays of a continuous-discrete-time linear stochastic system. The simulations for this case suggest......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...... computational resources. The identification method is suitable for predictive control....

  9. Assessment of performance of survival prediction models for cancer prognosis

    Directory of Open Access Journals (Sweden)

    Chen Hung-Chia

    2012-07-01

    Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in

  10. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “any fall” and “recurrent falls.” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  11. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  12. The Advancement Value Chain: An Exploratory Model

    Science.gov (United States)

    Leonard, Edward F., III

    2005-01-01

    Since the introduction of the value chain concept in 1985, several varying, yet virtually similar, value chains have been developed for the business enterprise. Shifting to higher education, can a value chain be found that links together the various activities of advancement so that an institution's leaders can actually look at the philanthropic…

  13. The Advancement Value Chain: An Exploratory Model

    Science.gov (United States)

    Leonard, Edward F., III

    2005-01-01

    Since the introduction of the value chain concept in 1985, several varying, yet virtually similar, value chains have been developed for the business enterprise. Shifting to higher education, can a value chain be found that links together the various activities of advancement so that an institution's leaders can actually look at the philanthropic…

  14. Predictive value of Sp1/Sp3/FLIP signature for prostate cancer recurrence.

    Directory of Open Access Journals (Sweden)

    Roble G Bedolla

    Full Text Available Prediction of prostate cancer prognosis is challenging and predictive biomarkers of recurrence remain elusive. Although prostate specific antigen (PSA has high sensitivity (90% at a PSA level of 4.0 ng/mL, its low specificity leads to many false positive results and considerable overtreatment of patients and its performance at lower ranges is poor. Given the histopathological and molecular heterogeneity of prostate cancer, we propose that a panel of markers will be a better tool than a single marker. We tested a panel of markers composed of the anti-apoptotic protein FLIP and its transcriptional regulators Sp1 and Sp3 using prostate tissues from 64 patients with recurrent and non-recurrent cancer who underwent radical prostatectomy as primary treatment for prostate cancer and were followed with PSA measurements for at least 5 years. Immunohistochemical staining for Sp1, Sp3, and FLIP was performed on these tissues and scored based on the proportion and intensity of staining. The predictive value of the FLIP/Sp1/Sp3 signature for clinical outcome (recurrence vs. non-recurrence was explored with logistic regression, and combinations of FLIP/Sp1/Sp3 and Gleason score were analyzed with a stepwise (backward and forward logistic model. The discrimination of the markers was identified by sensitivity-specificity analysis and the diagnostic value of FLIP/Sp1/Sp3 was determined using area under the curve (AUC for receiver operator characteristic curves. The AUCs for FLIP, Sp1, Sp3, and Gleason score for predicting PSA failure and non-failure were 0.71, 0.66, 0.68, and 0.76, respectively. However, this increased to 0.93 when combined. Thus, the "biomarker signature" of FLIP/Sp1/Sp3 combined with Gleason score predicted disease recurrence and stratified patients who are likely to benefit from more aggressive treatment.

  15. Prediction of pKa values using the PM6 semiempirical method

    Directory of Open Access Journals (Sweden)

    Jimmy C. Kromann

    2016-08-01

    Full Text Available The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs of 0.6–0.7 pH units. For carboxylic acids, the accuracy (0.7–1.0 pH units is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5–0.6, slightly worse (0.5–1.0, and worse (1.0–2.5, provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2–5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

  16. Experimental study on prediction model for maximum rebound ratio

    Institute of Scientific and Technical Information of China (English)

    LEI Wei-dong; TENG Jun; A.HEFNY; ZHAO Jian; GUAN Jiong

    2007-01-01

    The proposed prediction model for estimating the maximum rebound ratio was applied to a field explosion test, Mandai test in Singapore.The estimated possible maximum Deak particle velocities(PPVs)were compared with the field records.Three of the four available field-recorded PPVs lie exactly below the estimated possible maximum values as expected.while the fourth available field-recorded PPV lies close to and a bit higher than the estimated maximum possible PPV The comparison results show that the predicted PPVs from the proposed prediction model for the maximum rebound ratio match the field.recorded PPVs better than those from two empirical formulae.The very good agreement between the estimated and field-recorded values validates the proposed prediction model for estimating PPV in a rock mass with a set of ipints due to application of a two dimensional compressional wave at the boundary of a tunnel or a borehole.

  17. Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

    Science.gov (United States)

    Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

    2015-01-01

    In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it. PMID:26241496

  18. Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

    Science.gov (United States)

    Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

    2015-01-01

    In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

  19. Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

    Directory of Open Access Journals (Sweden)

    Young Bin Kim

    Full Text Available In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs. Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

  20. 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 p

  1. Childhood asthma prediction models: a systematic review.

    Science.gov (United States)

    Smit, Henriette A; Pinart, Mariona; Antó, Josep M; Keil, Thomas; Bousquet, Jean; Carlsen, Kai H; Moons, Karel G M; Hooft, Lotty; Carlsen, Karin C Lødrup

    2015-12-01

    Early identification of children at risk of developing asthma at school age is crucial, but the usefulness of childhood asthma prediction models in clinical practice is still unclear. We systematically reviewed all existing prediction models to identify preschool children with asthma-like symptoms at risk of developing asthma at school age. Studies were included if they developed a new prediction model or updated an existing model in children aged 4 years or younger with asthma-like symptoms, with assessment of asthma done between 6 and 12 years of age. 12 prediction models were identified in four types of cohorts of preschool children: those with health-care visits, those with parent-reported symptoms, those at high risk of asthma, or children in the general population. Four basic models included non-invasive, easy-to-obtain predictors only, notably family history, allergic disease comorbidities or precursors of asthma, and severity of early symptoms. Eight extended models included additional clinical tests, mostly specific IgE determination. Some models could better predict asthma development and other models could better rule out asthma development, but the predictive performance of no single model stood out in both aspects simultaneously. This finding suggests that there is a large proportion of preschool children with wheeze for which prediction of asthma development is difficult.

  2. Predictive value of clinical history compared with urodynamic study in 1,179 women

    Directory of Open Access Journals (Sweden)

    Jorge Milhem Haddad

    2016-02-01

    Full Text Available SUMMARY Objective: to determine the positive predictive value of clinical history in comparison with urodynamic study for the diagnosis of urinary incontinence. Methods: retrospective analysis comparing clinical history and urodynamic evaluation of 1,179 women with urinary incontinence. The urodynamic study was considered the gold standard, whereas the clinical history was the new test to be assessed. This was established after analyzing each method as the gold standard through the difference between their positive predictive values. Results: the positive predictive values of clinical history compared with urodynamic study for diagnosis of stress urinary incontinence, overactive bladder and mixed urinary incontinence were, respectively, 37% (95% CI 31-44, 40% (95% CI 33-47 and 16% (95% CI 14-19. Conclusion: we concluded that the positive predictive value of clinical history was low compared with urodynamic study for urinary incontinence diagnosis. The positive predictive value was low even among women with pure stress urinary incontinence.

  3. SOFTWARE EFFORT PREDICTION: AN EMPIRICAL EVALUATION OF METHODS TO TREAT MISSING VALUES WITH RAPIDMINER ®

    Directory of Open Access Journals (Sweden)

    OLGA FEDOTOVA

    2011-07-01

    Full Text Available Missing values is a common problem in the data analysis in all areas, being software engineering not an exception. Particularly, missing data is a widespread phenomenon observed during the elaboration of effort prediction models (EPMs required for budget, time and functionalities planning. Current work presents the results of a study carried out on a Portuguese medium-sized software development organization in order to obtain a formal method for EPMs elicitation in development processes. This study focuses on the performanceevaluation of several regression-based EPMs induced from data after applying three different methods to treat missing values. Results show that regression imputation offers substantial improvements over traditional techniques (case deletion and mean substitution. All the machine learning methods were implemented in RapidMiner®, one of the leading open-source data mining applications.

  4. Validating proposed migration equation and parameters' values as a tool to reproduce and predict (137)Cs vertical migration activity in Spanish soils.

    Science.gov (United States)

    Olondo, C; Legarda, F; Herranz, M; Idoeta, R

    2017-01-05

    This paper shows the procedure performed to validate the migration equation and the migration parameters' values presented in a previous paper (Legarda et al., 2011) regarding the migration of (137)Cs in Spanish mainland soils. In this paper, this model validation has been carried out checking experimentally obtained activity concentration values against those predicted by the model. This experimental data come from the measured vertical activity profiles of 8 new sampling points which are located in northern Spain. Before testing predicted values of the model, the uncertainty of those values has been assessed with the appropriate uncertainty analysis. Once establishing the uncertainty of the model, both activity concentration values, experimental versus model predicted ones, have been compared. Model validation has been performed analyzing its accuracy, studying it as a whole and also at different depth intervals. As a result, this model has been validated as a tool to predict (137)Cs behaviour in a Mediterranean environment.

  5. Predictive value of the smell identification test for nigrostriatal dopaminergic depletion in Korean tremor patients.

    Science.gov (United States)

    Hong, Jin Yong; Chung, Seok Jong; Lee, Ji E; Sunwoo, Mun Kyung; Lee, Phil Hyu; Sohn, Young H

    2013-11-01

    The predictive value of Cross-Cultural Smell Identification Test for nigrostriatal dopaminergic depletion in Korean tremor patients has yet to be assessed. Three hundred nineteen drug-naive patients who visited our clinic for the diagnosis of their tremor, and took both Cross-Cultural Smell Identification Test and dopamine transporter PET were included in the data analysis. Visual grading of each PET image was performed by two independent neurologists. Smell test scores were significantly correlated to the striatal dopaminergic activity (Kendall's τb = -0.291, p smell test score alone appeared to have relatively weak power for predicting dopaminergic depletion (area under the curve = 0.693). Multivariate logistic regression model with inclusion of the patient's age and symptom duration as independent variables enhanced predictive power for dopaminergic depletion (area under the curve = 0.812). These results demonstrated that Cross-Cultural Smell Identification Test measurements alone may be insufficient to predict striatal dopaminergic depletion in Korean tremor patients. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Increased tumour ADC value during chemotherapy predicts improved survival in unresectable pancreatic cancer

    Energy Technology Data Exchange (ETDEWEB)

    Nishiofuku, Hideyuki; Tanaka, Toshihiro; Kichikawa, Kimihiko [Nara Medical University, Department of Radiology and IVR Center, Kashihara-city, Nara (Japan); Marugami, Nagaaki [Nara Medical University, Department of Endoscopy and Ultrasound, Kashihara-city, Nara (Japan); Sho, Masayuki; Akahori, Takahiro; Nakajima, Yoshiyuki [Nara Medical University, Department of Surgery, Kashihara-city, Nara (Japan)

    2016-06-15

    To investigate whether changes to the apparent diffusion coefficient (ADC) of primary tumour in the early period after starting chemotherapy can predict progression-free survival (PFS) or overall survival (OS) in patients with unresectable pancreatic adenocarcinoma. Subjects comprised 43 patients with histologically confirmed unresectable pancreatic cancer treated with first-line chemotherapy. Minimum ADC values in primary tumour were measured using the selected area ADC (sADC), which excluded cystic and necrotic areas and vessels, and the whole tumour ADC (wADC), which included whole tumour components. Relative changes in ADC were calculated from baseline to 4 weeks after initiation of chemotherapy. Relationships between ADC and both PFS and OS were modelled by Cox proportional hazards regression. Median PFS and OS were 6.1 and 11.0 months, respectively. In multivariate analysis, sADC change was the strongest predictor of PFS (hazard ratio (HR), 4.5; 95 % confidence interval (CI), 1.7-11.9; p = 0.002). Multivariate Cox regression analysis for OS revealed sADC change and CRP as independent predictive markers, with sADC change as the strongest predictive biomarker (HR, 6.7; 95 % CI, 2.7-16.6; p = 0.001). Relative changes in sADC could provide a useful imaging biomarker to predict PFS and OS with chemotherapy for unresectable pancreatic adenocarcinoma. (orig.)

  7. Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study

    Science.gov (United States)

    Trautwein, Ulrich; Marsh, Herbert W.; Nagengast, Benjamin; Ludtke, Oliver; Nagy, Gabriel; Jonkmann, Kathrin

    2012-01-01

    In modern expectancy-value theory (EVT) in educational psychology, expectancy and value beliefs additively predict performance, persistence, and task choice. In contrast to earlier formulations of EVT, the multiplicative term Expectancy x Value in regression-type models typically plays no major role in educational psychology. The present study…

  8. Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study

    Science.gov (United States)

    Trautwein, Ulrich; Marsh, Herbert W.; Nagengast, Benjamin; Ludtke, Oliver; Nagy, Gabriel; Jonkmann, Kathrin

    2012-01-01

    In modern expectancy-value theory (EVT) in educational psychology, expectancy and value beliefs additively predict performance, persistence, and task choice. In contrast to earlier formulations of EVT, the multiplicative term Expectancy x Value in regression-type models typically plays no major role in educational psychology. The present study…

  9. Classification of missing values in spatial data using spin models

    CERN Document Server

    Žukovič, Milan; 10.1103/PhysRevE.80.011116

    2013-01-01

    A problem of current interest is the estimation of spatially distributed processes at locations where measurements are missing. Linear interpolation methods rely on the Gaussian assumption, which is often unrealistic in practice, or normalizing transformations, which are successful only for mild deviations from the Gaussian behavior. We propose to address the problem of missing values estimation on two-dimensional grids by means of spatial classification methods based on spin (Ising, Potts, clock) models. The "spin" variables provide an interval discretization of the process values, and the spatial correlations are captured in terms of interactions between the spins. The spins at the unmeasured locations are classified by means of the "energy matching" principle: the correlation energy of the entire grid (including prediction sites) is estimated from the sample-based correlations. We investigate the performance of the spin classifiers in terms of computational speed, misclassification rate, class histogram an...

  10. Determining the Clinical Utility of an Absolute Procalcitonin Value for Predicting a Positive Culture Result.

    Science.gov (United States)

    Caffarini, Erica M; DeMott, Joshua; Patel, Gourang; Lat, Ishaq

    2017-05-01

    Various procalcitonin ranges have been established to guide antimicrobial therapy; however, there are no data that establish whether the initial procalcitonin value can determine the likelihood of a positive culture result. This study aimed to establish if the initial procalcitonin value, on clinical presentation, has a positive predictive value for any positive culture result. This was a retrospective study of 813 medical intensive care unit patients. Data collected included patient demographics, procalcitonin assay results, sources of infection, culture results, and lengths of stay. Patients were excluded if they were immunocompromised. The primary outcome of this study was to determine a procalcitonin value that would predict any positive culture. Secondary outcomes included the sensitivity, specificity, positive predictive value, and negative predictive value for procalcitonin. After exclusions, a total of 519 patient charts were reviewed to determine the impact of the initial procalcitonin value on culture positivity. In our analyses, the receiver operating characteristic values were 0.62 for all cultures, 0.49 for pulmonary infections, 0.43 for urinary tract infections, and 0.78 for bacteremia. A procalcitonin value of 3.61 ng/ml was determined to be the threshold value for a positive blood culture result (prevalence, 4%). For bacteremia, the sensitivity of procalcitonin was 75%, the specificity was 72%, the positive predictive value was 20%, and the negative predictive value was 97%. Procalcitonin was a poor predictor of culture positivity. An initial procalcitonin value of less than 3.61 ng/ml may be useful in predicting whether bacteremia is absent. Procalcitonin should not be used as the only predictor for determining initiation of antibiotic therapy. Copyright © 2017 American Society for Microbiology.

  11. Development and ex post validation of prediction equations of corn energy values for growing pigs

    Directory of Open Access Journals (Sweden)

    Everardo Ayres Correia Ellery

    2015-06-01

    Full Text Available The aim of this study was to determine and validate prediction equations for digestible (DE and metabolizable energy (ME of corn for growing pigs. The prediction equations were developed based on data on the chemical composition, digestible and metabolizable energy of corn grain (30 samples evaluated in experiments in Embrapa Suínos e Aves, Brazil. The equations were evaluated using regression analysis, and adjusted R² was the criterion for selection of the best models. Two equations were tested for DE and ME, each. To validate the equations, 1 experiment with 2 assays was performed to determine the values of DE and ME of 5 corn cultivars. In each assay, we used 24 growing pigs with initial average weight of 54.21 ± 1.68 kg in complete randomized block design with 6 treatments and 4 replicates. Treatments consisted of a reference diet and 5 ration tests composed of 60% of the reference diet and 40% of corn (1 of the 5 cultivars. Based on the results of the metabolic experiment and predicted values obtained in the equations, the validation of the equations was conducted using the lowest prediction error (pe as a criterion for selection. The equations that produced the most accurate estimates of DE and ME of corn were as follows: DE = 11812 – 1015.9CP – 837.9EE – 1641ADF + 2616.3Ash + 47.5(CP2 + 114.7(CF2 + 46(ADF2 – 1.6(NDF2 – 997.1(Ash2 + 151.9EECF + 23.2EENDF – 126.4CPCF + 136.4CPADF – 4.0CPNDF, with R2 = 0.81 and pe = 2.33; ME = 12574 – 1254.9CP – 1140.5EE – 1359.9ADF + 2816.3Ash + 77.6(CP2 + 92.3(CF2 + 54.1(ADF2 – 1.8(NDF2 – 1097.2(Ash2 + 240.6EECF + 26.3EENDF – 157.4CPCF + 96.5CPADF – 4.4CPNDF, with R2 = 0.89 and pe = 2.24. Thus, using the data on chemical composition, it is possible to derive prediction equations for DE and ME of corn for pigs; these equations seem to be valid because of the small prediction errors suggestive of high accuracy of these models.

  12. 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...

  13. A simple method for predicting the lower heating value of municipal solid waste in China based on wet physical composition.

    Science.gov (United States)

    Lin, Xuebin; Wang, Fei; Chi, Yong; Huang, Qunxing; Yan, Jianhua

    2015-02-01

    A rapid and cost-effective prediction method based on wet physical composition has been developed to determine the lower heating value (LHV) of municipal solid waste (MSW) for practical applications in China. The heating values (HVs) of clean combustibles were measured in detail, and the effect of combustibles, food waste, and ash content on HV was studied to develop the model. The weighted average HV can be used to predict the MSW HV with high accuracy. Based on the moisture measurements of each major real combustible and the HV of clean solid waste, a predictive model of the LHV of real MSW was developed. To assess the prediction performance, information was collected on 103 MSW samples from 31 major cities in China from 1994 to 2012. Compared with five predictive models based on the wet physical composition from different regions in the world, the predictive result of the developed model is the most accurate. The prediction performance can be improved further if the MSW is sorted better and if more information is collected on the individual moisture contents of the waste. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Analysis of Predictive Values Based on Individual Risk Factors in Multi-Modality Trials

    Directory of Open Access Journals (Sweden)

    Katharina Lange

    2013-03-01

    Full Text Available The accuracy of diagnostic tests with binary end-points is most frequently measured by sensitivity and specificity. However, from the clinical perspective, the main purpose of a diagnostic agent is to assess the probability of a patient actually being diseased and hence predictive values are more suitable here. As predictive values depend on the pre-test probability of disease, we provide a method to take risk factors influencing the patient’s prior probability of disease into account, when calculating predictive values. Furthermore, approaches to assess confidence intervals and a methodology to compare predictive values by statistical tests are presented. Hereby the methods can be used to analyze predictive values of factorial diagnostic trials, such as multi-modality, multi-reader-trials. We further performed a simulation study assessing length and coverage probability for different types of confidence intervals, and we present the R-Package facROC that can be used to analyze predictive values in factorial diagnostic trials in particular. The methods are applied to a study evaluating CT-angiography as a noninvasive alternative to coronary angiography for diagnosing coronary artery disease. Hereby the patients’ symptoms are considered as risk factors influencing the respective predictive values.

  15. Analysis of Predictive Values Based on Individual Risk Factors in Multi-Modality Trials.

    Science.gov (United States)

    Lange, Katharina; Brunner, Edgar

    2013-03-15

    The accuracy of diagnostic tests with binary end-points is most frequently measured by sensitivity and specificity. However, from the clinical perspective, the main purpose of a diagnostic agent is to assess the probability of a patient actually being diseased and hence predictive values are more suitable here. As predictive values depend on the pre-test probability of disease, we provide a method to take risk factors influencing the patient's prior probability of disease into account, when calculating predictive values. Furthermore, approaches to assess confidence intervals and a methodology to compare predictive values by statistical tests are presented. Hereby the methods can be used to analyze predictive values of factorial diagnostic trials, such as multi-modality, multi-reader-trials. We further performed a simulation study assessing length and coverage probability for different types of confidence intervals, and we present the R-Package facROC that can be used to analyze predictive values in factorial diagnostic trials in particular. The methods are applied to a study evaluating CT-angiography as a noninvasive alternative to coronary angiography for diagnosing coronary artery disease. Hereby the patients' symptoms are considered as risk factors influencing the respective predictive values.

  16. Prior probability (the pretest best guess) affects predictive values of diagnostic tests.

    Science.gov (United States)

    Erb, Hollis N

    2011-06-01

    Authors who publish evaluations of dichotomous (yes/no) diagnostic tests often include the predictive values of their test at a single prior probability (eg, the prevalence of the target disease within the evaluation data set). The objectives of this technical note are to demonstrate why single-probability predictive values are misleading and to show a better way to display positive predictive values (PPV) and negative predictive values (NPV) for a newly evaluated test. Secondly, this technical note will show readers how to calculate predictive values from only sensitivity and specificity for any desired prior probability. As prior probability increases from 0% to 100%, PPV increases from 0% to 100%, but NPV goes in the opposite direction (drops from 100% to 0%). Because prior probabilities vary so greatly across situations, predictive values should be provided in publications for the full range of potential prior probabilities (if provided at all). This is easily done with a 2-curve graph displaying the predictive values (y-axis) against the prior probability (x-axis).

  17. The role of non-epistemic values in engineering models.

    Science.gov (United States)

    Diekmann, Sven; Peterson, Martin

    2013-03-01

    We argue that non-epistemic values, including moral ones, play an important role in the construction and choice of models in science and engineering. Our main claim is that non-epistemic values are not only "secondary values" that become important just in case epistemic values leave some issues open. Our point is, on the contrary, that non-epistemic values are as important as epistemic ones when engineers seek to develop the best model of a process or problem. The upshot is that models are neither value-free, nor depend exclusively on epistemic values or use non-epistemic values as tie-breakers.

  18. 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...

  19. Massive Predictive Modeling using Oracle R Enterprise

    CERN Document Server

    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...

  20. The New Digital Media Value Network: Proposing an Interactive Model of Digital Media Value Activities

    Directory of Open Access Journals (Sweden)

    Sylvia Chan-Olmsted

    2016-07-01

    Full Text Available This study models the dynamic nature of today’s media markets using the framework of value-adding activities in the provision and consumption of media products. The proposed user-centric approach introduces the notion that the actions of external users, social media, and interfaces affect the internal value activities of media firms via a feedback loop, and therefore should themselves be considered value activities. The model also suggests a more comprehensive list of indicators for value assessment.

  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. 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.

  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. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Prediction of chemical composition and peroxide value in unground pet foods by near-infrared spectroscopy.

    Science.gov (United States)

    De Marchi, M; Righi, F; Meneghesso, M; Manfrin, D; Ricci, R

    2016-12-20

    The massive development of the pet food industry in recent years has lead to the formulation of hundreds of canine and feline complete extruded foods with the objective of meeting both the needs of the animals and numerous demands from pet owners. In the meantime, highly variable raw material compositions and the industry's new production techniques oblige manufacturers to monitor all phases of the extrusion process closely in order to ensure the targeted composition and quality of the products. This study aimed at evaluating the potential of infrared technology (visible and near-infrared spectrophotometer; 570-1842 nm) in predicting the chemical composition and peroxide value (PV) of unground commercial extruded dog foods. Six hundred and forty-nine commercial extruded dog foods were collected. For each product, an unground aliquot was analysed by infrared instrument while a second aliquot was sent to a laboratory for proximate analysis and PV quantification. The wide range of extruded dog food typologies included in the study was responsible for the wide variability observed within each nutritional trait, especially crude fibre and ash. The mean value of the 208 pet foods sampled for PV quantification was 17.49 mEq O2 /kg fat (min 2.2 and max 94.10 mEq O2 /kg fat). The coefficients of determination in cross-validation of NIRS prediction models were 0.77, 0.97, 0.83, 0.86, 0.78 and 0.94 for moisture, crude protein, crude fat, crude fibre, ash and nitrogen-free extract (NFE) respectively. PV prediction was less precise, as demonstrated by the coefficient of determination in cross-validation (0.66). The results demonstrated the potential of NIRS in predicting chemical composition in unground samples, with lower accuracy for moisture and ash, while PV prediction models suggest use for screening purposes only. Journal of Animal Physiology and Animal Nutrition © 2016 Blackwell Verlag GmbH.

  14. Mantis: Predicting System Performance through Program Analysis and Modeling

    CERN Document Server

    Chun, Byung-Gon; Lee, Sangmin; Maniatis, Petros; Naik, Mayur

    2010-01-01

    We present Mantis, a new framework that automatically predicts program performance with high accuracy. Mantis integrates techniques from programming language and machine learning for performance modeling, and is a radical departure from traditional approaches. Mantis extracts program features, which are information about program execution runs, through program instrumentation. It uses machine learning techniques to select features relevant to performance and creates prediction models as a function of the selected features. Through program analysis, it then generates compact code slices that compute these feature values for prediction. Our evaluation shows that Mantis can achieve more than 93% accuracy with less than 10% training data set, which is a significant improvement over models that are oblivious to program features. The system generates code slices that are cheap to compute feature values.

  15. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  16. Prediction of arterial blood gas values from arterialized earlobe blood gas values in patients treated with mechanical ventilation

    Directory of Open Access Journals (Sweden)

    Honarmand Azim

    2008-01-01

    Full Text Available Background/Objective: Arterial blood gas (ABG analysis is useful in evaluation of the clinical condition of critically ill patients; however, arterial puncture or insertion of an arterial catheter may sometimes be difficult and cause many complications. Arterialized ear lobe blood samples have been described as adequate to gauge gas exchange in acute and chronically ill pediatric patients. Purpose: This study evaluates whether pH, partial pressure of oxygen (PO 2 , partial pressure of carbon dioxide (PCO 2 , base excess (BE, and bicarbonate (HCO 3 values of arterialized earlobe blood samples could accurately predict their arterial blood gas analogs for adult patients treated by mechanical ventilation in an intensive care unit (ICU. Setting: A prospective descriptive study Methods: Sixty-seven patients who were admitted to ICU and treated with mechanical ventilation were included in this study. Blood samples were drawn simultaneously from the radial artery and arterialized earlobe of each patient. Results: Regression equations and mean percentage-difference equations were derived to predict arterial pH, PCO 2 , PO 2 , BE, and HCO 3 -values from their earlobe analogs. pH, PCO 2 , BE, and HCO 3 all significantly correlated in ABG and earlobe values. In spite of a highly significant correlation, the limits of agreement between the two methods were wide for PO 2 . Regression equations for prediction of pH, PCO 2 , BE, and HCO3- values were: arterial pH (pHa = 1.81+ 0.76 x earlobe pH (pHe [r = 0.791, P < 0.001]; PaCO 2 = 1.224+ 1.058 x earlobePCO 2 (PeCO 2 [r = 0.956, P < 0.001]; arterial BE (BEa = 1.14+ 0.95 x earlobe BE (BEe [r= 0.894, P < 0.001], and arterial HCO 3 - (HCO 3 -a = 1.41+ earlobe HCO 3 (HCO 3 -e [r = 0.874, P < 0.001]. The predicted ABG values from the mean percentage-difference equations were derived as follows: pHa = pHe x 1.001; PaCO 2 = PeCO 2 x 0.33; BEa = BEe x 0.57; and HCO 3 -a = HCO 3 -e x 1.06. Conclusions: Arterialized

  17. A Course in... Model Predictive Control.

    Science.gov (United States)

    Arkun, Yaman; And Others

    1988-01-01

    Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)

  18. The value of multivariate model sophistication

    DEFF Research Database (Denmark)

    Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco

    2014-01-01

    in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable...... of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way...

  19. Equivalency and unbiasedness of grey prediction models

    Institute of Scientific and Technical Information of China (English)

    Bo Zeng; Chuan Li; Guo Chen; Xianjun Long

    2015-01-01

    In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction mo-dels, the equivalence and unbiasedness of grey prediction mo-dels are analyzed and verified. The results show that al the grey prediction models that are strictly derived from x(0)(k) +az(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homoge-neous exponential sequence can be accomplished. However, the models derived from dx(1)/dt+ax(1) =b are only close to those derived from x(0)(k)+az(1)(k)=b provided that|a|has to satisfy|a| < 0.1; neither could the unbiased simulation for the homoge-neous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

  20. Predictive value of hormonal parameters for live birth in women with unexplained infertility and male infertility

    National Research Council Canada - National Science Library

    Murto, Tiina; Bjuresten, Kerstin; Landgren, Britt-Marie; Stavreus-Evers, Anneli

    2013-01-01

    .... The objective of the present study was to characterize hormone levels in an arbitrary menstrual cycle in women with unexplained infertility and male infertility, and to determine the predictive value...

  1. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam Study.

    NARCIS (Netherlands)

    M.L. Bots (Michiel); A. Hofman (Albert); A.I. Sol (Antonio Iglesias); D.A. van der Kuip (Deirdre); J.C.M. Witteman (Jacqueline)

    2004-01-01

    textabstractBACKGROUND: Several noninvasive methods are available to investigate the severity of extracoronary atherosclerotic disease. No population-based study has yet examined whether differences exist between these measures with regard to their predictive value for myocardial i

  2. Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

    National Research Council Canada - National Science Library

    Kim, Young Bin; Lee, Sang Hyeok; Kang, Shin Jin; Choi, Myung Jin; Lee, Jung; Kim, Chang Hun

    2015-01-01

    In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs...

  3. Statistical procedures for evaluating daily and monthly hydrologic model predictions

    Science.gov (United States)

    Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.

    2004-01-01

    The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.

  4. Validation of an internal hardwood log defect prediction model

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The type, size, and location of internal defects dictate the grade and value of lumber sawn from hardwood logs. However, acquiring internal defect knowledge with x-ray/computed-tomography or magnetic-resonance imaging technology can be expensive both in time and cost. An alternative approach uses prediction models based on correlations among external defect indicators...

  5. The Added Value of Business Models

    NARCIS (Netherlands)

    Vliet, Harry van

    2014-01-01

    An overview of innovations in a particular area, for example retail developments in the fashion sector (Van Vliet, 2014), and a subsequent discussion about the probability as to whether these innovations will realise a ‘breakthrough’, has to be supplemented with the question of what the added value

  6. Modeling value creation with enterprise architecture

    NARCIS (Netherlands)

    Singh, Prince Mayurank; Jonkers, H.; Iacob, Maria Eugenia; van Sinderen, Marten J.

    2014-01-01

    Firms may not succeed in business if strategies are not properly implemented in practice. Every firm needs to know, represent and master its value creation logic, not only to stay in business but also to keep growing. This paper is about focusing on an important topic in the field of strategic

  7. Hybrid modeling and prediction of dynamical systems

    Science.gov (United States)

    Lloyd, Alun L.; Flores, Kevin B.

    2017-01-01

    Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data. PMID:28692642

  8. 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.

  9. A CHAID Based Performance Prediction Model in Educational Data Mining

    Directory of Open Access Journals (Sweden)

    R. Bhaskaran

    2010-01-01

    Full Text Available The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO. A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student records, which were used for CHAID prediction model construction. A set of prediction rules were extracted from CHIAD prediction model and the efficiency of the generated CHIAD prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory.

  10. BeBoP: A Cost Effective Predictor Infrastructure for Superscalar Value Prediction

    OpenAIRE

    Perais, Arthur; Seznec, André

    2015-01-01

    International audience; Up to recently, it was considered that a performance-effective implementation of Value Prediction (VP) would add tremendous complexity and power consumption in the pipeline, especially in the Out-of-Order engine and the predictor infrastructure.Despite recent progress in the field of Value Prediction, this remains partially true. Indeed, if the recent EOLE architecture proposition suggests that the OoO engine need not be altered to accommodate VP, complexity in the pre...

  11. Value of routine blood tests for prediction of mortality risk in hip fracture patients

    DEFF Research Database (Denmark)

    Mosfeldt, Mathias; Pedersen, Ole B; Riis, Troels

    2012-01-01

    There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission.......There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission....

  12. Property predictions using microstructural modeling

    Energy Technology Data Exchange (ETDEWEB)

    Wang, K.G. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)]. E-mail: wangk2@rpi.edu; Guo, Z. [Sente Software Ltd., Surrey Technology Centre, 40 Occam Road, Guildford GU2 7YG (United Kingdom); Sha, W. [Metals Research Group, School of Civil Engineering, Architecture and Planning, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom); Glicksman, M.E. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States); Rajan, K. [Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, CII 9219, 110 8th Street, Troy, NY 12180-3590 (United States)

    2005-07-15

    Precipitation hardening in an Fe-12Ni-6Mn maraging steel during overaging is quantified. First, applying our recent kinetic model of coarsening [Phys. Rev. E, 69 (2004) 061507], and incorporating the Ashby-Orowan relationship, we link quantifiable aspects of the microstructures of these steels to their mechanical properties, including especially the hardness. Specifically, hardness measurements allow calculation of the precipitate size as a function of time and temperature through the Ashby-Orowan relationship. Second, calculated precipitate sizes and thermodynamic data determined with Thermo-Calc[copyright] are used with our recent kinetic coarsening model to extract diffusion coefficients during overaging from hardness measurements. Finally, employing more accurate diffusion parameters, we determined the hardness of these alloys independently from theory, and found agreement with experimental hardness data. Diffusion coefficients determined during overaging of these steels are notably higher than those found during the aging - an observation suggesting that precipitate growth during aging and precipitate coarsening during overaging are not controlled by the same diffusion mechanism.

  13. Variability of Automated Intraoperative ST Segment Values Predicts Postoperative Troponin Elevation.

    Science.gov (United States)

    Maile, Michael D; Engoren, Milo C; Tremper, Kevin K; Tremper, Theodore T; Jewell, Elizabeth S; Kheterpal, Sachin

    2016-03-01

    Intraoperative electrocardiographic monitoring is considered a standard of care. However, there are no evidence-based algorithms for using intraoperative ST segment data to identify patients at high risk for adverse perioperative cardiac events. Therefore, we performed an exploratory study of statistical measures summarizing intraoperative ST segment values determine whether the variability of these measurements was associated with adverse postoperative events. We hypothesized that elevation, depression, and variability of ST segments captured in an anesthesia information management system are associated with postoperative serum troponin elevation. We conducted a single-institution, retrospective study of intraoperative automated ST segment measurements from leads I, II, and III, which were recorded in the electronic anesthesia record of adult patients undergoing noncardiac surgery. The maximum, minimum, mean, and SD of ST segment values were entered into logistic regression models to find independent associations with myocardial injury, defined as an elevated serum troponin concentration during the 7 days after surgery. Performance of these models was assessed by measuring the area under the receiver operator characteristic curve. The net reclassification improvement was calculated to quantify the amount of information that the ST segment values analysis added regarding the ability to predict postoperative troponin elevation. Of 81,011 subjects, 4504 (5.6%) had postoperative myocardial injury. After adjusting for patient characteristics, the ST segment maximal depression (e.g., lead I: odds ratio [OR], 1.66; 95% confidence interval [CI], 1.26-2.19; P = 0.0004), maximal elevation (e.g., lead I: OR, 1.70; 95% CI, 1.34-2.17; P accounting for the maximal amount of ST segment depression and elevation and for patient characteristics. The ST segment summary statistics model had fair discrimination, with an area under the receiver operator characteristic curve of 0.71 (95

  14. 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.

  15. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... connectivity approach. The development of these models requires measured property data and based on them, the regression of model parameters is performed. Although this class of models is empirical by nature, they do allow extrapolation from the regressed model parameters to predict properties of chemicals...... not included in the measured data-set. Therefore, they are also considered as predictive models. The paper will highlight different issues/challenges related to the role of the databases and the mathematical and thermodynamic consistency of the measured/estimated data, the predictive nature of the developed...

  16. Groundwater Level Prediction using M5 Model Trees

    Science.gov (United States)

    Nalarajan, Nitha Ayinippully; Mohandas, C.

    2015-01-01

    Groundwater is an important resource, readily available and having high economic value and social benefit. Recently, it had been considered a dependable source of uncontaminated water. During the past two decades, increased rate of extraction and other greedy human actions have resulted in the groundwater crisis, both qualitatively and quantitatively. Under prevailing circumstances, the availability of predicted groundwater levels increase the importance of this valuable resource, as an aid in the planning of groundwater resources. For this purpose, data-driven prediction models are widely used in the present day world. M5 model tree (MT) is a popular soft computing method emerging as a promising method for numeric prediction, producing understandable models. The present study discusses the groundwater level predictions using MT employing only the historical groundwater levels from a groundwater monitoring well. The results showed that MT can be successively used for forecasting groundwater levels.

  17. Valuing structured professional judgment: predictive validity, decision-making, and the clinical-actuarial conflict.

    Science.gov (United States)

    Falzer, Paul R

    2013-01-01

    Structured professional judgment (SPJ) has received considerable attention as an alternative to unstructured clinical judgment and actuarial assessment, and as a means of resolving their ongoing conflict. However, predictive validity studies have typically relied on receiver operating characteristic (ROC) analysis, the same technique commonly used to validate actuarial assessment tools. This paper presents SPJ as distinct from both unstructured clinical judgment and actuarial assessment. A key distinguishing feature of SPJ is the contribution of modifiable factors, either dynamic or protective, to summary risk ratings. With modifiable factors, the summary rating scheme serves as a prognostic model rather than a classification procedure. However, prognostic models require more extensive and thorough predictive validity testing than can be provided by ROC analysis. It is proposed that validation should include calibration and reclassification techniques, as well as additional measures of discrimination. Several techniques and measures are described and illustrated. The paper concludes by tracing the limitations of ROC analysis to its philosophical foundation and its origin as a statistical theory of decision-making. This foundation inhibits the performance of crucial tasks, such as determining the sufficiency of a risk assessment and examining the evidentiary value of statistical findings. The paper closes by noting a current effort to establish a viable and complementary relationship between SPJ and decision-making theory.

  18. Predicting Ebola Infection and Survival Using qRT-PCR and Basic Clinical Labs: Comparing Ebola Virus Survival, Clinical Features, and Laboratory Values in 3 Non Human Primate Models

    Science.gov (United States)

    2016-12-22

    sample collection and yielded a panel of 46 routine clinical lab values [21]. Viral RNA Viral burden was measured on sera collected at pre...tested predictor. The measurement of the area under the ROC curve, known as ROC AUC, transfers the performance curve into a value range between 0.5 and... present our findings in a systematic format concentrating on laboratory values that we think reflect EBOV disease pathogenesis and that are easily

  19. The predictive value of bronchial histamine challenge in the diagnosis of bronchial asthma

    DEFF Research Database (Denmark)

    Madsen, F; Holstein-Rathlou, N H; Mosbech, H

    1985-01-01

    A prospective survey aiming to study the predictive value of bronchial histamine challenge was performed on 151 patients with a forced expiratory volume1 (FEV1) above 60% of predicted. According to variations in peak expiratory flow rate (PEFR) and medical history the patients were classified...... as asthmatics (n = 97) or non-asthmatics (n = 54). The diagnostic properties of the challenge were calculated using the statement of Baye. Considering PC20 values below 4.00 mg/ml as positive, the predictive value of a positive test was about 0.80 and the predictive value of a negative about 0.76. When PC20...... was below 0.125 mg/ml the predictive value of a positive test was 1.00, but an increase in PC20 in the range from 4.00 to 16 mg/ml did not increase the predictive value of a negative test. In this study the prevalence of asthma was about 0.6. We therefore conclude that bronchial histamine challenge...

  20. Predictive value of upper-limb accelerometry in acute stroke with hemiparesis

    NARCIS (Netherlands)

    Gebruers, Nick; Truijen, Steven; Engelborghs, Sebastiaan; De Deyn, Peter P.

    2013-01-01

    Few studies have investigated how well early activity measurements by accelerometers predict recovery after stroke. First, we assessed the predictive value of accelerometer-based measurements of upper-limb activity in patients with acute stroke with a hemiplegic arm. Second, we established the

  1. Appropriate model selection methods for nonstationary generalized extreme value models

    Science.gov (United States)

    Kim, Hanbeen; Kim, Sooyoung; Shin, Hongjoon; Heo, Jun-Haeng

    2017-04-01

    Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.

  2. 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...... that describes the variation between subjects. The ODE setup implies that the variation for a single subject is described by a single parameter (or vector), namely the variance (covariance) of the residuals. Furthermore the prediction of the states is given as the solution to the ODEs and hence assumed...... 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...

  3. Precision Plate Plan View Pattern Predictive Model

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yang; YANG Quan; HE An-rui; WANG Xiao-chen; ZHANG Yun

    2011-01-01

    According to the rolling features of plate mill, a 3D elastic-plastic FEM (finite element model) based on full restart method of ANSYS/LS-DYNA was established to study the inhomogeneous plastic deformation of multipass plate rolling. By analyzing the simulation results, the difference of head and tail ends predictive models was found and modified. According to the numerical simulation results of 120 different kinds of conditions, precision plate plan view pattern predictive model was established. Based on these models, the sizing MAS (mizushima automatic plan view pattern control system) method was designed and used on a 2 800 mm plate mill. Comparing the rolled plates with and without PVPP (plan view pattern predictive) model, the reduced width deviation indicates that the olate !olan view Dattern predictive model is preeise.

  4. A Prediction Model of MF Radiation in Environmental Assessment

    Institute of Scientific and Technical Information of China (English)

    HE-SHAN GE; YAN-FENG HONG

    2006-01-01

    Objective To predict the impact of MF radiation on human health.Methods The vertical distribution of field intensity was estimated by analogism on the basis of measured values from simulation measurement. Results A kind of analogism on the basis of geometric proportion decay pattern is put forward in the essay. It showed that with increasing of height the field intensity increased according to geometric proportion law. Conclusion This geometric proportion prediction model can be used to estimate the impact of MF radiation on inhabited environment, and can act as a reference pattern in predicting the environmental impact level of MF radiation.

  5. NBC Hazard Prediction Model Capability Analysis

    Science.gov (United States)

    1999-09-01

    Puff( SCIPUFF ) Model Verification and Evaluation Study, Air Resources Laboratory, NOAA, May 1998. Based on the NOAA review, the VLSTRACK developers...TO SUBSTANTIAL DIFFERENCES IN PREDICTIONS HPAC uses a transport and dispersion (T&D) model called SCIPUFF and an associated mean wind field model... SCIPUFF is a model for atmospheric dispersion that uses the Gaussian puff method - an arbitrary time-dependent concentration field is represented

  6. The predictive value of selected serum microRNAs for acute GVHD by TaqMan MicroRNA arrays.

    Science.gov (United States)

    Zhang, Chunyan; Bai, Nan; Huang, Wenrong; Zhang, Pengjun; Luo, Yuan; Men, Shasha; Wen, Ting; Tong, Hongli; Wang, Shuhong; Tian, Ya-Ping

    2016-10-01

    Currently, the diagnosis of acute graft-versus-host disease (aGVHD) is mainly based on clinical symptoms and biopsy results. This study was designed to further explore new no noninvasive biomarkers for aGVHD prediction/diagnosis. We profiled miRNAs in serum pools from patients with aGVHD (grades II-IV) (n = 9) and non-aGVHD controls (n = 9) by real-time qPCR-based TaqMan MicroRNA arrays. Then, predictive models were established using related miRNAs (n = 38) and verified by a double-blind trial (n = 54). We found that miR-411 was significantly down regulated when aGVHD developed and recovered when aGVHD was controlled, which demonstrated that miR-411 has potential as an indicator for aGVHD monitoring. We developed and validated a predictive model and a diagnostic model for aGVHD. The predictive model included two miRNAs (miR-26b and miR-374a), which could predict an increased risk for aGVHD 1 or 2 weeks in advance, with an AUC, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) of 0.722, 76.19 %, and 69.70 %, respectively. The diagnostic model included three miRNAs (miR-28-5p, miR-489, and miR-671-3p) with an AUC, PPV, and NPV of 0.841, 85.71 % and 83.33 %, respectively. Our results show that circulating miRNAs (miR-26b and miR-374a, miR-28-5p, miR-489 and miR-671-3p) may serve as biomarkers for the prediction and diagnosis of grades II-IV aGVHD.

  7. Predicting entrepreneurial career intentions: Values and the theory of planned behavior.

    NARCIS (Netherlands)

    M.J. Gorgievski-Duijvesteijn (Marjan); U. Stephan (Ute); M. Laguna (Mariola); J.A. Moriano (Juan)

    2017-01-01

    textabstractIntegrating predictions from the theory of human values with the theory of planned behavior (TPB), our primary goal is to investigate mechanisms through which individual values are related to entrepreneurial career intentions using a sample of 823 students from four European countries.

  8. The predictive value of ovarian reserve tests for spontaneous pregnancy in subfertile ovulatory women

    NARCIS (Netherlands)

    Haadsma, M. L.; Groen, H.; Fidler, V.; Bukman, A.; Roeloffzen, E. M. A.; Groenewoud, E. R.; Broekmans, F. J. M.; Heineman, M. J.; Hoek, A.

    2008-01-01

    BACKGROUND: The predictive value of ovarian reserve tests (ORTs) for spontaneous pregnancy is unclear. Our study aimed to determine whether ORTs have added value to previously identified prognostic factors for spontaneous pregnancy in subfertile ovulatory couples. METHODS: A prospective cohort study

  9. Concordance and predictive value of two adverse drug event data sets

    OpenAIRE

    Cami, Aurel; Reis, Ben Y

    2014-01-01

    Background: Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single “gold standard” ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. Methods: We systemati...

  10. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis....... While use of experimentally measured values of the needed properties is desirable in these tasks, the experimental data of the properties of interest may not be available or may not be measurable in many cases. Therefore, property models that are reliable, predictive and easy to use are necessary....... However, which models should be used to provide the reliable estimates of the required properties? And, how much measured data is necessary to regress the model parameters? How to ensure predictive capabilities in the developed models? Also, as it is necessary to know the associated uncertainties...

  11. 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

  12. Modelling Chemical Reasoning to Predict Reactions

    CERN Document Server

    Segler, Marwin H S

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180,000 randomly selected binary reactions. We show that our data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-) discovering novel transformations (even including transition-metal catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph, and because each single reaction prediction is typically ac...

  13. Predictive value of derived calcium figures based on the measurement of ionised calcium.

    Science.gov (United States)

    Gardner, M D; Dryburgh, F J; Fyffe, J A; Jenkins, A S

    1981-03-01

    The algorithms used in this hospital to assess calcium status are calculated ionised serum calcium and the serum calcium concentration adjusted for albumin. In order to establish their clinical usefulness, they were compared with the ionised calcium concentration measured on the Nova 2 instrument in patients with various calcium and protein abnormalities. Good correlation was found between the measured and calculated values. The predictive values for the calculated results and for total serum calcium concentrations are presented. In this series, the derived values were useful in predicting the serum ionised calcium concentration of the patients studied.

  14. Prediction model for spring dust weather frequency in North China

    Institute of Scientific and Technical Information of China (English)

    LANG XianMei

    2008-01-01

    It is of great social and scientific importance and also very difficult to make reliable prediction for dust weather frequency (DWF) in North China. In this paper, the correlation between spring DWF in Beijing and Tianjin observation stations, taken as examples in North China, and seasonally averaged surface air temperature, precipitation, Arctic Oscillation, Antarctic Oscillation, South Oscillation, near surface meridional wind and Eurasian westerly index is respectively calculated so as to construct a prediction model for spring DWF in North China by using these climatic factors. Two prediction models, I.e. Model-Ⅰ and model-Ⅱ, are then set up respectively based on observed climate data and the 32-year (1970--2001) extra-seasonal hindcast experiment data as reproduced by the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM). It is indicated that the correlation coefficient between the observed and predicted DWF reaches 0.933 in the model-Ⅰ, suggesting a high prediction skill one season ahead. The corresponding value is high up to 0.948 for the subsequent model-Ⅱ, which involves synchronous spring climate data reproduced by the IAP9L-AGCM relative to the model-Ⅰ. The model-Ⅱ can not only make more precise prediction but also can bring forward the lead time of real-time prediction from the model-Ⅰ's one season to half year. At last, the real-time predictability of the two models is evaluated. It follows that both the models display high prediction skill for both the interannual variation and linear trend of spring DWF in North China, and each is also featured by different advantages. As for the model-Ⅱ, the prediction skill is much higher than that of original approach by use of the IAP9L-AGCM alone. Therefore, the prediction idea put forward here should be popularized in other regions in China where dust weather occurs frequently.

  15. Prediction model for spring dust weather frequency in North China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    It is of great social and scientific importance and also very difficult to make reliable prediction for dust weather frequency (DWF) in North China. In this paper, the correlation between spring DWF in Beijing and Tianjin observation stations, taken as examples in North China, and seasonally averaged surface air temperature, precipitation, Arctic Oscillation, Antarctic Oscillation, South Oscillation, near surface meridional wind and Eurasian westerly index is respectively calculated so as to construct a prediction model for spring DWF in North China by using these climatic factors. Two prediction models, i.e. model-I and model-II, are then set up respectively based on observed climate data and the 32-year (1970 -2001) extra-seasonal hindcast experiment data as reproduced by the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM). It is indicated that the correlation coefficient between the observed and predicted DWF reaches 0.933 in the model-I, suggesting a high prediction skill one season ahead. The corresponding value is high up to 0.948 for the subsequent model-II, which involves synchronous spring climate data reproduced by the IAP9L-AGCM relative to the model-I. The model-II can not only make more precise prediction but also can bring forward the lead time of real-time prediction from the model-I’s one season to half year. At last, the real-time predictability of the two models is evaluated. It follows that both the models display high prediction skill for both the interannual variation and linear trend of spring DWF in North China, and each is also featured by different advantages. As for the model-II, the prediction skill is much higher than that of original approach by use of the IAP9L-AGCM alone. Therefore, the prediction idea put forward here should be popularized in other regions in China where dust weather occurs frequently.

  16. Predictive value of Pre-treatment Amygdala volume for Electroconvulsive Therapy Response in Severely Depressed Patients

    Directory of Open Access Journals (Sweden)

    Freek eTen Doesschate

    2014-11-01

    Full Text Available Background Electroconvulsive therapy (ECT is an effective treatment for patients with severe depression. Knowledge on factors predicting therapeutic response may help to identify patients who will benefit most from the intervention. Based on the neuroplasticity hypothesis, volumes of the amygdala and hippocampus are possible candidates for predicting treatment outcome. Therefore, this prospective cohort study examines the predictive value of amygdala and hippocampal volumes for the effectiveness of ECT.Methods Prior to ECT, 53 severely unipolar depressed patients (mean age 57±14 years; 40% [n=21] male received structural magnetic resonance imaging at 1.5 Tesla. Normalized amygdala and hippocampal volumes were calculated based on automatic segmentation by FreeSurfer. Regression analyses were used to test if the normalized volumes could predict the response to a course of ECT, based on the Montgomery-Åsberg Depression Rating Scale (MADRS scores. ResultsA larger amygdala volume independently and significantly predicted a lower post-ECT MADRS score (β = -0.347, P=0.013. The left amygdala volume had greater predictive value for treatment outcome relative to the right amygdala volume. Hippocampal volume had no independent predictive value.Conclusion A larger pretreatment amygdala volume predicted more effective ECT, independent of other known predictors. Almost all patients continued their medication during the study, which might have influenced the course of treatment in ways that were not taken into account.

  17. 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.

  18. Is the prediction of pKa values by constant-pH molecular dynamics being hindered by inherited problems?

    Science.gov (United States)

    Machuqueiro, Miguel; Baptista, António M

    2011-12-01

    In this study, we investigate two factors that can hinder the performance of constant-pH molecular dynamics methods in predicting protein pK(a) values, using hen egg white lysozyme as a test system. The first factor is related to the molecular definition and pK(a) value of model compounds in the Poisson-Boltzmann framework. We address this by defining the model compound as a molecular fragment with an associated pK(a) value that is calibrated against experimental data, which results in a decrease of 0.12 units in pK(a) errors. The second addressed factor is the possibility that detrimental structural distortions are being introduced in the simulations by the underlying molecular mechanics force field. This issue is investigated by analyzing how the gradual structural rearrangements affect the predicted pK(a) values. The two GROMOS force fields studied here (43A1 and 53A6) yield good pK(a) predictions, although a time-dependent performance is observed: 43A1 performs better after a few nanoseconds of structural reorganization (pK(a) errors of ~0.45), while 53A6 gives the best prediction right at the first nanosecond (pK(a) errors of 0.42). These results suggest that the good performance of constant-pH molecular dynamics methods could be further improved if these force field limitations were overcome. Copyright © 2011 Wiley-Liss, Inc.

  19. An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction.

    Science.gov (United States)

    Ak, Ronay; Vitelli, Valeria; Zio, Enrico

    2015-11-01

    We consider the task of performing prediction with neural networks (NNs) on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the prediction model. A multilayer perceptron NN is trained to map interval-valued input data onto interval outputs, representing the prediction intervals (PIs) of the real target values. The NN training is performed by nondominated sorting genetic algorithm-II, so that the PIs are optimized both in terms of accuracy (coverage probability) and dimension (width). Demonstration of the proposed method is given in two case studies: 1) a synthetic case study, in which the data have been generated with a 5-min time frequency from an autoregressive moving average model with either Gaussian or Chi-squared innovation distribution and 2) a real case study, in which experimental data consist of wind speed measurements with a time step of 1 h. Comparisons are given with a crisp (single-valued) approach. The results show that the crisp approach is less reliable than the interval-valued input approach in terms of capturing the variability in input.

  20. Genetic models of homosexuality: generating testable predictions

    OpenAIRE

    Gavrilets, Sergey; Rice, William R.

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality inclu...

  1. Allostasis: a model of predictive regulation.

    Science.gov (United States)

    Sterling, Peter

    2012-04-12

    The premise of the standard regulatory model, "homeostasis", is flawed: the goal of regulation is not to preserve constancy of the internal milieu. Rather, it is to continually adjust the milieu to promote survival and reproduction. Regulatory mechanisms need to be efficient, but homeostasis (error-correction by feedback) is inherently inefficient. Thus, although feedbacks are certainly ubiquitous, they could not possibly serve as the primary regulatory mechanism. A newer model, "allostasis", proposes that efficient regulation requires anticipating needs and preparing to satisfy them before they arise. The advantages: (i) errors are reduced in magnitude and frequency; (ii) response capacities of different components are matched -- to prevent bottlenecks and reduce safety factors; (iii) resources are shared between systems to minimize reserve capacities; (iv) errors are remembered and used to reduce future errors. This regulatory strategy requires a dedicated organ, the brain. The brain tracks multitudinous variables and integrates their values with prior knowledge to predict needs and set priorities. The brain coordinates effectors to mobilize resources from modest bodily stores and enforces a system of flexible trade-offs: from each organ according to its ability, to each organ according to its need. The brain also helps regulate the internal milieu by governing anticipatory behavior. Thus, an animal conserves energy by moving to a warmer place - before it cools, and it conserves salt and water by moving to a cooler one before it sweats. The behavioral strategy requires continuously updating a set of specific "shopping lists" that document the growing need for each key component (warmth, food, salt, water). These appetites funnel into a common pathway that employs a "stick" to drive the organism toward filling the need, plus a "carrot" to relax the organism when the need is satisfied. The stick corresponds broadly to the sense of anxiety, and the carrot broadly to

  2. 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)

  3. Modeling churn using customer lifetime value

    OpenAIRE

    Glady, Nicolas; Baesens, Bart; Croux, Christophe

    2009-01-01

    The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric vi...

  4. Modeling customer loyalty using customer lifetime value.

    OpenAIRE

    Glady, N.; Baesens, Bart; Croux, Christophe

    2006-01-01

    The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution will be to redefine the notion of customer's loyalty by considering it from a customer-cen...

  5. Modeling the Marginal Value of Rainforest Losses

    OpenAIRE

    Strand, Jon

    2015-01-01

    A rainforest can be modeled as a dynamic asset subject to various risks, including risk of fire. Any small part of the forest can be in one of two states: either untouched by forest fire, or already damaged by fire, in which case there is both a local forest loss and increased dryness over a broader area. In this paper, two Bellman equations are constructed, one for unharmed forest and a s...

  6. Incidence, Mortality and Positive Predictive Value of Type 1 Cardiorenal Syndrome in Acute Coronary Syndrome

    Science.gov (United States)

    Pimienta González, Raquel; Couto Comba, Patricia; Rodríguez Esteban, Marcos; Alemán Sánchez, José Juan; Hernández Afonso, Julio; Rodríguez Pérez, María del Cristo; Marcelino Rodríguez, Itahisa; Brito Díaz, Buenaventura; Elosua, Roberto; Cabrera de León, Antonio

    2016-01-01

    Objectives To determine whether the risk of cardiovascular mortality associated with cardiorenal syndrome subtype 1 (CRS1) in patients who were hospitalized for acute coronary syndrome (ACS) was greater than the expected risk based on the sum of its components, to estimate the predictive value of CRS1, and to determine whether the severity of CRS1 worsens the prognosis. Methods Follow-up study of 1912 incident cases of ACS for 1 year after discharge. Cox regression models were estimated with time to event (in-hospital death, and readmission or death during the first year after discharge) as the dependent variable. Results The incidence of CRS1 was 9.2/1000 person-days of hospitalization (95% CI = 8.1–10.5), but these patients accounted for 56.6% (95% CI = 47.4–65.) of all mortality. The positive predictive value of CRS1 was 29.6% (95% CI = 23.9–36.0) for in-hospital death, and 51.4% (95% CI = 44.8–58.0) for readmission or death after discharge. The risk of in-hospital death from CRS1 (RR = 18.3; 95% CI = 6.3–53.2) was greater than the sum of risks associated with either acute heart failure (RR = 7.6; 95% CI = 1.8–31.8) or acute kidney injury (RR = 2.8; 95% CI = 0.9–8.8). The risk of events associated with CRS1 also increased with syndrome severity, reaching a RR of 10.6 (95% CI = 6.2–18.1) for in-hospital death at the highest severity level. Conclusions The effect of CRS1 on in-hospital mortality is greater than the sum of the effects associated with each of its components, and it increases with the severity of the syndrome. CRS1 accounted for more than half of all mortality, and its positive predictive value approached 30% in-hospital and 50% after discharge. PMID:27907067

  7. Some Remarks on CFD Drag Prediction of an Aircraft Model

    Science.gov (United States)

    Peng, S. H.; Eliasson, P.

    Observed in CFD drag predictions for the DLR-F6 aircraft model with various configurations, some issues are addressed. The emphasis is placed on the effect of turbulence modeling and grid resolution. With several different turbulence models, the predicted flow feature around the aircraft is highlighted. It is shown that the prediction of the separation bubble in the wing-body junction is closely related to the inherent modeling mechanism of turbulence production. For the configuration with an additional fairing, which has effectively removed the separation bubble, it is illustrated that the drag prediction may be altered even for attached turbulent boundary layer when different turbulence models are used. Grid sensitivity studies are performed with two groups of subsequently refined grids. It is observed that, in contrast to the lift, the drag prediction is rather sensitive to the grid refinement, as well as to the artificial diffusion added for solving the turbulence transport equation. It is demonstrated that an effective grid refinement should drive the predicted drag components monotonically and linearly converged to a finite value.

  8. Development of a new proximate analysis based correlation to predict calorific value of coal

    Energy Technology Data Exchange (ETDEWEB)

    A.K. Majumder; Rachana Jain; P. Banerjee; J.P. Barnwal [Advanced Materials and Processes Research Institute (CSIR), Bhopal (India). Department of Mineral Engineering

    2008-10-15

    The experimental determination of higher heating value (HHV) of solid fuels is a cost intensive process, as it requires special instrumentation and highly trained analyst to operate it, where as proximate analysis data can be obtained relatively easily using an ordinary muffle furnace. Therefore, to simplify the task and to reduce the cost of analysis many correlations were developed for determining HHV from proximate analysis of solid fuels. An attempt has been made in this paper to evaluate the applicability of these correlations with a special focus on Indian coals. It has been observed that the developed correlations are either complex in nature or by-pass the effect of important variables like moisture and ash contents of coals. An effort has, therefore, been made to develop a simple correlation based on proximate analysis data for predicting HHV of coal (as-received basis). The model presented here is developed using analyses of 250 coal samples and its significance lies in involvement of all the major variables affecting the HHV. The developed model appears to be better than the existing models. 10 refs., 4 figs., 2 tabs.

  9. Outcome Prediction in Mathematical Models of Immune Response to Infection.

    Directory of Open Access Journals (Sweden)

    Manuel Mai

    Full Text Available Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of 'virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability v in the ODE models by randomly selecting the model parameters from distributions with coefficients of variation v that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near 100% accuracy for v = 0, and the accuracy decreases with increasing v for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for v > 0. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.

  10. Predictive model for segmented poly(urea

    Directory of Open Access Journals (Sweden)

    Frankl P.

    2012-08-01

    Full Text Available Segmented poly(urea has been shown to be of significant benefit in protecting vehicles from blast and impact and there have been several experimental studies to determine the mechanisms by which this protective function might occur. One suggested route is by mechanical activation of the glass transition. In order to enable design of protective structures using this material a constitutive model and equation of state are needed for numerical simulation hydrocodes. Determination of such a predictive model may also help elucidate the beneficial mechanisms that occur in polyurea during high rate loading. The tool deployed to do this has been Group Interaction Modelling (GIM – a mean field technique that has been shown to predict the mechanical and physical properties of polymers from their structure alone. The structure of polyurea has been used to characterise the parameters in the GIM scheme without recourse to experimental data and the equation of state and constitutive model predicts response over a wide range of temperatures and strain rates. The shock Hugoniot has been predicted and validated against existing data. Mechanical response in tensile tests has also been predicted and validated.

  11. Ventral striatum encodes past and predicted value independent of motor contingencies.

    Science.gov (United States)

    Goldstein, Brandon L; Barnett, Brian R; Vasquez, Gloria; Tobia, Steven C; Kashtelyan, Vadim; Burton, Amanda C; Bryden, Daniel W; Roesch, Matthew R

    2012-02-08

    The ventral striatum (VS) is thought to signal the predicted value of expected outcomes. However, it is still unclear whether VS can encode value independently from variables often yoked to value such as response direction and latency. Expectations of high value reward are often associated with a particular action and faster latencies. To address this issue we trained rats to perform a task in which the size of the predicted reward was signaled before the instrumental response was instructed. Instrumental directional cues were presented briefly at a variable onset to reduce accuracy and increase reaction time. Rats were more accurate and slower when a large versus small reward was at stake. We found that activity in VS was high during odors that predicted large reward even though reaction times were slower under these conditions. In addition to these effects, we found that activity before the reward predicting cue reflected past and predicted reward. These results demonstrate that VS can encode value independent of motor contingencies and that the role of VS in goal-directed behavior is not just to increase vigor of specific actions when more is at stake.

  12. Positive predictive values of abused drug immunoassays on the Beckman Synchron in a veteran population.

    Science.gov (United States)

    Dietzen, D J; Ecos, K; Friedman, D; Beason, S

    2001-04-01

    The pressure to reduce the cost of analytic testing makes it tempting to discontinue routine confirmation of urine specimens positive for drugs of abuse by immunoassay. Beyond the economic motivation, the requirement for confirmation should be driven by the positive predictive value of the screening tests. We have quantitated positive predictive values of our screening immunoassays in a large metropolitan Veterans Affairs Medical Center. We reviewed the confirmatory rate of urine specimens positive for drugs of abuse with Beckman Synchron reagents from June 1998 to June 1999 and tabulated the false-positive screening rate. There were 175 instances of false-positive screens during the 13 months we analyzed. Positive predictive values ranged from 0% (amphetamine) to 100% (THC). We determined that the low positive predictive value of the amphetamine assay in our laboratory was primarily due to the use of ranitidine (Zantac). Urine specimens containing greater than 43 microg/mL ranitidine were positive in our amphetamine assay. This concentration is routinely exceeded in our patients taking ranitidine. In our clinical and analytic setting, the Beckman THC assay did not require confirmation. The positive predictive values of the Beckman opiate, cocaine, barbiturate, propoxyphene, and methadone immunoassays dictate routine confirmatory testing in specimens that screen positive for these substances. Finally, because of its extreme sensitivity to ranitidine, the Beckman amphetamine assay has little utility in our laboratory setting.

  13. 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.

  14. Predictive QSAR modeling of phosphodiesterase 4 inhibitors.

    Science.gov (United States)

    Kovalishyn, Vasyl; Tanchuk, Vsevolod; Charochkina, Larisa; Semenuta, Ivan; Prokopenko, Volodymyr

    2012-02-01

    A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Predicting soil acidification trends at Plynlimon using the SAFE model

    Directory of Open Access Journals (Sweden)

    B. Reynolds

    1997-01-01

    Full Text Available The SAFE model has been applied to an acid grassland site, located on base-poor stagnopodzol soils derived from Lower Palaeozoic greywackes. The model predicts that acidification of the soil has occurred in response to increased acid deposition following the industrial revolution. Limited recovery is predicted following the decline in sulphur deposition during the mid to late 1970s. Reducing excess sulphur and NOx deposition in 1998 to 40% and 70% of 1980 levels results in further recovery but soil chemical conditions (base saturation, soil water pH and ANC do not return to values predicted in pre-industrial times. The SAFE model predicts that critical loads (expressed in terms of the (Ca+Mg+K:Alcrit ratio for six vegetation species found in acid grassland communities are not exceeded despite the increase in deposited acidity following the industrial revolution. The relative growth response of selected vegetation species characteristic of acid grassland swards has been predicted using a damage function linking growth to soil solution base cation to aluminium ratio. The results show that very small growth reductions can be expected for 'acid tolerant' plants growing in acid upland soils. For more sensitive species such as Holcus lanatus, SAFE predicts that growth would have been reduced by about 20% between 1951 and 1983, when acid inputs were greatest. Recovery to c. 90% of normal growth (under laboratory conditions is predicted as acidic inputs decline.

  16. The HackensackUMC Value-Based Care Model: Building Essentials for Value-Based Purchasing.

    Science.gov (United States)

    Douglas, Claudia; Aroh, Dianne; Colella, Joan; Quadri, Mohammed

    2016-01-01

    The Affordable Care Act, 2010, and the subsequent shift from a quantity-focus to a value-centric reimbursement model led our organization to create the HackensackUMC Value-Based Care Model to improve our process capability and performance to meet and sustain the triple aims of value-based purchasing: higher quality, lower cost, and consumer perception. This article describes the basics of our model and illustrates how we used it to reduce the costs of our patient sitter program.

  17. Simple clinical variables predict liver histology in hepatitis C: prospective validation of a clinical prediction model.

    Science.gov (United States)

    Romagnuolo, Joseph; Andrews, Christopher N; Bain, Vincent G; Bonacini, Maurizio; Cotler, Scott J; Ma, Mang; Sherman, Morris

    2005-11-01

    A recent single-center multivariate analysis of hepatitis C (HCV) patients showed that having any two criteria: 1) ferritin > or =200 microg/l and 2) spider nevi and/or albumin clinical prediction model using an independent multicenter sample. Eighty-one patients with previously untreated active chronic HCV underwent physical examination, laboratory investigation, and liver biopsy. Biopsies were read, in blinded fashion, by a single pathologist, using a modified Hytiroglou (1995) scale. The clinical scoring system was correlated with histology; likelihood ratios (LRs), Fisher's exact p-values, and receiver operating characteristics (ROCs) were calculated. Data recording was complete in 77 and 38 patients regarding fibrotic stage and inflammatory grade, respectively. For fibrosis, 3/3 patients with any three criteria (LR 17, positive predictive value (PPV) 100%), 4/5 patients with any two criteria (LR 5.1), and 15/47 with no criteria (LR 0.6, negative predictive value (NPV) 68%) had stage 2 or greater fibrosis on biopsy (p=0.01). For inflammation, 5/5 patients with both criteria (LR 15, PPV 100%), and 8/19 patients with no criteria (LR 0.5, NPV 58%) had moderate-severe inflammation on liver biopsy (p=0.036). When missing variables were assumed to be normal, recalculated LRs were almost identical. An alanine aminotransferase (ALAT) level data set has validated our published model which uses simple clinical variables accurately and significantly to predict hepatic fibrosis and inflammation in HCV patients.

  18. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  19. Simple Predictive Models for Saturated Hydraulic Conductivity of Technosands

    DEFF Research Database (Denmark)

    Arthur, Emmanuel; Razzaghi, Fatemeh; Møldrup, Per

    2012-01-01

    Accurate estimation of saturated hydraulic conductivity (Ks) of technosands (gravel-free, coarse sands with negligible organic matter content) is important for irrigation and drainage management of athletic fields and golf courses. In this study, we developed two simple models for predicting Ks......-connectivity parameter (m) obtained for pure coarse sand after fitting to measured Ks data was 1.68 for both models and in good agreement with m values obtained from recent solute and gas diffusion studies. Both the modified K-C and R-C models are easy to use and require limited parameter input, and both models gave...

  20. The predictive performance and stability of six species distribution models.

    Directory of Open Access Journals (Sweden)

    Ren-Yan Duan

    Full Text Available Predicting species' potential geographical range by species distribution models (SDMs is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials. We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05, while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05, and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points.According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  1. Predictive value of acute coronary syndrome discharge diagnoses in the Danish national patioent registry

    DEFF Research Database (Denmark)

    Joensen, Albert Marni; Jensen, Majken K.; Overvad, Kim

    (95.3%) registered with a first-time ACS diagnosis. The overall positive predictive value for ACS was 65.4% (95% confidence interval (CI) 62.9 - 67.7%). Stratification by subdiagnoses and hospital department showed significantly higher positive predictive values for myocardial infarction diagnoses (81......Background: Updated data on the predictive value of acute coronary syndrome (ACS) diagnoses, including unstable angina pectoris, myocardial infarction and cardiac arrest, in hospital discharge registries are sparse. Design: Validation study. Methods: All first-time ACS diagnoses in the Danish.......9%; 95% CI 79.5 - 84.2%) and among patients diagnosed with an ACS diagnosis from a ward (80.1%;  95% CI 77.7 - 82.3%), respectively. Conclusion: The ACS diagnoses in hospital discharge registries should be used with caution. If validation is not possible, restricting analyses to patients with myocardial...

  2. Time-dependent Predictive Values of Prognostic Biomarkers with Failure Time Outcome.

    Science.gov (United States)

    Zheng, Yingye; Cai, Tianxi; Pepe, Margaret S; Levy, Wayne C

    2008-01-01

    In a prospective cohort study, information on clinical parameters, tests and molecular markers is often collected. Such information is useful to predict patient prognosis and to select patients for targeted therapy. We propose a new graphical approach, the positive predictive value (PPV) curve, to quantify the predictive accuracy of prognostic markers measured on a continuous scale with censored failure time outcome. The proposed method highlights the need to consider both predictive values and the marker distribution in the population when evaluating a marker, and it provides a common scale for comparing different markers. We consider both semiparametric and nonparametric based estimating procedures. In addition, we provide asymptotic distribution theory and resampling based procedures for making statistical inference. We illustrate our approach with numerical studies and datasets from the Seattle Heart Failure Study.

  3. Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

    Science.gov (United States)

    Yang, Bin

    2017-07-01

    Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

  4. Three-model ensemble wind prediction in southern Italy

    Science.gov (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013) three-model ensemble (TME) experiment for wind prediction is considered. The models employed, run operationally at National Research Council - Institute of Atmospheric Sciences and Climate (CNR-ISAC), are RAMS (Regional Atmospheric Modelling System), BOLAM (BOlogna Limited Area Model), and MOLOCH (MOdello LOCale in H coordinates). The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System). Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System) of the ECMWF (European Centre for Medium-Range Weather Forecast) for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  5. Calibrated predictions for multivariate competing risks models.

    Science.gov (United States)

    Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni

    2014-04-01

    Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

  6. Implicit Theories, Expectancies, and Values Predict Mathematics Motivation and Behavior across High School and College.

    Science.gov (United States)

    Priess-Groben, Heather A; Hyde, Janet Shibley

    2017-06-01

    Mathematics motivation declines for many adolescents, which limits future educational and career options. The present study sought to identify predictors of this decline by examining whether implicit theories assessed in ninth grade (incremental/entity) predicted course-taking behaviors and utility value in college. The study integrated implicit theory with variables from expectancy-value theory to examine potential moderators and mediators of the association of implicit theories with college mathematics outcomes. Implicit theories and expectancy-value variables were assessed in 165 American high school students (47 % female; 92 % White), who were then followed into their college years, at which time mathematics courses taken, course-taking intentions, and utility value were assessed. Implicit theories predicted course-taking intentions and utility value, but only self-concept of ability predicted courses taken, course-taking intentions, and utility value after controlling for prior mathematics achievement and baseline values. Expectancy for success in mathematics mediated associations between self-concept of ability and college outcomes. This research identifies self-concept of ability as a stronger predictor than implicit theories of mathematics motivation and behavior across several years: math self-concept is critical to sustained engagement in mathematics.

  7. Modelling language evolution: Examples and predictions.

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  8. Modelling language evolution: Examples and predictions

    Science.gov (United States)

    Gong, Tao; Shuai, Lan; Zhang, Menghan

    2014-06-01

    We survey recent computer modelling research of language evolution, focusing on a rule-based model simulating the lexicon-syntax coevolution and an equation-based model quantifying the language competition dynamics. We discuss four predictions of these models: (a) correlation between domain-general abilities (e.g. sequential learning) and language-specific mechanisms (e.g. word order processing); (b) coevolution of language and relevant competences (e.g. joint attention); (c) effects of cultural transmission and social structure on linguistic understandability; and (d) commonalities between linguistic, biological, and physical phenomena. All these contribute significantly to our understanding of the evolutions of language structures, individual learning mechanisms, and relevant biological and socio-cultural factors. We conclude the survey by highlighting three future directions of modelling studies of language evolution: (a) adopting experimental approaches for model evaluation; (b) consolidating empirical foundations of models; and (c) multi-disciplinary collaboration among modelling, linguistics, and other relevant disciplines.

  9. The value of metabolic imaging to predict tumour response after chemoradiation in locally advanced rectal cancer

    Directory of Open Access Journals (Sweden)

    Gómez-Río Manuel

    2010-12-01

    Full Text Available Abstract Background We aim to investigate the possibility of using 18F-positron emission tomography/computer tomography (PET-CT to predict the histopathologic response in locally advanced rectal cancer (LARC treated with preoperative chemoradiation (CRT. Methods The study included 50 patients with LARC treated with preoperative CRT. All patients were evaluated by PET-CT before and after CRT, and results were compared to histopathologic response quantified by tumour regression grade (patients with TRG 1-2 being defined as responders and patients with grade 3-5 as non-responders. Furthermore, the predictive value of metabolic imaging for pathologic complete response (ypCR was investigated. Results Responders and non-responders showed statistically significant differences according to Mandard's criteria for maximum standardized uptake value (SUVmax before and after CRT with a specificity of 76,6% and a positive predictive value of 66,7%. Furthermore, SUVmax values after CRT were able to differentiate patients with ypCR with a sensitivity of 63% and a specificity of 74,4% (positive predictive value 41,2% and negative predictive value 87,9%; This rather low sensitivity and specificity determined that PET-CT was only able to distinguish 7 cases of ypCR from a total of 11 patients. Conclusions We conclude that 18-F PET-CT performed five to seven weeks after the end of CRT can visualise functional tumour response in LARC. In contrast, metabolic imaging with 18-F PET-CT is not able to predict patients with ypCR accurately.

  10. Towards predictive food process models: A protocol for parameter estimation.

    Science.gov (United States)

    Vilas, Carlos; Arias-Méndez, Ana; Garcia, Miriam R; Alonso, Antonio A; Balsa-Canto, E

    2016-05-31

    Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.

  11. Global Solar Dynamo Models: Simulations and Predictions

    Indian Academy of Sciences (India)

    Mausumi Dikpati; Peter A. Gilman

    2008-03-01

    Flux-transport type solar dynamos have achieved considerable success in correctly simulating many solar cycle features, and are now being used for prediction of solar cycle timing and amplitude.We first define flux-transport dynamos and demonstrate how they work. The essential added ingredient in this class of models is meridional circulation, which governs the dynamo period and also plays a crucial role in determining the Sun’s memory about its past magnetic fields.We show that flux-transport dynamo models can explain many key features of solar cycles. Then we show that a predictive tool can be built from this class of dynamo that can be used to predict mean solar cycle features by assimilating magnetic field data from previous cycles.

  12. Model Predictive Control of Sewer Networks

    Science.gov (United States)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    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 controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  13. Predictive Response Value of Pre- and Postchemoradiotherapy Variables in Rectal Cancer: An Analysis of Histological Data.

    Science.gov (United States)

    Santos, Marisa D; Silva, Cristina; Rocha, Anabela; Nogueira, Carlos; Matos, Eduarda; Lopes, Carlos

    2016-01-01

    Background. Neoadjuvant chemoradiotherapy (nCRT) followed by curative surgery in locally advanced rectal cancer (LARC) improves pelvic disease control. Survival improvement is achieved only if pathological response occurs. Mandard tumor regression grade (TRG) proved to be a valid system to measure nCRT response. Potential predictive factors for Mandard response are analyzed. Materials and Methods. 167 patients with LARC were treated with nCRT and curative surgery. Tumor biopsies and surgical specimens were reviewed and analyzed regarding mitotic count, necrosis, desmoplastic reaction, and inflammatory infiltration grade. Surgical specimens were classified according to Mandard TRG. The patients were divided as "good responders" (Mandard TRG1-2) and "bad responders" (Mandard TRG3-5). According to results from our previous data, good responders have better prognosis than bad responders. We examined predictive factors for Mandard response and performed statistical analysis. Results. In univariate analysis, distance from anal verge and ten other postoperative variables related with nCRT tumor response had predictive value for Mandard response. In multivariable analysis only mitotic count, necrosis, and differentiation grade in surgical specimen had predictive value. Conclusions. There is a lack of clinical and pathological preoperative variables able to predict Mandard response. Only postoperative pathological parameters related with nCRT response have predictive value.

  14. Extended Range Hydrological Predictions: Uncertainty Associated with Model Parametrization

    Science.gov (United States)

    Joseph, J.; Ghosh, S.; Sahai, A. K.

    2016-12-01

    The better understanding of various atmospheric processes has led to improved predictions of meteorological conditions at various temporal scale, ranging from short term which cover a period up to 2 days to long term covering a period of more than 10 days. Accurate prediction of hydrological variables can be done using these predicted meteorological conditions, which would be helpful in proper management of water resources. Extended range hydrological simulation includes the prediction of hydrological variables for a period more than 10 days. The main sources of uncertainty in hydrological predictions include the uncertainty in the initial conditions, meteorological forcing and model parametrization. In the present study, the Extended Range Prediction developed for India for monsoon by Indian Institute of Tropical Meteorology (IITM), Pune is used as meteorological forcing for the Variable Infiltration Capacity (VIC) model. Sensitive hydrological parameters, as derived from literature, along with a few vegetation parameters are assumed to be uncertain and 1000 random values are generated given their prescribed ranges. Uncertainty bands are generated by performing Monte-Carlo Simulations (MCS) for the generated sets of parameters and observed meteorological forcings. The basins with minimum human intervention, within the Indian Peninsular region, are identified and validation of results are carried out using the observed gauge discharge. Further, the uncertainty bands are generated for the extended range hydrological predictions by performing MCS for the same set of parameters and extended range meteorological predictions. The results demonstrate the uncertainty associated with the model parametrisation for the extended range hydrological simulations. Keywords: Extended Range Prediction, Variable Infiltration Capacity model, Monte Carlo Simulation.

  15. Does measurement of preoperative anxiety have added value for predicting postoperative nausea and vomiting?

    Science.gov (United States)

    Van den Bosch, Jolanda E; Moons, Karel G; Bonsel, Gouke J; Kalkman, Cor J

    2005-05-01

    Preoperative anxiety has been suggested as a predictor of postoperative nausea and vomiting (PONV), but supporting data are lacking. We quantified the added predictive value of preoperative anxiety to established predictors of PONV in 1389 surgical inpatients undergoing various procedures, by using multivariate logistic regression analysis. Investigated predictors were a history of PONV or motion sickness, smoking, sex, age, ethnicity, body mass index, ASA physical status, surgery type, duration of anesthesia, anesthetic technique, and postoperative opioid analgesia. Anxiety was measured by the Spielberger State-Trait Anxiety Inventory and the Amsterdam Preoperative Anxiety and Information Scale. The outcome was the occurrence of PONV in the first 24 h after surgery. The area under the receiver operating characteristic curve of a multivariate (logistic regression) model including sex, age, smoking, history of PONV or motion sickness, surgery type, and anesthetic technique was 0.72 (95% confidence interval, 0.70-0.74). There was a weak but significant association of anxiety with PONV, but the addition of anxiety to the model did not further increase the area under the receiver operating characteristic curve. Therefore, routine preoperative measurement of anxiety does not seem warranted, provided that the other predictors are already considered.

  16. DKIST Polarization Modeling and Performance Predictions

    Science.gov (United States)

    Harrington, David

    2016-05-01

    Calibrating the Mueller matrices of large aperture telescopes and associated coude instrumentation requires astronomical sources and several modeling assumptions to predict the behavior of the system polarization with field of view, altitude, azimuth and wavelength. The Daniel K Inouye Solar Telescope (DKIST) polarimetric instrumentation requires very high accuracy calibration of a complex coude path with an off-axis f/2 primary mirror, time dependent optical configurations and substantial field of view. Polarization predictions across a diversity of optical configurations, tracking scenarios, slit geometries and vendor coating formulations are critical to both construction and contined operations efforts. Recent daytime sky based polarization calibrations of the 4m AEOS telescope and HiVIS spectropolarimeter on Haleakala have provided system Mueller matrices over full telescope articulation for a 15-reflection coude system. AEOS and HiVIS are a DKIST analog with a many-fold coude optical feed and similar mirror coatings creating 100% polarization cross-talk with altitude, azimuth and wavelength. Polarization modeling predictions using Zemax have successfully matched the altitude-azimuth-wavelength dependence on HiVIS with the few percent amplitude limitations of several instrument artifacts. Polarization predictions for coude beam paths depend greatly on modeling the angle-of-incidence dependences in powered optics and the mirror coating formulations. A 6 month HiVIS daytime sky calibration plan has been analyzed for accuracy under a wide range of sky conditions and data analysis algorithms. Predictions of polarimetric performance for the DKIST first-light instrumentation suite have been created under a range of configurations. These new modeling tools and polarization predictions have substantial impact for the design, fabrication and calibration process in the presence of manufacturing issues, science use-case requirements and ultimate system calibration

  17. Modelling Chemical Reasoning to Predict Reactions

    OpenAIRE

    Segler, Marwin H. S.; Waller, Mark P.

    2016-01-01

    The ability to reason beyond established knowledge allows Organic Chemists to solve synthetic problems and to invent novel transformations. Here, we propose a model which mimics chemical reasoning and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outpe...

  18. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert; Knox, James

    2016-01-01

    Fully predictive models of the Four Bed Molecular Sieve of the Carbon Dioxide Removal Assembly on the International Space Station 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.

  19. Raman Model Predicting Hardness of Covalent Crystals

    OpenAIRE

    Zhou, Xiang-Feng; Qian, Quang-Rui; Sun, Jian; Tian, Yongjun; Wang, Hui-Tian

    2009-01-01

    Based on the fact that both hardness and vibrational Raman spectrum depend on the intrinsic property of chemical bonds, we propose a new theoretical model for predicting hardness of a covalent crystal. The quantitative relationship between hardness and vibrational Raman frequencies deduced from the typical zincblende covalent crystals is validated to be also applicable for the complex multicomponent crystals. This model enables us to nondestructively and indirectly characterize the hardness o...

  20. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts

  1. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

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

    2008-01-01

    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...

  2. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  3. Prediction modelling for population conviction data

    NARCIS (Netherlands)

    Tollenaar, N.

    2017-01-01

    In this thesis, the possibilities of using prediction models for judicial penal case data are investigated. The development and refinement of a risk taxation scale based on these data is discussed. When false positives are weighted equally severe as false negatives, 70% can be classified correctly.

  4. A Predictive Model for MSSW Student Success

    Science.gov (United States)

    Napier, Angela Michele

    2011-01-01

    This study tested a hypothetical model for predicting both graduate GPA and graduation of University of Louisville Kent School of Social Work Master of Science in Social Work (MSSW) students entering the program during the 2001-2005 school years. The preexisting characteristics of demographics, academic preparedness and culture shock along with…

  5. A revised prediction model for natural conception

    NARCIS (Netherlands)

    Bensdorp, A.J.; Steeg, J.W. van der; Steures, P.; Habbema, J.D.; Hompes, P.G.; Bossuyt, P.M.; Veen, F. van der; Mol, B.W.; Eijkemans, M.J.; Kremer, J.A.M.; et al.,

    2017-01-01

    One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis

  6. 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...

  7. Predictive Modelling of Mycotoxins in Cereals

    NARCIS (Netherlands)

    Fels, van der H.J.; Liu, C.

    2015-01-01

    In dit artikel worden de samenvattingen van de presentaties tijdens de 30e bijeenkomst van de Werkgroep Fusarium weergegeven. De onderwerpen zijn: Predictive Modelling of Mycotoxins in Cereals.; Microbial degradation of DON.; Exposure to green leaf volatiles primes wheat against FHB but boosts produ

  8. Leptogenesis in minimal predictive seesaw models

    CERN Document Server

    Björkeroth, Fredrik; Varzielas, Ivo de Medeiros; King, Stephen F

    2015-01-01

    We estimate the Baryon Asymmetry of the Universe (BAU) arising from leptogenesis within a class of minimal predictive seesaw models involving two right-handed neutrinos and simple Yukawa structures with one texture zero. The two right-handed neutrinos are dominantly responsible for the "atmospheric" and "solar" neutrino masses with Yukawa couplings to $(\

  9. Predictive modeling of coral disease distribution within a reef system.

    Directory of Open Access Journals (Sweden)

    Gareth J Williams

    Full Text Available Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1 coral diseases show distinct associations with multiple environmental factors, 2 incorporating interactions (synergistic collinearities among environmental variables is important when predicting coral disease spatial patterns, and 3 modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA, Porites tissue loss (PorTL, Porites trematodiasis (PorTrem, and Montipora white syndrome (MWS, and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response, led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to

  10. Extreme value prediction of the wave-induced vertical bending moment in large container ships

    DEFF Research Database (Denmark)

    Andersen, Ingrid Marie Vincent; Jensen, Jørgen Juncher

    2015-01-01

    in the present paper is based on time series of full scale measurements from three large container ships of 8600, 9400 and 14000 TEU. When carrying out the extreme value estimation the peak-over-threshold (POT) method combined with an appropriate extreme value distribution is applied. The choice of a proper...... increase the extreme hull girder response significantly. Focus in the present paper is on the influence of the hull girder flexibility on the extreme response amidships, namely the wave-induced vertical bending moment (VBM) in hogging, and the prediction of the extreme value of the same. The analysis...... threshold level as well as the statistical correlation between clustered peaks influence the extreme value prediction and are taken into consideration in the present paper....

  11. Academic Achievement in College: the Predictive Value of Subjective Evaluations of Intelligence and Academic Self-concept

    Directory of Open Access Journals (Sweden)

    Tatiana V. Kornilova

    2009-01-01

    Full Text Available The study examined the relationship between self-, peer- and test-estimated intelligence, academic self-concept and academic achievement. Subjective evaluations of intelligence and academic self-concept had incremental predictive value over conventional intelligence when predicting achievement accounting for more than 40% of its variance. The obtained pattern of results is presented via SEM-model which accounts for 75% variance in the latent factor of academic achievement. Author suggests the importance of further studying complex sets of achievement predictors from ability, personality and mediating domains.

  12. Predictive value of MSH2 gene expression in colorectal cancer treated with capecitabine

    DEFF Research Database (Denmark)

    Jensen, Lars H; Danenberg, Kathleen D; Danenberg, Peter V;

    2007-01-01

    was associated with a hazard ratio of 0.5 (95% confidence interval, 0.23-1.11; P = 0.083) in survival analysis. CONCLUSION: The higher gene expression of MSH2 in responders and the trend for predicting overall survival indicates a predictive value of this marker in the treatment of advanced CRC with capecitabine.......PURPOSE: The objective of the present study was to evaluate the gene expression of the DNA mismatch repair gene MSH2 as a predictive marker in advanced colorectal cancer (CRC) treated with first-line capecitabine. PATIENTS AND METHODS: Microdissection of paraffin-embedded tumor tissue, RNA...

  13. Cultural values predict coping using culture as an individual difference variable in multi-cultural samples.

    OpenAIRE

    Bardi, Anat; Guerra, V. M.

    2011-01-01

    Three studies establish the relations between cultural values and coping using multicultural samples of international students. Study 1 established the cross-cultural measurement invariance of subscales of the Cope inventory (Carver, Scheier, & Weintraub, 1989) used in the paper. The cultural value dimensions of embeddedness vs. autonomy and hierarchy vs. egalitarianism predicted how international students from 28 (Study 2) and 38 (Study 3) countries coped with adapting to living in a new cou...

  14. Specialized Language Models using Dialogue Predictions

    CERN Document Server

    Popovici, C; Popovici, Cosmin; Baggia, Paolo

    1996-01-01

    This paper analyses language modeling in spoken dialogue systems for accessing a database. The use of several language models obtained by exploiting dialogue predictions gives better results than the use of a single model for the whole dialogue interaction. For this reason several models have been created, each one for a specific system question, such as the request or the confirmation of a parameter. The use of dialogue-dependent language models increases the performance both at the recognition and at the understanding level, especially on answers to system requests. Moreover other methods to increase performance, like automatic clustering of vocabulary words or the use of better acoustic models during recognition, does not affect the improvements given by dialogue-dependent language models. The system used in our experiments is Dialogos, the Italian spoken dialogue system used for accessing railway timetable information over the telephone. The experiments were carried out on a large corpus of dialogues coll...

  15. The Role of Non-Epistemic Values in Engineering Models

    OpenAIRE

    Diekmann, Sven; Peterson, Martin

    2011-01-01

    We argue that non-epistemic values, including moral ones, play an important role in the construction and choice of models in science and engineering. Our main claim is that non-epistemic values are not only “secondary values” that become important just in case epistemic values leave some issues open. Our point is, on the contrary, that non-epistemic values are as important as epistemic ones when engineers seek to develop the best model of a process or problem. The upshot is that models are ne...

  16. Caries risk assessment models in caries prediction

    Directory of Open Access Journals (Sweden)

    Amila Zukanović

    2013-11-01

    Full Text Available Objective. The aim of this research was to assess the efficiency of different multifactor models in caries prediction. Material and methods. Data from the questionnaire and objective examination of 109 examinees was entered into the Cariogram, Previser and Caries-Risk Assessment Tool (CAT multifactor risk assessment models. Caries risk was assessed with the help of all three models for each patient, classifying them as low, medium or high-risk patients. The development of new caries lesions over a period of three years [Decay Missing Filled Tooth (DMFT increment = difference between Decay Missing Filled Tooth Surface (DMFTS index at baseline and follow up], provided for examination of the predictive capacity concerning different multifactor models. Results. The data gathered showed that different multifactor risk assessment models give significantly different results (Friedman test: Chi square = 100.073, p=0.000. Cariogram is the model which identified the majority of examinees as medium risk patients (70%. The other two models were more radical in risk assessment, giving more unfavorable risk –profiles for patients. In only 12% of the patients did the three multifactor models assess the risk in the same way. Previser and CAT gave the same results in 63% of cases – the Wilcoxon test showed that there is no statistically significant difference in caries risk assessment between these two models (Z = -1.805, p=0.071. Conclusions. Evaluation of three different multifactor caries risk assessment models (Cariogram, PreViser and CAT showed that only the Cariogram can successfully predict new caries development in 12-year-old Bosnian children.

  17. Disease prediction models and operational readiness.

    Directory of Open Access Journals (Sweden)

    Courtney D Corley

    Full Text Available The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011. We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4, spatial (26, ecological niche (28, diagnostic or clinical (6, spread or response (9, and reviews (3. The model parameters (e.g., etiology, climatic, spatial, cultural and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological were recorded and reviewed. A component of this review is the identification of verification and validation (V&V methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology

  18. 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...

  19. Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle.

    Science.gov (United States)

    Chen, L; Vinsky, M; Li, C

    2015-02-01

    Accuracy of predicting genomic breeding values for carcass merit traits including hot carcass weight, longissimus muscle area (REA), carcass average backfat thickness (AFAT), lean meat yield (LMY) and carcass marbling score (CMAR) was evaluated based on 543 Angus and 400 Charolais steers genotyped on the Illumina BovineSNP50 Beadchip. For the genomic prediction within Angus, the average accuracy was 0.35 with a range from 0.32 (LMY) to 0.37 (CMAR) across different training/validation data-splitting strategies and statistical methods. The within-breed genomic prediction for Charolais yielded an average accuracy of 0.36 with a range from 0.24 (REA) to 0.46 (AFAT). The across-breed prediction had the lowest accuracy, which was on average near zero. When the data from the two breeds were combined to predict the breeding values of either breed, the prediction accuracy averaged 0.35 for Angus with a range from 0.33 (REA) to 0.39 (CMAR) and averaged 0.33 for Charolais with a range from 0.18 (REA) to 0.46 (AFAT). The prediction accuracy was slightly higher on average when the data were split by animal's birth year than when the data were split by sire family. These results demonstrate that the genetic relationship or relatedness of selection candidates with the training population has a great impact on the accuracy of predicting genomic breeding values under the density of the marker panel used in this study. © 2014 Her Majesty the Queen in Right of Canada. Animal Genetics © 2014 Stichting International Foundation for Animal Genetics.

  20. ENSO Prediction using Vector Autoregressive Models

    Science.gov (United States)

    Chapman, D. R.; Cane, M. A.; Henderson, N.; Lee, D.; Chen, C.

    2013-12-01

    A recent comparison (Barnston et al, 2012 BAMS) shows the ENSO forecasting skill of dynamical models now exceeds that of statistical models, but the best statistical models are comparable to all but the very best dynamical models. In this comparison the leading statistical model is the one based on the Empirical Model Reduction (EMR) method. Here we report on experiments with multilevel Vector Autoregressive models using only sea surface temperatures (SSTs) as predictors. VAR(L) models generalizes Linear Inverse Models (LIM), which are a VAR(1) method, as well as multilevel univariate autoregressive models. Optimal forecast skill is achieved using 12 to 14 months of prior state information (i.e 12-14 levels), which allows SSTs alone to capture the effects of other variables such as heat content as well as seasonality. The use of multiple levels allows the model advancing one month at a time to perform at least as well for a 6 month forecast as a model constructed to explicitly forecast 6 months ahead. We infer that the multilevel model has fully captured the linear dynamics (cf. Penland and Magorian, 1993 J. Climate). Finally, while VAR(L) is equivalent to L-level EMR, we show in a 150 year cross validated assessment that we can increase forecast skill by improving on the EMR initialization procedure. The greatest benefit of this change is in allowing the prediction to make effective use of information over many more months.

  1. 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.)

  2. Gas explosion prediction using CFD models

    Energy Technology Data Exchange (ETDEWEB)

    Niemann-Delius, C.; Okafor, E. [RWTH Aachen Univ. (Germany); Buhrow, C. [TU Bergakademie Freiberg Univ. (Germany)

    2006-07-15

    A number of CFD models are currently available to model gaseous explosions in complex geometries. Some of these tools allow the representation of complex environments within hydrocarbon production plants. In certain explosion scenarios, a correction is usually made for the presence of buildings and other complexities by using crude approximations to obtain realistic estimates of explosion behaviour as can be found when predicting the strength of blast waves resulting from initial explosions. With the advance of computational technology, and greater availability of computing power, computational fluid dynamics (CFD) tools are becoming increasingly available for solving such a wide range of explosion problems. A CFD-based explosion code - FLACS can, for instance, be confidently used to understand the impact of blast overpressures in a plant environment consisting of obstacles such as buildings, structures, and pipes. With its porosity concept representing geometry details smaller than the grid, FLACS can represent geometry well, even when using coarse grid resolutions. The performance of FLACS has been evaluated using a wide range of field data. In the present paper, the concept of computational fluid dynamics (CFD) and its application to gas explosion prediction is presented. Furthermore, the predictive capabilities of CFD-based gaseous explosion simulators are demonstrated using FLACS. Details about the FLACS-code, some extensions made to FLACS, model validation exercises, application, and some results from blast load prediction within an industrial facility are presented. (orig.)

  3. Genetic models of homosexuality: generating testable predictions.

    Science.gov (United States)

    Gavrilets, Sergey; Rice, William R

    2006-12-22

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism.

  4. Characterizing Attention with Predictive Network Models.

    Science.gov (United States)

    Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

    2017-04-01

    Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Mean Value Modelling of an SI Engine with EGR

    DEFF Research Database (Denmark)

    Føns, Michael; Muller, Martin; Chevalier, Alain

    1999-01-01

    Mean Value Engine Models (MVEMs) are simplified, dynamic engine models which are physically based. Such models are useful for control studies, for engine control system analysis and for model based control systems. Very few published MVEMs have included the effects of Exhaust Gas Recirculation (E...

  6. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    R. G. SILVA

    1999-03-01

    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. Predicted values of cardiopulmonary exercise testing in healthy individuals (a pilot study).

    Science.gov (United States)

    Mohammad, Majid Malek; Dadashpour, Shahdak; Adimi, Parisa

    2012-01-01

    Cardiopulmonary exercise testing evaluates the ability of one's cardiovascular and respiratory system in maximal exercise. This was a descriptive cross-sectional pilot study conducted at Masih Daneshvari Hospital in order to determine predicted values of cardiopulmonary exercise testing in individuals with normal physical activity patterns. Thirty four individuals (14 women, 20 men) between 18-57 years of age were chosen using simple sampling method and evaluated with an incremental progressive cycle-ergometer test to a symptom-limited maximal tolerable work load. Subjects with a history of ischemic heart disease, pulmonary disease or neuromuscular disease were excluded from the study. Smokers were included but we made sure that all subjects had normal FEV1 and FEV1/FVC. This study aimed to compare measured values of VO2, VCO2, VO2/Kg, RER, O2pulse, HRR, HR, Load, Ant, BF, BR, VE, EQCO2, and EQO2 with previously published predicted values. We found that our obtained values for VO2 max, HRR max and HR max were different from standard tables but such difference was not observed for other understudy variables. Multiple linear regression analysis was done for height, weight and age (due to the small number of samples, no difference was detected between males and females). VO2 max and load max had reverse correlation with age and direct correlation with weight and height (P < 0.05) but the greatest correlation was observed for height. Due to the small number of samples and poor correlations it was not possible to do regression analysis for other variables. In the next study with a larger sample size predicted values for all variables will be calculated. If the future study also indicates a significant difference between the predicted values and the reference values, we will need standard tables made specifically for our own country, Iran.

  8. Recipes Prediction by Matching to K/S Values Based on New Two constant Theory

    Institute of Scientific and Technical Information of China (English)

    HE Guo-xing; XING Huai-zhong; ZHOU Ming-xun

    2006-01-01

    A concept of new two-constant of colorant, both (k/St) and (s/St), is introduced based on the Kubelka-Munk theory.A new two-constant theory for color matching is presented.Basic equations used in matching to K/S values are given in matrix form based on the new two-constant theory.Algorithm for a least-squares match to K/S values of a sample is developed by use of the new two-constant theory.The algorithm is suitable for single-constant theory as well as two-constant theory. The experimental data show that calculating K/S values of disperse dyes based on new two-constant theory are accordant with the measuring ones. The reoipes predicted by new two-constant theory are closer to the actual recipes of the standard sample than the recipes predicted by single-constant theory. The sample according to the recipe predicted by new two-constant theory has smaller color difference against for the standard than the sample according to the recipe predicted by single-constant theory.The results show that the scattering of disperse dyes cannot be negligible, and that the recipes match to textiles colored by disperse dyes should be predicted by using of new two-constant theory.

  9. Predictive value of brain perfusion SPECT for ketamine response in hyperalgesic fibromyalgia

    Energy Technology Data Exchange (ETDEWEB)

    Guedj, Eric; Cammilleri, Serge; Colavolpe, Cecile; Taieb, David; Laforte, Catherine de; Mundler, Olivier [Centre Hospitalo-Universitaire de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Assistance Publique des Hopitaux de Marseille, Marseille Cedex 5 (France); Niboyet, Jean [Clinique La Phoceanne, Unite d' Etude et de Traitement de la Douleur, Marseille (France)

    2007-08-15

    Ketamine has been used successfully in various proportions of fibromyalgia (FM) patients. However, the response to this specific treatment remains largely unpredictable. We evaluated brain SPECT perfusion before treatment with ketamine, using voxel-based analysis. The objective was to determine the predictive value of brain SPECT for ketamine response. Seventeen women with FM (48 {+-} 11 years; ACR criteria) were enrolled in the study. Brain SPECT was performed before any change was made in therapy in the pain care unit. We considered that a patient was a good responder to ketamine if the VAS score for pain decreased by at least 50% after treatment. A voxel-by-voxel group analysis was performed using SPM2, in comparison to a group of ten healthy women matched for age. The VAS score for pain was 81.8 {+-} 4.2 before ketamine and 31.8 {+-} 27.1 after ketamine. Eleven patients were considered ''good responders'' to ketamine. Responder and non-responder subgroups were similar in terms of pain intensity before ketamine. In comparison to responding patients and healthy subjects, non-responding patients exhibited a significant reduction in bilateral perfusion of the medial frontal gyrus. This cluster of hypoperfusion was highly predictive of non-response to ketamine (positive predictive value 100%, negative predictive value 91%). Brain perfusion SPECT may predict response to ketamine in hyperalgesic FM patients. (orig.)

  10. The predictive value of first day bilirubin levels for early discharged newborns.

    Directory of Open Access Journals (Sweden)

    MUSTAFA TOLGA ÜNSÜR

    2015-09-01

    Full Text Available Objective: Early discharge of newborns is essential because of social, economic an medical reasons in our area, but it increases readmission rates especially for hyperbilirubinemia. Hence, predicting the high risk neonates for subsequent hyperbilirubinemia is required. This study was designed to investigate which level of total serum bilirubin (TSB at the first day could be used to predict hyperbilirubinemia .Methods: The venous blood samples obtained from 300 newborns at post-partum 24±6 hours for blood group, direct coomb’s, TSB and direct bilirubin level (DBL. These newborns were followed up during 5- day and TSB and DBLwere detected in 90 newborns with jaundice again according to Kramer dermal zones at 120±6 hours of age.Results: In 23.3% of 90 newborns phototherapy was needed. The cut off value of TSB at the first day to define newborns at high risk for subsequent hyperbilirubinemia was 6.50 mg/dl with positive predictive value 19.75%, negative predictive value 97.72%. At that point sensitivity was 76.19%, specificity was 76.70%.Conclusion: The cut-off point of 6.5 mg/dl of TSB at the first day might be used to predict subsequent hyperbilirubinemia risk at healthy, full-term early discharged newborns as the test is economic and available in all healthcare units.

  11. Preterm birth and cerebral palsy. Predictive value of pregnancy complications, mode of delivery, and Apgar scores

    DEFF Research Database (Denmark)

    Topp, Monica Wedell; Langhoff-Roos, J; Uldall, P

    1997-01-01

    .01), and low Apgar scores at 1 minute (45% vs. 36%, p or = 3 (adjusted OR = 1.53 (95% CI 1.00-2.34), p ... complications preceding preterm birth did not imply a higher risk of cerebral palsy. Delivery by Cesarean section was a prognostic factor for developing cerebral palsy, and the predictive value of Apgar scores was highly limited....

  12. Predictive value of self-reported and observer-rated defense style in depression treatment.

    NARCIS (Netherlands)

    Van, H.L.; Dekker, J.J.M.; Peen, J.; Abraham, R.E.; Schoevers, R.

    2009-01-01

    This study explored the predictive value of observer-rated and self-reported defensive functioning on the outcome of psychotherapy for the treatment of depression. Defense styles were measured according to the Developmental Profile (DP) and the Defense Style Questionnaire (DSQ) in 81 moderately seve

  13. Predictive value of HATCH score on atrial fibrillation recurrence post radiofrequency catheter ablation

    Institute of Scientific and Technical Information of China (English)

    缪丹丹

    2012-01-01

    Objective To determine the predictive value of HATCH score on recurrence of atrial fibrillation(AF) after radiofrequency catheter ablation (RFCA). Methods The data of 123 consecutive AF patients(74 paroxysmal and 49 persistent AF) who underwent RFCA between April 2009 and December 2010 in our department were retrospectively

  14. The predictive value of physical fitness for falls in older adults with intellectual disabilities

    NARCIS (Netherlands)

    Oppewal, Alyt; Hilgenkamp, Thessa I. M.; van Wijck, Ruud; Schoufour, Josje D.; Evenhuis, Heleen M.

    2014-01-01

    A high incidence of falls is seen in people with intellectual disabilities (ID), along with poor balance, strength, muscular endurance, and slow gait speed, which are well-established risk factors for falls in the general population. The aim of this study was to assess the predictive value of these

  15. Impact of the endoscopist's experience on the negative predictive value of capsule endoscopy.

    Science.gov (United States)

    Velayos Jiménez, Benito; Alcaide Suárez, Noelia; González Redondo, Guillermo; Fernández Salazar, Luis; Aller de la Fuente, Rocío; Del Olmo Martínez, Lourdes; Ruiz Rebollo, Lourdes; González Hernández, José Manuel

    2017-01-01

    The impact of the accumulated experience of the capsule endoscopy (CE) reader on the accuracy of this test is discussed. To determine whether the negative predictive value of CE findings changes along the learning curve. We reviewed the first 900 CE read by 3 gastroenterologists experienced in endoscopy over 8 years. These 900 CE were divided into 3 groups (300 CE each): group 1 consisted of the sum of the first 100 CE read by each of the 3 endoscopists; group 2, the sum of the second 100 and groups 3, the sum of the third 100. Patients with normal CE were monitored for at least 28 months to estimate the negative predictive value. A total of 54 (18%) CE in group 1, 58 (19.3%) in group 2 and 47 (15.6%) in group 3 were normal, although only 34 patients in group 1, 38 in group 2 and 36 in group 3 with normal CE completed follow up and were eventually studied. The negative predictive value was 88.2% in group 1, 89.5% in group 2 and 97% in group 3 (P>.05). The negative predictive value tended to increase, but remained high and did not change significantly after the first 100 when readers are experienced in conventional endoscopy and have preliminary specific training. Copyright © 2016 Elsevier España, S.L.U., AEEH y AEG. All rights reserved.

  16. Value of plasma ADMA in predicting cardiac structure and function of patients with chronic kidney diseases

    Institute of Scientific and Technical Information of China (English)

    叶建华

    2012-01-01

    Objective To explore the predicting value of plasma asymmetric dimethylarginine (ADMA) in cardiac structure and function of patients with chronic kidney diseases(CKD). Methods A total of 100 CKD patients were enrolled in this cross-sectional study. According to staging of the

  17. Bayes' theorem applied to perimetric progression detection in glaucoma : from specificity to positive predictive value

    NARCIS (Netherlands)

    Jansonius, NM

    2005-01-01

    Purpose: To estimate the specificity of a clinical evaluation of a series of visual fields and to calculate the positive predictive value of progression. Methods: The specificity of a clinical evaluation of a series of visual fields was estimated using nonparametric ranking and probability calculus.

  18. Multisource ratings on managerial competencies and their predictive value for managerial and organizational effectiveness

    NARCIS (Netherlands)

    Semeijn, J.; Heijden, B.I.J.M. van der; Lee, A.T van der

    2014-01-01

    This study examined the predictive value of multisource ratings of managerial competencies for managerial and organizational effectiveness. Data from 155 subordinates, 59 peers, and 28 supervisors were gathered in order to provide insight into their perceptions on managerial competencies for their m

  19. The predictive value of mild renal insufficiency on the prognosis of patients with acute coronary syndrome

    Institute of Scientific and Technical Information of China (English)

    张建华

    2014-01-01

    Objective To investigate the predictive value of mild renal insufficiency on the endpoint events in patients with acute coronary syndrome(ACS).Methods A total of 552 patients with ACS were enrolled in the present study.According to the levels of estimated glomerular filtration rate(eGFR),patients were divided into two groups,normal

  20. Predictive value of the official cancer alarm symptoms in general practice

    DEFF Research Database (Denmark)

    Krasnik Huggenberger, Ivan; Andersen, John Sahl

    2015-01-01

    Introduction: The objective of this study was to investigate the evidence for positive predictive value (PPV) of alarm symptoms and combinations of symptoms for colorectal cancer, breast cancer, prostate cancer and lung cancer in general practice. Methods: This study is based on a literature search...

  1. Sensitivity, specificity and predictive value of blood cultures from cattle clinically suspected of bacterial endocarditis

    DEFF Research Database (Denmark)

    Houe, Hans; Eriksen, L.; Jungersen, Gregers;

    1993-01-01

    This study investigated the number of blood culture-positive cattle among 215 animals clinically suspected of having bacterial endocarditis. For animals that were necropsied, the sensitivity, specificity and predictive value of the diagnosis of endocarditis were calculated on the basis...

  2. The neonatal tetrahydrobiopterin loading test in phenylketonuria: : what is the predictive value?

    NARCIS (Netherlands)

    Anjema, Karen; Hofstede, Floris C.; Bosch, Annet M.; Rubio-Gozalbo, M. Estela; de Vries, Maaike C.; Boelen, Carolien C. A.; van Rijn, Margreet; van Spronsen, Francjan J.

    2016-01-01

    Background: It is unknown whether the neonatal tetrahydrobiopterin (BH4) loading test is adequate to diagnose long-term BH4 responsiveness in PKU. Therefore we compared the predictive value of the neonatal (test I) versus the 48-h BH4 loading test (test II) and long-term BH4 responsiveness. Methods:

  3. Assessment of dual tasking has no clinical value for fall prediction in Parkinson's disease

    NARCIS (Netherlands)

    Smulders, K.; Esselink, R.A.J.; Weiss, A.; Kessels, R.P.C.; Geurts, A.C.H.; Bloem, B.R.

    2012-01-01

    The objective of this study is to investigate the value of dual-task performance for the prediction of falls inpatients with Parkinson's disease (PD). Two hundred sixty three patients with PD (H&Y 1-3, 65.2 +/- 7.9 years)walked two times along a 10-m trajectory, both under single-task and dual-task

  4. Improving the Positive Predictive Value of Screening for Developmental Language Disorder.

    Science.gov (United States)

    Klee, Thomas; Pearce, Kim; Carson, David K.

    2000-01-01

    This study evaluated application of a revised criterion for the Language Development Survey, a parent-report screening measure designed to identify young children with language delays. The revised criterion generated fewer false positives, improved specificity, and improved positive predictive value while maintaining the high sensitivity and high…

  5. Alternative Approaches for Measuring Values: Direct and Indirect Assessments in Performance Prediction.

    Science.gov (United States)

    Mumford, Michael D.; Connelly, Mary Shane; Helton, Whitney B.; Van Doorn, Judy R.; Osburn, Holly K.

    2002-01-01

    Undergraduates (n=195) completed direct and indirect measures of values before working on entrepreneurial, consulting, and marketing tasks. Regression analysis showed both types of measures were effective predictors. Indirect measures yielded better prediction and better discrimination of cross-task performance differences. (Contains 55…

  6. Predictive Value of Tokuhashi Scoring Systems in Spinal Metastases, Focusing on Various Primary Tumor Groups

    DEFF Research Database (Denmark)

    Wang, Miao; Bünger, Cody; Haisheng, Li

    2012-01-01

    STUDY DESIGN: We conducted a prospective cohort study of 448 patients with spinal metastases from a variety of cancer groups. OBJECTIVE: To determine the specific predictive value of the Tokuhashi scoring system (T12) and its revised version (T15) in spinal metastases of various primary tumors. S...

  7. The predictive value of optical coherence tomography after grid laser photocoagulation for diffuse diabetic macular oedema

    DEFF Research Database (Denmark)

    Soliman, W.; Sander, B.; Soliman, K.A.E.N.

    2008-01-01

    Purpose: To assess the predictive value of optical coherence tomography (OCT) mapping of retinal thickness and intraretinal morphological changes after macular grid for diffuse diabetic macular oedema (DMO). Methods: We carried out a prospective, non-controlled, case series study, in which 28 con...

  8. Comparing predictions made by a prediction model, clinical score, and physicians: pediatric asthma exacerbations in the emergency department.

    Science.gov (United States)

    Farion, K J; Wilk, S; Michalowski, W; O'Sullivan, D; Sayyad-Shirabad, J

    2013-01-01

    Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2 data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar's test it is not possible to conclude that the differences between predictions are statistically significant. Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy.

  9. Performance model to predict overall defect density

    Directory of Open Access Journals (Sweden)

    J Venkatesh

    2012-08-01

    Full Text Available Management by metrics is the expectation from the IT service providers to stay as a differentiator. Given a project, the associated parameters and dynamics, the behaviour and outcome need to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. In most of the cases, the actions taken are re-active. It is too late in the life cycle. Root cause analysis and corrective actions can be implemented only to the benefit of the next project. The focus has to shift left, towards the execution phase than waiting for lessons to be learnt post the implementation. How do we pro-actively predict defect metrics and have a preventive action plan in place. This paper illustrates the process performance model to predict overall defect density based on data from projects in an organization.

  10. 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.

  11. The predictive performance and stability of six species distribution models.

    Science.gov (United States)

    Duan, Ren-Yan; Kong, Xiao-Quan; Huang, Min-Yi; Fan, Wei-Yi; Wang, Zhi-Gao

    2014-01-01

    Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (pMAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.

  12. Using a computer-controlled simulated digestion system to predict the energetic value of corn for ducks.

    Science.gov (United States)

    Zhao, F; Zhang, L; Mi, B M; Zhang, H F; Hou, S S; Zhang, Z Y

    2014-06-01

    Two experiments were conducted to develop a computer-controlled digestion system to simulate the digestion process of duck for predicting the concentration of ME and the metabolizability of gross energy (GE) in corn. In a calibration experiment, 30 corn-based calibration samples with a previously published ME concentration in 2008 were used to develop the prediction models for in vivo energetic values. The linear relationships were established between in vivo ME concentration and in vitro digestible energy (IVDE) concentration, and between in vivo metabolizability of GE (ME/GE) and in vitro digestibility of GE (IVDE/GE), respectively. In a validation experiment, 6 sources of corn with previously published ME concentration in 2008 randomly selected from the primary corn-growing regions of China were used to validate the prediction models established in the calibration experiment. The results showed that in calibration samples, the IVDE concentration was positively correlated with the AME (r = 0.9419), AMEn (r = 0.9480), TME (r = 0.9403), and TMEn concentration (r = 0.9473). Similarly, the IVDE/GE was positively correlated with the AME/GE (r = 0.95987), AMEn/GE (r = 0.9641), TME/GE (r = 0.9588), and TMEn/GE (r = 0.9637). The coefficient of determination greater than 0.88 and 0.91, and residual SD less than 45 kcal/kg of DM and 1.01% were observed in the prediction models for ME concentrations and ME/GE, respectively. Twenty-nine out of 30 calibration samples showed differences less than 100 kcal/kg of DM and 2.4% between determined and predicted values for 4 ME (AME, AMEn, TME, and TMEn) and for 4 ME/GE (AME/GE, AMEn/GE, TME/GE, and TMEn/GE), respectively. Using prediction models developed from 30 calibration samples, 6 validation samples further showed differences less than 100 kcal/kg of DM and 2% between determined and predicted values for ME and ME/GE, respectively. Therefore, the computer-controlled simulated digestion system can be used to predict the ME and ME

  13. Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment.

    Science.gov (United States)

    Bitan, Yuval; O'Connor, Michael F

    2012-01-01

    Alarm fatigue from high false alarm rate is a well described phenomenon in the intensive care unit (ICU). Progress to further reduce false alarms must employ a new strategy. Highly sensitive alarms invariably have a very high false alarm rate. Clinically useful alarms have a high Positive-Predictive Value. Our goal is to demonstrate one approach to suppressing false alarms using an algorithm that correlates information across sensors and replicates the ways that human evaluators discriminate artifact from real signal. After obtaining IRB approval and waiver of informed consent, a set of definitions, (hypovolemia, left ventricular shock, tamponade, hemodynamically significant ventricular tachycardia, and hemodynamically significant supraventricular tachycardia), were installed in the monitors in a 10 bed cardiothoracic ICU and evaluated over an 85 day study period. The logic of the algorithms was intended to replicate the logic of practitioners, and correlated information across sensors in a way similar to that used by practitioners. The performance of the alarms was evaluated via a daily interview with the ICU attending and review of the tracings recorded over the previous 24 hours in the monitor. True alarms and false alarms were identified by an expert clinician, and the performance of the algorithms evaluated using the standard definitions of sensitivity, specificity, positive predictive value, and negative predictive value. Between 1 and 221 instances of defined events occurred over the duration of the study, and the positive predictive value of the definitions varied between 4.1% and 84%. Correlation of information across alarms can suppress artifact, increase the positive predictive value of alarms, and can employ more sophisticated definitions of alarm events than present single-sensor based systems.

  14. Predictive values of serum amyloid-A and CRP for infection in febrile neutropenic cancer patients

    Directory of Open Access Journals (Sweden)

    Ayşe Batırel

    2014-12-01

    Full Text Available Objectives: To evaluate predictive values of serum amyloid A (SAA and C-reactive protein (CRP for infection and mor­tality in patients with febrile neutropenia (FEN. Methods: Daily measurement of serum SAA and CRP levels of patients during antibiotherapy for FEN. Results: Sixty-five FEN episodes of 52 patients were evaluated. Median CRP and SAA levels on 1st day of FEN were 137 mg/L (23-420 mg/L and 547 mg/L (11-1660 mg/L, respectively. For detection of infection of infection the sensitivity, positive predictive value (PPV, and negative predictive value (NPV of SAA at a level of >80 mg/L were as 100%, 48% and 100%. Whilethe sensitivity, PPV, and NPV of CRP at a level of >50mg/L were as 86%, 47% and 60%, respectively. Predictive values of initial SAA and CRP levels for infection didn’t differ significantly (CRP: p=0.24, SAA: p=0.39. SAA and CRP levels on the last day of FEN course were significant for infection and mortality (for infection: p=0.003 for CRP and p=0.026 for SAA; for mortality: p<0.001 for CRP and p=0.021 for SAA. Both initial and daily SAA and CRP levels correlated with each other positively and statistically significantly (p<0.001. The area under the curve (AUC on the re­ceiver operating character (ROC curve for CRP and SAA were 0.72 (p=0.003, 95% CI: 0.59-0.86 and 0.68 (p=0.19, 95% CI: 0.54-0.82, respectively. Conclusions: Despite low predictive values in decision of initial therapy, these parameters would be helpful in decision of modification and evaluation of response to therapy. J Microbiol Infect Dis 2014; 4(4: 128-135

  15. In silico modeling to predict drug-induced phospholipidosis

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Sadrieh, Nakissa

    2013-06-01

    Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the construction and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL.

  16. The predictive value of Muller's maneuvre for CPAP titration in OSAHS patients.

    Science.gov (United States)

    Bosi, Marcello; De Vito, Andrea; Vicini, Claudio; Poletti, Venerino

    2013-08-01

    To investigate the role of awake upper airways (UA) endoscopy assessment as a parameter of prediction for CPAP titration in OSAHS patient therapy. Retrospective analysis of UA endoscopic assessment with Mueller's maneuvre and the application of the nose oropharynx hypopharynx score (NOHs) was conducted to obtain a numeric score representing the grade of severity of UA obstruction. Other commonly used predictive parameters for CPAP titration were also included in the study: anthropometric [BMI, neck circumference (NC)] and polysomnographic parameters (AHI, ODI). 3 groups of patients were identified: (1) 67/90 patients requiring intermediate CPAP values, (2) 13/90 patients requiring high CPAP values, and (3) 10/90 patients requiring low pressure values. BMI (p = 0.0013) was the only monitored parameter to show significant statistical value as a CPAP titration predictor. However, higher values of anthropometric parameters (NOHs ≥9, BMI >35, NC >45) showed a sensitivity of 69.2% as a single parameter and 76.9% as combined parameters, and specificity between 66.2 and 72.7% as a single parameter and 43.4% as combined parameters, unequivocally identifying patients requiring high therapeutic CPAP value. A lower cut-off of anthropometric parameters (NOHs ≤6, BMI ≤29, NC <42) showed sensitivity between 40 and 60% as a single parameter and of 90% as combined parameters, and specificity between 68.7 and 80.2% as a single parameter which increased to 93.7% as combined parameters, identifying patients requiring a low therapeutic CPAP value. The results show that anthropometric and polygraphic parameters have no significant independent predictive value for CPAP titration, with the exception of BMI. However, anthropometric parameters showed good levels of sensitivity and specificity in OSAHS patients requiring high or low levels of CPAP therapy.

  17. A two process model of burnout and work engagement: distinct implications of demands and values.

    Science.gov (United States)

    Leiter, M P

    2008-01-01

    A model of job burnout proposes two distinct processes. The first process concerns balance of demands to resources. A poor balance leads to chronic exhaustion, an integral aspect of the burnout syndrome. The second process concerns the congruence of individual and organizational values. The model proposes that value conflicts have implications for all three aspects of burnout. It also proposes that the impact of value conflicts has only minor implications for the exhaustion aspect of burnout; they are more relevant for the cynicism and inefficacy aspects of the syndrome. The model considers distinct processes at work that concern employees' perception of organizational justice and their trust in leadership. With a sample of 725 nurses, the analysis tested one component of the theory: the extent to which value congruence enhances the prediction of burnout beyond the prediction provided by demands and resources. Future directions are discussed.

  18. A value model for evaluating homeland security decisions.

    Science.gov (United States)

    Keeney, Ralph L; von Winterfeldt, Detlof

    2011-09-01

    One of the most challenging tasks of homeland security policymakers is to allocate their limited resources to reduce terrorism risks cost effectively. To accomplish this task, it is useful to develop a comprehensive set of homeland security objectives, metrics to measure each objective, a utility function, and value tradeoffs relevant for making homeland security investments. Together, these elements form a homeland security value model. This article develops a homeland security value model based on literature reviews, a survey, and experience with building value models. The purposes of the article are to motivate the use of a value model for homeland security decision making and to illustrate its use to assess terrorism risks, assess the benefits of countermeasures, and develop a severity index for terrorism attacks. © 2011 Society for Risk Analysis.

  19. Seasonal Predictability in a Model Atmosphere.

    Science.gov (United States)

    Lin, Hai

    2001-07-01

    The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

  20. Accuracy of genomic prediction using deregressed breeding values estimated from purebred and crossbred offspring phenotypes in pigs.

    Science.gov (United States)

    Hidalgo, A M; Bastiaansen, J W M; Lopes, M S; Veroneze, R; Groenen, M A M; de Koning, D-J

    2015-07-01

    Genomic selection is applied to dairy cattle breeding to improve the genetic progress of purebred (PB) animals, whereas in pigs and poultry the target is a crossbred (CB) animal for which a different strategy appears to be needed. The source of information used to estimate the breeding values, i.e., using phenotypes of CB or PB animals, may affect the accuracy of prediction. The objective of our study was to assess the direct genomic value (DGV) accuracy of CB and PB pigs using different sources of phenotypic information. Data used were from 3 populations: 2,078 Dutch Landrace-based, 2,301 Large White-based, and 497 crossbreds from an F1 cross between the 2 lines. Two female reproduction traits were analyzed: gestation length (GLE) and total number of piglets born (TNB). Phenotypes used in the analyses originated from offspring of genotyped individuals. Phenotypes collected on CB and PB animals were analyzed as separate traits using a single-trait model. Breeding values were estimated separately for each trait in a pedigree BLUP analysis and subsequently deregressed. Deregressed EBV for each trait originating from different sources (CB or PB offspring) were used to study the accuracy of genomic prediction. Accuracy of prediction was computed as the correlation between DGV and the DEBV of the validation population. Accuracy of prediction within PB populations ranged from 0.43 to 0.62 across GLE and TNB. Accuracies to predict genetic merit of CB animals with one PB population in the training set ranged from 0.12 to 0.28, with the exception of using the CB offspring phenotype of the Dutch Landrace that resulted in an accuracy estimate around 0 for both traits. Accuracies to predict genetic merit of CB animals with both parental PB populations in the training set ranged from 0.17 to 0.30. We conclude that prediction within population and trait had good predictive ability regardless of the trait being the PB or CB performance, whereas using PB population(s) to predict

  1. Defining the cutoff value of MGMT gene promoter methylation and its predictive capacity in glioblastoma.

    Science.gov (United States)

    Brigliadori, Giovanni; Foca, Flavia; Dall'Agata, Monia; Rengucci, Claudia; Melegari, Elisabetta; Cerasoli, Serenella; Amadori, Dino; Calistri, Daniele; Faedi, Marina

    2016-06-01

    Despite advances in the treatment of glioblastoma (GBM), median survival is 12-15 months. O6-methylguanine-DNA methyltransferase (MGMT) gene promoter methylation status is acknowledged as a predictive marker for temozolomide (TMZ) treatment. When MGMT promoter values fall into a "methylated" range, a better response to chemotherapy is expected. However, a cutoff that discriminates between "methylated" and "unmethylated" status has yet to be defined. We aimed to identify the best cutoff value and to find out whether variability in methylation profiles influences the predictive capacity of MGMT promoter methylation. Data from 105 GBM patients treated between 2008 and 2013 were analyzed. MGMT promoter methylation status was determined by analyzing 10 CpG islands by pyrosequencing. Patients were treated with radiotherapy followed by TMZ. MGMT promoter methylation status was classified into unmethylated 0-9 %, methylated 10-29 % and methylated 30-100 %. Statistical analysis showed that an assumed methylation cutoff of 9 % led to an overestimation of responders. All patients in the 10-29 % methylation group relapsed before the 18-month evaluation. Patients with a methylation status ≥30 % showed a median overall survival of 25.2 months compared to 15.2 months in all other patients, confirming this value as the best methylation cutoff. Despite wide variability among individual profiles, single CpG island analysis did not reveal any correlation between single CpG island methylation values and relapse or death. Specific CpG island methylation status did not influence the predictive value of MGMT. The predictive role of MGMT promoter methylation was maintained only with a cutoff value ≥30 %.

  2. The predictive value of the NICE "red traffic lights" in acutely ill children.

    Directory of Open Access Journals (Sweden)

    Evelien Kerkhof

    Full Text Available OBJECTIVE: Early recognition and treatment of febrile children with serious infections (SI improves prognosis, however, early detection can be difficult. We aimed to validate the predictive rule-in value of the National Institute for Health and Clinical Excellence (NICE most severe alarming signs or symptoms to identify SI in children. DESIGN, SETTING AND PARTICIPANTS: The 16 most severe ("red" features of the NICE traffic light system were validated in seven different primary care and emergency department settings, including 6,260 children presenting with acute illness. MAIN OUTCOME MEASURES: We focussed on the individual predictive value of single red features for SI and their combinations. Results were presented as positive likelihood ratios, sensitivities and specificities. We categorised "general" and "disease-specific" red features. Changes in pre-test probability versus post-test probability for SI were visualised in Fagan nomograms. RESULTS: Almost all red features had rule-in value for SI, but only four individual red features substantially raised the probability of SI in more than one dataset: "does not wake/stay awake", "reduced skin turgor", "non-blanching rash", and "focal neurological signs". The presence of ≥ 3 red features improved prediction of SI but still lacked strong rule-in value as likelihood ratios were below 5. CONCLUSIONS: The rule-in value of the most severe alarming signs or symptoms of the NICE traffic light system for identifying children with SI was limited, even when multiple red features were present. Our study highlights the importance of assessing the predictive value of alarming signs in clinical guidelines prior to widespread implementation in routine practice.

  3. Self-Service Banking: Value Creation Models and Information Exchange

    Directory of Open Access Journals (Sweden)

    Ragnvald Sannes

    2001-01-01

    Full Text Available This paper argues that most banks have failed to exploit the potential of self-service banking because they base their service design on an incomplete business model for self-service. A framework for evaluation of self-service banking concepts is developed on the basis of Stabell and Fjeldstad's three value configurations. The value network and the value shop are consistent with self-service banking while the value chain is inappropriate. The impact of the value configurations on information exchange and self-service functionality is discussed, and a framework for design of such services proposed. Current self-service banking practices are compared to the framework, and it is concluded that current practice matches the concept of a value network and not the value shop. However, current practices are only a partial implementation of a value network-based self-service banking concept.

  4. A kinetic model for predicting biodegradation.

    Science.gov (United States)

    Dimitrov, S; Pavlov, T; Nedelcheva, D; Reuschenbach, P; Silvani, M; Bias, R; Comber, M; Low, L; Lee, C; Parkerton, T; Mekenyan, O

    2007-01-01

    Biodegradation plays a key role in the environmental risk assessment of organic chemicals. The need to assess biodegradability of a chemical for regulatory purposes supports the development of a model for predicting the extent of biodegradation at different time frames, in particular the extent of ultimate biodegradation within a '10 day window' criterion as well as estimating biodegradation half-lives. Conceptually this implies expressing the rate of catabolic transformations as a function of time. An attempt to correlate the kinetics of biodegradation with molecular structure of chemicals is presented. A simplified biodegradation kinetic model was formulated by combining the probabilistic approach of the original formulation of the CATABOL model with the assumption of first order kinetics of catabolic transformations. Nonlinear regression analysis was used to fit the model parameters to OECD 301F biodegradation kinetic data for a set of 208 chemicals. The new model allows the prediction of biodegradation multi-pathways, primary and ultimate half-lives and simulation of related kinetic biodegradation parameters such as biological oxygen demand (BOD), carbon dioxide production, and the nature and amount of metabolites as a function of time. The model may also be used for evaluating the OECD ready biodegradability potential of a chemical within the '10-day window' criterion.

  5. Disease Prediction Models and Operational Readiness

    Energy Technology Data Exchange (ETDEWEB)

    Corley, Courtney D.; Pullum, Laura L.; Hartley, David M.; Benedum, Corey M.; Noonan, Christine F.; Rabinowitz, Peter M.; Lancaster, Mary J.

    2014-03-19

    INTRODUCTION: The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. One of the primary goals of this research was to characterize the viability of biosurveillance models to provide operationally relevant information for decision makers to identify areas for future research. Two critical characteristics differentiate this work from other infectious disease modeling reviews. First, we reviewed models that attempted to predict the disease event, not merely its transmission dynamics. Second, we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). Methods: We searched dozens of commercial and government databases and harvested Google search results for eligible models utilizing terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche-modeling, The publication date of search results returned are bound by the dates of coverage of each database and the date in which the search was performed, however all searching was completed by December 31, 2010. This returned 13,767 webpages and 12,152 citations. After de-duplication and removal of extraneous material, a core collection of 6,503 items was established and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. Next, PNNL’s IN-SPIRE visual analytics software was used to cross-correlate these publications with the definition for a biosurveillance model resulting in the selection of 54 documents that matched the criteria resulting Ten of these documents, However, dealt purely with disease spread models, inactivation of bacteria, or the modeling of human immune system responses to pathogens rather than predicting disease events. As a result, we systematically reviewed 44 papers and the

  6. 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...

  7. Sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate prediction in Sahelian goats after progesterone impregnated sponge synchronization

    Directory of Open Access Journals (Sweden)

    Justin Kouamo

    2014-09-01

    Full Text Available Aim: This study was aimed to evaluate the sensitivity, specificity, predictive value and accuracy of ultrasonography in pregnancy rate (PR prediction in Sahelian goats after progesterone impregnated sponge synchronization within the framework of caprine artificial insemination (AI program in Fatick (Senegal. Materials and Methods: Of 193 candidate goats in AI program, 167 were selected (day 50 in six villages. Estrus was synchronized by progesterone impregnated sponges installed for 11 days. Two days before the time of sponge removal (day 4, each goat was treated with 500 IU of equine chorionic gonadotropin and 50 μg of dcloprostenol. All goats were inseminated (day 0 with alpine goat semen from France at 45±3 h after sponge removal (day 2. Real-time B-mode ultrasonography was performed at day 50, day 13, day 0, day 40 and day 60 post-AI. Results: Selection rate, estrus response rate, AI rate, PR at days 40 and days 60 were 86.53%; 71.85%; 83.34%; 51% and 68% (p<0.05 respectively. Value of sensitivity, specificity, positive and negative predictive value, accuracy, total conformity, conformity of correct positive, conformity of correct negative and discordance of pregnancy diagnosis by trans-abdominal ultrasonography (TU were 98.03%; 63.26%; 73.52%; 3.12%; 81%; 81%; 50%; 31% and 19%, respectively. Conclusion: These results indicate that the TU can be performed in goats under traditional condition and emphasized the importance of re-examination of goats with negative or doubtful TU diagnoses performed at day 40 post-AI.

  8. Reiteration of Hankel singular value decomposition for modeling of complex-valued signal

    Science.gov (United States)

    Staniszewski, Michał; Skorupa, Agnieszka; Boguszewicz, Łukasz; Wicher, Magdalena; Konopka, Marek; Sokół, Maria; Polański, Andrzej

    2016-06-01

    Modeling signal which forms complex values is a common scientific problem, which is present in many applications, i.e. in medical signals, computer graphics and vision. One of the possible solution is utilization of Hankel Singular Value Decomposition. In the first step complex-valued signal is arranged in a special form called Hankel matrix, which is in the next step decomposed in operation of Singular Value Decomposition. Obtained matrices can be then reformulated in order to get parameters describing system. Basic method can be applied for fitting whole signal but it fails in modeling each particular component of signal. Modification of basic HSVD method, which relies on reiteration and is used for main components, and application of prior knowledge solves presented problem.

  9. Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Bultmann, Ute; Groothoff, Johan W.; Twisk, Jos W. R.; Heymans, Martijn W.

    2015-01-01

    Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (a parts per thousand yen30) SA days and high (a parts per thousand yen3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic model

  10. Hydrologic predictions in a changing environment: behavioral modeling

    Directory of Open Access Journals (Sweden)

    B. Schaefli

    2010-10-01

    Full Text Available Most hydrological models are valid at most only in a few places and cannot be reasonably transferred to other places or to far distant time periods. Transfer in space is difficult because the models are conditioned on past observations at particular places to define parameter values and unobservable processes that are needed to fully characterize the structure and functioning of the landscape. Transfer in time has to deal with the likely temporal changes to both parameters and processes under future changed conditions. This remains an important obstacle to addressing some of the most urgent prediction questions in hydrology, such as prediction in ungauged basins and prediction under global change. In this paper, we propose a new approach to catchment hydrological modeling, based on universal principles that do not change in time and that remain valid across many places. The key to this framework, which we call behavioral modeling, is to assume that these universal and time-invariant organizing principles can be used to identify the most appropriate model structure (including parameter values and responses for a given ecosystem at a given moment in time. The organizing principles may be derived from fundamental physical or biological laws, or from empirical laws that have been demonstrated to be time-invariant and to hold at many places and scales. Much fundamental research remains to be undertaken to help discover these organizing principles on the basis of exploration of observed patterns of landscape structure and hydrological behavior and their interpretation as legacy effects of past co-evolution of climate, soils, topography, vegetation and humans. Our hope is that the new behavioral modeling framework will be a step forward towards a new vision for hydrology where models are capable of more confidently predicting the behavior of catchments beyond what has been observed or experienced before.

  11. HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling

    Directory of Open Access Journals (Sweden)

    S. J. Schymanski

    2011-02-01

    Full Text Available Most hydrological models are valid at most only in a few places and cannot be reasonably transferred to other places or to far distant time periods. Transfer in space is difficult because the models are conditioned on past observations at particular places to define parameter values and unobservable processes that are needed to fully characterize the structure and functioning of the landscape. Transfer in time has to deal with the likely temporal changes to both parameters and processes under future changed conditions. This remains an important obstacle to addressing some of the most urgent prediction questions in hydrology, such as prediction in ungauged basins and prediction under global change. In this paper, we propose a new approach to catchment hydrological modeling, based on universal principles that do not change in time and that remain valid across many places. The key to this framework, which we call behavioral modeling, is to assume that there are universal and time-invariant organizing principles that can be used to identify the most appropriate model structure (including parameter values and responses for a given ecosystem at a given moment in time. These organizing principles may be derived from fundamental physical or biological laws, or from empirical laws that have been demonstrated to be time-invariant and to hold at many places and scales. Much fundamental research remains to be undertaken to help discover these organizing principles on the basis of exploration of observed patterns of landscape structure and hydrological behavior and their interpretation as legacy effects of past co-evolution of climate, soils, topography, vegetation and humans. Our hope is that the new behavioral modeling framework will be a step forward towards a new vision for hydrology where models are capable of more confidently predicting the behavior of catchments beyond what has been observed or experienced before.

  12. 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.

  13. Environment Modeling Using Runtime Values for JPF-Android

    Science.gov (United States)

    van der Merwe, Heila; Tkachuk, Oksana; Nel, Seal; van der Merwe, Brink; Visser, Willem

    2015-01-01

    Software applications are developed to be executed in a specific environment. This environment includes external native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on- the-fly that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.

  14. Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

    Science.gov (United States)

    Cavanagh, Sean E; Wallis, Joni D; Kennerley, Steven W; Hunt, Laurence T

    2016-01-01

    Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations. DOI: http://dx.doi.org/10.7554/eLife.18937.001 PMID:27705742

  15. Chemosensitivity profile assay of circulating cancer cells: prognostic and predictive value in epithelial tumors.

    Science.gov (United States)

    Gazzaniga, Paola; Naso, Giuseppe; Gradilone, Angela; Cortesi, Enrico; Gandini, Orietta; Gianni, Walter; Fabbri, Maria Agnese; Vincenzi, Bruno; di Silverio, Franco; Frati, Luigi; Aglianò, Anna Maria; Cristofanilli, Massimo

    2010-05-15

    The prognostic value associated with the detection of circulating tumor cells (CTCs) in metastatic breast cancer by the CellSearch technology raise additional issues regarding the biological value of this information. We postulated that a drug-resistance profile of CTCs may predict response to chemotherapy in cancer patients and therefore could be used for patient selection. One hundred 5 patients with diagnosis of carcinoma were enrolled in a prospective trial. CTCs were isolated from peripheral blood, and positive samples were evaluated for the expression of a panel of genes involved in anticancer drugs resistance. The drug-resistance profile was correlated with disease-free survival (DFS; patients in adjuvant setting) and time to progression (TTP; metastatic patients) in a 24-months follow-up. Objective response correlation was a secondary end point. Fifty-one percent of patients were found positive for CTCs while all blood samples from healthy donors were negative. The drug-resistance profile correlates with DFS and TTP (p < 0.001 in both). Sensitivity of the test: able to predict treatment response in 98% of patients. Specificity of the test: 100%; no sample from healthy subject was positive for the presence of CTCs. Positive and negative predictive values were found to be 96.5 and 100%, respectively. We identified a drug-resistance profile of CTCs, which is predictive of response to chemotherapy, independent of tumor type and stage of disease. This approach may represent a first step toward the individualization of chemotherapy in cancer patients.

  16. Probabilistic prediction models for aggregate quarry siting

    Science.gov (United States)

    Robinson, G.R.; Larkins, P.M.

    2007-01-01

    Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.

  17. Prediction of Frost Occurrences Using Statistical Modeling Approaches

    Directory of Open Access Journals (Sweden)

    Hyojin Lee

    2016-01-01

    Full Text Available We developed the frost prediction models in spring in Korea using logistic regression and decision tree techniques. Hit Rate (HR, Probability of Detection (POD, and False Alarm Rate (FAR from both models were calculated and compared. Threshold values for the logistic regression models were selected to maximize HR and POD and minimize FAR for each station, and the split for the decision tree models was stopped when change in entropy was relatively small. Average HR values were 0.92 and 0.91 for logistic regression and decision tree techniques, respectively, average POD values were 0.78 and 0.80 for logistic regression and decision tree techniques, respectively, and average FAR values were 0.22 and 0.28 for logistic regression and decision tree techniques, respectively. The average numbers of selected explanatory variables were 5.7 and 2.3 for logistic regression and decision tree techniques, respectively. Fewer explanatory variables can be more appropriate for operational activities to provide a timely warning for the prevention of the frost damages to agricultural crops. We concluded that the decision tree model can be more useful for the timely warning system. It is recommended that the models should be improved to reflect local topological features.

  18. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing s...... as it pinpoints which decisions to be concerned about when the goal is to predict footbridge response. The studies involve estimating footbridge responses using Monte-Carlo simulations and focus is on estimating vertical structural response to single person loading....

  19. Nonconvex Model Predictive Control for Commercial Refrigeration

    DEFF Research Database (Denmark)

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

    2013-01-01

    is to minimize 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...... 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...

  20. A prediction model for ocular damage - Experimental validation.

    Science.gov (United States)

    Heussner, Nico; Vagos, Márcia; Spitzer, Martin S; Stork, Wilhelm

    2015-08-01

    With the increasing number of laser applications in medicine and technology, accidental as well as intentional exposure of the human eye to laser sources has become a major concern. Therefore, a prediction model for ocular damage (PMOD) is presented within this work and validated for long-term exposure. This model is a combination of a raytracing model with a thermodynamical model of the human and an application which determines the thermal damage by the implementation of the Arrhenius integral. The model is based on our earlier work and is here validated against temperature measurements taken with porcine eye samples. For this validation, three different powers were used: 50mW, 100mW and 200mW with a spot size of 1.9mm. Also, the measurements were taken with two different sensing systems, an infrared camera and a fibre optic probe placed within the tissue. The temperatures were measured up to 60s and then compared against simulations. The measured temperatures were found to be in good agreement with the values predicted by the PMOD-model. To our best knowledge, this is the first model which is validated for both short-term and long-term irradiations in terms of temperature and thus demonstrates that temperatures can be accurately predicted within the thermal damage regime. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Mean Value Modelling of a Turbocharged SI Engine

    DEFF Research Database (Denmark)

    Müller, Martin; Hendricks, Elbert; Sorenson, Spencer C.

    1998-01-01

    An important paradigm for the modelling of naturallly aspirated (NA) spark ignition (SI) engines for control purposes is the Mean Value Engine Model (MVEM). Such models have a time resolution which is just sufficient to capture the main details of the dynamic performance of NA SI engines but not ...

  2. Predictive value of brain perfusion SPECT for rTMS response in pharmacoresistant depression

    Energy Technology Data Exchange (ETDEWEB)

    Richieri, Raphaelle; Lancon, Christophe [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); Boyer, Laurent [La Timone University, EA 3279 - Self-perceived Health Assessment Research Unit, School of Medicine, Marseille (France); La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Department of Public Health, Marseille (France); Farisse, Jean [Sainte-Marguerite University Hospital, Department of Psychiatry, Marseille (France); Colavolpe, Cecile; Mundler, Olivier [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Guedj, Eric [La Timone University Hospital, Assistance Publique - Hopitaux de Marseille, Service Central de Biophysique et Medecine Nucleaire, Marseille (France); Universite de la Mediterranee, Centre Europeen de Recherche en Imagerie Medicale (CERIMED), Marseille (France); Hopital de la Timone, Service Central de Biophysique et de Medecine Nucleaire, Marseille Cedex 5 (France)

    2011-09-15

    The aim of this study was to determine the predictive value of whole-brain voxel-based regional cerebral blood flow (rCBF) for repetitive transcranial magnetic stimulation (rTMS) response in patients with pharmacoresistant depression. Thirty-three right-handed patients who met DSM-IV criteria for major depressive disorder (unipolar or bipolar depression) were included before rTMS. rTMS response was defined as at least 50% reduction in the baseline Beck Depression Inventory scores. The predictive value of {sup 99m}Tc-ethyl cysteinate dimer (ECD) single photon emission computed tomography (SPECT) for rTMS response was studied before treatment by comparing rTMS responders to non-responders at voxel level using Statistical Parametric Mapping (SPM) (p < 0.001, uncorrected). Of the patients, 18 (54.5%) were responders to rTMS and 15 were non-responders (45.5%). There were no statistically significant differences in demographic and clinical characteristics (p > 0.10). In comparison to responders, non-responders showed significant hypoperfusions (p < 0.001, uncorrected) in the left medial and bilateral superior frontal cortices (BA10), the left uncus/parahippocampal cortex (BA20/BA35) and the right thalamus. The area under the curve for the combination of SPECT clusters to predict rTMS response was 0.89 (p < 0.001). Sensitivity, specificity, positive predictive value and negative predictive value for the combination of clusters were: 94, 73, 81 and 92%, respectively. This study shows that, in pharmacoresistant depression, pretreatment rCBF of specific brain regions is a strong predictor for response to rTMS in patients with homogeneous demographic/clinical features. (orig.)

  3. Constructing predictive models of human running.

    Science.gov (United States)

    Maus, Horst-Moritz; Revzen, Shai; Guckenheimer, John; Ludwig, Christian; Reger, Johann; Seyfarth, Andre

    2015-02-06

    Running is an essential mode of human locomotion, during which ballistic aerial phases alternate with phases when a single foot contacts the ground. The spring-loaded inverted pendulum (SLIP) provides a starting point for modelling running, and generates ground reaction forces that resemble those of the centre of mass (CoM) of a human runner. Here, we show that while SLIP reproduces within-step kinematics of the CoM in three dimensions, it fails to reproduce stability and predict future motions. We construct SLIP control models using data-driven Floquet analysis, and show how these models may be used to obtain predictive models of human running with six additional states comprising the position and velocity of the swing-leg ankle. Our methods are general, and may be applied to any rhythmic physical system. We provide an approach for identifying an event-driven linear controller that approximates an observed stabilization strategy, and for producing a reduced-state model which closely recovers the observed dynamics. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  4. Statistical Seasonal Sea Surface based Prediction Model

    Science.gov (United States)

    Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima

    2014-05-01

    The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.

  5. Evaluation of models to predict insolation on tilted surfaces

    Science.gov (United States)

    Klucher, T. M.

    1979-01-01

    An empirical study was performed to evaluate the validity of various insolation models which employ either an isotropic or an anisotropic distribution approximation for sky light when predicting insolation on tilted surfaces. Data sets of measured hourly insolation values were obtained over a 6-month period using pyranometers which received diffuse and total solar radiation on a horizontal plane and total radiation on surfaces tilted toward the equator at 37 degrees and 60 degrees angles above the horizon. Data on the horizontal surfaces were used in the insolation models to predict insolation on the tilted surface; comparisons of measured vs calculated insolation on the tilted surface were examined to test the validity of the sky light approximations. It was found that the Liu-Jordan isotropic distribution model provides a good fit to empirical data under overcast skies but underestimates the amount of solar radiation incident on tilted surfaces under clear and partly cloudy conditions.

  6. Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad

    2013-01-01

    The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...... by the effective wind speed on the rotor disc. We take the wind speed as a scheduling variable. The wind speed is measurable ahead of the turbine using LIDARs, therefore, the scheduling variable is known for the entire prediction horizon. By taking the advantage of having future values of the scheduling variable...... on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point...

  7. Hemostatic system changes predictive value in patients with ischemic brain disorders

    Directory of Open Access Journals (Sweden)

    Raičević Ranko

    2002-01-01

    Full Text Available The aim of this research was to determine the importance of tracking the dynamics of changes of the hemostatic system factors (aggregation of thrombocytes, D-dimer, PAI-1, antithrombin III, protein C and protein S, factor VII and factor VIII, fibrin degradation products, euglobulin test and the activated partial thromboplastin time – aPTPV in relation to the level of the severity of ischemic brain disorders (IBD and the level of neurological and functional deficiency in the beginning of IBD manifestation from 7 to 10 days, 19 to 21 day, and after 3 to 6 months. The research results confirmed significant predictive value of changes of hemostatic system with the predomination of procoagulant factors, together with the insufficiency of fibrinolysis. Concerning the IBD severity and it's outcome, the significant predictive value was shown in the higher levels of PAI-1 and the lower level of antithrombin III, and borderline significant value was shown in the accelerated aggregation of thrombocytes and the increased concentration of D-dimer. It could be concluded that the tracking of the dynamics of changes in parameters of hemostatic system proved to be an easily accessible method with the significant predictive value regarding the development of more severe. IBD cases and the outcome of the disease itself.

  8. Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions

    Directory of Open Access Journals (Sweden)

    Jabar H. Yousif

    2017-07-01

    Full Text Available The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV panels based on a self-organizing feature map (SOFM model. The proposed model is compared with other models, including the multi-layer perceptron (MLP and support vector machine (SVM models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE, mean absolute error (MAE, correlation (R, and coefficient of determination (R2. The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.

  9. Regional differences in prediction models of lung function in Germany

    Directory of Open Access Journals (Sweden)

    Schäper Christoph

    2010-04-01

    Full Text Available Abstract Background Little is known about the influencing potential of specific characteristics on lung function in different populations. The aim of this analysis was to determine whether lung function determinants differ between subpopulations within Germany and whether prediction equations developed for one subpopulation are also adequate for another subpopulation. Methods Within three studies (KORA C, SHIP-I, ECRHS-I in different areas of Germany 4059 adults performed lung function tests. The available data consisted of forced expiratory volume in one second, forced vital capacity and peak expiratory flow rate. For each study multivariate regression models were developed to predict lung function and Bland-Altman plots were established to evaluate the agreement between predicted and measured values. Results The final regression equations for FEV1 and FVC showed adjusted r-square values between 0.65 and 0.75, and for PEF they were between 0.46 and 0.61. In all studies gender, age, height and pack-years were significant determinants, each with a similar effect size. Regarding other predictors there were some, although not statistically significant, differences between the studies. Bland-Altman plots indicated that the regression models for each individual study adequately predict medium (i.e. normal but not extremely high or low lung function values in the whole study population. Conclusions Simple models with gender, age and height explain a substantial part of lung function variance whereas further determinants add less than 5% to the total explained r-squared, at least for FEV1 and FVC. Thus, for different adult subpopulations of Germany one simple model for each lung function measures is still sufficient.

  10. Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals.

    Directory of Open Access Journals (Sweden)

    A van Giessen

    Full Text Available The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI. However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it.In a large Dutch population cohort (n = 21,992 we classified individuals to low (< 5% and high (≥ 5% fatal cardiovascular disease risk by the Framingham risk score (FRS and reclassified them based on the systematic coronary risk evaluation (SCORE. Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE.Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%] within the events, 0.06% (95% CI [-0.08%; 0.22%] within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]. Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3% improved the NRI to 5.32% (95% CI [-0.13%; 12.06%] within the events, 0.24% (95% CI [0.10%; 0.36%] within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]. Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate.In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly

  11. Trust Model for Social Network using Singular Value Decomposition

    OpenAIRE

    Davis Bundi Ntwiga; Patrick Weke; Michael Kiura Kirumbu

    2016-01-01

    For effective interactions to take place in a social network, trust is important. We model trust of agents using the peer to peer reputation ratings in the network that forms a real valued matrix. Singular value decomposition discounts the reputation ratings to estimate the trust levels as trust is the subjective probability of future expectations based on current reputation ratings. Reputation and trust are closely related and singular value decomposition can estimate trust using the...

  12. SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles.

    Science.gov (United States)

    Franceschini, Andrea; Lin, Jianyi; von Mering, Christian; Jensen, Lars Juhl

    2016-04-01

    A successful approach for predicting functional associations between non-homologous genes is to compare their phylogenetic distributions. We have devised a phylogenetic profiling algorithm, SVD-Phy, which uses truncated singular value decomposition to address the problem of uninformative profiles giving rise to false positive predictions. Benchmarking the algorithm against the KEGG pathway database, we found that it has substantially improved performance over existing phylogenetic profiling methods. The software is available under the open-source BSD license at https://bitbucket.org/andrea/svd-phy lars.juhl.jensen@cpr.ku.dk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  13. Plasma levels of catecholamines and asymmetric dimethylarginine levels as predictive values of mortality among hemodialysis patients

    Directory of Open Access Journals (Sweden)

    Dziedzic Marcin

    2014-06-01

    Full Text Available The aim of the study was to assess plasma concentration of catecholamines and asymmetric dimethyl arginine levels and a possible relationship to predict the mortality rates among hemodialysis patients. The study population comprised 27 subjects, aged 65-70 years. Each patient underwent dialysis thrice a week. Furthermore, the median duration of hemodialysis was 3.5 years. Based on the conducted research, it can be concluded that the concentrations of adrenaline and the level of asymmetric dimethylarginine have predictive value of mortality among hemodialysis patients. Of note, lowering plasma asymmetric dimethylarginine concentration may represent therapeutic target for prevention of progressive renal damage.

  14. Stakeholder Theory and Value Creation Models in Brazilian Firms

    Directory of Open Access Journals (Sweden)

    Natalia Giugni Vidal

    2015-09-01

    Full Text Available Objective – The purpose of this study is to understand how top Brazilian firms think about and communicate value creation to their stakeholders. Design/methodology/approach – We use qualitative content analysis methodology to analyze the sustainability or annual integrated reports of the top 25 Brazilian firms by sales revenue. Findings – Based on our analysis, these firms were classified into three main types of stakeholder value creation models: narrow, broad, or transitioning from narrow to broad. We find that many of the firms in our sample are in a transition state between narrow and broad stakeholder value creation models. We also identify seven areas of concentration discussed by firms in creating value for stakeholders: better stakeholder relationships, better work environment, environmental preservation, increased customer base, local development, reputation, and stakeholder dialogue. Practical implications – This study shows a trend towards broader stakeholder value creation models in Brazilian firms. The findings of this study may inform practitioners interested in broadening their value creation models. Originality/value – This study adds to the discussion of stakeholder theory in the Brazilian context by understanding variations in value creation orientation in Brazil.

  15. Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

    Science.gov (United States)

    Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

    2015-11-01

    Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

  16. Predictive modeling by the cerebellum improves proprioception.

    Science.gov (United States)

    Bhanpuri, Nasir H; Okamura, Allison M; Bastian, Amy J

    2013-09-04

    Because sensation is delayed, real-time movement control requires not just sensing, but also predicting limb position, a function hypothesized for the cerebellum. Such cerebellar predictions could contribute to perception of limb position (i.e., proprioception), particularly when a person actively moves the limb. Here we show that human cerebellar patients have proprioceptive deficits compared with controls during active movement, but not when the arm is moved passively. Furthermore, when healthy subjects move in a force field with unpredictable dynamics, they have active proprioceptive deficits similar to cerebellar patients. Therefore, muscle activity alone is likely insufficient to enhance proprioception and predictability (i.e., an internal model of the body and environment) is important for active movement to benefit proprioception. We conclude that cerebellar patients have an active proprioceptive deficit consistent with disrupted movement prediction rather than an inability to generally enhance peripheral proprioceptive signals during action and suggest that active proprioceptive deficits should be considered a fundamental cerebellar impairment of clinical importance.

  17. Mixing height computation from a numerical weather prediction model

    Energy Technology Data Exchange (ETDEWEB)

    Jericevic, A. [Croatian Meteorological and Hydrological Service, Zagreb (Croatia); Grisogono, B. [Univ. of Zagreb, Zagreb (Croatia). Andrija Mohorovicic Geophysical Inst., Faculty of Science

    2004-07-01

    Dispersion models require hourly values of the mixing height, H, that indicates the existence of turbulent mixing. The aim of this study was to investigate a model ability and characteristics in the prediction of H. The ALADIN, limited area numerical weather prediction (NWP) model for short-range 48-hour forecasts was used. The bulk Richardson number (R{sub iB}) method was applied to determine the height of the atmospheric boundary layer at one grid point nearest to Zagreb, Croatia. This specific location was selected because there were available radio soundings and the verification of the model could be done. Critical value of bulk Richardson number R{sub iBc}=0.3 was used. The values of H, modelled and measured, for 219 days at 12 UTC are compared, and the correlation coefficient of 0.62 is obtained. This indicates that ALADIN can be used for the calculation of H in the convective boundary layer. For the stable boundary layer (SBL), the model underestimated H systematically. Results showed that R{sub iBc} evidently increases with the increase of stability. Decoupling from the surface in the very SBL was detected, which is a consequence of the flow ease resulting in R{sub iB} becoming very large. Verification of the practical usage of the R{sub iB} method for H calculations from NWP model was performed. The necessity for including other stability parameters (e.g., surface roughness length) was evidenced. Since ALADIN model is in operational use in many European countries, this study would help the others in pre-processing NWP data for input to dispersion models. (orig.)

  18. Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

    Science.gov (United States)

    Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

    2017-03-01

    This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

  19. Sensation and perception of sucrose and fat stimuli predict the reinforcing value of food.

    Science.gov (United States)

    Panek-Scarborough, Leah M; Dewey, Amber M; Temple, Jennifer L

    2012-03-20

    Chronic overeating can lead to weight gain and obesity. Sensory system function may play a role in the types of foods people select and the amount of food people eat. Several studies have shown that the orosensory components of eating play a strong role in driving food intake and food selection. In addition, previous work has shown that motivation to get food, or the reinforcing value of food, is a predictor of energy intake. The purpose of this study was to test the hypothesis that higher detection thresholds and lower suprathreshold intensity ratings of sweet and fat stimuli are associated with greater reinforcing value of food. In addition, we sought to determine if the sensory ratings of the stimuli would differ depending on whether they were expectorated or swallowed. The reinforcing value of food was measured by having participants perform operant responses for food on progressive ratio schedules of reinforcement. Taste detection thresholds and suprathresholds for solutions containing varied concentrations of sucrose and fat were also measured in two different Experiments. In Experiment 1, we found that sucrose, but not fat, detection predicted the reinforcing value of food with the reinforcing value of food increasing as sucrose detection threshold increased (indicating poorer detection). In Experiment 2, we found that lower suprathreshold ratings of expectorated fat and sucrose predicted greater reinforcing value of food. In addition, higher detection thresholds for fat stimuli (indicating poorer detection) were associated with greater reinforcing value of food. When taken together, these studies suggest that there is a relationship between taste detection and perception and reinforcing value of food and that these relationships vary based on whether the stimulus is swallowed or expectorated. Copyright © 2012 Elsevier Inc. All rights reserved.

  20. Value Creation Challenges in Multichannel Retail Business Models

    Directory of Open Access Journals (Sweden)

    Mika Yrjölä

    2014-08-01

    Full Text Available Purpose: The purpose of the paper is to identify and analyze the challenges of value creation in multichannel retail business models. Design/methodology/approach: With the help of semi-structured interviews with top executives from different retailing environments, this study introduces a model of value creation challenges in the context of multichannel retailing. The challenges are analyzed in terms of three retail business model elements, i.e., format, activities, and governance. Findings: Adopting a multichannel retail business model requires critical rethinking of the basic building blocks of value creation. First of all, as customers effortlessly move between multiple channels, multichannel formats can lead to a mismatch between customer and firm value. Secondly, retailers face pressures to use their activities to form integrated total offerings to customers. Thirdly, multiple channels might lead to organizational silos with conflicting goals. A careful orchestration of value creation is needed to determine the roles and incentives of the channel parties involved. Research limitations/implications: In contrast to previous business model literature, this study did not adopt a network-centric view. By embracing the boundary-spanning nature of the business model, other challenges and elements might have been discovered (e.g., challenges in managing relationships with suppliers. Practical implications: As a practical contribution, this paper has analyzed the challenges retailers face in adopting multichannel business models. Customer tendencies for showrooming behavior highlight the need for generating efficient lock-in strategies. Customized, personal offers and information are ways to increase customer value, differentiate from competition, and achieve lock-in. Originality/value: As a theoretical contribution, this paper empirically investigates value creation challenges in a specific context, lowering the level of abstraction in the mostly

  1. Integral-Value Models for Outcomes over Continuous Time

    DEFF Research Database (Denmark)

    Harvey, Charles M.; Østerdal, Lars Peter

    Models of preferences between outcomes over continuous time are important for individual, corporate, and social decision making, e.g., medical treatment, infrastructure development, and environmental regulation. This paper presents a foundation for such models. It shows that conditions...... on preferences between real- or vector-valued outcomes over continuous time are satisfied if and only if the preferences are represented by a value function having an integral form...

  2. A prediction model for Clostridium difficile recurrence

    Directory of Open Access Journals (Sweden)

    Francis D. LaBarbera

    2015-02-01

    Full Text Available Background: Clostridium difficile infection (CDI is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR; however, there is little consensus on the impact of most of the identified risk factors. Methods: Our study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR from February 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods. Results: We came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results. Conclusions: We hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

  3. 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...

  4. 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.

  5. 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.

  6. 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.

  7. A generative model for predicting terrorist incidents

    Science.gov (United States)

    Verma, Dinesh C.; Verma, Archit; Felmlee, Diane; Pearson, Gavin; Whitaker, Roger

    2017-05-01

    A major concern in coalition peace-support operations is the incidence of terrorist activity. In this paper, we propose a generative model for the occurrence of the terrorist incidents, and illustrate that an increase in diversity, as measured by the number of different social groups to which that an individual belongs, is inversely correlated with the likelihood of a terrorist incident in the society. A generative model is one that can predict the likelihood of events in new contexts, as opposed to statistical models which are used to predict the future incidents based on the history of the incidents in an existing context. Generative models can be useful in planning for persistent Information Surveillance and Reconnaissance (ISR) since they allow an estimation of regions in the theater of operation where terrorist incidents may arise, and thus can be used to better allocate the assignment and deployment of ISR assets. In this paper, we present a taxonomy of terrorist incidents, identify factors related to occurrence of terrorist incidents, and provide a mathematical analysis calculating the likelihood of occurrence of terrorist incidents in three common real-life scenarios arising in peace-keeping operations

  8. Predicted values of exercise capacity in heart failure: where we are, where to go.

    Science.gov (United States)

    Gargiulo, Paola; Olla, Sergio; Boiti, Costanza; Contini, Mauro; Perrone-Filardi, Pasquale; Agostoni, Piergiuseppe

    2014-09-01

    Cardiopulmonary exercise testing (CPET) is a procedure widely used in daily clinical activity to investigate cardiac and pulmonary disorders. Peak oxygen consumption (VO2 peak) is the most validated and clinically accepted parameter used to report aerobic capacity in healthy individuals and in different clinical settings. However, peak VO2 is influenced by several factors, whose variability is nowadays particularly evident due to the extensive use of CPET even in very young and very old subgroups of patients. Thus, its diagnostic and prognostic significance may be improved by the use of % of predicted VO2. At present, many sets of normal values are available, making the identification of the most proper max VO2 predicted value a challenging problem. In fact, normal value sets have been obtained from studies whose accuracy was reduced by important limitations, such as small sample size, low grade of heterogeneity of the population enrolled, poor rigorousness of methods, or difficulty in interpreting results. Accordingly, the aim of the present review is threefold: (A) to report some illustrative cases to show how the choice of the normal value set can influence the report of CPET; (B) to describe the most known and used reference value sets, highlighting the main characteristics of sample population, the most important methodological aspects, and the major limitations of the studies; (C) to suggest which equation should be used, if any, and to underline its weakness.

  9. Dissatisfaction with performance of valued activities predicts depression in age-related macular degeneration.

    Science.gov (United States)

    Rovner, Barry W; Casten, Robin J; Hegel, Mark T; Hauck, Walter W; Tasman, William S

    2007-08-01

    To determine whether dissatisfaction with performance of valued activities predicts depression in patients with age-related macular degeneration (AMD). Two hundred and six patients with newly diagnosed neovascular AMD in one eye and pre-existing AMD in the fellow eye who were participating in a clinical trial of a psychosocial intervention to prevent depression. Structured clinical evaluations of vision function, depression, visual acuity, contrast sensitivity and medical morbidity. Subjects were classified as dissatisfied if they indicated that they were dissatisfied with their performance of a valued activity. Subjects who were dissatisfied with performance of valued activities (n = 71) had similar demographic characteristics to satisfied subjects (n = 135) but had worse visual acuity (p < 0.054), greater medical comorbidity (p < 0.006), and lower vision function (p < 0.001). Dissatisfied subjects were almost 2.5 times more likely (OR = 2.41; [95% CI 1.02, 5.65]; p = 0.044) to become depressed within 2 months than satisfied subjects independent of baseline visual acuity, vision function, and medical comorbidity. Dissatisfaction with performance of valued activities in older persons with AMD predicts depression over a 2-month period. Assessing the ability to pursue valued activities may identify patients at risk for depression and prompt clinicians to initiate rehabilitative interventions and careful surveillance for depression.

  10. Risk models to predict hypertension: a systematic review.

    Directory of Open Access Journals (Sweden)

    Justin B Echouffo-Tcheugui

    Full Text Available BACKGROUND: As well as being a risk factor for cardiovascular disease, hypertension is also a health condition in its own right. Risk prediction models may be of value in identifying those individuals at risk of developing hypertension who are likely to benefit most from interventions. METHODS AND FINDINGS: To synthesize existing evidence on the performance of these models, we searched MEDLINE and EMBASE; examined bibliographies of retrieved articles; contacted experts in the field; and searched our own files. Dual review of identified studies was conducted. Included studies had to report on the development, validation, or impact analysis of a hypertension risk prediction model. For each publication, information was extracted on study design and characteristics, predictors, model discrimination, calibration and reclassification ability, validation and impact analysis. Eleven studies reporting on 15 different hypertension prediction risk models were identified. Age, sex, body mass index, diabetes status, and blood pressure variables were the most common predictor variables included in models. Most risk models had acceptable-to-good discriminatory ability (C-statistic>0.70 in the derivation sample. Calibration was less commonly assessed, but overall acceptable. Two hypertension risk models, the Framingham and Hopkins, have been externally validated, displaying acceptable-to-good discrimination, and C-statistic ranging from 0.71 to 0.81. Lack of individual-level data precluded analyses of the risk models in subgroups. CONCLUSIONS: The discrimination ability of existing hypertension risk prediction tools is acceptable, but the impact of using these tools on prescriptions and outcomes of hypertension prevention is unclear.

  11. Maximal aerobic capacity in ageing subjects: actual measurements versus predicted values

    OpenAIRE

    Cristina Pistea; Evelyne Lonsdorfer; Stéphane Doutreleau; Monique Oswald; Irina Enache; Anne Charloux

    2016-01-01

    We evaluated the impact of selection of reference values on the categorisation of measured maximal oxygen consumption (V′O2 peak) as “normal” or “abnormal” in an ageing population. We compared measured V′O2 peak with predicted values and the lower limit of normal (LLN) calculated with five equations. 99 (58 males and 41 females) disease-free subjects aged ≥70 years completed an incremental maximal exercise test on a cycle ergometer. Mean V′O2 peak was 1.88 L·min−1 in men and 1.26 L·min−1 in w...

  12. Bayesian prediction of breeding values by accounting for genotype-by-environment interaction in self-pollinating crops.

    Science.gov (United States)

    Bauer, A M; Hoti, F; Reetz, T C; Schuh, W-D; Léon, J; Sillanpää, M J

    2009-06-01

    In self-pollinating populations, individuals are characterized by a high degree of inbreeding. Additionally, phenotypic observations are highly influenced by genotype-by-environment interaction effects. Usually, Bayesian approaches to predict breeding values (in self-pollinating crops) omit genotype-by-environment interactions in the statistical model, which may result in biased estimates. In our study, a Bayesian Gibbs sampling algorithm was developed that is adapted to the high degree of inbreeding in self-pollinated crops and accounts for interaction effects between genotype and environment. As related lines are supposed to show similar genotype-by-environment interaction effects, an extended genetic relationship matrix is included in the Bayesian model. Additionally, since the coefficient matrix C in the mixed model equations can be characterized by rank deficiencies, the pseudoinverse of C was calculated by using the nullspace, which resulted in a faster computation time. In this study, field data of spring barley lines and data of a 'virtual' parental population of self-pollinating crops, generated by computer simulation, were used. For comparison, additional breeding values were predicted by a frequentist approach. In general, standard Bayesian Gibbs sampling and a frequentist approach resulted in similar estimates if heritability of the regarded trait was high. For low heritable traits, the modified Bayesian model, accounting for relatedness between lines in genotype-by-environment interaction, was superior to the standard model.

  13. Prediction models of prevalent radiographic vertebral fractures among older men.

    Science.gov (United States)

    Schousboe, John T; Rosen, Harold R; Vokes, Tamara J; Cauley, Jane A; Cummings, Steven R; Nevitt, Michael C; Black, Dennis M; Orwoll, Eric S; Kado, Deborah M; Ensrud, Kristine E

    2014-01-01

    No studies have compared how well different prediction models discriminate older men who have a radiographic prevalent vertebral fracture (PVFx) from those who do not. We used area under receiver operating characteristic curves and a net reclassification index to compare how well regression-derived prediction models and nonregression prediction tools identify PVFx among men age ≥65 yr with femoral neck T-score of -1.0 or less enrolled in the Osteoporotic Fractures in Men Study. The area under receiver operating characteristic for a model with age, bone mineral density, and historical height loss (HHL) was 0.682 compared with 0.692 for a complex model with age, bone mineral density, HHL, prior non-spine fracture, body mass index, back pain, grip strength, smoking, and glucocorticoid use (p values for difference in 5 bootstrapped samples 0.14-0.92). This complex model, using a cutpoint prevalence of 5%, correctly reclassified only a net 5.7% (p = 0.13) of men as having or not having a PVFx compared with a simple criteria list (age ≥ 80 yr, HHL >4 cm, or glucocorticoid use). In conclusion, simple criteria identify older men with PVFx and regression-based models. Future research to identify additional risk factors that more accurately identify older men with PVFx is needed.

  14. Positive predictive value of diagnosis coding for hemolytic anemias in the Danish National Patient Register

    DEFF Research Database (Denmark)

    Hansen, Dennis Lund; Overgaard, Ulrik Malthe; Pedersen, Lars

    2016-01-01

    PURPOSE: The nationwide public health registers in Denmark provide a unique opportunity for evaluation of disease-associated morbidity if the positive predictive values (PPVs) of the primary diagnosis are known. The aim of this study was to evaluate the predictive values of hemolytic anemias...... registered in the Danish National Patient Register. PATIENTS AND METHODS: All patients with a first-ever diagnosis of hemolytic anemia from either specialist outpatient clinic contact or inpatient admission at Odense University Hospital from January 1994 through December 2011 were considered for inclusion....... Patients with mechanical reason for hemolysis such as an artificial heart valve, and patients with vitamin-B12 or folic acid deficiency were excluded. RESULTS: We identified 412 eligible patients: 249 with a congenital hemolytic anemia diagnosis and 163 with acquired hemolytic anemia diagnosis. In all...

  15. Prevalence and predictive value of islet cell antibodies and insulin autoantibodies in women with gestational diabetes

    DEFF Research Database (Denmark)

    Damm, P; Kühl, C; Buschard, K

    1994-01-01

    The objective of the present study was to investigate the predictive value of islet cell antibodies (ICA) and insulin autoantibodies (IAA) for development of diabetes in women with previous gestational diabetes (GDM). Two hundred and forty-one previous diet-treated GDM patients and 57 women without...... previous GDM were examined 2-11 years after the index pregnancy. In subgroups, plasma from the diagnostic OGTT during index pregnancy was analysed for ICA and IAA. Among the previous GDM patients, 3.7% had developed Type 1 diabetes and 13.7% Type 2 diabetes. Four (2.9%) of the 139 GDM patients tested...... for ICA were ICA-positive and three of these had Type 1 diabetes at follow-up, as well as three ICA-negative patients. The sensitivity, specificity, and predictive value of ICA-positivity for later development of diabetes were 50%, 99%, and 75%, respectively. None of the women was IAA-positive during...

  16. A two step Bayesian approach for genomic prediction of breeding values

    DEFF Research Database (Denmark)

    Mahdi Shariati, Mohammad; Sørensen, Peter; Janss, Luc

    2012-01-01

    . A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df. Methods: The simulated data from the 15th QTL...... of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances. Conclusions: Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker...... variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization...

  17. Optimal feedback scheduling of model predictive controllers

    Institute of Scientific and Technical Information of China (English)

    Pingfang ZHOU; Jianying XIE; Xiaolong DENG

    2006-01-01

    Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.

  18. Predictive value of fever and palmar pallor for P. falciparum parasitaemia in children from an endemic area.

    Directory of Open Access Journals (Sweden)

    Christof David Vinnemeier

    Full Text Available INTRODUCTION: Although the incidence of Plasmodium falciparum malaria in some parts of sub-Saharan Africa is reported to decline and other conditions, causing similar symptoms as clinical malaria are gaining in relevance, presumptive anti-malarial treatment is still common. This study traced for age-dependent signs and symptoms predictive for P. falciparum parasitaemia. METHODS: In total, 5447 visits of 3641 patients between 2-60 months of age who attended an outpatient department (OPD of a rural hospital in the Ashanti Region, Ghana, were analysed. All Children were examined by a paediatrician and a full blood count and thick smear were done. A Classification and Regression Tree (CART model was used to generate a clinical decision tree to predict malarial parasitaemia a7nd predictive values of all symptoms were calculated. RESULTS: Malarial parasitaemia was detected in children between 2-12 months and between 12-60 months of age with a prevalence of 13.8% and 30.6%, respectively. The CART-model revealed age-dependent differences in the ability of the variables to predict parasitaemia. While palmar pallor was the most important symptom in children between 2-12 months, a report of fever and an elevated body temperature of ≥37.5°C gained in relevance in children between 12-60 months. The variable palmar pallor was significantly (p<0.001 associated with lower haemoglobin levels in children of all ages. Compared to the Integrated Management of Childhood Illness (IMCI algorithm the CART-model had much lower sensitivities, but higher specificities and positive predictive values for a malarial parasitaemia. CONCLUSIONS: Use of age-derived algorithms increases the specificity of the prediction for P. falciparum parasitaemia. The predictive value of palmar pallor should be underlined in health worker training. Due to a lack of sensitivity neither the best algorithm nor palmar pallor as a single sign are eligible for decision-making and cannot replace

  19. Prediction models from CAD models of 3D objects

    Science.gov (United States)

    Camps, Octavia I.

    1992-11-01

    In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.

  20. Should we believe model predictions of future climate change? (Invited)

    Science.gov (United States)

    Knutti, R.

    2009-12-01

    for an effect to be real, but some features of the current models are perfectly robust yet known to be wrong. How much we can actually learn from more models of the same type is therefore an open question. A case is made here that the community must think harder on how to quantify the uncertainty and skill of their models, that making the models ever more complicated and expensive to run is unlikely to reduce uncertainties in predictions unless new data is used to constrain and calibrate the models, and that the demand for predictions and the data produced by the models is likely to quickly outgrow our capacity to understand the model and to analyze the results. More quantitative methods to quantify model performance are therefore critical to maximize the value of climate change projections from global climate models.

  1. Model predictive control of MSMPR crystallizers

    Science.gov (United States)

    Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc

    2005-02-01

    A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.

  2. Local morphology predicts functional organization of experienced value signals in the human orbitofrontal cortex.

    Science.gov (United States)

    Li, Yansong; Sescousse, Guillaume; Amiez, Céline; Dreher, Jean-Claude

    2015-01-28

    Experienced value representations within the human orbitofrontal cortex (OFC) are thought to be organized through an antero-posterior gradient corresponding to secondary versus primary rewards. Whether this gradient depends upon specific morphological features within this region, which displays considerable intersubject variability, remains unknown. To test the existence of such relationships, we performed a subject-by-subject analysis of fMRI data taking into account the local morphology of each individual. We tested 38 subjects engaged in a simple incentive delay task manipulating both monetary and visual erotic rewards, focusing on reward outcome (experienced value signal). The results showed reliable and dissociable primary (erotic) and secondary (monetary) experienced value signals at specific OFC sulci locations. More specifically, experienced value signal induced by monetary reward outcome was systematically located in the rostral portion of the medial orbital sulcus. Experienced value signal related to erotic reward outcome was located more posteriorly, that is, at the intersection between the caudal portion of the medial orbital sulcus and transverse orbital sulcus. Thus, the localizations of distinct experienced value signals can be predicted from the organization of the human orbitofrontal sulci. This study provides insights into the anatomo-functional parcellation of the anteroposterior OFC gradient observed for secondary versus primary rewards because there is a direct relationship between value signals at the time of reward outcome and unique OFC sulci locations.

  3. An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments

    NARCIS (Netherlands)

    Wientjes, Y.C.J.; Bijma, P.; Veerkamp, R.F.; Calus, M.P.L.

    2016-01-01

    Predicting the accuracy of estimated genomic values using genome-wide marker information is an important step in designing training populations. Currently, different deterministic equations are available to predict accuracy within populations, but not for multipopulation scenarios where data from

  4. Data from: An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments

    NARCIS (Netherlands)

    Wientjes, Y.C.J.; Bijma, P.; Veerkamp, R.F.; Calus, M.P.L.

    2015-01-01

    Predicting the accuracy of estimated genomic values using genome-wide marker information is an important step in designing training populations. Currently, different deterministic equations are available to predict accuracy within populations, but not for multipopulation scenarios where data from

  5. 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.

  6. Possibilistic Fuzzy Net Present Value Model and Application

    Directory of Open Access Journals (Sweden)

    S. S. Appadoo

    2014-01-01

    Full Text Available The cash flow values and the interest rate in the net present value (NPV model are usually specified by either crisp numbers or random variables. In this paper, we first discuss some of the recent developments in possibility theory and find closed form expressions for fuzzy possibilistic net present value (FNPV. Then, following Carlsson and Fullér (2001, we discuss some of the possibilistic moments related to FNPV model along with an illustrative numerical example. We also give a unified approach to find higher order moments of FNPV by using the moment generating function introduced by Paseka et al. (2011.

  7. Models of consumer value cocreation in health care.

    Science.gov (United States)

    Nambisan, Priya; Nambisan, Satish

    2009-01-01

    In recent years, consumer participation in health care has gained critical importance as health care organizations (HCOs) seek varied avenues to enhance the quality and the value of their offerings. Many large HCOs have established online health communities where health care consumers (patients) can interact with one another to share knowledge and offer emotional support in disease management and care. Importantly, the focus of consumer participation in health care has moved beyond such personal health care management as the potential for consumers to participate in innovation and value creation in varied areas of the health care industry becomes increasingly evident. Realizing such potential, however, will require HCOs to develop a better understanding of the varied types of consumer value cocreation that are enabled by new information and communication technologies such as online health communities and Web 2.0 (social media) technologies. This article seeks to contribute toward such an understanding by offering a concise and coherent theoretical framework to analyze consumer value cocreation in health care. We identify four alternate models of consumer value cocreation-the partnership model, the open-source model, the support-group model, and the diffusion model-and discuss their implications for HCOs. We develop our theoretical framework by drawing on theories and concepts in knowledge creation, innovation management, and online communities. A set of propositions are developed by combining theoretical insights from these areas with real-world examples of consumer value cocreation in health care. The theoretical framework offered here informs on the potential impact of the different models of consumer value cocreation on important organizational variables such as innovation cost and time, service quality, and consumer perceptions of HCO. An understanding of the four models of consumer value cocreation can help HCOs adopt appropriate strategies and practices to

  8. Predictive modelling of contagious deforestation in the Brazilian Amazon.

    Directory of Open Access Journals (Sweden)

    Isabel M D Rosa

    Full Text Available Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1 it is probabilistic and quantifies uncertainty around predictions and parameters; (2 the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3 deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia"-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy, annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is

  9. Predictive modelling of contagious deforestation in the Brazilian Amazon.

    Science.gov (United States)

    Rosa, Isabel M D; Purves, Drew; Souza, Carlos; Ewers, Robert M

    2013-01-01

    Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently

  10. Pricing for Catastrophe Bonds Based on Expected-value Model

    Directory of Open Access Journals (Sweden)

    Junfei Chen

    2013-02-01

    Full Text Available As the catastrophes cannot be avoided and result in huge economic losses, therefore the compensation issue for catastrophe losses become an important research topic. Catastrophe bonds can effectively disperse the catastrophe risks which mainly undertaken by the government and the insurance companies currently and focus on capital more effectively in broad capital market, therefore to be an ideal catastrophe securities product. This study adopts Expectancy Theory to supplement and improve the pricing of catastrophe bonds based on Value Theory. A model of expected utility is established to determine the conditions of the expected revenue R of catastrophe bonds. The pricing model of the value function is used to get the psychological value of R,U (R-R‾, for catastrophe bonds. Finally, the psychological value is improved by the value according to expected utility and this can more accurately evaluate catastrophe bonds at a reasonable price. This research can provide decision-making for the pricing of catastrophe bonds.

  11. Predictive value of digital subtraction angiography in patients with tuberculous meningitis

    Energy Technology Data Exchange (ETDEWEB)

    Rojas-Echeverri, L.A. [Dept. of Neuroimaging and Endovascular Therapy, National Inst. for Neurology and Neurosurgery, Mexico City (Mexico); Soto-Hernandez, J.L. [Infectious Disease Dept., National Inst. for Neurology and Neurosurgery, Tlalpan (Mexico); Garza, S. [Div. of Neurology, National Inst. for Neurology and Neurosurgery, Tlalpan (Mexico); Martinez-Zubieta, R. [Div. of Neurology, National Inst. for Neurology and Neurosurgery, Tlalpan (Mexico); Miranda, L.I. [Div. of Neurology, National Inst. for Neurology and Neurosurgery, Tlalpan (Mexico); Garcia-Ramos, G. [Div. of Neurology, National Inst. for Neurology and Neurosurgery, Tlalpan (Mexico); Zenteno, M. [Dept. of Neuroimaging and Endovascular Therapy, National Inst. for Neurology and Neurosurgery, Mexico City (Mexico)

    1996-01-01

    Digital subtraction angiography (DSA) was performed in 24 adults with tuberculous meningitis (TBM) and results were correlated with 24 admission and 16 follow-up CT examinations. 19 MRI studies and clinical outcome at a mean follow-up of 44 weeks. DSA was abnormal in 11 patients. Abnormal DSA was associated with advenced clinical stages of the Medical Research Council classification, admission CT with hydrocephalus or gyral cortical enhancement. MRI disclosed brain infarcts not seen on initial CT in 8 cases. Of seven patients who died, 4 had abnormal and 3 normal DSA. Among patients who survived, those with normal DSA had a better functional outcome by Karnofsky scores. During follow-up infarcts were evident in 16 patients. Abnormal DSA in relation to brain infarcts had a sensitivity of 0.56, specificity 0.75, positive predictive value 0.82 and negative predictive value 0.46. A single arteriogram does not predict the outcome in patients with TBM and its value is limited in the assessment of vascular complications of TBM. Angiography in TBM is justified only in specific clinical trials to assess new therapeutic modalities against infarcts. (orig.)

  12. Testing the additional predictive value of high-dimensional molecular data

    Directory of Open Access Journals (Sweden)

    Boulesteix Anne-Laure

    2010-02-01

    Full Text Available Abstract Background While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature. Results We suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations. In simulations, our novel approach is found to have very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to the two publicly available cancer data sets. Conclusions Our simple and computationally efficient approach can be used to globally assess the additional predictive power of a large number of candidate predictors given that a few clinical covariates or a known prognostic index are already available. It is implemented in the R package "globalboosttest" which is publicly available from R-forge and will be sent to the CRAN as soon as possible.

  13. Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action.

    Science.gov (United States)

    Bissonette, Gregory B; Roesch, Matthew R

    2016-01-01

    Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum.

  14. Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action

    Science.gov (United States)

    Roesch, Matthew R.

    2017-01-01

    Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum. PMID:26276036

  15. Maximal aerobic capacity in ageing subjects: actual measurements versus predicted values.

    Science.gov (United States)

    Pistea, Cristina; Lonsdorfer, Evelyne; Doutreleau, Stéphane; Oswald, Monique; Enache, Irina; Charloux, Anne

    2016-01-01

    We evaluated the impact of selection of reference values on the categorisation of measured maximal oxygen consumption (V'O2peak) as "normal" or "abnormal" in an ageing population. We compared measured V'O2peak with predicted values and the lower limit of normal (LLN) calculated with five equations. 99 (58 males and 41 females) disease-free subjects aged ≥70 years completed an incremental maximal exercise test on a cycle ergometer. Mean V'O2peak was 1.88 L·min(-1) in men and 1.26 L·min(-1) in women. V'O2peak ranged from 89% to 108% of predicted in men, and from 88% to 164% of predicted in women, depending on the reference equation used. The proportion of subjects below the LLN ranged from 5% to 14% in men and 0-22% in women, depending on the reference equation. The LLN was lacking in one study, and was unsuitable for women in another. Most LLNs ranged between 53% and 73% of predicted. Therefore, choosing an 80% cut-off leads to overestimation of the proportion of "abnormal" subjects. To conclude, the proportion of subjects aged ≥70 years with a "low" V'O2peak differs markedly according to the chosen reference equations. In clinical practice, it is still relevant to test a sample of healthy volunteers and select the reference equations that better characterise this sample.

  16. QSAR Models for the Prediction of Plasma Protein Binding

    Directory of Open Access Journals (Sweden)

    Zeshan Amin

    2013-02-01

    Full Text Available Introduction: The prediction of plasma protein binding (ppb is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure – Activity Relationships (QSAR for the prediction of plasma protein binding. Methods: Protein binding values for 794 compounds were collated from literature. The data was partitioned into a training set of 662 compounds and an external validation set of 132 compounds. Physicochemical and molecular descriptors were calculated for each compound using ACD labs/logD, MOE (Chemical Computing Group and Symyx QSAR software packages. Several data mining tools were employed for the construction of models. These included stepwise regression analysis, Classification and Regression Trees (CART, Boosted trees and Random Forest. Results: Several predictive models were identified; however, one model in particular produced significantly superior prediction accuracy for the external validation set as measured using mean absolute error and correlation coefficient. The selected model was a boosted regression tree model which had the mean absolute error for training set of 13.25 and for validation set of 14.96. Conclusion: Plasma protein binding can be modeled using simple regression trees or multiple linear regressions with reasonable model accuracies. These interpretable models were able to identify the governing molecular factors for a high ppb that included hydrophobicity, van der Waals surface area parameters, and aromaticity. On the other hand, the more complicated ensemble method of boosted regression trees produced the most accurate ppb estimations for the external validation set.

  17. Prediction intervals for future BMI values of individual children: a non-parametric approach by quantile boosting.

    Science.gov (United States)

    Mayr, Andreas; Hothorn, Torsten; Fenske, Nora

    2012-01-25

    The construction of prediction intervals (PIs) for future body mass index (BMI) values of individual children based on a recent German birth cohort study with n = 2007 children is problematic for standard parametric approaches, as the BMI distribution in childhood is typically skewed depending on age. We avoid distributional assumptions by directly modelling the borders of PIs by additive quantile regression, estimated by boosting. We point out the concept of conditional coverage to prove the accuracy of PIs. As conditional coverage can hardly be evaluated in practical applications, we conduct a simulation study before fitting child- and covariate-specific PIs for future BMI values and BMI patterns for the present data. The results of our simulation study suggest that PIs fitted by quantile boosting cover future observations with the predefined coverage probability and outperform the benchmark approach. For the prediction of future BMI values, quantile boosting automatically selects informative covariates and adapts to the age-specific skewness of the BMI distribution. The lengths of the estimated PIs are child-specific and increase, as expected, with the age of the child. Quantile boosting is a promising approach to construct PIs with correct conditional coverage in a non-parametric way. It is in particular suitable for the prediction of BMI patterns depending on covariates, since it provides an interpretable predictor structure, inherent variable selection properties and can even account for longitudinal data structures.

  18. Predictive value of adenosine 5'-monophosphate challenge in preschool children for the diagnosis of asthma 5 years later.

    Science.gov (United States)

    Cohen, Shlomo; Avital, Avraham; Hevroni, Avigdor; Avenshtein, Alina; Hadi, Ronen; Springer, Chaim

    2012-07-01

    We evaluated the predictive values of preschool bronchial challenge with nebulized adenosine 5'-monophosphate (AMP) using the auscultation method for having asthma 5 years later. Preschool AMP challenge had a high negative (90%) and a moderate positive (67%) predictive value for asthma 5 years later. Positive predictive value increased with the age at which the challenge was performed. The degree of preschool response to AMP was associated with the severity of asthma at school age.

  19. [Healthcare value chain: a model for the Brazilian healthcare system].

    Science.gov (United States)

    Pedroso, Marcelo Caldeira; Malik, Ana Maria

    2012-10-01

    This article presents a model of the healthcare value chain which consists of a schematic representation of the Brazilian healthcare system. The proposed model is adapted for the Brazilian reality and has the scope and flexibility for use in academic activities and analysis of the healthcare sector in Brazil. It places emphasis on three components: the main activities of the value chain, grouped in vertical and horizontal links; the mission of each link and the main value chain flows. The proposed model consists of six vertical and three horizontal links, amounting to nine. These are: knowledge development; supply of products and technologies; healthcare services; financial intermediation; healthcare financing; healthcare consumption; regulation; distribution of healthcare products; and complementary and support services. Four flows can be used to analyze the value chain: knowledge and innovation; products and services; financial; and information.

  20. Predictive modelling of ferroelectric tunnel junctions

    Science.gov (United States)

    Velev, Julian P.; Burton, John D.; Zhuravlev, Mikhail Ye; Tsymbal, Evgeny Y.

    2016-05-01

    Ferroelectric tunnel junctions combine the phenomena of quantum-mechanical tunnelling and switchable spontaneous polarisation of a nanometre-thick ferroelectric film into novel device functionality. Switching the ferroelectric barrier polarisation direction produces a sizable change in resistance of the junction—a phenomenon known as the tunnelling electroresistance effect. From a fundamental perspective, ferroelectric tunnel junctions and their version with ferromagnetic electrodes, i.e., multiferroic tunnel junctions, are testbeds for studying the underlying mechanisms of tunnelling electroresistance as well as the interplay between electric and magnetic degrees of freedom and their effect on transport. From a practical perspective, ferroelectric tunnel junctions hold promise for disruptive device applications. In a very short time, they have traversed the path from basic model predictions to prototypes for novel non-volatile ferroelectric random access memories with non-destructive readout. This remarkable progress is to a large extent driven by a productive cycle of predictive modelling and innovative experimental effort. In this review article, we outline the development of the ferroelectric tunnel junction concept and the role of theoretical modelling in guiding experimental work. We discuss a wide range of physical phenomena that control the functional properties of ferroelectric tunnel junctions and summarise the state-of-the-art achievements in the field.