Genetic Programming for Sea Level Predictions in an Island Environment

Directory of Open Access Journals (Sweden)

M.A. Ghorbani

2010-03-01

Full Text Available Accurate predictions of sea-level are important for geodetic applications, navigation, coastal, industrial and tourist activities. In the current work, the Genetic Programming (GP and artificial neural networks (ANNs were applied to forecast half-daily and daily sea-level variations from 12 hours to 5 days ahead. The measurements at the Cocos (Keeling Islands in the Indian Ocean were used for training and testing of the employed artificial intelligence techniques. A comparison was performed of the predictions from the GP model and the ANN simulations. Based on the comparison outcomes, it was found that the Genetic Programming approach can be successfully employed in forecasting of sea level variations.

POLYAR, a new computer program for prediction of poly(A sites in human sequences

Directory of Open Access Journals (Sweden)

Qamar Raheel

2010-11-01

Full Text Available Abstract Background mRNA polyadenylation is an essential step of pre-mRNA processing in eukaryotes. Accurate prediction of the pre-mRNA 3'-end cleavage/polyadenylation sites is important for defining the gene boundaries and understanding gene expression mechanisms. Results 28761 human mapped poly(A sites have been classified into three classes containing different known forms of polyadenylation signal (PAS or none of them (PAS-strong, PAS-weak and PAS-less, respectively and a new computer program POLYAR for the prediction of poly(A sites of each class was developed. In comparison with polya_svm (till date the most accurate computer program for prediction of poly(A sites while searching for PAS-strong poly(A sites in human sequences, POLYAR had a significantly higher prediction sensitivity (80.8% versus 65.7% and specificity (66.4% versus 51.7% However, when a similar sort of search was conducted for PAS-weak and PAS-less poly(A sites, both programs had a very low prediction accuracy, which indicates that our knowledge about factors involved in the determination of the poly(A sites is not sufficient to identify such polyadenylation regions. Conclusions We present a new classification of polyadenylation sites into three classes and a novel computer program POLYAR for prediction of poly(A sites/regions of each of the class. In tests, POLYAR shows high accuracy of prediction of the PAS-strong poly(A sites, though this program's efficiency in searching for PAS-weak and PAS-less poly(A sites is not very high but is comparable to other available programs. These findings suggest that additional characteristics of such poly(A sites remain to be elucidated. POLYAR program with a stand-alone version for downloading is available at http://cub.comsats.edu.pk/polyapredict.htm.

Predicting introductory programming performance: A multi-institutional multivariate study

Science.gov (United States)

Bergin, Susan; Reilly, Ronan

2006-12-01

A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

Genomic prediction in a breeding program of perennial ryegrass

DEFF Research Database (Denmark)

Fé, Dario; Ashraf, Bilal; Greve-Pedersen, Morten

2015-01-01

We present a genomic selection study performed on 1918 rye grass families (Lolium perenne L.), which were derived from a commercial breeding program at DLF-Trifolium, Denmark. Phenotypes were recorded on standard plots, across 13 years and in 6 different countries. Variants were identified...... this set. Estimated Breeding Value and prediction accuracies were calculated trough two different cross-validation schemes: (i) k-fold (k=10); (ii) leaving out one parent combination at the time, in order to test for accuracy of predicting new families. Accuracies ranged between 0.56 and 0.97 for scheme (i....... A larger set of 1791 F2s were used as training set to predict EBVs of 127 synthetic families (originated from poly-crosses between 5-11 single plants) for heading date and crown rust resistance. Prediction accuracies were 0.93 and 0.57 respectively. Results clearly demonstrate considerable potential...

PBF/LOFT Lead Rod Test Program experiment predictions document

International Nuclear Information System (INIS)

Varacalle, D.J.; Cox, W.R.; Niebruegge, D.A.; Seiber, S.J.; Brake, T.E.; Driskell, W.E.; Nigg, D.W.; Tolman, E.L.

1978-12-01

The PBF/LOFT Lead Rod (LLR) Test Program is being conducted to provide experimental information on the behavior of nuclear fuel under normal and accident conditions in the Power Burst Facility (PBF) at the Idaho National Engineering Laboratory. The PBF/LLR tests are designed to simulate the test conditions for the LOFT Power Ascension Tests L2-3 through L2-5. The test program has been designed to provide a parametric evaluation of the LOFT fuel (center and peripheral modules) over a wide range of power. This report presents the experiment predictions for the three four-rod LOCA tests

Evaluation of Monticello Nuclear Power Plant, Environmental Impact Prediction, based on monitoring programs

International Nuclear Information System (INIS)

Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

1976-11-01

This report evaluates quantitatively the nonradiological environmental monitoring programs at Monticello Nuclear Generating Plant. The general objective of the study is to assess the effectiveness of monitoring programs in the measurement of environmental impacts. Specific objectives include the following: (1) Assess the validity of environmental impact predictions made in the Environmental Statement by analysis of nonradiological monitoring data; (2) evaluate the general adequacy of environmental monitoring programs for detecting impacts and their responsiveness to Technical Specifications objectives; (3) assess the adequacy of preoperational monitoring programs in providing a sufficient data base for evaluating operational impacts; (4) identify possible impacts that were not predicted in the environmental statement and identify monitoring activities that need to be added, modified or deleted; and (5) assist in identifying environmental impacts, monitoring methods, and measurement problems that need additional research before quantitative predictions can be attempted. Preoperational as well as operational monitoring data were examined to test the usefulness of baseline information in evaluating impacts. This included an examination of the analytical methods used to measure ecological and physical parameters, and an assessment of sampling periodicity and sensitivity where appropriate data were available

Activity, exposure rate and spectrum prediction with Java programming

International Nuclear Information System (INIS)

Sahin, D.; Uenlue, K.

2009-01-01

In order to envision the radiation exposure during Neutron Activation Analysis (NAA) experiments, a software called Activity Predictor is developed using Java TM programming language. The Activity Predictor calculates activities, exposure rates and gamma spectra of activated samples for NAA experiments performed at Radiation Science and Engineering Center (RSEC), Penn State Breazeale Reactor (PSBR). The calculation procedure for predictions involves both analytical and Monte Carlo methods. The Activity Predictor software is validated with a series of activation experiments. It has been found that Activity Predictor software calculates the activities and exposure rates precisely. The software also predicts gamma spectrum for each measurement. The predicted spectra agreed partially with measured spectra. The error in net photo peak areas varied from 4.8 to 51.29%, which is considered to be due to simplistic modeling, statistical fluctuations and unknown contaminants in the samples. (author)

Participatory cues and program familiarity predict young children’s learning from educational television

NARCIS (Netherlands)

Piotrowski, J.

2014-01-01

The capacity model is designed to predict young children's learning from educational television. It posits that select program features and individual child characteristics can support this learning either by increasing total working memory allocated to the program or altering the allocation of

Experimental validation of the twins prediction program for rolling noise. Pt.2: results

NARCIS (Netherlands)

Thompson, D.J.; Fodiman, P.; Mahé, H.

1996-01-01

Two extensive measurement campaigns have been carried out to validate the TWINS prediction program for rolling noise, as described in part 1 of this paper. This second part presents the experimental results of vibration and noise during train pass-bys and compares them with predictions from the

Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs.

Science.gov (United States)

Lado, Bettina; Battenfield, Sarah; Guzmán, Carlos; Quincke, Martín; Singh, Ravi P; Dreisigacker, Susanne; Peña, R Javier; Fritz, Allan; Silva, Paula; Poland, Jesse; Gutiérrez, Lucía

2017-07-01

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses. Copyright © 2017 Crop Science Society of America.

Predicting compliance with an information-based residential outdoor water conservation program

Science.gov (United States)

Landon, Adam C.; Kyle, Gerard T.; Kaiser, Ronald A.

2016-05-01

Residential water conservation initiatives often involve some form of education or persuasion intended to change the attitudes and behaviors of residential consumers. However, the ability of these instruments to change attitudes toward conservation and their efficacy in affecting water use remains poorly understood. In this investigation the authors examine consumer attitudes toward complying with a persuasive water conservation program, the extent to which those attitudes predict compliance, and the influence of environmental contextual factors on outdoor water use. Results indicate that the persuasive program was successful in developing positive attitudes toward compliance, and that those attitudes predict water use. However, attitudinal variables explain a relatively small proportion of the variance in objectively measured water use behavior. Recommendations for policy are made stressing the importance of understanding both the effects of attitudes and environmental contextual factors in behavior change initiatives in the municipal water sector.

Reliability of nine programs of topological predictions and their application to integral membrane channel and carrier proteins.

Science.gov (United States)

Reddy, Abhinay; Cho, Jaehoon; Ling, Sam; Reddy, Vamsee; Shlykov, Maksim; Saier, Milton H

2014-01-01

We evaluated topological predictions for nine different programs, HMMTOP, TMHMM, SVMTOP, DAS, SOSUI, TOPCONS, PHOBIUS, MEMSAT-SVM (hereinafter referred to as MEMSAT), and SPOCTOPUS. These programs were first evaluated using four large topologically well-defined families of secondary transporters, and the three best programs were further evaluated using topologically more diverse families of channels and carriers. In the initial studies, the order of accuracy was: SPOCTOPUS > MEMSAT > HMMTOP > TOPCONS > PHOBIUS > TMHMM > SVMTOP > DAS > SOSUI. Some families, such as the Sugar Porter Family (2.A.1.1) of the Major Facilitator Superfamily (MFS; TC #2.A.1) and the Amino Acid/Polyamine/Organocation (APC) Family (TC #2.A.3), were correctly predicted with high accuracy while others, such as the Mitochondrial Carrier (MC) (TC #2.A.29) and the K(+) transporter (Trk) families (TC #2.A.38), were predicted with much lower accuracy. For small, topologically homogeneous families, SPOCTOPUS and MEMSAT were generally most reliable, while with large, more diverse superfamilies, HMMTOP often proved to have the greatest prediction accuracy. We next developed a novel program, TM-STATS, that tabulates HMMTOP, SPOCTOPUS or MEMSAT-based topological predictions for any subdivision (class, subclass, superfamily, family, subfamily, or any combination of these) of the Transporter Classification Database (TCDB; www.tcdb.org) and examined the following subclasses: α-type channel proteins (TC subclasses 1.A and 1.E), secreted pore-forming toxins (TC subclass 1.C) and secondary carriers (subclass 2.A). Histograms were generated for each of these subclasses, and the results were analyzed according to subclass, family and protein. The results provide an update of topological predictions for integral membrane transport proteins as well as guides for the development of more reliable topological prediction programs, taking family-specific characteristics into account. © 2014 S. Karger AG, Basel.

Gstat: a program for geostatistical modelling, prediction and simulation

Science.gov (United States)

Pebesma, Edzer J.; Wesseling, Cees G.

1998-01-01

Gstat is a computer program for variogram modelling, and geostatistical prediction and simulation. It provides a generic implementation of the multivariable linear model with trends modelled as a linear function of coordinate polynomials or of user-defined base functions, and independent or dependent, geostatistically modelled, residuals. Simulation in gstat comprises conditional or unconditional (multi-) Gaussian sequential simulation of point values or block averages, or (multi-) indicator sequential simulation. Besides many of the popular options found in other geostatistical software packages, gstat offers the unique combination of (i) an interactive user interface for modelling variograms and generalized covariances (residual variograms), that uses the device-independent plotting program gnuplot for graphical display, (ii) support for several ascii and binary data and map file formats for input and output, (iii) a concise, intuitive and flexible command language, (iv) user customization of program defaults, (v) no built-in limits, and (vi) free, portable ANSI-C source code. This paper describes the class of problems gstat can solve, and addresses aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details.

RFI Math Model programs for predicting intermodulation interference

Science.gov (United States)

Stafford, J. M.

1974-01-01

Receivers operating on a space vehicle or an aircraft having many on-board transmitters are subject to intermodulation interference from mixing in the transmitting antenna systems, the external environment, or the receiver front-ends. This paper presents the techniques utilized in RFI Math Model computer programs that were developed to aid in the prevention of interference by predicting problem areas prior to occurrence. Frequencies and amplitudes of possible intermodulation products generated in the external environment are calculated and compared to receiver sensitivities. Intermodulation products generated in receivers are evaluated to determine the adequacy of preselector ejection.

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean

International Nuclear Information System (INIS)

1990-01-01

The proposed COPS (Coastal Ocean Prediction Systems) program is concerned with combining numerical models with observations (through data assimilation) to improve our predictive knowledge of the coastal ocean. It is oriented toward applied research and development and depends upon the continued pursuit of basic research in programs like COOP (Coastal Ocean Processes); i.e., to a significant degree it is involved with ''technology transfer'' from basic knowledge to operational and management applications. This predictive knowledge is intended to address a variety of societal problems: (1) ship routing, (2) trajectories for search and rescue operations, (3) oil spill trajectory simulations, (4) pollution assessments, (5) fisheries management guidance, (6) simulation of the coastal ocean's response to climate variability, (7) calculation of sediment transport, (8) calculation of forces on structures, and so forth. The initial concern is with physical models and observations in order to provide a capability for the estimation of physical forces and transports in the coastal ocean. For all these applications, there are common needs for physical field estimates: waves, tides, currents, temperature, and salinity, including mixed layers, thermoclines, fronts, jets, etc. However, the intent is to work with biologists, chemists, and geologists in developing integrated multidisciplinary prediction systems as it becomes feasible to do so. From another perspective, by combining observations with models through data assimilation, a modern approach to monitoring is provided through whole-field estimation

Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

Science.gov (United States)

Yukselturk, Erman; Ozekes, Serhat; Turel, Yalin Kilic

2014-01-01

This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…

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

Science.gov (United States)

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

2016-01-01

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

Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

International Nuclear Information System (INIS)

Xu Ruirui; Chen Tianlun; Gao Chengfeng

2006-01-01

Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

Gene expression programming for prediction of scour depth downstream of sills

Science.gov (United States)

Azamathulla, H. Md.

2012-08-01

SummaryLocal scour is crucial in the degradation of river bed and the stability of grade control structures, stilling basins, aprons, ski-jump bucket spillways, bed sills, weirs, check dams, etc. This short communication presents gene-expression programming (GEP), which is an extension to genetic programming (GP), as an alternative approach to predict scour depth downstream of sills. Published data were compiled from the literature for the scour depth downstream of sills. The proposed GEP approach gives satisfactory results (R2 = 0.967 and RMSE = 0.088) compared to the existing predictors (Chinnarasri and Kositgittiwong, 2008) with R2 = 0.87 and RMSE = 2.452 for relative scour depth.

Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.

Science.gov (United States)

Legendre, Audrey; Angel, Eric; Tahi, Fariza

2018-01-15

RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

Linear genetic programming application for successive-station monthly streamflow prediction

Science.gov (United States)

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

2014-09-01

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

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

Science.gov (United States)

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

Science.gov (United States)

Danandeh Mehr, Ali; Kahya, Ercan

2017-06-01

Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi

Science.gov (United States)

Hayes, Catherine

2005-07-01

This study sought to identify a variable or variables predictive of attrition among baccalaureate nursing students. The study was quantitative in design and multivariate correlational statistics and discriminant statistical analysis were used to identify a model for prediction of attrition. The analysis then weighted variables according to their predictive value to determine the most parsimonious model with the greatest predictive value. Three public university nursing education programs in Mississippi offering a Bachelors Degree in Nursing were selected for the study. The population consisted of students accepted and enrolled in these three programs for the years 2001 and 2002 and graduating in the years 2003 and 2004 (N = 195). The categorical dependent variable was attrition (includes academic failure or withdrawal) from the program of nursing education. The ten independent variables selected for the study and considered to have possible predictive value were: Grade Point Average for Pre-requisite Course Work; ACT Composite Score, ACT Reading Subscore, and ACT Mathematics Subscore; Letter Grades in the Courses: Anatomy & Physiology and Lab I, Algebra I, English I (101), Chemistry & Lab I, and Microbiology & Lab I; and Number of Institutions Attended (Universities, Colleges, Junior Colleges or Community Colleges). Descriptive analysis was performed and the means of each of the ten independent variables was compared for students who attrited and those who were retained in the population. The discriminant statistical analysis performed created a matrix using the ten variable model that was able to correctly predicted attrition in the study's population in 77.6% of the cases. Variables were then combined and recombined to produce the most efficient and parsimonious model for prediction. A six variable model resulted which weighted each variable according to predictive value: GPA for Prerequisite Coursework, ACT Composite, English I, Chemistry & Lab I, Microbiology

Genomic selection accuracy using multi-family prediction models in a wheat breeding program

Science.gov (United States)

Genomic selection (GS) uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotyp...

MHA admission criteria and program performance: do they predict career performance?

Science.gov (United States)

Porter, J; Galfano, V J

1987-01-01

The purpose of this study was to determine to what extent admission criteria predict graduate school and career performance. The study also analyzed which objective and subjective criteria served as the best predictors. MHA graduates of the University of Minnesota from 1974 to 1977 were surveyed to assess career performance. Student files served as the data base on admission criteria and program performance. Career performance was measured by four variables: total compensation, satisfaction, fiscal responsibility, and level of authority. High levels of MHA program performance were associated with women who had high undergraduate GPAs from highly selective undergraduate colleges, were undergraduate business majors, and participated in extracurricular activities. High levels of compensation were associated with relatively low undergraduate GPAs, high levels of participation in undergraduate extracurricular activities, and being single at admission to graduate school. Admission to MHA programs should be based upon both objective and subjective criteria. Emphasis should be placed upon the selection process for MHA students since admission criteria are shown to explain 30 percent of the variability in graduate program performance, and as much as 65 percent of the variance in level of position authority.

Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming

KAUST Repository

Kouramas, K.I.; Faí sca, N.P.; Panos, C.; Pistikopoulos, E.N.

2011-01-01

This work presents a new algorithm for solving the explicit/multi- parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

OpenAIRE

Mingjie Tan; Peiji Shao

2015-01-01

The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Ne...

Microcomputer based program for predicting heat transfer under reactor accident conditions. Volume I

International Nuclear Information System (INIS)

Cheng, S.C.; Groeneveld, D.C.; Leung, L.K.H.; Wong, Y.L.; Nguyen, C.

1987-07-01

A microcomputer based program called Heat Transfer Prediction Software (HTPS) has been developed. It calculates the heat transfer for the tube and bundle geometries for steady state and transient conditions. This program is capable of providing the best estimated of the hot pin temperatures during slow transients for 37- and 28-element CANDU type fuel bundles. The program is designed for an IBM-PC AT/XT (or IBM-PC compatible computer) equipped with a Math Co-processor. The following input parameters are required: pressure, mass flux, hydraulic diameter, and quality. For the steady state case, the critical heat flux (CHF), the critical heat flux temperature, the minimum film boiling temperature, and the minimum film boiling heat flux are the primary outputs. With either the surface heat flux or wall temperature specified, the program determines the heat transfer regime and calculates the surface heat flux, wall temperatures and heat transfer coefficient. For the slow transient case, the pressure, mass flux, quality, and volumetric heat generation rate are the time dependent input parameters required to calculate the hot pin sheath temperatures and surface heat fluxes. A simple routine for generating properties has been developed for light water to support the above program. It contains correlations that have been verified for pressures ranging from 0.6kPa to 30 MPa, and temperatures up to 1100 degrees Celcius. The thermodynamic and transport properties that can be generated from this routine are: density, specific volume, enthalpy, specific heat capacity, conductivity, viscosity, surface tension and Prandtl number for saturated liquid, saturated vapour, subcooled liquid for superheated vapour. A software for predicting flow regime has also been developed. It determines the flow pattern at specific flow conditions, and provides a correction factor for calculating the CHF during partially stratified horizontal flow. The technical bases for the program and its

Microcomputer based program for predicting heat transfer under reactor accident conditions. Volume II

International Nuclear Information System (INIS)

Cheng, S.C.; Groeneveld, D.C.; Leung, L.K.H.; Wong, Y.L.; Nguyen, C.

1987-07-01

A microcomputer based program called Heat Transfer Prediction Software (HTPS) has been developed. It calculates the heat transfer for tube and bundle geometries for steady state and transient conditions. This program is capable of providing the best estimated of the hot pin temperatures during slow transients for 37- and 28-element CANDU type fuel bundles. The program is designed for an IBM-PC AT/XT (or IBM-PC compatible computer) equipped with a Math Co-processor. The following input parameters are required: pressure, mass flux, hydraulic diameter, and quality. For the steady state case, the critical heat flux (CHF), the critical heat flux temperature, the minimum film boiling temperature, and the minimum film boiling heat flux are the primary outputs. With either the surface heat flux or wall temperature specified, the program determines the heat transfer regime and calculates the surface heat flux, wall temperature and heat transfer coefficient. For the slow transient case, the pressure, mass flux, quality, and volumetric heat generation rate are the time dependent input parameters are required to calculate the hot pin sheath temperatures and surface heat fluxes. A simple routine for generating properties has been developed for light water to support the above program. It contains correlations that have been verified for pressures ranging from 0.6kPa to 30 MPa, and temperatures up to 1100 degrees Celcius. The thermodynamic and transport properties that can be generated from this routine are: density, specific volume, enthalpy, specific heat capacity, conductivity, viscosity, surface tension and Prandtle number for saturated liquid, saturated vapour, subcooled liquid of superheated vapour. A software for predicting flow regime has also been developed. It determines the flow pattern at specific flow conditions, and provides a correction factor for calculating the CHF during partially stratified horizontal flow. The technical bases for the program and its structure

Predicting Improvement After a Bystander Program for the Prevention of Sexual and Dating Violence.

Science.gov (United States)

Hines, Denise A; Palm Reed, Kathleen M

2015-07-01

Although evidence suggests that bystander prevention programs are promising interventions for decreasing sexual violence and dating violence on college campuses, there have been no studies to date evaluating moderators of bystander program effectiveness. The current study evaluates whether different demographic characteristics, attitudes, knowledge, and behaviors at pretest predict change over a 6-month follow-up for students who participated in a bystander prevention program. Participants in the three assessments (pretest, posttest, 6-month follow-up) included 296 college students who were mandated to attend a bystander program during their first year orientation. Analyses showed that with few exceptions, the bystander program worked best for students who were most at risk given their pretest demographics and levels of attitudes condoning dating violence and sexual violence, bystander efficacy, and bystander behaviors. Results are discussed in terms of suggestions for future research. © 2014 Society for Public Health Education.

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

Science.gov (United States)

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

2001-01-01

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

Predicting Workplace Transfer of Learning: A Study of Adult Learners Enrolled in a Continuing Professional Education Training Program

Science.gov (United States)

Nafukho, Fredrick Muyia; Alfred, Mary; Chakraborty, Misha; Johnson, Michelle; Cherrstrom, Catherine A.

2017-01-01

Purpose: The primary purpose of this study was to predict transfer of learning to workplace among adult learners enrolled in a continuing professional education (CPE) training program, specifically training courses offered through face-to-face, blended and online instruction formats. The study examined the predictive capacity of trainee…

Genomic Selection Accuracy using Multifamily Prediction Models in a Wheat Breeding Program

Directory of Open Access Journals (Sweden)

Elliot L. Heffner

2011-03-01

Full Text Available Genomic selection (GS uses genome-wide molecular marker data to predict the genetic value of selection candidates in breeding programs. In plant breeding, the ability to produce large numbers of progeny per cross allows GS to be conducted within each family. However, this approach requires phenotypes of lines from each cross before conducting GS. This will prolong the selection cycle and may result in lower gains per year than approaches that estimate marker-effects with multiple families from previous selection cycles. In this study, phenotypic selection (PS, conventional marker-assisted selection (MAS, and GS prediction accuracy were compared for 13 agronomic traits in a population of 374 winter wheat ( L. advanced-cycle breeding lines. A cross-validation approach that trained and validated prediction accuracy across years was used to evaluate effects of model selection, training population size, and marker density in the presence of genotype × environment interactions (G×E. The average prediction accuracies using GS were 28% greater than with MAS and were 95% as accurate as PS. For net merit, the average accuracy across six selection indices for GS was 14% greater than for PS. These results provide empirical evidence that multifamily GS could increase genetic gain per unit time and cost in plant breeding.

The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks.

Science.gov (United States)

Witt, Clara J; Richards, Allen L; Masuoka, Penny M; Foley, Desmond H; Buczak, Anna L; Musila, Lillian A; Richardson, Jason H; Colacicco-Mayhugh, Michelle G; Rueda, Leopoldo M; Klein, Terry A; Anyamba, Assaf; Small, Jennifer; Pavlin, Julie A; Fukuda, Mark M; Gaydos, Joel; Russell, Kevin L; Wilkerson, Richard C; Gibbons, Robert V; Jarman, Richard G; Myint, Khin S; Pendergast, Brian; Lewis, Sheri; Pinzon, Jorge E; Collins, Kathrine; Smith, Matthew; Pak, Edwin; Tucker, Compton; Linthicum, Kenneth; Myers, Todd; Mansour, Moustafa; Earhart, Ken; Kim, Heung Chul; Jiang, Ju; Schnabel, Dave; Clark, Jeffrey W; Sang, Rosemary C; Kioko, Elizabeth; Abuom, David C; Grieco, John P; Richards, Erin E; Tobias, Steven; Kasper, Matthew R; Montgomery, Joel M; Florin, Dave; Chretien, Jean-Paul; Philip, Trudy L

2011-03-04

The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program's ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia.

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming.

Science.gov (United States)

Chen, Ruoying; Zhang, Zhiwang; Wu, Di; Zhang, Peng; Zhang, Xinyang; Wang, Yong; Shi, Yong

2011-01-21

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids. Copyright © 2010 Elsevier Ltd. All rights reserved.

FREC-4A: a computer program to predict fuel rod performance under normal reactor operation

International Nuclear Information System (INIS)

Harayama, Yasuo; Izumi, Fumio

1981-10-01

The program FREC-4A (Fuel Reliability Evaluation Code-version 4A) is used for predicting fuel rod performance in normal reactor operation. The performance is calculated in accordance with the irradiation history of fuel rods. Emphasis is placed on the prediction of the axial elongation of claddings induced by pellet-cladding mechanical interaction, including the influence of initially preloaded springs inserted in fuel rod lower plenums. In the FREC-4A, an fuel rod is divided into axial segments. In each segment, it is assumed that the temperature, stress and strain are axi-symmetrical, and the axial strain in constant in fuel pellets and in a cladding, though the values in the pellets and in the cladding are different. The calculation of the contact load and the clearance along the length of a fuel rod and the stress and strain in each segment is explained. The method adopted in the FREC-4A is simple, and suitable to predict the deformation of fuel rods over their full length. This report is described on the outline of the program, the method of solving the stiffness equations, the calculation models, the input data such as irradiation history, output distribution, material properties and pores, the printing-out of input data and calculated results. (Kako, I.)

Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

International Nuclear Information System (INIS)

Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor

2017-01-01

Highlights: • Genetic programming based ensemble of neural networks is employed for short term wind power prediction. • Proposed predictor shows resilience against abrupt changes in weather. • Genetic programming evolves nonlinear mapping between meteorological measures and wind-power. • Proposed approach gives mathematical expressions of wind power to its independent variables. • Proposed model shows relatively accurate and steady wind-power prediction performance. - Abstract: The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean. Volume 1: Strategic summary

Energy Technology Data Exchange (ETDEWEB)

1990-05-15

The proposed COPS (Coastal Ocean Prediction Systems) program is concerned with combining numerical models with observations (through data assimilation) to improve our predictive knowledge of the coastal ocean. It is oriented toward applied research and development and depends upon the continued pursuit of basic research in programs like COOP (Coastal Ocean Processes); i.e., to a significant degree it is involved with ``technology transfer`` from basic knowledge to operational and management applications. This predictive knowledge is intended to address a variety of societal problems: (1) ship routing, (2) trajectories for search and rescue operations, (3) oil spill trajectory simulations, (4) pollution assessments, (5) fisheries management guidance, (6) simulation of the coastal ocean`s response to climate variability, (7) calculation of sediment transport, (8) calculation of forces on structures, and so forth. The initial concern is with physical models and observations in order to provide a capability for the estimation of physical forces and transports in the coastal ocean. For all these applications, there are common needs for physical field estimates: waves, tides, currents, temperature, and salinity, including mixed layers, thermoclines, fronts, jets, etc. However, the intent is to work with biologists, chemists, and geologists in developing integrated multidisciplinary prediction systems as it becomes feasible to do so. From another perspective, by combining observations with models through data assimilation, a modern approach to monitoring is provided through whole-field estimation.

Logic programming to predict cell fate patterns and retrodict genotypes in organogenesis.

Science.gov (United States)

Hall, Benjamin A; Jackson, Ethan; Hajnal, Alex; Fisher, Jasmin

2014-09-06

Caenorhabditis elegans vulval development is a paradigm system for understanding cell differentiation in the process of organogenesis. Through temporal and spatial controls, the fate pattern of six cells is determined by the competition of the LET-23 and the Notch signalling pathways. Modelling cell fate determination in vulval development using state-based models, coupled with formal analysis techniques, has been established as a powerful approach in predicting the outcome of combinations of mutations. However, computing the outcomes of complex and highly concurrent models can become prohibitive. Here, we show how logic programs derived from state machines describing the differentiation of C. elegans vulval precursor cells can increase the speed of prediction by four orders of magnitude relative to previous approaches. Moreover, this increase in speed allows us to infer, or 'retrodict', compatible genomes from cell fate patterns. We exploit this technique to predict highly variable cell fate patterns resulting from dig-1 reduced-function mutations and let-23 mosaics. In addition to the new insights offered, we propose our technique as a platform for aiding the design and analysis of experimental data. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program.

Science.gov (United States)

Bernal-Vasquez, Angela-Maria; Gordillo, Andres; Schmidt, Malthe; Piepho, Hans-Peter

2017-05-31

The use of multiple genetic backgrounds across years is appealing for genomic prediction (GP) because past years' data provide valuable information on marker effects. Nonetheless, single-year GP models are less complex and computationally less demanding than multi-year GP models. In devising a suitable analysis strategy for multi-year data, we may exploit the fact that even if there is no replication of genotypes across years, there is plenty of replication at the level of marker loci. Our principal aim was to evaluate different GP approaches to simultaneously model genotype-by-year (GY) effects and breeding values using multi-year data in terms of predictive ability. The models were evaluated under different scenarios reflecting common practice in plant breeding programs, such as different degrees of relatedness between training and validation sets, and using a selected fraction of genotypes in the training set. We used empirical grain yield data of a rye hybrid breeding program. A detailed description of the prediction approaches highlighting the use of kinship for modeling GY is presented. Using the kinship to model GY was advantageous in particular for datasets disconnected across years. On average, predictive abilities were 5% higher for models using kinship to model GY over models without kinship. We confirmed that using data from multiple selection stages provides valuable GY information and helps increasing predictive ability. This increase is on average 30% higher when the predicted genotypes are closely related with the genotypes in the training set. A selection of top-yielding genotypes together with the use of kinship to model GY improves the predictive ability in datasets composed of single years of several selection cycles. Our results clearly demonstrate that the use of multi-year data and appropriate modeling is beneficial for GP because it allows dissecting GY effects from genomic estimated breeding values. The model choice, as well as ensuring

The Climate Variability & Predictability (CVP) Program at NOAA - Observing and Understanding Processes Affecting the Propagation of Intraseasonal Oscillations in the Maritime Continent Region

Science.gov (United States)

Lucas, S. E.

2017-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). In 2017, the CVP Program had a call for proposals focused on observing and understanding processes affecting the propagation of intraseasonal oscillations in the Maritime Continent region. This poster will present the recently funded CVP projects, the expected scientific outcomes, the geographic areas of their work in the Maritime Continent region, and the collaborations with the Office of Naval Research, Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and other partners.

Predicting Social Responsibility and Belonging in Urban After-School Physical Activity Programs with Underserved Children

Science.gov (United States)

Martin, Jeffrey J.; Byrd, Brigid; Garn, Alex; McCaughtry, Nate; Kulik, Noel; Centeio, Erin

2016-01-01

The purpose of this cross sectional study was to predict feelings of belonging and social responsibility based on the motivational climate perceptions and contingent self-worth of children participating in urban after-school physical activity programs. Three-hundred and four elementary school students from a major Midwestern city participated.…

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

Science.gov (United States)

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks

Science.gov (United States)

2011-01-01

The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program’s ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia. PMID:21388561

Predictive Models of Duration of Ground Delay Programs in New York Area Airports

Science.gov (United States)

Kulkarni, Deepak

2011-01-01

Initially planned GDP duration often turns out to be an underestimate or an overestimate of the actual GDP duration. This, in turn, results in avoidable airborne or ground delays in the system. Therefore, better models of actual duration have the potential of reducing delays in the system. The overall objective of this study is to develop such models based on logs of GDPs. In a previous report, we described descriptive models of Ground Delay Programs. These models were defined in terms of initial planned duration and in terms of categorical variables. These descriptive models are good at characterizing the historical errors in planned GDP durations. This paper focuses on developing predictive models of GDP duration. Traffic Management Initiatives (TMI) are logged by Air Traffic Control facilities with The National Traffic Management Log (NTML) which is a single system for automated recoding, coordination, and distribution of relevant information about TMIs throughout the National Airspace System. (Brickman, 2004 Yuditsky, 2007) We use 2008-2009 GDP data from the NTML database for the study reported in this paper. NTML information about a GDP includes the initial specification, possibly one or more revisions, and the cancellation. In the next section, we describe general characteristics of Ground Delay Programs. In the third section, we develop models of actual duration. In the fourth section, we compare predictive performance of these models. The final section is a conclusion.

Predicting changes in hypertension control using electronic health records from a chronic disease management program.

Science.gov (United States)

Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

2014-01-01

Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans.

Predicting changes in hypertension control using electronic health records from a chronic disease management program

Science.gov (United States)

Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

2014-01-01

Objective Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. Method In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. Results The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). Conclusions This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans. PMID:24045907

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Science.gov (United States)

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Directory of Open Access Journals (Sweden)

Ching-Hsue Cheng

2018-01-01

Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming

KAUST Repository

Kouramas, K.I.

2011-08-01

This work presents a new algorithm for solving the explicit/multi- parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step. © 2011 Elsevier Ltd. All rights reserved.

Establishing a predictive maintenance (PdM) program at the Hanford Site

International Nuclear Information System (INIS)

Murray, W.A.; Winslow, R.G.

1994-02-01

The production reactors have been shut down for some time. But for the rest of the site, there is currently about 16,000 people engaged in a multi-billion dollar effort to safely process wastes which have been stored at the site since the 1940's. This effort also includes demolition of some older facilities and environmental restoration of much of the site. This is expected to take approximately 30 to 40 years. The concept of a site-wide predictive maintenance (PdM) program began to form in early 1993. Several informal studies showed that the stand alone predictive maintenance groups which had prevailed on site to date were less than 15% effective at trending equipment conditions and predicting failures. To improve the effectiveness of PdM within the company, an engineering analysis by Rick Winslow confirmed that utilization of software networking technology which was now available would significantly overcome many of these built in handicaps. A site-wide predictive maintenance network would make PdM technology accessible to all of the areas and facilities at the site regardless of geographical distances and company division lines. Site resident vibration experts can easily be located and provide consultations on the network. However, it was recognized that strong leadership and management skills would be required within each of the two organizations for effective implementation. To start this process, a letter of understanding and agreement between the facilities and Tank Farm divisions was drafted and endorsed by company management. The agreement assigned the primary responsibility of acquiring the network software and licensee to the Tank Farms division. The acquisition and installation of the network server would be the responsibility of the facilities division. This paper describes the rest of the network development and implementation process

Viral IRES prediction system - a web server for prediction of the IRES secondary structure in silico.

Directory of Open Access Journals (Sweden)

Jun-Jie Hong

Full Text Available The internal ribosomal entry site (IRES functions as cap-independent translation initiation sites in eukaryotic cells. IRES elements have been applied as useful tools for bi-cistronic expression vectors. Current RNA structure prediction programs are unable to predict precisely the potential IRES element. We have designed a viral IRES prediction system (VIPS to perform the IRES secondary structure prediction. In order to obtain better results for the IRES prediction, the VIPS can evaluate and predict for all four different groups of IRESs with a higher accuracy. RNA secondary structure prediction, comparison, and pseudoknot prediction programs were implemented to form the three-stage procedure for the VIPS. The backbone of VIPS includes: the RNAL fold program, aimed to predict local RNA secondary structures by minimum free energy method; the RNA Align program, intended to compare predicted structures; and pknotsRG program, used to calculate the pseudoknot structure. VIPS was evaluated by using UTR database, IRES database and Virus database, and the accuracy rate of VIPS was assessed as 98.53%, 90.80%, 82.36% and 80.41% for IRES groups 1, 2, 3, and 4, respectively. This advance useful search approach for IRES structures will facilitate IRES related studies. The VIPS on-line website service is available at http://140.135.61.250/vips/.

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Directory of Open Access Journals (Sweden)

Mingjie Tan

2015-02-01

Full Text Available The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN, Decision Tree (DT and Bayesian Networks (BNs. A large sample of 62375 students was utilized in the procedures of model training and testing. The results of each model were presented in confusion matrix, and analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective in student dropout prediction, and DT presented a better performance. Finally, some suggestions were made for considerable future research.

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

Science.gov (United States)

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

2014-09-01

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

An influence function method based subsidence prediction program for longwall mining operations in inclined coal seams

Energy Technology Data Exchange (ETDEWEB)

Yi Luo; Jian-wei Cheng [West Virginia University, Morgantown, WV (United States). Department of Mining Engineering

2009-09-15

The distribution of the final surface subsidence basin induced by longwall operations in inclined coal seam could be significantly different from that in flat coal seam and demands special prediction methods. Though many empirical prediction methods have been developed, these methods are inflexible for varying geological and mining conditions. An influence function method has been developed to take the advantage of its fundamentally sound nature and flexibility. In developing this method, significant modifications have been made to the original Knothe function to produce an asymmetrical influence function. The empirical equations for final subsidence parameters derived from US subsidence data and Chinese empirical values have been incorporated into the mathematical models to improve the prediction accuracy. A corresponding computer program is developed. A number of subsidence cases for longwall mining operations in coal seams with varying inclination angles have been used to demonstrate the applicability of the developed subsidence prediction model. 9 refs., 8 figs.

Development of a Program for Predicting Flow Instability in a Once-through Sodium-Heated Steam Generator (III)

Energy Technology Data Exchange (ETDEWEB)

Kim, Eui Kwang; Yoon, Jung; Kim, Jong Bum; Jeong, Jiyoung [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

Two-phase flow systems can be subjected to several types of instability problems. Density-wave oscillation is the most common and important type of instability in boiling channels. Such instability gives difficulties in predictions of system performance and system control, and component failure due to thermal fatigue. A computer program developed for predicting two-phase flow instability in a steam generator heated by liquid sodium was presented in the previous works. Limit cycle was predicted even in a fixed node system. The amplitude of inlet flow rate is larger than that of outlet flow rate. The amplitude of phase change location oscillation within boiling-to-vapor boundary node is larger than that of liquid-to-boiling boundary node. Sodium and steam temperature are invariant at tube exit.

Modeling transducer impulse responses for predicting calibrated pressure pulses with the ultrasound simulation program Field II

DEFF Research Database (Denmark)

Bæk, David; Jensen, Jørgen Arendt; Willatzen, Morten

2010-01-01

FIELD II is a simulation software capable of predicting the field pressure in front of transducers having any complicated geometry. A calibrated prediction with this program is, however, dependent on an exact voltage-to-surface acceleration impulse response of the transducer. Such impulse response...... is not calculated by FIELD II. This work investigates the usability of combining a one-dimensional multilayer transducer modeling principle with the FIELD II software. Multilayer here refers to a transducer composed of several material layers. Measurements of pressure and current from Pz27 piezoceramic disks...... transducer model and the FIELD II software in combination give good agreement with measurements....

PREDICTION OF MEAT PRODUCT QUALITY BY THE MATHEMATICAL PROGRAMMING METHODS

Directory of Open Access Journals (Sweden)

A. B. Lisitsyn

2016-01-01

Full Text Available Abstract Use of the prediction technologies is one of the directions of the research work carried out both in Russia and abroad. Meat processing is accompanied by the complex physico-chemical, biochemical and mechanical processes. To predict the behavior of meat raw material during the technological processing, a complex of physico-technological and structural-mechanical indicators, which objectively reflects its quality, is used. Among these indicators are pH value, water binding and fat holding capacities, water activity, adhesiveness, viscosity, plasticity and so on. The paper demonstrates the influence of animal proteins (beef and pork on the physico-chemical and functional properties before and after thermal treatment of minced meat made from meat raw material with different content of the connective and fat tissues. On the basis of the experimental data, the model (stochastic dependence parameters linking the quantitative resultant and factor variables were obtained using the regression analysis, and the degree of the correlation with the experimental data was assessed. The maximum allowable levels of meat raw material replacement with animal proteins (beef and pork were established by the methods of mathematical programming. Use of the information technologies will significantly reduce the costs of the experimental search and substantiation of the optimal level of replacement of meat raw material with animal proteins (beef, pork, and will also allow establishing a relationship of product quality indicators with quantity and quality of minced meat ingredients.

Pulverized coal devolatilization prediction

International Nuclear Information System (INIS)

Rojas, Andres F; Barraza, Juan M

2008-01-01

The aim of this study was to predict the two bituminous coals devolatilization at low rate of heating (50 Celsius degrade/min), with program FG-DVC (functional group Depolymerization. Vaporization and crosslinking), and to compare the devolatilization profiles predicted by program FG-DVC, which are obtained in the thermogravimetric analyzer. It was also study the volatile liberation at (10 4 k/s) in a drop-tube furnace. The tar, methane, carbon monoxide, and carbon dioxide, formation rate profiles, and the hydrogen, oxygen, nitrogen and sulphur, elemental distribution in the devolatilization products by FG-DVC program at low rate of heating was obtained; and the liberation volatile and R factor at high rate of heating was calculated. it was found that the program predicts the bituminous coals devolatilization at low rate heating, at high rate heating, a volatile liberation around 30% was obtained

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming.

Directory of Open Access Journals (Sweden)

Stephen Gang Wu

2016-04-01

Full Text Available 13C metabolic flux analysis (13C-MFA has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (http://mflux.org that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM, k-Nearest Neighbors (k-NN, and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species.

Prenatal attitudes and parity predict selection into a U.S. child health program: a short report.

Science.gov (United States)

Martin-Anderson, Sarah

2013-10-01

Public policies are a determinant of child health disparities; sound evaluation of these programs is essential for good governance. It is impossible in most countries to randomize assignment into child health programs that directly offer benefits. In the absence of this, researchers face the threat of selection bias-the idea that there are innate, immeasurable differences between those who take-up treatment and those who don't. In the field of Program Evaluation we are most concerned with the differences between the eligible people who take-up a program and the eligible people who choose not to enroll. Using a case study of a large U.S. nutrition program, this report illustrates how the perceived benefits of participation may affect the decision to take-up a program. In turn, this highlights sources of potential selection bias. Using data from a longitudinal study of mothers and infants conducted between May and December of 2005, I show that attitudes and beliefs prenatally toward breastfeeding determine enrollment in a U.S nutrition program that offers free Infant Formula. I also find that the significance of the selection bias differs by parity. Analysis reveals that maternal attitudinal responses are more highly predictive of future behavior, compared to standard demographic variables. In sum, this paper makes a case for rigorously understanding the factors that determine take-up of a program and how those factors can modify the results of a program evaluation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gate valve performance prediction

International Nuclear Information System (INIS)

Harrison, D.H.; Damerell, P.S.; Wang, J.K.; Kalsi, M.S.; Wolfe, K.J.

1994-01-01

The Electric Power Research Institute is carrying out a program to improve the performance prediction methods for motor-operated valves. As part of this program, an analytical method to predict the stem thrust required to stroke a gate valve has been developed and has been assessed against data from gate valve tests. The method accounts for the loads applied to the disc by fluid flow and for the detailed mechanical interaction of the stem, disc, guides, and seats. To support development of the method, two separate-effects test programs were carried out. One test program determined friction coefficients for contacts between gate valve parts by using material specimens in controlled environments. The other test program investigated the interaction of the stem, disc, guides, and seat using a special fixture with full-sized gate valve parts. The method has been assessed against flow-loop and in-plant test data. These tests include valve sizes from 3 to 18 in. and cover a considerable range of flow, temperature, and differential pressure. Stem thrust predictions for the method bound measured results. In some cases, the bounding predictions are substantially higher than the stem loads required for valve operation, as a result of the bounding nature of the friction coefficients in the method

Prediction of drug-related cardiac adverse effects in humans--B: use of QSAR programs for early detection of drug-induced cardiac toxicities.

Science.gov (United States)

Frid, Anna A; Matthews, Edwin J

2010-04-01

This report describes the use of three quantitative structure-activity relationship (QSAR) programs to predict drug-related cardiac adverse effects (AEs), BioEpisteme, MC4PC, and Leadscope Predictive Data Miner. QSAR models were constructed for 9 cardiac AE clusters affecting Purkinje nerve fibers (arrhythmia, bradycardia, conduction disorder, electrocardiogram, palpitations, QT prolongation, rate rhythm composite, tachycardia, and Torsades de pointes) and 5 clusters affecting the heart muscle (coronary artery disorders, heart failure, myocardial disorders, myocardial infarction, and valve disorders). The models were based on a database of post-marketing AEs linked to 1632 chemical structures, and identical training data sets were configured for three QSAR programs. Model performance was optimized and shown to be affected by the ratio of the number of active to inactive drugs. Results revealed that the three programs were complementary and predictive performances using any single positive, consensus two positives, or consensus three positives were as follows, respectively: 70.7%, 91.7%, and 98.0% specificity; 74.7%, 47.2%, and 21.0% sensitivity; and 138.2, 206.3, and 144.2 chi(2). In addition, a prospective study using AE data from the U.S. Food and Drug Administration's (FDA's) MedWatch Program showed 82.4% specificity and 94.3% sensitivity. Furthermore, an external validation study of 18 drugs with serious cardiotoxicity not considered in the models had 88.9% sensitivity. Published by Elsevier Inc.

A general strategy for performing temperature-programming in high performance liquid chromatography--prediction of segmented temperature gradients.

Science.gov (United States)

Wiese, Steffen; Teutenberg, Thorsten; Schmidt, Torsten C

2011-09-28

In the present work it is shown that the linear elution strength (LES) model which was adapted from temperature-programming gas chromatography (GC) can also be employed to predict retention times for segmented-temperature gradients based on temperature-gradient input data in liquid chromatography (LC) with high accuracy. The LES model assumes that retention times for isothermal separations can be predicted based on two temperature gradients and is employed to calculate the retention factor of an analyte when changing the start temperature of the temperature gradient. In this study it was investigated whether this approach can also be employed in LC. It was shown that this approximation cannot be transferred to temperature-programmed LC where a temperature range from 60°C up to 180°C is investigated. Major relative errors up to 169.6% were observed for isothermal retention factor predictions. In order to predict retention times for temperature gradients with different start temperatures in LC, another relationship is required to describe the influence of temperature on retention. Therefore, retention times for isothermal separations based on isothermal input runs were predicted using a plot of the natural logarithm of the retention factor vs. the inverse temperature and a plot of the natural logarithm of the retention factor vs. temperature. It could be shown that a plot of lnk vs. T yields more reliable isothermal/isocratic retention time predictions than a plot of lnk vs. 1/T which is usually employed. Hence, in order to predict retention times for temperature-gradients with different start temperatures in LC, two temperature gradient and two isothermal measurements have been employed. In this case, retention times can be predicted with a maximal relative error of 5.5% (average relative error: 2.9%). In comparison, if the start temperature of the simulated temperature gradient is equal to the start temperature of the input data, only two temperature

Coupled information diffusion--pest dynamics models predict delayed benefits of farmer cooperation in pest management programs.

Science.gov (United States)

Rebaudo, François; Dangles, Olivier

2011-10-01

Worldwide, the theory and practice of agricultural extension system have been dominated for almost half a century by Rogers' "diffusion of innovation theory". In particular, the success of integrated pest management (IPM) extension programs depends on the effectiveness of IPM information diffusion from trained farmers to other farmers, an important assumption which underpins funding from development organizations. Here we developed an innovative approach through an agent-based model (ABM) combining social (diffusion theory) and biological (pest population dynamics) models to study the role of cooperation among small-scale farmers to share IPM information for controlling an invasive pest. The model was implemented with field data, including learning processes and control efficiency, from large scale surveys in the Ecuadorian Andes. Our results predict that although cooperation had short-term costs for individual farmers, it paid in the long run as it decreased pest infestation at the community scale. However, the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in IPM diffusion and applications. We further showed that if individuals learn from others about the benefits of early prevention of new pests, then educational effort may have a sustainable long-run impact. Consistent with models of information diffusion theory, our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of IPM programs. This approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations.

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

Science.gov (United States)

Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

2016-01-01

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

Predicting Success: How Predictive Analytics Are Transforming Student Support and Success Programs

Science.gov (United States)

Boerner, Heather

2015-01-01

Every year, Lone Star College in Texas hosts a "Men of Honor" program to provide assistance and programming to male students, but particularly those who are Hispanic and black, in hopes their academic performance will improve. Lone Star might have kept directing its limited resources toward these students--and totally missed the subset…

Grading Prediction of Enterprise Financial Crisis Based on Nonlinear Programming Evaluation: A Case Study of Chinese Transportation Industry

Directory of Open Access Journals (Sweden)

Zhi-yuan Li

2014-01-01

Full Text Available As the core of the effective financial crisis prevention, enterprise finance crisis prediction has been the focal attention of both theorists and businessmen. Financial crisis predictions need to apply a variety of financial and operating indicators for its analysis. Therefore, a new evaluation model based on nonlinear programming is established, the nature of the model is proved, the detailed solution steps of the model are given, and the significance and algorithm of the model are thoroughly discussed in this study. The proposed model can deal with the case of missing data, and has the good isotonic property and profound theoretical background. In the empirical analysis to predict the financial crisis and through the comparison of the analysis of historical data and the real enterprises with financial crisis, we find that the results are in accordance with the real enterprise financial conditions and the proposed model has a good predictive ability.

Mental Models and Programming Aptitude

DEFF Research Database (Denmark)

Caspersen, Michael Edelgaard; Bennedsen, Jens; Larsen, Kasper Dalgaard

2007-01-01

Predicting the success of students participating in introductory programming courses has been an active research area for more than 25 years. Until recently, no variables or tests have had any significant predictive power. However, Dehnadi and Bornat claim to have found a simple test for programm......Predicting the success of students participating in introductory programming courses has been an active research area for more than 25 years. Until recently, no variables or tests have had any significant predictive power. However, Dehnadi and Bornat claim to have found a simple test...... for programming aptitude to cleanly separate programming sheep from non-programming goats. We briefly present their theory and test instrument. We have repeated their test in our local context in order to verify and perhaps generalise their findings, but we could not show that the test predicts students' success...... in our introductory program-ming course. Based on this failure of the test instrument, we discuss various explanations for our differing results and suggest a research method from which it may be possible to generalise local results in this area. Furthermore, we discuss and criticize Dehnadi and Bornat...

Evaluation of Haddam Neck (Connecticut Yankee) Nuclear Power Plant, environmental impact prediction, based on monitoring programs

International Nuclear Information System (INIS)

Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Mahaffey, J.A.; Waton, D.G.

1976-12-01

A study was undertaken by the U.S. Nuclear Regulatory Commission (NRC) to evaluate the nonradiological environmental data obtained from three nuclear power plants operating for a period of one year or longer. The document presented reports the second of three nuclear power plants to be evaluated in detail by Battelle, Pacific Northwest Laboratories. Haddam Neck (Connecticut Yankee) Nuclear Power Plant nonradiological monitoring data were assessed to determine their effectiveness in the measurement of environmental impacts. Efforts were made to determine if: (1) monitoring programs, as designed, can detect environmental impacts, (2) appropriate statistical analyses were performed and if they were sensitive enough to detect impacts, (3) predicted impacts could be verified by monitoring programs, and (4) monitoring programs satisfied the requirements of the Environmental Technical Specifications. Both preoperational and operational monitoring data were examined to test the usefulness of baseline information in evaluating impacts. This included an examination of the methods used to measure ecological, chemical, and physical parameters, and an assessment of sampling periodicity and sensitivity where appropriate data sets were available. From this type of analysis, deficiencies in both preoperational and operational monitoring programs may be identified and provide a basis for suggested improvement

Flow discharge prediction in compound channels using linear genetic programming

Science.gov (United States)

Azamathulla, H. Md.; Zahiri, A.

2012-08-01

SummaryFlow discharge determination in rivers is one of the key elements in mathematical modelling in the design of river engineering projects. Because of the inundation of floodplains and sudden changes in river geometry, flow resistance equations are not applicable for compound channels. Therefore, many approaches have been developed for modification of flow discharge computations. Most of these methods have satisfactory results only in laboratory flumes. Due to the ability to model complex phenomena, the artificial intelligence methods have recently been employed for wide applications in various fields of water engineering. Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena. In this paper, a precise dimensionless equation has been derived for prediction of flood discharge using LGP. The proposed model was developed using published data compiled for stage-discharge data sets for 394 laboratories, and field of 30 compound channels. The results indicate that the LGP model has a better performance than the existing models.

Earthquake prediction

International Nuclear Information System (INIS)

Ward, P.L.

1978-01-01

The state of the art of earthquake prediction is summarized, the possible responses to such prediction are examined, and some needs in the present prediction program and in research related to use of this new technology are reviewed. Three basic aspects of earthquake prediction are discussed: location of the areas where large earthquakes are most likely to occur, observation within these areas of measurable changes (earthquake precursors) and determination of the area and time over which the earthquake will occur, and development of models of the earthquake source in order to interpret the precursors reliably. 6 figures

Babcock and Wilcox revisions to CONTEMPT, computer program for predicting containment pressure-temperature response to a loss-of-coolant accident

International Nuclear Information System (INIS)

Hsii, Y.H.

1975-01-01

The CONTEMPT computer program predicts the pressure-temperature response of a single-volume reactor building to a loss-of-coolant accident. The analytical model used for the program is described. CONTEMPT assumes that the loss-of-coolant accident can be separated into two phases; the primary system blowdown and reactor building pressurization. The results of the blowdown analysis serve as the boundary conditions and are input to the CONTEMPT program. Thus, the containment model is only concerned with the pressure and temperature in the reactor building and the temperature distribution through the reactor building structures. The program also calculates building leakage and the effects of engineered safety features such as reactor building sprays, decay heat coolers, sump coolers, etc. 11 references. (U.S.)

Predicted effect of landscape position on wildlife habitat value of Conservation Reserve Enhancement Program wetlands in a tile-drained agricultural region

Science.gov (United States)

Otis, David L.; Crumpton, William R.; Green, David; Loan-Wilsey, Anna; Cooper, Tom; Johnson, Rex R.

2013-01-01

Justification for investment in restored or constructed wetland projects are often based on presumed net increases in ecosystem services. However, quantitative assessment of performance metrics is often difficult and restricted to a single objective. More comprehensive performance assessments could help inform decision-makers about trade-offs in services provided by alternative restoration program design attributes. The primary goal of the Iowa Conservation Reserve Enhancement Program is to establish wetlands that efficiently remove nitrates from tile-drained agricultural landscapes. A secondary objective is provision of wildlife habitat. We used existing wildlife habitat models to compare relative net change in potential wildlife habitat value for four alternative landscape positions of wetlands within the watershed. Predicted species richness and habitat value for birds, mammals, amphibians, and reptiles generally increased as the wetland position moved lower in the watershed. However, predicted average net increase between pre- and post-project value was dependent on taxonomic group. The increased average wetland area and changes in surrounding upland habitat composition among landscape positions were responsible for these differences. Net change in predicted densities of several grassland bird species at the four landscape positions was variable and species-dependent. Predicted waterfowl breeding activity was greater for lower drainage position wetlands. Although our models are simplistic and provide only a predictive index of potential habitat value, we believe such assessment exercises can provide a tool for coarse-level comparisons of alternative proposed project attributes and a basis for constructing informed hypotheses in auxiliary empirical field studies.

A Homogeneous and Self-Dual Interior-Point Linear Programming Algorithm for Economic Model Predictive Control

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Frison, Gianluca; Skajaa, Anders

2015-01-01

We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...... system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that 1) the proposed algorithm...

Evaluation of the 1996 predictions of the run-timing of wild migrant spring/summer yearling chinook in the Snake River Basin using Program RealTime

International Nuclear Information System (INIS)

Townsend, R.L.; Yasuda, D.; Skalski, J.R.

1997-03-01

This report is a post-season analysis of the accuracy of the 1996 predictions from the program RealTime. Observed 1996 migration data collected at Lower Granite Dam were compared to the predictions made by RealTime for the spring outmigration of wild spring/summer chinook. Appendix A displays the graphical reports of the RealTime program that were interactively accessible via the World Wide Web during the 1996 migration season. Final reports are available at address http://www.cqs.washington.edu/crisprt/. The CRISP model incorporated the predictions of the run status to move the timing forecasts further down the Snake River to Little Goose, Lower Monumental and McNary Dams. An analysis of the dams below Lower Granite Dam is available separately

Predicting Attrition in a Military Special Program Training Command

Science.gov (United States)

2016-05-20

made by assessing additional psychological factors. Specifically, motivation (s) to enter the training program (e.g., intrinsic versus extrinsic ...this and other training programs. Motivations to enter the training program could be assessed using a measure such as the Work Extrinsic and...MEDICINE GRADUATE PROGRAMS Graduate Education Office (A 1045), 4301 Jones Bridge Road, Bethesda, MD 20814 APPROVAL OF THE DOCTORAL DISSERTATION IN THE

Prediction methodologies for target scene generation in the aerothermal targets analysis program (ATAP)

Science.gov (United States)

Hudson, Douglas J.; Torres, Manuel; Dougherty, Catherine; Rajendran, Natesan; Thompson, Rhoe A.

2003-09-01

The Air Force Research Laboratory (AFRL) Aerothermal Targets Analysis Program (ATAP) is a user-friendly, engineering-level computational tool that features integrated aerodynamics, six-degree-of-freedom (6-DoF) trajectory/motion, convective and radiative heat transfer, and thermal/material response to provide an optimal blend of accuracy and speed for design and analysis applications. ATAP is sponsored by the Kinetic Kill Vehicle Hardware-in-the-Loop Simulator (KHILS) facility at Eglin AFB, where it is used with the CHAMP (Composite Hardbody and Missile Plume) technique for rapid infrared (IR) signature and imagery predictions. ATAP capabilities include an integrated 1-D conduction model for up to 5 in-depth material layers (with options for gaps/voids with radiative heat transfer), fin modeling, several surface ablation modeling options, a materials library with over 250 materials, options for user-defined materials, selectable/definable atmosphere and earth models, multiple trajectory options, and an array of aerodynamic prediction methods. All major code modeling features have been validated with ground-test data from wind tunnels, shock tubes, and ballistics ranges, and flight-test data for both U.S. and foreign strategic and theater systems. Numerous applications include the design and analysis of interceptors, booster and shroud configurations, window environments, tactical missiles, and reentry vehicles.

Programming Useful Life Prediction (PULP), Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Accurately predicting Remaining Useful Life (RUL) provides significant benefits—it increases safety and reduces financial and labor resource requirements. Relying on...

PISCES: A Tool for Predicting Software Testability

Science.gov (United States)

Voas, Jeffrey M.; Miller, Keith W.; Payne, Jeffery E.

1991-01-01

Before a program can fail, a software fault must be executed, that execution must alter the data state, and the incorrect data state must propagate to a state that results directly in an incorrect output. This paper describes a tool called PISCES (developed by Reliable Software Technologies Corporation) for predicting the probability that faults in a particular program location will accomplish all three of these steps causing program failure. PISCES is a tool that is used during software verification and validation to predict a program's testability.

Predicting daily physical activity in a lifestyle intervention program

NARCIS (Netherlands)

Long, Xi; Pauws, S.C.; Pijl, M.; Lacroix, J.; Goris, A.H.C.; Aarts, R.M.; Gottfried, B.; Aghajan, H.

2011-01-01

The growing number of people adopting a sedentary lifestyle these days creates a serious need for effective physical activity promotion programs. Often, these programs monitor activity, provide feedback about activity and offer coaching to increase activity. Some programs rely on a human coach who

Assessing the Effectiveness of Statistical Classification Techniques in Predicting Future Employment of Participants in the Temporary Assistance for Needy Families Program

Science.gov (United States)

Montoya, Isaac D.

2008-01-01

Three classification techniques (Chi-square Automatic Interaction Detection [CHAID], Classification and Regression Tree [CART], and discriminant analysis) were tested to determine their accuracy in predicting Temporary Assistance for Needy Families program recipients' future employment. Technique evaluation was based on proportion of correctly…

Beyond the GRE: using a composite score to predict 
the success of Puerto Rican students in a biomedical 
PhD program.

Science.gov (United States)

Pacheco, Wendy I; Noel, Richard J; Porter, James T; Appleyard, Caroline B

2015-01-01

The use and validity of the Graduate Record Examination General Test (GRE) to predict the success of graduate school applicants is heavily debated, especially for its possible impact on the selection of underrepresented minorities into science, technology, engineering, and math fields. To better identify candidates who would succeed in our program with less reliance on the GRE and grade point average (GPA), we developed and tested a composite score (CS) that incorporates additional measurable predictors of success to evaluate incoming applicants. Uniform numerical values were assigned to GPA, GRE, research experience, advanced course work or degrees, presentations, and publications. We compared the CS of our students with their achievement of program goals and graduate school outcomes. The average CS was significantly higher in those students completing the graduate program versus dropouts (p thesis defense. In contrast, these outcomes were not predicted by GPA, science GPA, or GRE. Recent implementation of an impromptu writing assessment during the interview suggests the CS can be improved further. We conclude that the CS provides a broader quantitative measure that better predicts success of students in our program and allows improved evaluation and selection of the most promising candidates. © 2015 W. I. Pacheco et al. CBE—Life Sciences Education © 2015 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

MARKETING PREDICTIONS IN ANTI-DRUG SOCIAL PROGRAMS: USE OF CAUSAL METHODS IN THE STUDY AND PREVENTION OF DRUG ABUSE

Directory of Open Access Journals (Sweden)

Serban Corina

2010-12-01

Full Text Available Drug use is one of the major challenges that todays society faces; its effects are felt at the level of various social, professional and age categories. Over 50 non-profit organizations are involved in the development of anti-drug social programs in Romania. Their role is to improve the degree of awareness of the target population concerning the risks associated with drug use, but also to steer consumers towards healthy areas, beneficial to their future. This paper aims to detail the issue of drug use in Romania, by making predictions based on the evolution of this phenomenon during the next five years. The obtained results have revealed the necessity to increase the number of programs preventing drug use, aswell as the need to continue social programs that have proved effective in previous years.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

Science.gov (United States)

Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A

2016-03-01

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/. © 2016 Wiley Periodicals, Inc.

The Sensitivity, Specificity and Predictive Values of Snellen Chart Compared to the Diagnostic Test in Amblyopia Screening Program in Iran

Directory of Open Access Journals (Sweden)

Fatemeh Rivakani

2015-12-01

Full Text Available Introduction Amblyopia is a leading cause of visual impairment in both childhood and adult populations. Our aim in this study was to assess the epidemiological characteristics of the amblyopia screening program in Iran. Materials and Methods A cross-sectional study was done on a randomly selected sample of 4,636 Iranian children who were referred to screening program in 2013 were participated in validity study, too. From each provinces the major city were selected. Screening and diagnostic tests were done by instructors in first stage and optometrists in second stage, respectively. Finally data were analyzed by Stata version 13. Results The sensitivity was ranged from 74% to 100% among the various provinces such that Fars and Ardabil province had maximum and minimum values, respectively. The pattern of specificity was differ and ranged 44% to 84% among the provinces; Hormozgan and Fars had maximum and minimum values, respectively. The positive predictive value was also ranged from 35% to %81 which was assigned to Khuzestan and Ardabil provinces, respectively. The range of Negative Predictive value was 61% to 100% which was belonged to Ardabil and Fars provinces. Conclusion The total sensitivity (89% and negative predictive values (93% of screening test among children aged 3-6 years is acceptable, but only 51% of children refereed to second stage are true positive and this imposes considerable cost to health system.

Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming

Science.gov (United States)

Kashid, Satishkumar S.; Maity, Rajib

2012-08-01

SummaryPrediction of Indian Summer Monsoon Rainfall (ISMR) is of vital importance for Indian economy, and it has been remained a great challenge for hydro-meteorologists due to inherent complexities in the climatic systems. The Large-scale atmospheric circulation patterns from tropical Pacific Ocean (ENSO) and those from tropical Indian Ocean (EQUINOO) are established to influence the Indian Summer Monsoon Rainfall. The information of these two large scale atmospheric circulation patterns in terms of their indices is used to model the complex relationship between Indian Summer Monsoon Rainfall and the ENSO as well as EQUINOO indices. However, extracting the signal from such large-scale indices for modeling such complex systems is significantly difficult. Rainfall predictions have been done for 'All India' as one unit, as well as for five 'homogeneous monsoon regions of India', defined by Indian Institute of Tropical Meteorology. Recent 'Artificial Intelligence' tool 'Genetic Programming' (GP) has been employed for modeling such problem. The Genetic Programming approach is found to capture the complex relationship between the monthly Indian Summer Monsoon Rainfall and large scale atmospheric circulation pattern indices - ENSO and EQUINOO. Research findings of this study indicate that GP-derived monthly rainfall forecasting models, that use large-scale atmospheric circulation information are successful in prediction of All India Summer Monsoon Rainfall with correlation coefficient as good as 0.866, which may appears attractive for such a complex system. A separate analysis is carried out for All India Summer Monsoon rainfall for India as one unit, and five homogeneous monsoon regions, based on ENSO and EQUINOO indices of months of March, April and May only, performed at end of month of May. In this case, All India Summer Monsoon Rainfall could be predicted with 0.70 as correlation coefficient with somewhat lesser Correlation Coefficient (C.C.) values for different

A dynamic food-chain model and program for predicting the radiological consequences of nuclear accident

International Nuclear Information System (INIS)

Hu Erbang; Gao Zhanrong; Zhang Heyuan; Wei Weiqiang

1996-12-01

A dynamic food-chain model and program, DYFOM-95, for predicting the radiological consequences of nuclear accident has been developed, which is not only suitable to the West food-chain but also to Chinese food chain. The following processes, caused by accident release which will make an impact on radionuclide concentration in the edible parts of vegetable are considered: dry and wet deposition interception and initial retention, translocation, percolation, root uptake and tillage. Activity intake rate of animals, effects of processing and activity intake of human through ingestion pathway are also considered in calculations. The effects of leaf area index LAI of vegetable are considered in dry deposition model. A method for calculating the contribution of rain with different period and different intensity to total wet deposition is established. The program contains 1 main code and 5 sub-codes to calculate dry and wet deposition on surface of vegetable and soil, translocation of nuclides in vegetable, nuclide concentration in the edible parts of vegetable and in animal products and activity intake of human and so on. (24 refs., 9 figs., 11 tabs.)

Bagging Approach for Increasing Classification Accuracy of CART on Family Participation Prediction in Implementation of Elderly Family Development Program

Directory of Open Access Journals (Sweden)

Wisoedhanie Widi Anugrahanti

2017-06-01

Full Text Available Classification and Regression Tree (CART was a method of Machine Learning where data exploration was done by decision tree technique. CART was a classification technique with binary recursive reconciliation algorithms where the sorting was performed on a group of data collected in a space called a node / node into two child nodes (Lewis, 2000. The aim of this study was to predict family participation in Elderly Family Development program based on family behavior in providing physical, mental, social care for the elderly. Family involvement accuracy using Bagging CART method was calculated based on 1-APER value, sensitivity, specificity, and G-Means. Based on CART method, classification accuracy was obtained 97,41% with Apparent Error Rate value 2,59%. The most important determinant of family behavior as a sorter was society participation (100,00000, medical examination (98,95988, providing nutritious food (68.60476, establishing communication (67,19877 and worship (57,36587. To improved the stability and accuracy of CART prediction, used CART Bootstrap Aggregating (Bagging with 100% accuracy result. Bagging CART classifies a total of 590 families (84.77% were appropriately classified into implement elderly Family Development program class.

Program overview: Subsurface science program

International Nuclear Information System (INIS)

1994-03-01

The OHER Subsurface Science Program is DOE's core basic research program concerned with subsoils and groundwater. These practices have resulted in contamination by mixtures of organic chemicals, inorganic chemicals, and radionuclides. A primary long-term goal is to provide a foundation of knowledge that will lead to the reduction of environmental risks and to cost-effective cleanup strategies. Since the Program was initiated in 1985, a substantial amount of research in hydrogeology, subsurface microbiology, and the geochemistry of organically complexed radionuclides has been completed, leading to a better understanding of contaminant transport in groundwater and to new insights into microbial distribution and function in the subsurface environments. The Subsurface Science Program focuses on achieving long-term scientific advances that will assist DOE in the following key areas: providing the scientific basis for innovative in situ remediation technologies that are based on a concept of decontamination through benign manipulation of natural systems; understanding the complex mechanisms and process interactions that occur in the subsurface; determining the influence of chemical and geochemical-microbial processes on co-contaminant mobility to reduce environmental risks; improving predictions of contaminant transport that draw on fundamental knowledge of contaminant behavior in the presence of physical and chemical heterogeneities to improve cleanup effectiveness and to predict environmental risks

[Prognostic prediction of the functional capacity and effectiveness of functional improvement program of the musculoskeletal system among users of preventive care service under long-term care insurance].

Science.gov (United States)

Sone, Toshimasa; Nakaya, Naoki; Tomata, Yasutake; Aida, Jun; Okubo, Ichiro; Ohara, Satoko; Obuchi, Shuichi; Sugiyama, Michiko; Yasumura, Seiji; Suzuki, Takao; Tsuji, Ichiro

2013-01-01

The purpose of this study was to examine the effectiveness of the Functional Improvement Program of the Musculoskeletal System among users of Preventive Care Service under Long-Term Care Insurance. A total of 3,073 subjects were analyzed. We used the prediction formula to estimate the predicted value of the Kihon Checklist after one year, and calculated the measured value minus the predicted value. The subjects were divided into two groups according to the measured value minus predicted value tertiles: the lowest and middle tertile (good-to-fair measured value) and the highest tertile (poor measured value). We used a multiple logistic regression model to calculate the odds ratio (OR) and 95% confidence interval (CI) of the good-to-fair measured values of the Kihon Checklist after one year, according to the Functional Improvement Program of the Musculoskeletal System. In potentially dependent elderly, the multivariate adjusted ORs (95% CI) of the good-to-fair measured values were 2.4 (1.3-4.4) for those who attended the program eight times or more in a month (vs those who attended it three times or less in a month), 1.3 (1.0-1.8) for those who engaged in strength training using machines (vs those who did not train), and 1.4 (1.0-1.9) for those who engaged in endurance training. In this study, among potentially dependent elderly, those who attended the program eight times or more in a month and those who engaged in strength training using machines or endurance training showed a significant improvement of their functional capacity.

Predicting Performance in Higher Education Using Proximal Predictors

Science.gov (United States)

Niessen, A. Susan M.; Meijer, Rob R.; Tendeiro, Jorge N.

2016-01-01

We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach, and specific skills tests in English and math. Test scores were used to predict academic achievement and progress after the first year, achievement in specific course types, enrollment, and dropout after the first year. All tests showed positive significant correlations with the criteria. The trial-studying test was consistently the best predictor in the admission procedure. We found no significant differences between the predictive validity of the trial-studying test and prior educational performance, and substantial shared explained variance between the two predictors. Only applicants with lower trial-studying scores were significantly less likely to enroll in the program. In conclusion, the trial-studying test yielded predictive validities similar to that of prior educational performance and possibly enabled self-selection. In admissions aimed at student-program fit, or in admissions in which past educational performance is difficult to use, a trial-studying test is a good instrument to predict academic performance. PMID:27073859

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

Science.gov (United States)

Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

2015-11-13

The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

Science.gov (United States)

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Can we predict nuclear proliferation

International Nuclear Information System (INIS)

Tertrais, Bruno

2011-01-01

The author aims at improving nuclear proliferation prediction capacities, i.e. the capacities to identify countries susceptible to acquire nuclear weapons, to interpret sensitive activities, and to assess nuclear program modalities. He first proposes a retrospective assessment of counter-proliferation actions since 1945. Then, based on academic studies, he analyzes what causes and motivates proliferation, with notably the possibility of existence of a chain phenomenon (mechanisms driving from one program to another). He makes recommendations for a global approach to proliferation prediction, and proposes proliferation indices and indicators

Development of a hot water tank simulation program with improved prediction of thermal stratification in the tank

DEFF Research Database (Denmark)

Fan, Jianhua; Furbo, Simon; Yue, Hongqiang

2015-01-01

A simulation program SpiralSol was developed in previous investigations to calculate thermal performance of a solar domestic hot water (SDHW) system with a hot water tank with a built-in heat exchanger spiral [1]. The simulation program is improved in the paper in term of prediction of thermal...... stratification in the tank. The transient fluid flow and heat transfer in the hot water tank during cooling caused by standby heat loss are investigated by validated computational fluid dynamics (CFD) calculations. Detailed CFD investigations are carried out to determine the influence of thickness and material...... property of the tank wall on thermal stratification in the tank. It is elucidated how thermal stratification in the tank is influenced by the natural convection and how the heat loss from the tank sides will be distributed at different levels of the tank at different thermal conditions. The existing...

Verification, validation, and reliability of predictions

International Nuclear Information System (INIS)

Pigford, T.H.; Chambre, P.L.

1987-04-01

The objective of predicting long-term performance should be to make reliable determinations of whether the prediction falls within the criteria for acceptable performance. Establishing reliable predictions of long-term performance of a waste repository requires emphasis on valid theories to predict performance. The validation process must establish the validity of the theory, the parameters used in applying the theory, the arithmetic of calculations, and the interpretation of results; but validation of such performance predictions is not possible unless there are clear criteria for acceptable performance. Validation programs should emphasize identification of the substantive issues of prediction that need to be resolved. Examples relevant to waste package performance are predicting the life of waste containers and the time distribution of container failures, establishing the criteria for defining container failure, validating theories for time-dependent waste dissolution that depend on details of the repository environment, and determining the extent of congruent dissolution of radionuclides in the UO 2 matrix of spent fuel. Prediction and validation should go hand in hand and should be done and reviewed frequently, as essential tools for the programs to design and develop repositories. 29 refs

Prediction Markets as a Way to Manage Acquisition Programs

Science.gov (United States)

2011-06-01

volume helps management set production levels, but if management increases advertising it will undermine the market . This becomes critical for the DoD...34 Corporate Strategy Board. Gaspoz, C. (2008). "Prediction markets as an innovative way to manage R&D portfolios." CAiSE Doctoral Consortium. Montpellier...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA MBA PROFESSIONAL REPORT Prediction Markets as a Way to Manage Acquisition

Phenology prediction component of GypsES

Science.gov (United States)

Jesse A. Logan; Lukas P. Schaub; F. William Ravlin

1991-01-01

Prediction of phenology is an important component of most pest management programs, and considerable research effort has been expended toward development of predictive tools for gypsy moth phenology. Although phenological prediction is potentially valuable for timing of spray applications (e.g. Bt, or Gypcheck) and other management activities (e.g. placement and...

MASBAL: A computer program for predicting the composition of nuclear waste glass produced by a slurry-fed ceramic melter

International Nuclear Information System (INIS)

Reimus, P.W.

1987-07-01

This report is a user's manual for the MASBAL computer program. MASBAL's objectives are to predict the composition of nuclear waste glass produced by a slurry-fed ceramic melter based on a knowledge of process conditions; to generate simulated data that can be used to estimate the uncertainty in the predicted glass composition as a function of process uncertainties; and to generate simulated data that can be used to provide a measure of the inherent variability in the glass composition as a function of the inherent variability in the feed composition. These three capabilities are important to nuclear waste glass producers because there are constraints on the range of compositions that can be processed in a ceramic melter and on the range of compositions that will be acceptable for disposal in a geologic repository. MASBAL was developed specifically to simulate the operation of the West Valley Component Test system, a commercial-scale ceramic melter system that will process high-level nuclear wastes currently stored in underground tanks at the site of the Western New York Nuclear Services Center (near West Valley, New York). The program is flexible enough, however, to simulate any slurry-fed ceramic melter system. 4 refs., 16 figs., 5 tabs

Analysis and monitoring of energy security and prediction of indicator values using conventional non-linear mathematical programming

Directory of Open Access Journals (Sweden)

Elena Vital'evna Bykova

2011-09-01

Full Text Available This paper describes the concept of energy security and a system of indicators for its monitoring. The indicator system includes more than 40 parameters that reflect the structure and state of fuel and energy complex sectors (fuel, electricity and heat & power, as well as takes into account economic, environmental and social aspects. A brief description of the structure of the computer system to monitor and analyze energy security is given. The complex contains informational, analytical and calculation modules, provides applications for forecasting and modeling energy scenarios, modeling threats and determining levels of energy security. Its application to predict the values of the indicators and methods developed for it are described. This paper presents a method developed by conventional nonlinear mathematical programming needed to address several problems of energy and, in particular, the prediction problem of the security. An example of its use and implementation of this method in the application, "Prognosis", is also given.

Adaptation and Implementation of Predictive Maintenance Technique with Nondestructive Testing for Power Plants

International Nuclear Information System (INIS)

Jung, Gye Jo; Jung, Nam Gun

2010-01-01

Many forces are pressuring utilities to reduce operating and maintenance costs without cutting back on reliability or availability. Many utility managers are re-evaluating maintenance strategies to meet these demands. To utilities how to reduce maintenance costs and extent the effective operating life of equipment, predictive maintenance technique can be adapted. Predictive maintenance had three types program which are in-house program, engineering company program and mixed program. We can approach successful predictive maintenance program with 'smart trust' concept

Can a fatigue test of the isolated lumbar extensor muscles of untrained young men predict strength progression in a resistance exercise program?

NARCIS (Netherlands)

Helmhout, P.; Staal, B.; Dijk, J. van; Harts, C.; Bertina, F.; Bie, R. de

2010-01-01

AIM: The aim of this exploratory study was to investigate the predictive value of a fatigue test of the lumbar extensor muscles for training progression in a group of 28 healthy but predominantly sedentary male students, in an 8-week resistance exercise program. METHODS: A three-phased fatigue test

Pulse Rate and Transit Time Analysis to Predict Hypotension Events After Spinal Anesthesia During Programmed Cesarean Labor.

Science.gov (United States)

Bolea, Juan; Lázaro, Jesús; Gil, Eduardo; Rovira, Eva; Remartínez, José M; Laguna, Pablo; Pueyo, Esther; Navarro, Augusto; Bailón, Raquel

2017-09-01

Prophylactic treatment has been proved to reduce hypotension incidence after spinal anesthesia during cesarean labor. However, the use of pharmacological prophylaxis could carry out undesirable side-effects on mother and fetus. Thus, the prediction of hypotension becomes an important challenge. Hypotension events are hypothesized to be related to a malfunctioning of autonomic nervous system (ANS) regulation of blood pressure. In this work, ANS responses to positional changes of 51 pregnant women programmed for a cesarean labor were explored for hypotension prediction. Lateral and supine decubitus, and sitting position were considered while electrocardiographic and pulse photoplethysmographic signals were recorded. Features based on heart rate variability, pulse rate variability (PRV) and pulse transit time (PTT) analysis were used in a logistic regression classifier. The results showed that PRV irregularity changes, assessed by approximate entropy, from supine to lateral decubitus, and standard deviation of PTT in supine decubitus were found as the combination of features that achieved the best classification results sensitivity of 76%, specificity of 70% and accuracy of 72%, being normotensive the positive class. Peripheral regulation and blood pressure changes, measured by PRV and PTT analysis, could help to predict hypotension events reducing prophylactic side-effects in the low-risk population.

Integrating genomics and proteomics data to predict drug effects using binary linear programming.

Science.gov (United States)

Ji, Zhiwei; Su, Jing; Liu, Chenglin; Wang, Hongyan; Huang, Deshuang; Zhou, Xiaobo

2014-01-01

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be

Benchmarking of gene prediction programs for metagenomic data.

Science.gov (United States)

Yok, Non; Rosen, Gail

2010-01-01

This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from one hundred species of diverse lineages. We defined four different types of fragments; two types come from the inter- and intra-coding regions and the other types are from the gene edges. Hoff et al. used only 12 species in their comparison; therefore, their sample is too small to represent an environmental sample. Also, no predecessors has separately examined fragments that contain gene edges as opposed to intra-coding regions. General observations in our results are that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show low annotation error in all of the programs if compared to the gene edge fragments. Overall, we found an upper-bound performance by combining all the methods.

Predictive maintenance primer

International Nuclear Information System (INIS)

Flude, J.W.; Nicholas, J.R.

1991-04-01

This Predictive Maintenance Primer provides utility plant personnel with a single-source reference to predictive maintenance analysis methods and technologies used successfully by utilities and other industries. It is intended to be a ready reference to personnel considering starting, expanding or improving a predictive maintenance program. This Primer includes a discussion of various analysis methods and how they overlap and interrelate. Additionally, eighteen predictive maintenance technologies are discussed in sufficient detail for the user to evaluate the potential of each technology for specific applications. This document is designed to allow inclusion of additional technologies in the future. To gather the information necessary to create this initial Primer the Nuclear Maintenance Applications Center (NMAC) collected experience data from eighteen utilities plus other industry and government sources. NMAC also contacted equipment manufacturers for information pertaining to equipment utilization, maintenance, and technical specifications. The Primer includes a discussion of six methods used by analysts to study predictive maintenance data. These are: trend analysis; pattern recognition; correlation; test against limits or ranges; relative comparison data; and statistical process analysis. Following the analysis methods discussions are detailed descriptions for eighteen technologies analysts have found useful for predictive maintenance programs at power plants and other industrial facilities. Each technology subchapter has a description of the operating principles involved in the technology, a listing of plant equipment where the technology can be applied, and a general description of the monitoring equipment. Additionally, these descriptions include a discussion of results obtained from actual equipment users and preferred analysis techniques to be used on data obtained from the technology. 5 refs., 30 figs

Accuracy of Genomic Prediction in a Commercial Perennial Ryegrass Breeding Program

Directory of Open Access Journals (Sweden)

Dario Fè

2016-11-01

Full Text Available The implementation of genomic selection (GS in plant breeding, so far, has been mainly evaluated in crops farmed as homogeneous varieties, and the results have been generally positive. Fewer results are available for species, such as forage grasses, that are grown as heterogenous families (developed from multiparent crosses in which the control of the genetic variation is far more complex. Here we test the potential for implementing GS in the breeding of perennial ryegrass ( L. using empirical data from a commercial forage breeding program. Biparental F and multiparental synthetic (SYN families of diploid perennial ryegrass were genotyped using genotyping-by-sequencing, and phenotypes for five different traits were analyzed. Genotypes were expressed as family allele frequencies, and phenotypes were recorded as family means. Different models for genomic prediction were compared by using practically relevant cross-validation strategies. All traits showed a highly significant level of genetic variance, which could be traced using the genotyping assay. While there was significant genotype × environment (G × E interaction for some traits, accuracies were high among F families and between biparental F and multiparental SYN families. We have demonstrated that the implementation of GS in grass breeding is now possible and presents an opportunity to make significant gains for various traits.

Ability of commercially available dairy ration programs to predict duodenal flows of protein and essential amino acids in dairy cows.

Science.gov (United States)

Pacheco, D; Patton, R A; Parys, C; Lapierre, H

2012-02-01

The objective of this analysis was to compare the rumen submodel predictions of 4 commonly used dairy ration programs to observed values of duodenal flows of crude protein (CP), protein fractions, and essential AA (EAA). The literature was searched and 40 studies, including 154 diets, were used to compare observed values with those predicted by AminoCow (AC), Agricultural Modeling and Training Systems (AMTS), Cornell-Penn-Miner (CPM), and National Research Council 2001 (NRC) models. The models were evaluated based on their ability to predict the mean, their root mean square prediction error (RMSPE), error bias, and adequacy of regression equations for each protein fraction. The models predicted the mean duodenal CP flow within 5%, with more than 90% of the variation due to random disturbance. The models also predicted within 5% the mean microbial CP flow except CPM, which overestimated it by 27%. Only NRC, however, predicted mean rumen-undegraded protein (RUP) flows within 5%, whereas AC and AMTS underpredicted it by 8 to 9% and CPM by 24%. Regarding duodenal flows of individual AA, across all diets, CPM predicted substantially greater (>10%) mean flows of Arg, His, Ile, Met, and Lys; AMTS predicted greater flow for Arg and Met, whereas AC and NRC estimations were, on average, within 10% of observed values. Overpredictions by the CPM model were mainly related to mean bias, whereas the NRC model had the highest proportion of bias in random disturbance for flows of EAA. Models tended to predict mean flows of EAA more accurately on corn silage and alfalfa diets than on grass-based diets, more accurately on corn grain-based diets than on non-corn-based diets, and finally more accurately in the mid range of diet types. The 4 models were accurate at predicting mean dry matter intake. The AC, AMTS, and NRC models were all sufficiently accurate to be used for balancing EAA in dairy rations under field conditions. Copyright © 2012 American Dairy Science Association

Development of computer program ENMASK for prediction of residual environmental masking-noise spectra, from any three independent environmental parameters

Energy Technology Data Exchange (ETDEWEB)

Chang, Y.-S.; Liebich, R. E.; Chun, K. C.

2000-03-31

Residual environmental sound can mask intrusive4 (unwanted) sound. It is a factor that can affect noise impacts and must be considered both in noise-impact studies and in noise-mitigation designs. Models for quantitative prediction of sensation level (audibility) and psychological effects of intrusive noise require an input with 1/3 octave-band spectral resolution of environmental masking noise. However, the majority of published residual environmental masking-noise data are given with either octave-band frequency resolution or only single A-weighted decibel values. A model has been developed that enables estimation of 1/3 octave-band residual environmental masking-noise spectra and relates certain environmental parameters to A-weighted sound level. This model provides a correlation among three environmental conditions: measured residual A-weighted sound-pressure level, proximity to a major roadway, and population density. Cited field-study data were used to compute the most probable 1/3 octave-band sound-pressure spectrum corresponding to any selected one of these three inputs. In turn, such spectra can be used as an input to models for prediction of noise impacts. This paper discusses specific algorithms included in the newly developed computer program ENMASK. In addition, the relative audibility of the environmental masking-noise spectra at different A-weighted sound levels is discussed, which is determined by using the methodology of program ENAUDIBL.

Prediction of Basic Math Course Failure Rate in the Physics, Meteorology, Mathematics, Actuarial Sciences and Pharmacy Degree Programs

Directory of Open Access Journals (Sweden)

Luis Rojas-Torres

2014-09-01

Full Text Available This paper summarizes a study conducted in 2013 with the purpose of predicting the failure rate of math courses taken by Pharmacy, Mathematics, Actuarial Science, Physics and Meteorology students at Universidad de Costa Rica (UCR. Using the Logistics Regression statistical techniques applied to the 2010 cohort, failure rates were predicted of students in the aforementioned programs in one of their Math introductory courses (Calculus 101 for Physics and Meteorology, Math Principles for Mathematics and Actuarial Science and Applied Differential Equations for Pharmacy. For these models, the UCR admission average, the student’s genre, and the average correct answers in the Quantitative Skills Test were used as predictor variables. The most important variable for all models was the Quantitative Skills Test, and the model with the highest correct classification rate was the Logistics Regression. For the estimated Physics-Meteorology, Pharmacy and Mathematics-Actuarial Science models, correct classifications were 89.8%, 73.6%, and 93.9%, respectively.

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

Science.gov (United States)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Part 2 -- current program integrating strategies and lubrication technology

International Nuclear Information System (INIS)

Johnson, B.

1996-01-01

This paper is the second of two that describe the Predictive Maintenance Program for rotating machinery at the Palo Verde Nuclear Generating Station. The Predictive Maintenance program has been enhanced through organizational changes and improved interdisciplinary usage of technology. This paper will discuss current program strategies that have improved the interaction between the Vibration and Lube Oil programs. The open-quotes Lube Oilclose quotes view of the combined program along with case studies will then be presented

Part 2 -- current program integrating strategies and lubrication technology

Energy Technology Data Exchange (ETDEWEB)

Johnson, B.

1996-12-01

This paper is the second of two that describe the Predictive Maintenance Program for rotating machinery at the Palo Verde Nuclear Generating Station. The Predictive Maintenance program has been enhanced through organizational changes and improved interdisciplinary usage of technology. This paper will discuss current program strategies that have improved the interaction between the Vibration and Lube Oil programs. The {open_quotes}Lube Oil{close_quotes} view of the combined program along with case studies will then be presented.

Predicting transportation routes for radioactive wastes

International Nuclear Information System (INIS)

Joy, D.S.; Johnson, P.E.; Clarke, D.B.; McGuire, S.C.

1981-01-01

Oak Ridge National Laboratory (ORNL) has been involved in transportation logistics of radioactive wastes as part of the overall waste transportation program. A Spent Fuel Logistics Model (SFLM), was developed to predict overall material balances representing the flow of spent fuel assemblies from reactors to away-from-reactor storage facilities and/or to federal repositories. The transportation requirements to make these shipments are also itemized. The next logical step in the overall transportation project was the development of a set of computer codes which would predict likely transportation routes for waste shipments. Two separate routing models are now operational at ORNL. Routes for truck transport can be estimated with the HIGHWAY program, and rail and barge routes can be predicted with the INTERLINE model. This paper discusses examples of the route estimates and applications of the routing models

Study on intelligent prediction of broken rock zone thickness of coal mine roadways

Energy Technology Data Exchange (ETDEWEB)

Xu, G.; Jing, H. [China University of Mining and Technology, Xuzhou (China). School of Architecture and Civil Engineering

2005-03-01

Considering the problem of obtaining the thickness of broken rock zone, a booming intelligent prediction method with ANFIS (adaptive neuro-fuzzy inference system) was introduced into the thickness prediction. A stand-alone program with functions of creating and applying prediction systems was designed on the platform of MATLAB6.5. Then the program was used to predict the broken rock zone thickness of dips in the 12th coal mine, Pingdingshan Group Company of Coal Industry. The results show that the predicted values correlate well with the in-situ measured ones. Thereby the validity of the program is validated and it can provide a new approach to obtaining the broken zone thickness. 10 refs., 4 figs., 1 tab.

Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.

Science.gov (United States)

Johnson, Tracy L; Brewer, Daniel; Estacio, Raymond; Vlasimsky, Tara; Durfee, Michael J; Thompson, Kathy R; Everhart, Rachel M; Rinehart, Deborath J; Batal, Holly

2015-01-01

The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health's (DH) integrated, safety net health care system $19.8 million to implement a "population health" approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner's Chronic Care Model (CCM) to achieve the "Triple Aim": improved health for populations, care to individuals, and lower per capita costs. This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective. In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables. We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.

Prediction of glycosylation sites using random forests

Directory of Open Access Journals (Sweden)

Hirst Jonathan D

2008-11-01

Full Text Available Abstract Background Post translational modifications (PTMs occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins. Glycosylation is one type of PTM, and is implicated in protein folding, transport and function. Results We use the random forest algorithm and pairwise patterns to predict glycosylation sites. We identify pairwise patterns surrounding glycosylation sites and use an odds ratio to weight their propensity of association with modified residues. Our prediction program, GPP (glycosylation prediction program, predicts glycosylation sites with an accuracy of 90.8% for Ser sites, 92.0% for Thr sites and 92.8% for Asn sites. This is significantly better than current glycosylation predictors. We use the trepan algorithm to extract a set of comprehensible rules from GPP, which provide biological insight into all three major glycosylation types. Conclusion We have created an accurate predictor of glycosylation sites and used this to extract comprehensible rules about the glycosylation process. GPP is available online at http://comp.chem.nottingham.ac.uk/glyco/.

Social support for healthy behaviors: Scale psychometrics and prediction of weight loss among women in a behavioral program

Science.gov (United States)

Kiernan, Michaela; Moore, Susan D.; Schoffman, Danielle E.; Lee, Katherine; King, Abby C.; Taylor, C. Barr; Kiernan, Nancy Ellen; Perri, Michael G.

2015-01-01

Social support could be a powerful weight-loss treatment moderator or mediator but is rarely assessed. We assessed the psychometric properties, initial levels, and predictive validity of a measure of perceived social support and sabotage from friends and family for healthy eating and physical activity (eight subscales). Overweight/obese women randomized to one of two 6-month, group-based behavioral weight-loss programs (N=267; mean BMI 32.1±3.5; 66.3% White) completed subscales at baseline, and weight loss was assessed at 6 months. Internal consistency, discriminant validity, and content validity were excellent for support subscales and adequate for sabotage subscales; qualitative responses revealed novel deliberate instances not reflected in current sabotage items. Most women (>75%) “never” or “rarely” experienced support from friends or family. Using non-parametric classification methods, we identified two subscales—support from friends for healthy eating and support from family for physical activity—that predicted three clinically meaningful subgroups who ranged in likelihood of losing ≥5% of initial weight at 6 months. Women who “never” experienced family support were least likely to lose weight (45.7% lost weight) whereas women who experienced both frequent friend and family support were more likely to lose weight (71.6% lost weight). Paradoxically, women who “never” experienced friend support were most likely to lose weight (80.0% lost weight), perhaps because the group-based programs provided support lacking from friendships. Psychometrics for support subscales were excellent; initial support was rare; and the differential roles of friend versus family support could inform future targeted weight-loss interventions to subgroups at risk. PMID:21996661

The role of predictive uncertainty in the operational management of reservoirs

Directory of Open Access Journals (Sweden)

E. Todini

2014-09-01

Full Text Available The present work deals with the operational management of multi-purpose reservoirs, whose optimisation-based rules are derived, in the planning phase, via deterministic (linear and nonlinear programming, dynamic programming, etc. or via stochastic (generally stochastic dynamic programming approaches. In operation, the resulting deterministic or stochastic optimised operating rules are then triggered based on inflow predictions. In order to fully benefit from predictions, one must avoid using them as direct inputs to the reservoirs, but rather assess the "predictive knowledge" in terms of a predictive probability density to be operationally used in the decision making process for the estimation of expected benefits and/or expected losses. Using a theoretical and extremely simplified case, it will be shown why directly using model forecasts instead of the full predictive density leads to less robust reservoir management decisions. Moreover, the effectiveness and the tangible benefits for using the entire predictive probability density instead of the model predicted values will be demonstrated on the basis of the Lake Como management system, operational since 1997, as well as on the basis of a case study on the lake of Aswan.

Accurate predictions for the LHC made easy

CERN Multimedia

CERN. Geneva

2014-01-01

The data recorded by the LHC experiments is of a very high quality. To get the most out of the data, precise theory predictions, including uncertainty estimates, are needed to reduce as much as possible theoretical bias in the experimental analyses. Recently, significant progress has been made in computing Next-to-Leading Order (NLO) computations, including matching to the parton shower, that allow for these accurate, hadron-level predictions. I shall discuss one of these efforts, the MadGraph5_aMC@NLO program, that aims at the complete automation of predictions at the NLO accuracy within the SM as well as New Physics theories. I’ll illustrate some of the theoretical ideas behind this program, show some selected applications to LHC physics, as well as describe the future plans.

Combining gene prediction methods to improve metagenomic gene annotation

Directory of Open Access Journals (Sweden)

Rosen Gail L

2011-01-01

Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

TFTR lithium blanket module program. Final design report. Volume VII. LBM instrumentation and Program of Experiments and Analysis

International Nuclear Information System (INIS)

Harker, Y.D.; Tsang, F.Y.; Jassby, D.L.

1983-07-01

The Program of Experiments and Analysis comprises 3 spheres of activity: (1) measurements of neutron fluence and flux spectra inside and around the LBM, and of tritium production in the LBM central zone; (2) neutronic-code modeling and analysis of the TFTR/LBM system to predict the quantities measured in (1); (3) comparisons of the predicted and measured quantities, and improvements of the code modeling and analysis and the experimental techniques, in order to resolve any discrepancy between prediction and measurement. The measurement techniques are discussed. Section 5 of this volume discusses the strategy for carrying out the measurement program, for making comparisons with the neutronics code predictions, and for resolving discrepancies

Thermoviscoelastic characterization and prediction of Kevlar/epoxy composite laminates

Science.gov (United States)

Gramoll, K. C.; Dillard, D. A.; Brinson, H. F.

1990-01-01

The thermoviscoelastic characterization of Kevlar 49/Fiberite 7714A epoxy composite lamina and the development of a numerical procedure to predict the viscoelastic response of any general laminate constructed from the same material were studied. The four orthotropic material properties, S sub 11, S sub 12, S sub 22, and S sub 66, were characterized by 20 minute static creep tests on unidirectional (0) sub 8, (10) sub 8, and (90) sub 16 lamina specimens. The Time-Temperature Superposition-Principle (TTSP) was used successfully to accelerate the characterization process. A nonlinear constitutive model was developed to describe the stress dependent viscoelastic response for each of the material properties. A numerical procedure to predict long term laminate properties from lamina properties (obtained experimentally) was developed. Numerical instabilities and time constraints associated with viscoelastic numerical techniques were discussed and solved. The numerical procedure was incorporated into a user friendly microcomputer program called Viscoelastic Composite Analysis Program (VCAP), which is available for IBM PC type computers. The program was designed for ease of use. The final phase involved testing actual laminates constructed from the characterized material, Kevlar/epoxy, at various temperatures and load level for 4 to 5 weeks. These results were compared with the VCAP program predictions to verify the testing procedure and to check the numerical procedure used in the program. The actual tests and predictions agreed for all test cases which included 1, 2, 3, and 4 fiber direction laminates.

Model predictive control for a thermostatic controlled system

DEFF Research Database (Denmark)

Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob

2013-01-01

This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperat......This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...

Psychosocial and nonclinical factors predicting hospital utilization in patients of a chronic disease management program: a prospective observational study.

Science.gov (United States)

Tran, Mark W; Weiland, Tracey J; Phillips, Georgina A

2015-01-01

Psychosocial factors such as marital status (odds ratio, 3.52; 95% confidence interval, 1.43-8.69; P = .006) and nonclinical factors such as outpatient nonattendances (odds ratio, 2.52; 95% confidence interval, 1.22-5.23; P = .013) and referrals made (odds ratio, 1.20; 95% confidence interval, 1.06-1.35; P = .003) predict hospital utilization for patients in a chronic disease management program. Along with optimizing patients' clinical condition by prescribed medical guidelines and supporting patient self-management, addressing psychosocial and nonclinical issues are important in attempting to avoid hospital utilization for people with chronic illnesses.

Babcock and Wilcox revisions to CONTEMPT, computer program for predicting containment pressure-temperature response to a loss-of-coolant accident

International Nuclear Information System (INIS)

Hsii, Y.H.

1976-06-01

The CONTEMPT computer program predicts the pressure-temperature response of a single-volume reactor building to a loss-of-coolant accident. The report describes the analytical model used for the program. CONTEMPT assumes that the loss-of-coolant accident can be separated into two phases; the primary system blowdown and reactor building pressurization. The results of the blowdown analysis serve as the boundary conditions and are input to the CONTEMPT program. Thus, the containment model is only concerned with the pressure and temperature in the reactor building and the temperature distribution through the reactor building structures. The user is required to input the description of the discharge of coolant, the boiling of residual water by reactor decay heat, the superheating of steam passing through the core, and metal-water reactions. The reactor building is separated into liquid and vapor regions. Each region is in thermal equilibrium itself, but the two may not be in thermal equilibrium; the liquid and gaseous regions may have different temperatures. The reactor building is represented as consisting of several heat-conducting structures whose thermal behavior can be described by the one-dimensional multi-region heat conduction equation. The program also calculates building leakage and the effects of engineered safety features such as reactor building sprays, decay heat coolers, sump coolers, etc

Nucleic acid secondary structure prediction and display.

OpenAIRE

Stüber, K

1986-01-01

A set of programs has been developed for the prediction and display of nucleic acid secondary structures. Information from experimental data can be used to restrict or enforce secondary structural elements. The predictions can be displayed either on normal line printers or on graphic devices like plotters or graphic terminals.

Beyond [lambda][subscript max] Part 2: Predicting Molecular Color

Science.gov (United States)

Williams, Darren L.; Flaherty, Thomas J.; Alnasleh, Bassam K.

2009-01-01

A concise roadmap for using computational chemistry programs (i.e., Gaussian 03W) to predict the color of a molecular species is presented. A color-predicting spreadsheet is available with the online material that uses transition wavelengths and peak-shape parameters to predict the visible absorbance spectrum, transmittance spectrum, chromaticity…

Fundamental radiation effects studies in the fusion materials program

International Nuclear Information System (INIS)

Doran, D.G.

1982-01-01

Fundamental radiation effects studies in the US Fusion Materials Program generally fall under the aegis of the Damage Analysis and Fundamental Studies (DAFS) Program. In a narrow sense, the problem addressed by the DAFS program is the prediction of radiation effects in fusion devices using data obtained in non-representative environments. From the onset, the program has had near-term and long-term components. The premise for the latter is that there will be large economic penalties for uncertainties in predictive capability. Fusion devices are expected to be large and complex and unanticipated maintenance will be costly. It is important that predictions are based on a maximum of understanding and a minimum of empiricism. Gaining this understanding is the thrust of the long-term component. (orig.)

Accurate microRNA target prediction correlates with protein repression levels

Directory of Open Access Journals (Sweden)

Simossis Victor A

2009-09-01

Full Text Available Abstract Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

Surveying the elements of successful infrared predictive maintenance programs

Science.gov (United States)

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

1991-03-01

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

Predicting Performance in Higher Education Using Proximal Predictors

NARCIS (Netherlands)

Niessen, A Susan M; Meijer, Rob R; Tendeiro, Jorge N

2016-01-01

We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach,

BIOPEP database and other programs for processing bioactive peptide sequences.

Science.gov (United States)

Minkiewicz, Piotr; Dziuba, Jerzy; Iwaniak, Anna; Dziuba, Marta; Darewicz, Małgorzata

2008-01-01

This review presents the potential for application of computational tools in peptide science based on a sample BIOPEP database and program as well as other programs and databases available via the World Wide Web. The BIOPEP application contains a database of biologically active peptide sequences and a program enabling construction of profiles of the potential biological activity of protein fragments, calculation of quantitative descriptors as measures of the value of proteins as potential precursors of bioactive peptides, and prediction of bonds susceptible to hydrolysis by endopeptidases in a protein chain. Other bioactive and allergenic peptide sequence databases are also presented. Programs enabling the construction of binary and multiple alignments between peptide sequences, the construction of sequence motifs attributed to a given type of bioactivity, searching for potential precursors of bioactive peptides, and the prediction of sites susceptible to proteolytic cleavage in protein chains are available via the Internet as are other approaches concerning secondary structure prediction and calculation of physicochemical features based on amino acid sequence. Programs for prediction of allergenic and toxic properties have also been developed. This review explores the possibilities of cooperation between various programs.

Mathematical model for dissolved oxygen prediction in Cirata ...

African Journals Online (AJOL)

This paper presents the implementation and performance of mathematical model to predict theconcentration of dissolved oxygen in Cirata Reservoir, West Java by using Artificial Neural Network (ANN). The simulation program was created using Visual Studio 2012 C# software with ANN model implemented in it. Prediction ...

A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff

Science.gov (United States)

Cingolani, Pablo; Platts, Adrian; Wang, Le Lily; Coon, Melissa; Nguyen, Tung; Wang, Luan; Land, Susan J.; Lu, Xiangyi; Ruden, Douglas M.

2012-01-01

We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w1118; iso-2; iso-3 strain and the reference y1; cn1 bw1 sp1 strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in the 5′UTR. We found, as expected, that the SNP frequency is proportional to the recombination frequency (i.e., highest in the middle of chromosome arms). We also found that start-gain or stop-lost SNPs in Drosophila melanogaster often result in additions of N-terminal or C-terminal amino acids that are conserved in other Drosophila species. It appears that the 5′ and 3′ UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus. As genome sequencing is becoming inexpensive and routine, SnpEff enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory. PMID:22728672

Prediction-based dynamic load-sharing heuristics

Science.gov (United States)

Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.

1993-01-01

The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

Predicting the Coupling Properties of Axially-Textured Materials

Science.gov (United States)

Fuentes-Cobas, Luis E.; Muñoz-Romero, Alejandro; Montero-Cabrera, María E.; Fuentes-Montero, Luis; Fuentes-Montero, María E.

2013-01-01

A description of methods and computer programs for the prediction of “coupling properties” in axially-textured polycrystals is presented. Starting data are the single-crystal properties, texture and stereography. The validity and proper protocols for applying the Voigt, Reuss and Hill approximations to estimate coupling properties effective values is analyzed. Working algorithms for predicting mentioned averages are given. Bunge’s symmetrized spherical harmonics expansion of orientation distribution functions, inverse pole figures and (single and polycrystals) physical properties is applied in all stages of the proposed methodology. The established mathematical route has been systematized in a working computer program. The discussion of piezoelectricity in a representative textured ferro-piezoelectric ceramic illustrates the application of the proposed methodology. Polycrystal coupling properties, predicted by the suggested route, are fairly close to experimentally measured ones. PMID:28788370

Predicting the Coupling Properties of Axially-Textured Materials

Directory of Open Access Journals (Sweden)

María E. Fuentes-Montero

2013-10-01

Full Text Available A description of methods and computer programs for the prediction of “coupling properties” in axially-textured polycrystals is presented. Starting data are the single-crystal properties, texture and stereography. The validity and proper protocols for applying the Voigt, Reuss and Hill approximations to estimate coupling properties effective values is analyzed. Working algorithms for predicting mentioned averages are given. Bunge’s symmetrized spherical harmonics expansion of orientation distribution functions, inverse pole figures and (single and polycrystals physical properties is applied in all stages of the proposed methodology. The established mathematical route has been systematized in a working computer program. The discussion of piezoelectricity in a representative textured ferro-piezoelectric ceramic illustrates the application of the proposed methodology. Polycrystal coupling properties, predicted by the suggested route, are fairly close to experimentally measured ones.

Predicting Employer's Benefits from Cooperative Education.

Science.gov (United States)

Wiseman, Richard L.; Page, Norman R.

1983-01-01

Attempts to predict employer benefits resulting from their involvement in cooperative education programs. Benefits include a good source of quality employees, increased worker motivation, and increased respect between students and employers. (JOW)

Modeling and prediction of retardance in citric acid coated ferrofluid using artificial neural network

International Nuclear Information System (INIS)

Lin, Jing-Fung; Sheu, Jer-Jia

2016-01-01

Citric acid coated (citrate-stabilized) magnetite (Fe 3 O 4 ) magnetic nanoparticles have been conducted and applied in the biomedical fields. Using Taguchi-based measured retardances as the training data, an artificial neural network (ANN) model was developed for the prediction of retardance in citric acid (CA) coated ferrofluid (FF). According to the ANN simulation results in the training stage, the correlation coefficient between predicted retardances and measured retardances was found to be as high as 0.9999998. Based on the well-trained ANN model, the predicted retardance at excellent program from Taguchi method showed less error of 2.17% compared with a multiple regression (MR) analysis of statistical significance. Meanwhile, the parameter analysis at excellent program by the ANN model had the guiding significance to find out a possible program for the maximum retardance. It was concluded that the proposed ANN model had high ability for the prediction of retardance in CA coated FF. - Highlights: • The feedforward ANN is applied for modeling of retardance in CA coated FFs. • ANN can predict the retardance at excellent program with acceptable error to MR. • The proposed ANN has high ability for the prediction of retardance.

Dinucleotide controlled null models for comparative RNA gene prediction.

Science.gov (United States)

Gesell, Tanja; Washietl, Stefan

2008-05-27

Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

Dinucleotide controlled null models for comparative RNA gene prediction

Directory of Open Access Journals (Sweden)

Gesell Tanja

2008-05-01

Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

Internet-Based Motivation Program for Women With Eating Disorders: Eating Disorder Pathology and Depressive Mood Predict Dropout

Science.gov (United States)

Hirschfeld, Gerrit; Rieger, Elizabeth; Schmidt, Ulrike; Kosfelder, Joachim; Hechler, Tanja; Schulte, Dietmar; Vocks, Silja

2014-01-01

Background One of the main problems of Internet-delivered interventions for a range of disorders is the high dropout rate, yet little is known about the factors associated with this. We recently developed and tested a Web-based 6-session program to enhance motivation to change for women with anorexia nervosa, bulimia nervosa, or related subthreshold eating pathology. Objective The aim of the present study was to identify predictors of dropout from this Web program. Methods A total of 179 women took part in the study. We used survival analyses (Cox regression) to investigate the predictive effect of eating disorder pathology (assessed by the Eating Disorders Examination-Questionnaire; EDE-Q), depressive mood (Hopkins Symptom Checklist), motivation to change (University of Rhode Island Change Assessment Scale; URICA), and participants’ age at dropout. To identify predictors, we used the least absolute shrinkage and selection operator (LASSO) method. Results The dropout rate was 50.8% (91/179) and was equally distributed across the 6 treatment sessions. The LASSO analysis revealed that higher scores on the Shape Concerns subscale of the EDE-Q, a higher frequency of binge eating episodes and vomiting, as well as higher depression scores significantly increased the probability of dropout. However, we did not find any effect of the URICA or age on dropout. Conclusions Women with more severe eating disorder pathology and depressive mood had a higher likelihood of dropping out from a Web-based motivational enhancement program. Interventions such as ours need to address the specific needs of women with more severe eating disorder pathology and depressive mood and offer them additional support to prevent them from prematurely discontinuing treatment. PMID:24686856

Internet-based motivation program for women with eating disorders: eating disorder pathology and depressive mood predict dropout.

Science.gov (United States)

von Brachel, Ruth; Hötzel, Katrin; Hirschfeld, Gerrit; Rieger, Elizabeth; Schmidt, Ulrike; Kosfelder, Joachim; Hechler, Tanja; Schulte, Dietmar; Vocks, Silja

2014-03-31

One of the main problems of Internet-delivered interventions for a range of disorders is the high dropout rate, yet little is known about the factors associated with this. We recently developed and tested a Web-based 6-session program to enhance motivation to change for women with anorexia nervosa, bulimia nervosa, or related subthreshold eating pathology. The aim of the present study was to identify predictors of dropout from this Web program. A total of 179 women took part in the study. We used survival analyses (Cox regression) to investigate the predictive effect of eating disorder pathology (assessed by the Eating Disorders Examination-Questionnaire; EDE-Q), depressive mood (Hopkins Symptom Checklist), motivation to change (University of Rhode Island Change Assessment Scale; URICA), and participants' age at dropout. To identify predictors, we used the least absolute shrinkage and selection operator (LASSO) method. The dropout rate was 50.8% (91/179) and was equally distributed across the 6 treatment sessions. The LASSO analysis revealed that higher scores on the Shape Concerns subscale of the EDE-Q, a higher frequency of binge eating episodes and vomiting, as well as higher depression scores significantly increased the probability of dropout. However, we did not find any effect of the URICA or age on dropout. Women with more severe eating disorder pathology and depressive mood had a higher likelihood of dropping out from a Web-based motivational enhancement program. Interventions such as ours need to address the specific needs of women with more severe eating disorder pathology and depressive mood and offer them additional support to prevent them from prematurely discontinuing treatment.

STUDY OF SOLUTION REPRESENTATION LANGUAGE INFLUENCE ON EFFICIENCY OF INTEGER SEQUENCES PREDICTION

Directory of Open Access Journals (Sweden)

A. S. Potapov

2015-01-01

Full Text Available Methods based on genetic programming for the problem solution of integer sequences extrapolation are the subjects for study in the paper. In order to check the hypothesis about the influence of language expression of program representation on the prediction effectiveness, the genetic programming method based on several limited languages for recurrent sequences has been developed. On the single sequence sample the implemented method with the use of more complete language has shown results, significantly better than the results of one of the current methods represented in literature based on artificial neural networks. Analysis of experimental comparison results for the realized method with the usage of different languages has shown that language extension increases the difficulty of consistent patterns search in languages, available for prediction in a simpler language though it makes new sequence classes accessible for prediction. This effect can be reduced but not eliminated completely at language extension by the constructions, which make solutions more compact. Carried out researches have drawn to the conclusion that alone the choice of an adequate language for solution representation is not enough for the full problem solution of integer sequences prediction (and, all the more, universal prediction problem. However, practically applied methods can be received by the usage of genetic programming.

Analytical method for predicting plastic flow in notched fiber composite materials

International Nuclear Information System (INIS)

Flynn, P.L.; Ebert, L.J.

1977-01-01

An analytical system was developed for prediction of the onset and progress of plastic flow of oriented fiber composite materials in which both externally applied complex stress states and stress raisers were present. The predictive system was a unique combination of two numerical systems, the ''SAAS II'' finite element analysis system and a micromechanics finite element program. The SAAS II system was used to generate the three-dimensional stress distributions, which were used as the input into the finite element micromechanics program. Appropriate yielding criteria were then applied to this latter program. The accuracy of the analytical system was demonstrated by the agreement between the analytically predicted and the experimentally measured flow values of externally notched tungsten wire reinforced copper oriented fiber composites, in which the fiber fraction was 50 vol pct

Predictive access control for distributed computation

DEFF Research Database (Denmark)

Yang, Fan; Hankin, Chris; Nielson, Flemming

2013-01-01

We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies — policies based on the future beh...... behavior of a program. A novel feature of our approach is that we can define policies concerning secondary use of data....

Attending to Structural Programming Features Predicts Differences in Learning and Motivation

Science.gov (United States)

Witherspoon, Eben B.; Schunn, Christian D.; Higashi, Ross M.; Shoop, Robin

2018-01-01

Educational robotics programs offer an engaging opportunity to potentially teach core computer science concepts and practices in K-12 classrooms. Here, we test the effects of units with different programming content within a virtual robotics context on both learning gains and motivational changes in middle school (6th-8th grade) robotics…

FFTF-cycle 10 program and future plan

Science.gov (United States)

Kohyama, Akira

1988-04-01

Brief outlines are provided of the FFTF cycle 10 program and future plans in consideration. The primary objective of the Japan-US collaboration program is to enable predictions of material behavior in MFRs to be made from data obtained in other irradiation environments. Major program goals are outlined.

Supplementation of Flow Accelerated Corrosion Prediction Program Using Numerical Analysis Technique

International Nuclear Information System (INIS)

Hwang, Kyeong Mo; Jin, Tae Eun; Park, Won; Oh, Dong Hoon

2010-01-01

Flow-accelerated corrosion (FAC) leads to thinning of steel pipe walls that are exposed to flowing water or wet steam. From experience, it is seen that FAC damage to piping at fossil and nuclear plants can result in outages that require expensive repairs and can affect plant reliability and safety. CHECWORKS have been utilized in domestic nuclear plants as a predictive tool to assist FAC engineers in planning inspections and evaluating the inspection data so that piping failures caused by FAC can be prevented. However, CHECWORKS may be occasionally ignore local susceptible portions when predicting FAC damage in a group of pipelines after constructing a database for all the secondary side piping in nuclear plants. This paper describes the methodologies that can complement CHECWORKS and the verifications of CHECWORKS prediction results using numerical analysis. FAC susceptible locations determined using CHECWORKS for two pipeline groups of a nuclear plant was compared with determined using the numerical-analysis-based FLUENT

[A prediction model for internet game addiction in adolescents: using a decision tree analysis].

Science.gov (United States)

Kim, Ki Sook; Kim, Kyung Hee

2010-06-01

This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors. The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors. From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents). The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.

Heavy Ion Collisions at the LHC - Last Call for Predictions

Energy Technology Data Exchange (ETDEWEB)

Armesto, N; Borghini, N; Jeon, S; Wiedemann, U A; Abreu, S; Akkelin, V; Alam, J; Albacete, J L; Andronic, A; Antonuv, D; Arleo, F; Armesto, N; Arsene, I C; Barnafoldi, G G; Barrette, J; Bauchle, B; Becattini, F; Betz, B; Bleicher, M; Bluhm, M; Boer, D; Bopp, F W; Braun-Munzinger, P; Bravina, L; Busza, W; Cacciari, M; Capella, A; Casalderrey-Solana, J; Chatterjee, R; Chen, L; Cleymans, J; Cole, B A; delValle, Z C; Csernai, L P; Cunqueiro, L; Dainese, A; de Deus, J D; Ding, H; Djordjevic, M; Drescher, H; Dremin, I M; Dumitru, A; El, A; Engel, R; d' Enterria, D; Eskola, K J; Fai, G; Ferreiro, E G; Fries, R J; Frodermann, E; Fujii, H; Gale, C; Gelis, F; Goncalves, V P; Greco, V; Gyulassy, M; van Hees, H; Heinz, U; Honkanen, H; Horowitz, W A; Iancu, E; Ingelman, G; Jalilian-Marian, J; Jeon, S; Kaidalov, A B; Kampfer, B; Kang, Z; Karpenko, I A; Kestin, G; Kharzeev, D; Ko, C M; Koch, B; Kopeliovich, B; Kozlov, M; Kraus, I; Kuznetsova, I; Lee, S H; Lednicky, R; Letessier, J; Levin, E; Li, B; Lin, Z; Liu, H; Liu, W; Loizides, C; Lokhtin, I P; Machado, M T; Malinina, L V; Managadze, A M; Mangano, M L; Mannarelli, M; Manuel, C; Martinez, G; Milhano, J G; Mocsy, A; Molnar, D; Nardi, M; Nayak, J K; Niemi, H; Oeschler, H; Ollitrault, J; Paic, G; Pajares, C; Pantuev, V S; Papp, G; Peressounko, D; Petreczky, P; Petrushanko, S V; Piccinini, F; Pierog, T; Pirner, H J; Porteboeuf, S; Potashnikova, I; Qin, G Y; Qiu, J; Rafelski, J; Rajagopal, K; Ranft, J; Rapp, R; Rasanen, S S; Rathsman, J; Rau, P; Redlich, K; Renk, T; Rezaeian, A H; Rischke, D; Roesler, S; Ruppert, J; Ruuskanen, P V; Salgado, C A; Sapeta, S; Sarcevic, I; Sarkar, S; Sarycheva, L I; Schmidt, I; Shoski, A I; Sinha, B; Sinyukov, Y M; Snigirev, A M; Srivastava, D K; Stachel, J; Stasto, A; Stocker, H; Teplov, C Y; Thews, R L; Torrieri, G; Pop, V T; Triantafyllopoulos, D N; Tuchin, K L; Turbide, S; Tywoniuk, K; Utermann, A; Venugopalan, R; Vitev, I; Vogt, R; Wang, E; Wang, X N; Werner, K; Wessels, E; Wheaton, S; Wicks, S; Wiedemann, U A; Wolschin, G; Xiao, B; Xu, Z; Yasui, S; Zabrodin, E; Zapp, K; Zhang, B

2008-02-25

In August 2006, the CERN Theory Unit announced to restructure its visitor program and to create a 'CERN Theory Institute', where 1-3 month long specific programs can take place. The first such Institute was held from 14 May to 10 June 2007, focusing on 'Heavy Ion Collisions at the LHC - Last Call for Predictions'. It brought together close to 100 scientists working on the theory of ultra-relativistic heavy ion collisions. The aim of this workshop was to review and document the status of expectations and predictions for the heavy ion program at the Large Hadron Collider LHC before its start. LHC will explore heavy ion collisions at {approx} 30 times higher center of mass energy than explored previously at the Relativistic Heavy Ion Collider RHIC. So, on the one hand, the charge of this workshop provided a natural forum for the exchange of the most recent ideas, and allowed to monitor how the understanding of heavy ion collisions has evolved in recent years with the data from RHIC, and with the preparation of the LHC experimental program. On the other hand, the workshop aimed at a documentation which helps to distinguish pre- from post-dictions. An analogous documentation of the 'Last Call for Predictions' [1] was prepared prior to the start of the heavy-ion program at the Relativistic Heavy Ion Collider RHIC, and it proved useful in the subsequent discussion and interpretation of RHIC data. The present write-up is the documentation of predictions for the LHC heavy ion program, received or presented during the CERN TH Institute. The set-up of the CERN TH Institute allowed us to aim for the wide-most coverage of predictions. There were more than 100 presentations and discussions during the workshop. Moreover, those unable to attend could still participate by submitting predictions in written form during the workshop. This followed the spirit that everybody interested in making a prediction had the right to be heard. To arrive at a concise

Child Development Program Evaluation Scale.

Science.gov (United States)

Fiene, Richard J.

The Child Development Program Evaluation Scale (CDPES) is actually two scales in one, a licensing scale and a quality scale. Licensing predictor items have been found to predict overall compliance of child day care centers with state regulations in four states. Quality scale items have been found to predict the overall quality of child day care…

Cancer predictive value of cytogenetic markers used in occupational health surveillance programs

DEFF Research Database (Denmark)

Hagmar, L; Bonassi, S; Strömberg, U

1998-01-01

It has not previously been clear whether cytogenetic biomarkers in healthy subjects will predict cancer. Earlier analyses of a Nordic and an Italian cohort indicated predictivity for chromosomal aberrations (CAS) but not for sister chromatid exchanges (SCES). A pooled analysis of the updated......, occupational exposures and smoking, will be assessed in a case-referent study within the study base....

Flight Experiment Verification of Shuttle Boundary Layer Transition Prediction Tool

Science.gov (United States)

Berry, Scott A.; Berger, Karen T.; Horvath, Thomas J.; Wood, William A.

2016-01-01

Boundary layer transition at hypersonic conditions is critical to the design of future high-speed aircraft and spacecraft. Accurate methods to predict transition would directly impact the aerothermodynamic environments used to size a hypersonic vehicle's thermal protection system. A transition prediction tool, based on wind tunnel derived discrete roughness correlations, was developed and implemented for the Space Shuttle return-to-flight program. This tool was also used to design a boundary layer transition flight experiment in order to assess correlation uncertainties, particularly with regard to high Mach-number transition and tunnel-to-flight scaling. A review is provided of the results obtained from the flight experiment in order to evaluate the transition prediction tool implemented for the Shuttle program.

Worksite health screening programs for predicting the development of Metabolic Syndrome in middle-aged employees: a five-year follow-up study

Directory of Open Access Journals (Sweden)

Chen Jong-Dar

2010-12-01

Full Text Available Abstract Background Metabolic syndrome (MetS management programs conventionally focus on the adults having MetS. However, risk assessment for MetS development is also important for many adults potentially at risk but do not yet fulfill MetS criteria at screening. Therefore, we conducted this follow-up study to explore whether initial screening records can be efficiently applied on the prediction of the MetS occurrence in healthy middle-aged employees. Methods Utilizing health examination data, a five-year follow-up observational study was conducted for 1384 middle-aged Taiwanese employees not fulfilling MetS criteria. Data analyzed included: gender, age, MetS components, uric acid, insulin, liver enzymes, sonographic fatty liver, hepatovirus infections and lifestyle factors. Multivariate logistic regression was used to estimate the adjusted odds ratios (OR and 95% confidence interval (CI of risk for MetS development. The synergistic index (SI values and their confidence intervals of risk factor combinations were calculated; and were used to estimate the interacting effects of coupling MetS components on MetS development. Results Within five years, 13% (175 out of 1384 participants fulfilled MetS criteria. The ORs for MetS development among adults initially having one or two MetS components were 2.8 and 7.3, respectively (both p Conclusion MetS component count and combination can be used in predicting MetS development for participants potentially at risk. Worksite MetS screening programs simultaneously allow for finding out cases and for assessing risk of MetS development.

BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1

Science.gov (United States)

Patricia L. Andrews

1986-01-01

Describes BURN Subsystem, Part 1, the operational fire behavior prediction subsystem of the BEHAVE fire behavior prediction and fuel modeling system. The manual covers operation of the computer program, assumptions of the mathematical models used in the calculations, and application of the predictions.

Topology and prediction of RNA pseudoknots

DEFF Research Database (Denmark)

Reidys, Christian; Huang, Fenix; Andersen, Jørgen Ellegaard

2011-01-01

Motivation: Several dynamic programming algorithms for predicting RNA structures with pseudoknots have been proposed that differ dramatically from one another in the classes of structures considered. Results: Here, we use the natural topological classification of RNA structures in terms...

Firebird-III program description

International Nuclear Information System (INIS)

Lin, M.R.; Prawirosochardjo, S.; Rennick, D.F.; Wessman, E.; Blain, R.J.D.; Wilson, J.M.

1979-09-01

The FIREBIRD-III digital computer program is a general network code developed primarily for predicting the thermalhydraulic behaviour of CANDU power reactors during a postulated loss-of-coolant accident and the subsequent emergency coolant injection. Because of its flexibility, the code can also be used to solve a large variety of general two-phase flow problems. This report describes the thermalhydraulic models and the computation methods used in the program

The Brand's PREACH Model: Predicting Readiness to Engage African American Churches in Health.

Science.gov (United States)

Brand, Dorine J; Alston, Reginald J

2017-09-01

Despite many attempts to reduce health disparities, health professionals face obstacles in improving poor health outcomes within the African American (AA) community. To promote change for improved health measures, it is important to implement culturally tailored programming through a trusted institution, such as the AA church. While churches have the potential to play an important role in positively impacting health among AAs, it is unclear what attributes are necessary to predict success or failure for health promotion within these institutions. The purpose of this study was to create a model, the Brand's PREACH ( Predicting Readiness to Engage African American Churches in Health) Model, to predict the readiness of AA churches to engage in health promotion programming. Thirty-six semistructured key informant interviews were conducted with 12 pastors, 12 health leaders, and 12 congregants to gain information on the relationship between church infrastructure (physical structure, personnel, funding, and social/cultural support), readiness, and health promotion programming. The findings revealed that church infrastructure has an association with and will predict the readiness of a church to engage in health promotion programming. The ability to identify readiness early on will be useful for developing, implementing, and evaluating faith-based interventions, in partnership with churches, which is a key factor for sustainable and effective programs.

Predicting RNA Structure Using Mutual Information

DEFF Research Database (Denmark)

Freyhult, E.; Moulton, V.; Gardner, P. P.

2005-01-01

, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. Results: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall...... package. Conclusion: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. Availability: MIfold is freely available from http......Background: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure...

Mathematical Model for Prediction of Flexural Strength of Mound ...

African Journals Online (AJOL)

The mound soil-cement blended proportions were mathematically optimized by using scheffe's approach and the optimization model developed. A computer program predicting the mix proportion for the model was written. The optimal proportion by the program was used prepare beam samples measuring 150mm x 150mm ...

GAPIT: genome association and prediction integrated tool.

Science.gov (United States)

Lipka, Alexander E; Tian, Feng; Wang, Qishan; Peiffer, Jason; Li, Meng; Bradbury, Peter J; Gore, Michael A; Buckler, Edward S; Zhang, Zhiwu

2012-09-15

Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. http://www.maizegenetics.net/GAPIT. zhiwu.zhang@cornell.edu Supplementary data are available at Bioinformatics online.

Rapid determination of thermodynamic parameters from one-dimensional programmed-temperature gas chromatography for use in retention time prediction in comprehensive multidimensional chromatography.

Science.gov (United States)

McGinitie, Teague M; Ebrahimi-Najafabadi, Heshmatollah; Harynuk, James J

2014-01-17

A new method for estimating the thermodynamic parameters of ΔH(T0), ΔS(T0), and ΔCP for use in thermodynamic modeling of GC×GC separations has been developed. The method is an alternative to the traditional isothermal separations required to fit a three-parameter thermodynamic model to retention data. Herein, a non-linear optimization technique is used to estimate the parameters from a series of temperature-programmed separations using the Nelder-Mead simplex algorithm. With this method, the time required to obtain estimates of thermodynamic parameters a series of analytes is significantly reduced. This new method allows for precise predictions of retention time with the average error being only 0.2s for 1D separations. Predictions for GC×GC separations were also in agreement with experimental measurements; having an average relative error of 0.37% for (1)tr and 2.1% for (2)tr. Copyright © 2013 Elsevier B.V. All rights reserved.

Predicting the performance of a strategic alliance: an analysis of the Community Clinical Oncology Program.

Science.gov (United States)

Kaluzny, A D; Lacey, L M; Warnecke, R; Hynes, D M; Morrissey, J; Ford, L; Sondik, E

1993-06-01

This study is designed to examine the effects of environment and structure of the Community Clinical Oncology Program (CCOP) on performance as measured by patient accrual to National Cancer Institute (NCI)-approved treatment protocols. Data and analysis are part of a larger evaluation of the NCI Community Clinical Oncology Program during its second funding cycle, June 1987-May 1990. Data, taken from primary and secondary sources, included a survey of selected informants in CCOPs and research bases, CCOP grant applications, CCOP annual progress reports, and site visits to a subsample of CCOPs (N = 20) and research bases (N = 5). Accrual data were obtained from NCI records. Analysis involved three complementary sets of factors: the local health care resources environment available to the CCOP, the larger policy environment as reflected by the relationship of the CCOP to selected research bases and the NCI, and the operational structure of the CCOP itself. A hierarchical model examined the separate and cumulative effects of local and policy environment and structure on performance. Other things equal, the primary predictors of treatment accrual were: (1) the larger policy environment, as measured by the attendance of nurses at research base meetings; and (2) operational structure, as measured by the number and character of components within participating CCOPs and the number of hours per week worked by data managers. These factors explained 73 percent of the total variance in accrual performance. Findings suggest criteria for selecting the types of organizations to participate in the alliance, as well as for establishing guidelines for managing such alliances. A future challenge is to determine the extent to which factors predicting accrual to cancer treatment clinical trials are equally important as predictors of accrual to cancer prevention and control trials.

Improved functional capacity evaluation performance predicts successful return to work one year after completing a functional restoration rehabilitation program.

Science.gov (United States)

Fore, Lisa; Perez, Yoheli; Neblett, Randy; Asih, Sali; Mayer, Tom G; Gatchel, Robert J

2015-04-01

To evaluate whether functional capacity evaluation (FCE) scores are responsive to functional restoration treatment, and to assess the ability of FCEs at program discharge to predict work outcomes. An interdisciplinary cohort study of prospectively collected data. A functional restoration center. A consecutive sample of 354 patients with chronic disabling occupational musculoskeletal disorders (CDOMDs) completed a functional restoration program consisting of quantitatively directed exercise progression and multi-modal disability management with interdisciplinary medical supervision. Each patient participated in an FCE at admission and discharge from treatment. The results of each FCE yielded the physical demand level (PDL) at which patients were functioning. Patients were initially divided into 5 PDL groups, based on job-of-injury lifting, carrying, and pushing/pulling requirements, for the pre- to posttreatment responsiveness analyses. Patients were subsequently divided into 5 PDL groups, based on their performance on the FCE upon program completion. Outcome measures included admission-to-discharge changes in PDLs and 2 specific FCE lifting tasks: isokinetic lifting; and the Progressive Isoinertial Lifting Evaluation (PILE). Socioeconomic outcomes were also evaluated, including post-discharge work return and work retention 1-year after treatment completion. Overall, 96% of the patients demonstrated improvement in their PDLs from admission to discharge. A majority of patients (56%) were able to achieve a discharge PDL that was comparable to their estimated job-of-injury lifting requirement or higher (P work return (P work retention (P work return after treatment completion and work retention 1 year later. Copyright © 2015 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Machine and lubricant condition monitoring for extended equipment lifetimes and predictive maintenance

Energy Technology Data Exchange (ETDEWEB)

Lukas, M; Anderson, D P [Spectro Incorporated, Littleton, Massachusetts (United States)

1998-12-31

Predictive maintenance has gained wide acceptance as a cost cutting strategy in modern industry. Condition monitoring by lubricant analysis is one of the basic tools of a predictive maintenance program along with vibration monitoring, performance monitoring and thermography. In today`s modern power generation, manufacturing, refinery, transportation, mining, and military operations, the cost of equipment maintenance, service, and lubricants are ever increasing. Parts, labor, equipment downtime and lubricant prices and disposal costs are a primary concern in a well run maintenance management program. Machine condition monitoring based on oil analysis has become a prerequisite in most maintenance programs. Few operations can afford not to implement a program if they wish to remain competitive, and in some cases, profitable. This presentation describes a comprehensive Machine Condition Monitoring Program based on oil analysis. Actual operational condition monitoring programs will be used to review basic components and analytical requirements. Case histories will be cited as examples of cost savings, reduced equipment downtime and increased efficiencies of maintenance programs through a well managed oil analysis program. (orig.)

Machine and lubricant condition monitoring for extended equipment lifetimes and predictive maintenance

Energy Technology Data Exchange (ETDEWEB)

Lukas, M.; Anderson, D.P. [Spectro Incorporated, Littleton, Massachusetts (United States)

1997-12-31

Predictive maintenance has gained wide acceptance as a cost cutting strategy in modern industry. Condition monitoring by lubricant analysis is one of the basic tools of a predictive maintenance program along with vibration monitoring, performance monitoring and thermography. In today`s modern power generation, manufacturing, refinery, transportation, mining, and military operations, the cost of equipment maintenance, service, and lubricants are ever increasing. Parts, labor, equipment downtime and lubricant prices and disposal costs are a primary concern in a well run maintenance management program. Machine condition monitoring based on oil analysis has become a prerequisite in most maintenance programs. Few operations can afford not to implement a program if they wish to remain competitive, and in some cases, profitable. This presentation describes a comprehensive Machine Condition Monitoring Program based on oil analysis. Actual operational condition monitoring programs will be used to review basic components and analytical requirements. Case histories will be cited as examples of cost savings, reduced equipment downtime and increased efficiencies of maintenance programs through a well managed oil analysis program. (orig.)

Degradation mode analysis: An approach to establish effective predictive maintenance tasks

International Nuclear Information System (INIS)

Sonnett, D.E.; Douglass, P.T.; Barnard, D.D.

1991-01-01

A significant number of nuclear generating stations have been employing Reliability Centered Maintenance methodology to arrive at applicable and effective maintenance tasks for their plant equipment. The resultant endpoint of most programs has been an increased emphasis on predictive maintenance as the task of choice for monitoring and trending plant equipment condition to address failure mechanisms of the analyses. Many of these plants have spent several years conducting reliability centered analysis before they seriously begin implementing predictive program improvements. In this paper we present another methodology, entitled Degradation Mode Analysis, which provides a more direct method to quickly and economically achieve the major benefit of reliability centered analysis, namely predictive maintenance. (author)

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

Science.gov (United States)

2017-06-01

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

Development of genomic prediction in sorghum

NARCIS (Netherlands)

Hunt, Colleen H.; Eeuwijk, van Fred A.; Mace, Emma S.; Hayes, Ben J.; Jordan, David R.

2018-01-01

Genomic selection can increase the rate of genetic gain in plant breeding programs by shortening the breeding cycle. Gain can also be increased through higher selection intensities, as the size of the population available for selection can be increased by predicting performance of nonphenotyped, but

Improving consensus contact prediction via server correlation reduction.

Science.gov (United States)

Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

2009-05-06

Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Improving consensus contact prediction via server correlation reduction

Directory of Open Access Journals (Sweden)

Xu Jinbo

2009-05-01

Full Text Available Abstract Background Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. Results In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Conclusion Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Quality of life predicts outcome in a heart failure disease management program.

LENUS (Irish Health Repository)

O'Loughlin, Christina

2012-02-01

BACKGROUND: Chronic heart failure (HF) is associated with a poor Health Related Quality of Life (HRQoL). HRQoL has been shown to be a predictor of HF outcomes however, variability in the study designs make it difficult to apply these findings to a clinical setting. The aim of this study was to establish if HRQoL is a predictor of long-term mortality and morbidity in HF patients followed-up in a disease management program (DMP) and if a HRQoL instrument could be applied to aid in identifying high-risk patients within a clinical context. METHODS: This is a retrospective analysis of HF patients attending a DMP with 18+\\/-9 months follow-up. Clinical and biochemical parameters were recorded on discharge from index HF admission and HRQoL measures were recorded at 2 weeks post index admission. RESULTS: 225 patients were enrolled into the study (mean age=69+\\/-12 years, male=61%, and 78%=systolic HF). In multivariable analysis, all dimensions of HRQoL (measured by the Minnesota Living with HF Questionnaire) were independent predictors of both mortality and readmissions particularly in patients <80 years. A significant interaction between HRQoL and age (Total((HRQoL))age: p<0.001) indicated that the association of HRQoL with outcomes diminished as age increased. CONCLUSIONS: These data demonstrate that HRQoL is a predictor of outcome in HF patients managed in a DMP. Younger patients (<65 years) with a Total HRQoL score of > or =50 are at high risk of an adverse outcome. In older patients > or =80 years HRQoL is not useful in predicting outcome.

Prediction of the semiscale blowdown heat transfer test S-02-8 (NRC Standard Problem Five)

International Nuclear Information System (INIS)

Fujita, N.; Irani, A.A.; Mecham, D.C.; Sawtelle, G.R.; Moore, K.V.

1976-10-01

Standard Problem Five was the prediction of test S-02-8 in the Semiscale Mod-1 experimental program. The Semiscale System is an electrically heated experiment designed to produce data on system performance typical of PWR thermal-hydraulic behavior. The RELAP4 program used for these analyses is a digital computer program developed to predict the thermal-hydraulic behavior of experimental systems and water-cooled nuclear reactors subjected to postulated transients. The RELAP4 predictions of Standard Problem 5 were in good overall agreement with the measured hydraulic data. Fortunately, sufficient experience has been gained with the semiscale break configuration and the critical flow models in RELAP4 to accurately predict the break flow and, hence the overall system depressurization. Generally, the hydraulic predictions are quite good in regions where homogeneity existed. Where separation effects occurred, predictions are not as good, and the data oscillations and error bands are larger. A large discrepancy existed among the measured heater rod temperature data as well as between these data and predicted values. Several potential causes for these differences were considered, and several post test analyses were performed in order to evaluate the discrepancies

Predicting High or Low Transfer Efficiency of Photovoltaic Systems Using a Novel Hybrid Methodology Combining Rough Set Theory, Data Envelopment Analysis and Genetic Programming

Directory of Open Access Journals (Sweden)

Lee-Ing Tong

2012-02-01

Full Text Available Solar energy has become an important energy source in recent years as it generates less pollution than other energies. A photovoltaic (PV system, which typically has many components, converts solar energy into electrical energy. With the development of advanced engineering technologies, the transfer efficiency of a PV system has been increased from low to high. The combination of components in a PV system influences its transfer efficiency. Therefore, when predicting the transfer efficiency of a PV system, one must consider the relationship among system components. This work accurately predicts whether transfer efficiency of a PV system is high or low using a novel hybrid model that combines rough set theory (RST, data envelopment analysis (DEA, and genetic programming (GP. Finally, real data-set are utilized to demonstrate the accuracy of the proposed method.

Fusion Simulation Program

International Nuclear Information System (INIS)

Greenwald, Martin

2011-01-01

Many others in the fusion energy and advanced scientific computing communities participated in the development of this plan. The core planning team is grateful for their important contributions. This summary is meant as a quick overview the Fusion Simulation Program's (FSP's) purpose and intentions. There are several additional documents referenced within this one and all are supplemental or flow down from this Program Plan. The overall science goal of the DOE Office of Fusion Energy Sciences (FES) Fusion Simulation Program (FSP) is to develop predictive simulation capability for magnetically confined fusion plasmas at an unprecedented level of integration and fidelity. This will directly support and enable effective U.S. participation in International Thermonuclear Experimental Reactor (ITER) research and the overall mission of delivering practical fusion energy. The FSP will address a rich set of scientific issues together with experimental programs, producing validated integrated physics results. This is very well aligned with the mission of the ITER Organization to coordinate with its members the integrated modeling and control of fusion plasmas, including benchmarking and validation activities. (1). Initial FSP research will focus on two critical Integrated Science Application (ISA) areas: ISA1, the plasma edge; and ISA2, whole device modeling (WDM) including disruption avoidance. The first of these problems involves the narrow plasma boundary layer and its complex interactions with the plasma core and the surrounding material wall. The second requires development of a computationally tractable, but comprehensive model that describes all equilibrium and dynamic processes at a sufficient level of detail to provide useful prediction of the temporal evolution of fusion plasma experiments. The initial driver for the whole device model will be prediction and avoidance of discharge-terminating disruptions, especially at high performance, which are a critical

Plasma Simulation Program

Energy Technology Data Exchange (ETDEWEB)

Greenwald, Martin

2011-10-04

Many others in the fusion energy and advanced scientific computing communities participated in the development of this plan. The core planning team is grateful for their important contributions. This summary is meant as a quick overview the Fusion Simulation Program's (FSP's) purpose and intentions. There are several additional documents referenced within this one and all are supplemental or flow down from this Program Plan. The overall science goal of the DOE Office of Fusion Energy Sciences (FES) Fusion Simulation Program (FSP) is to develop predictive simulation capability for magnetically confined fusion plasmas at an unprecedented level of integration and fidelity. This will directly support and enable effective U.S. participation in International Thermonuclear Experimental Reactor (ITER) research and the overall mission of delivering practical fusion energy. The FSP will address a rich set of scientific issues together with experimental programs, producing validated integrated physics results. This is very well aligned with the mission of the ITER Organization to coordinate with its members the integrated modeling and control of fusion plasmas, including benchmarking and validation activities. [1]. Initial FSP research will focus on two critical Integrated Science Application (ISA) areas: ISA1, the plasma edge; and ISA2, whole device modeling (WDM) including disruption avoidance. The first of these problems involves the narrow plasma boundary layer and its complex interactions with the plasma core and the surrounding material wall. The second requires development of a computationally tractable, but comprehensive model that describes all equilibrium and dynamic processes at a sufficient level of detail to provide useful prediction of the temporal evolution of fusion plasma experiments. The initial driver for the whole device model will be prediction and avoidance of discharge-terminating disruptions, especially at high performance, which are a

Supervisor's role in training programs as a manager of learning program

Directory of Open Access Journals (Sweden)

2011-06-01

Full Text Available According to the training literature, a supervisor's role in training programs has two major elements: supervisor support and supervisor communication. The ability of supervisors to play effective roles in training programs may increase employees' motivation to learn. The nature of this relationship is interesting, but the role of supervisor's role as a predicting variable is less emphasized in a training program models. Therefore, this study was conducted to examine the effect of supervisor's role in training programs on motivation to learn using 152 usable questionnaires gathered from non-academic employees who have worked in a technological based public university, Malaysia. The outcomes of stepwise regression analysis showed that the supervisor support and supervisor communication significantly associated with motivation to learn. Statistically, this result demonstrates that supervisor's role in training programs does act as an important predictor of motivation to learn in the organizational sample. In addition, discussion, implication and conclusion are elaborated.

PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes.

Science.gov (United States)

Yu, Nancy Y; Wagner, James R; Laird, Matthew R; Melli, Gabor; Rey, Sébastien; Lo, Raymond; Dao, Phuong; Sahinalp, S Cenk; Ester, Martin; Foster, Leonard J; Brinkman, Fiona S L

2010-07-01

PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. http://www.psort.org/psortb (download open source software or use the web interface). psort-mail@sfu.ca Supplementary data are available at Bioinformatics online.

Generalizability of GMAT[R] Validity to Programs outside the U.S.

Science.gov (United States)

Talento-Miller, Eileen

2008-01-01

This study explores the predictive validity of GMAT[R] scores for predicting performance in graduate management programs outside the United States. Results suggest that the validity estimates based on the combination of GMAT[R] scores were about a third of a standard deviation higher for non-U.S. programs compared with existing data on U.S.…

Developing a comprehensive training curriculum for integrated predictive maintenance

Science.gov (United States)

Wurzbach, Richard N.

2002-03-01

On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

International Nuclear Information System (INIS)

Wurzbach, Richard N.

2002-01-01

On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of

Motivation and Treatment Credibility Predicts Dropout, Treatment Adherence, and Clinical Outcomes in an Internet-Based Cognitive Behavioral Relaxation Program: A Randomized Controlled Trial.

Science.gov (United States)

Alfonsson, Sven; Olsson, Erik; Hursti, Timo

2016-03-08

In previous research, variables such as age, education, treatment credibility, and therapeutic alliance have shown to affect patients' treatment adherence and outcome in Internet-based psychotherapy. A more detailed understanding of how such variables are associated with different measures of adherence and clinical outcomes may help in designing more effective online therapy. The aims of this study were to investigate demographical, psychological, and treatment-specific variables that could predict dropout, treatment adherence, and treatment outcomes in a study of online relaxation for mild to moderate stress symptoms. Participant dropout and attrition as well as data from self-report instruments completed before, during, and after the online relaxation program were analyzed. Multiple linear and logistical regression analyses were conducted to predict early dropout, overall attrition, online treatment progress, number of registered relaxation exercises, posttreatment symptom levels, and reliable improvement. Dropout was significantly predicted by treatment credibility, whereas overall attrition was associated with reporting a focus on immediate consequences and experiencing a low level of intrinsic motivation for the treatment. Treatment progress was predicted by education level and treatment credibility, whereas number of registered relaxation exercises was associated with experiencing intrinsic motivation for the treatment. Posttreatment stress symptoms were positively predicted by feeling external pressure to participate in the treatment and negatively predicted by treatment credibility. Reporting reliable symptom improvement after treatment was predicted by treatment credibility and therapeutic bond. This study confirmed that treatment credibility and a good working alliance are factors associated with successful Internet-based psychotherapy. Further, the study showed that measuring adherence in different ways provides somewhat different results, which

Survival prediction algorithms miss significant opportunities for improvement if used for case selection in trauma quality improvement programs.

Science.gov (United States)

Heim, Catherine; Cole, Elaine; West, Anita; Tai, Nigel; Brohi, Karim

2016-09-01

Quality improvement (QI) programs have shown to reduce preventable mortality in trauma care. Detailed review of all trauma deaths is a time and resource consuming process and calculated probability of survival (Ps) has been proposed as audit filter. Review is limited on deaths that were 'expected to survive'. However no Ps-based algorithm has been validated and no study has examined elements of preventability associated with deaths classified as 'expected'. The objective of this study was to examine whether trauma performance review can be streamlined using existing mortality prediction tools without missing important areas for improvement. We conducted a retrospective study of all trauma deaths reviewed by our trauma QI program. Deaths were classified into non-preventable, possibly preventable, probably preventable or preventable. Opportunities for improvement (OPIs) involve failure in the process of care and were classified into clinical and system deviations from standards of care. TRISS and PS were used for calculation of probability of survival. Peer-review charts were reviewed by a single investigator. Over 8 years, 626 patients were included. One third showed elements of preventability and 4% were preventable. Preventability occurred across the entire range of the calculated Ps band. Limiting review to unexpected deaths would have missed over 50% of all preventability issues and a third of preventable deaths. 37% of patients showed opportunities for improvement (OPIs). Neither TRISS nor PS allowed for reliable identification of OPIs and limiting peer-review to patients with unexpected deaths would have missed close to 60% of all issues in care. TRISS and PS fail to identify a significant proportion of avoidable deaths and miss important opportunities for process and system improvement. Based on this, all trauma deaths should be subjected to expert panel review in order to aim at a maximal output of performance improvement programs. Copyright © 2016 Elsevier

Use of Admission Criteria to Predict Performance of Students in an Entry-Level Master's Program on Fieldwork Placements and in Academic Courses.

Science.gov (United States)

Kirchner, G L; Stone, R G; Holm, M B

2001-01-01

The relationships among clinical outcomes, academic success, and predictors used to screen applicants for entrance into a Master in Occupational Therapy Program (MOT) were examined. The dependent variables were grade point average in occupational therapy courses (OT-GPA), client therapy outcomes at the clinic, and ratings of MOT students by Level II Fieldwork supervisors. Predictor variables included undergraduate GPA, scores on the Graduate Record Examination (GRE), and an essay. Both undergraduate GPA and scores on the GRE were found to predict OT-GPA. The analytical section of the GRE was also positively correlated with fieldwork supervisors' ratings of students.

Predicting Secretory Proteins with SignalP

DEFF Research Database (Denmark)

Nielsen, Henrik

2017-01-01

SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history...

Predictive microbiology for food packaging applications

Science.gov (United States)

Mathematical modeling has been applied to describe the microbial growth and inactivation in foods for decades and is also known as ‘Predictive microbiology’. When models are developed and validated, their applications may save cost and time. The Pathogen Modeling Program (PMP), a collection of mode...

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics.

Science.gov (United States)

Hoyt, Robert Eugene; Snider, Dallas; Thompson, Carla; Mantravadi, Sarita

2016-10-11

We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix

Theoretical Predictions of Springing and Their Comparison with Full Scale Measurements

DEFF Research Database (Denmark)

Gu, X.; Storhaug, G.; Vidic-Perunovic, Jelena

2003-01-01

The present paper considers a large ocean going ship with significant springing responses, which have made a large contribution to the fatigue cracking for certain structural details. Four different theories for predicting ship responses and associated computer programs for predictions of springing...

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)

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

Science.gov (United States)

Hunter, H. E.; Amato, R. A.

1972-01-01

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

Evaluation of Millstone Nuclear Power Plant, Environmental Impact prediction, based on monitoring programs

International Nuclear Information System (INIS)

Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

1977-02-01

This report evaluates the nonradiological monitoring program at Millstone Nuclear Power Plant. Both operational as well as preoperational monitoring programs were analyzed to produce long-term (5 yr or longer) data sets, where possible. In order to determine the effectiveness of these monitoring programs, the appropriate data sets have to be analyzed by the appropriate statistical analysis. Thus, both open literature and current statistical analysis being developed at Pacific Northwest Laboratories (PNL) were employed in data analysis

Evaluation of Millstone Nuclear Power Plant, Environmental Impact prediction, based on monitoring programs

Energy Technology Data Exchange (ETDEWEB)

Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

1977-02-01

This report evaluates the nonradiological monitoring program at Millstone Nuclear Power Plant. Both operational as well as preoperational monitoring programs were analyzed to produce long-term (5 yr or longer) data sets, where possible. In order to determine the effectiveness of these monitoring programs, the appropriate data sets have to be analyzed by the appropriate statistical analysis. Thus, both open literature and current statistical analysis being developed at Pacific Northwest Laboratories (PNL) were employed in data analysis.

Year-ahead prediction of US landfalling hurricane numbers: intense hurricanes

OpenAIRE

Khare, Shree; Jewson, Stephen

2005-01-01

We continue with our program to derive simple practical methods that can be used to predict the number of US landfalling hurricanes a year in advance. We repeat an earlier study, but for a slightly different definition landfalling hurricanes, and for intense hurricanes only. We find that the averaging lengths needed for optimal predictions of numbers of intense hurricanes are longer than those needed for optimal predictions of numbers of hurricanes of all strengths.

MetWAMer: eukaryotic translation initiation site prediction

Directory of Open Access Journals (Sweden)

Brendel Volker

2008-09-01

Full Text Available Abstract Background Translation initiation site (TIS identification is an important aspect of the gene annotation process, requisite for the accurate delineation of protein sequences from transcript data. We have developed the MetWAMer package for TIS prediction in eukaryotic open reading frames of non-viral origin. MetWAMer can be used as a stand-alone, third-party tool for post-processing gene structure annotations generated by external computational programs and/or pipelines, or directly integrated into gene structure prediction software implementations. Results MetWAMer currently implements five distinct methods for TIS prediction, the most accurate of which is a routine that combines weighted, signal-based translation initiation site scores and the contrast in coding potential of sequences flanking TISs using a perceptron. Also, our program implements clustering capabilities through use of the k-medoids algorithm, thereby enabling cluster-specific TIS parameter utilization. In practice, our static weight array matrix-based indexing method for parameter set lookup can be used with good results in data sets exhibiting moderate levels of 5'-complete coverage. Conclusion We demonstrate that improvements in statistically-based models for TIS prediction can be achieved by taking the class of each potential start-methionine into account pending certain testing conditions, and that our perceptron-based model is suitable for the TIS identification task. MetWAMer represents a well-documented, extensible, and freely available software system that can be readily re-trained for differing target applications and/or extended with existing and novel TIS prediction methods, to support further research efforts in this area.

Water Quality Analysis Simulation Program (WASP)

Science.gov (United States)

The Water Quality Analysis Simulation Program (WASP) model helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions.

OSMOSE an experimental program for improving neutronic predictions of advanced nuclear fuels.

Energy Technology Data Exchange (ETDEWEB)

Klann, R. T.; Aliberti, G.; Zhong, Z.; Graczyk, D.; Loussi, A.; Nuclear Engineering Division; Commissariat a l Energie Atomique

2007-10-18

This report describes the technical results of tasks and activities conducted in FY07 to support the DOE-CEA collaboration on the OSMOSE program. The activities are divided into five high-level tasks: reactor modeling and pre-experiment analysis, sample fabrication and analysis, reactor experiments, data treatment and analysis, and assessment for relevance to high priority advanced reactor programs (such as GNEP and Gen-IV).

Small Spacecraft Technology Initiative Education Program

Science.gov (United States)

1995-01-01

A NASA engineer with the Commercial Remote Sensing Program (CRSP) at Stennis Space Center works with students from W.P. Daniels High School in New Albany, Miss., through NASA's Small Spacecraft Technology Initiative Program. CRSP is teaching students to use remote sensing to locate a potential site for a water reservoir to offset a predicted water shortage in the community's future.

Evaluation of environmental impact predictions

International Nuclear Information System (INIS)

Cunningham, P.A.; Adams, S.M.; Kumar, K.D.

1977-01-01

An analysis and evaluation of the ecological monitoring program at the Surry Nuclear Power Plant showed that predictions of potential environmental impact made in the Final Environmental Statement (FES), which were based on generally accepted ecological principles, were not completely substantiated by environmental monitoring data. The Surry Nuclear Power Plant (Units 1 and 2) was chosen for study because of the facility's relatively continuous operating history and the availability of environmental data adequate for analysis. Preoperational and operational fish monitoring data were used to assess the validity of the FES prediction that fish would congregate in the thermal plume during winter months and would avoid the plume during summer months. Analysis of monitoring data showed that fish catch per unit effort (CPE) was generally high in the thermal plume during winter months; however, the highest fish catches occurred in the plume during the summer. Possible explanations for differences between the FES prediction and results observed in analysis of monitoring data are discussed, and general recommendations are outlined for improving impact assessment predictions

Prediction of natural gas consumption

International Nuclear Information System (INIS)

Zhang, R.L.; Walton, D.J.; Hoskins, W.D.

1993-01-01

Distributors of natural gas need to predict future consumption in order to purchase a sufficient supply on contract. Distributors that offer their customers equal payment plans need to predict the consumption of each customer 12 months in advance. Estimates of previous consumption are often used for months when meters are inaccessible, or bimonthly-read meters. Existing methods of predicting natural gas consumption, and a proposed new method for each local region are discussed. The proposed model distinguishes the consumption load factors from summer to other seasons by attempting to adjust them by introducing two parameters. The problem is then reduced to a quadratic programming problem. However, since it is not necessary to use both parameters simultaneously, the problem can be solved with a simple iterative procedure. Results show that the new model can improve the two-equation model to a certain scale. The adjustment to heat load factor can reduce the error of prediction markedly while that to base load factor influences the error marginally. 3 refs., 11 figs., 2 tabs

TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts

Energy Technology Data Exchange (ETDEWEB)

Shen Yang; Delaglio, Frank [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States); Cornilescu, Gabriel [National Magnetic Resonance Facility (United States); Bax, Ad [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)], E-mail: bax@nih.gov

2009-08-15

NMR chemical shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between {sup 13}C, {sup 15}N and {sup 1}H chemical shifts and backbone torsion angles {phi} and {psi} (Cornilescu et al. J Biomol NMR 13 289-302, 1999). Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addition of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those observed in the crystalline state, the accuracy of predicted {phi} and {psi} angles, equals {+-}13{sup o}. Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chemical shifts, and the neural network component of the program also predicts secondary structure with good accuracy.

Genomic Selection for Predicting Fusarium Head Blight Resistance in a Wheat Breeding Program

Directory of Open Access Journals (Sweden)

Marcio P. Arruda

2015-11-01

Full Text Available Genomic selection (GS is a breeding method that uses marker–trait models to predict unobserved phenotypes. This study developed GS models for predicting traits associated with resistance to head blight (FHB in wheat ( L.. We used genotyping-by-sequencing (GBS to identify 5054 single-nucleotide polymorphisms (SNPs, which were then treated as predictor variables in GS analysis. We compared how the prediction accuracy of the genomic-estimated breeding values (GEBVs was affected by (i five genotypic imputation methods (random forest imputation [RFI], expectation maximization imputation [EMI], -nearest neighbor imputation [kNNI], singular value decomposition imputation [SVDI], and the mean imputation [MNI]; (ii three statistical models (ridge-regression best linear unbiased predictor [RR-BLUP], least absolute shrinkage and operator selector [LASSO], and elastic net; (iii marker density ( = 500, 1500, 3000, and 4500 SNPs; (iv training population (TP size ( = 96, 144, 192, and 218; (v marker-based and pedigree-based relationship matrices; and (vi control for relatedness in TPs and validation populations (VPs. No discernable differences in prediction accuracy were observed among imputation methods. The RR-BLUP outperformed other models in nearly all scenarios. Accuracies decreased substantially when marker number decreased to 3000 or 1500 SNPs, depending on the trait; when sample size of the training set was less than 192; when using pedigree-based instead of marker-based matrix; or when no control for relatedness was implemented. Overall, moderate to high prediction accuracies were observed in this study, suggesting that GS is a very promising breeding strategy for FHB resistance in wheat.

Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection

Science.gov (United States)

Kashid, S. S.; Ghosh, Subimal; Maity, Rajib

2010-12-01

SummarySimultaneous variations in weather and climate over widely separated regions are commonly known as "hydroclimatic teleconnections". Rainfall and runoff patterns, over continents, are found to be significantly teleconnected, with large-scale circulation patterns, through such hydroclimatic teleconnections. Though such teleconnections exist in nature, it is very difficult to model them, due to their inherent complexity. Statistical techniques and Artificial Intelligence (AI) tools gain popularity in modeling hydroclimatic teleconnection, based on their ability, in capturing the complicated relationship between the predictors (e.g. sea surface temperatures) and predictand (e.g., rainfall). Genetic Programming is such an AI tool, which is capable of capturing nonlinear relationship, between predictor and predictand, due to its flexible functional structure. In the present study, gridded multi-site weekly rainfall is predicted from El Niño Southern Oscillation (ENSO) indices, Equatorial Indian Ocean Oscillation (EQUINOO) indices, Outgoing Longwave Radiation (OLR) and lag rainfall at grid points, over the catchment, using Genetic Programming. The predicted rainfall is further used in a Genetic Programming model to predict streamflows. The model is applied for weekly forecasting of streamflow in Mahanadi River, India, and satisfactory performance is observed.

An interprofessional palliative care oncology rehabilitation program: effects on function and predictors of program completion.

Science.gov (United States)

Chasen, M R; Feldstain, A; Gravelle, D; Macdonald, N; Pereira, J

2013-12-01

After treatment, patients with active cancer face a considerable burden from the effects of both the disease and its treatment. The Palliative Rehabilitation Program (prp) is designed to ameliorate disease effects and to improve the patient's functioning. The present study evaluated predictors of program completion and changes in functioning, symptoms, and well-being after the program. The program received referrals for 173 patients who had finished anticancer therapy. Of those 173 patients, 116 with advanced cancer were eligible and enrolled in the 8-week interprofessional prp; 67 completed it. Measures of physical, nutritional, social, and psychological functioning were evaluated at entry to the program and at completion. Participants experienced significant improvements in physical performance (p program not challenging enough), death, and personal or unknown reasons. A normal level of C-reactive protein (program completion. Patients living with advanced cancers who underwent the interprofessional prp experienced significant improvement in functioning across several domains. Program completion can be predicted by a normal level of C-reactive protein.

Program of telluric lines monitoring

Directory of Open Access Journals (Sweden)

Vince I.

2006-01-01

Full Text Available A new observational program of telluric lines monitoring was introduced at Belgrade Astronomical Observatory. The ultimate goal of this program is to investigate the properties of Earth’s atmosphere through modeling the observed profiles of telluric lines. The program is intend to observe infrared molecular oxygen lines that were selected according to spectral sensitivity of the available CCD camera. In this paper we give the initial and the final selection criteria for spectral lines included in the program the description of equipment and procedures used for observations and reduction, a review of preliminary observational results with the estimated precision, and a short discussion on the comparison of the theoretical predictions and the measurements.

Predictive factors of adherence to frequency and duration components in home exercise programs for neck and low back pain: an observational study

Directory of Open Access Journals (Sweden)

Jimeno-Serrano Francisco J

2009-12-01

Full Text Available Abstract Background Evidence suggests that to facilitate physical activity sedentary people may adhere to one component of exercise prescriptions (intensity, duration or frequency without adhering to other components. Some experts have provided evidence for determinants of adherence to different components among healthy people. However, our understanding remains scarce in this area for patients with neck or low back pain. The aims of this study are to determine whether patients with neck or low back pain have different rates of adherence to exercise components of frequency per week and duration per session when prescribed with a home exercise program, and to identify if adherence to both exercise components have distinct predictive factors. Methods A cohort of one hundred eighty-four patients with chronic neck or low back pain who attended physiotherapy in eight primary care centers were studied prospectively one month after intervention. The study had three measurement periods: at baseline (measuring characteristics of patients and pain, at the end of physiotherapy intervention (measuring characteristics of the home exercise program and a month later (measuring professional behaviors during clinical encounters, environmental factors and self-efficacy, and adherence behavior. Results Adherence to duration per session (70.9% ± 7.1 was more probable than adherence to frequency per week (60.7% ± 7.0. Self-efficacy was a relevant factor for both exercise components (p Conclusion We have shown in a clinic-based study that adherence to exercise prescription frequency and duration components have distinct levels and predictive factors. We recommend additional study, and advise that differential attention be given in clinical practice to each exercise component for improving adherence.

Predicting temperature drop rate of mass concrete during an initial cooling period using genetic programming

Science.gov (United States)

Bhattarai, Santosh; Zhou, Yihong; Zhao, Chunju; Zhou, Huawei

2018-02-01

Thermal cracking on concrete dams depends upon the rate at which the concrete is cooled (temperature drop rate per day) within an initial cooling period during the construction phase. Thus, in order to control the thermal cracking of such structure, temperature development due to heat of hydration of cement should be dropped at suitable rate. In this study, an attempt have been made to formulate the relation between cooling rate of mass concrete with passage of time (age of concrete) and water cooling parameters: flow rate and inlet temperature of cooling water. Data measured at summer season (April-August from 2009 to 2012) from recently constructed high concrete dam were used to derive a prediction model with the help of Genetic Programming (GP) software “Eureqa”. Coefficient of Determination (R) and Mean Square Error (MSE) were used to evaluate the performance of the model. The value of R and MSE is 0.8855 and 0.002961 respectively. Sensitivity analysis was performed to evaluate the relative impact on the target parameter due to input parameters. Further, testing the proposed model with an independent dataset those not included during analysis, results obtained from the proposed GP model are close enough to the real field data.

A Predictive Method of Teacher's Structure in China's University (1985--2000)

OpenAIRE

Chen Ling; Pen Guozhong

1987-01-01

In this paper, a predictive model is designed to provide the Educational Department of China with the information necessary to draw up the program (1985--2000) for education development. This model contains two submodels, that is, the predictive model to the annual teacher's number of each title and the fuzzy predictive model to the annual teacher's number of each age. The first submodel is a Markov Chain model and the second submodel is a fuzzy predictive model. Before establishing the secon...

Butterfly valve torque prediction methodology

International Nuclear Information System (INIS)

Eldiwany, B.H.; Sharma, V.; Kalsi, M.S.; Wolfe, K.

1994-01-01

As part of the Motor-Operated Valve (MOV) Performance Prediction Program, the Electric Power Research Institute has sponsored the development of methodologies for predicting thrust and torque requirements of gate, globe, and butterfly MOVs. This paper presents the methodology that will be used by utilities to calculate the dynamic torque requirements for butterfly valves. The total dynamic torque at any disc position is the sum of the hydrodynamic torque, bearing torque (which is induced by the hydrodynamic force), as well as other small torque components (such as packing torque). The hydrodynamic torque on the valve disc, caused by the fluid flow through the valve, depends on the disc angle, flow velocity, upstream flow disturbances, disc shape, and the disc aspect ratio. The butterfly valve model provides sets of nondimensional flow and torque coefficients that can be used to predict flow rate and hydrodynamic torque throughout the disc stroke and to calculate the required actuation torque and the maximum transmitted torque throughout the opening and closing stroke. The scope of the model includes symmetric and nonsymmetric discs of different shapes and aspects ratios in compressible and incompressible fluid applications under both choked and nonchoked flow conditions. The model features were validated against test data from a comprehensive flowloop and in situ test program. These tests were designed to systematically address the effect of the following parameters on the required torque: valve size, disc shapes and disc aspect ratios, upstream elbow orientation and its proximity, and flow conditions. The applicability of the nondimensional coefficients to valves of different sizes was validated by performing tests on 42-in. valve and a precisely scaled 6-in. model. The butterfly valve model torque predictions were found to bound test data from the flow-loop and in situ testing, as shown in the examples provided in this paper

U.S.-Japan Quake Prediction Research

Science.gov (United States)

Kisslinger, Carl; Mikumo, Takeshi; Kanamori, Hiroo

For the seventh time since 1964, a seminar on earthquake prediction has been convened under the U.S.-Japan Cooperation in Science Program. The purpose of the seminar was to provide an opportunity for researchers from the two countries to share recent progress and future plans in the continuing effort to develop the scientific basis for predicting earthquakes and practical means for implementing prediction technology as it emerges. Thirty-six contributors, 15 from Japan and 21 from the U.S., met in Morro Bay, Calif.September 12-14. The following day they traveled to nearby sections of the San Andreas fault, including the site of the Parkfield prediction experiment. The conveners of the seminar were Hiroo Kanamori, Seismological Laboratory, California Institute of Technology (Caltech), for the U.S., and Takeshi Mikumo, Disaster Prevention Research Institute, Kyoto University, for Japan . Funding for the participants came from the U.S. National Science Foundation and the Japan Society forthe Promotion of Science, supplemented by other agencies in both countries.

Fuel channel in-service inspection programs program design for maximum cost effectiveness

International Nuclear Information System (INIS)

Van den Brekel, N.C.

1995-01-01

Inspection is an integral part of fuel channel life management strategy. Inspection data is used to assess the state of reactor core integrity and provide the information necessary to optimize long term maintenance programs. This paper will provide an overview of the structured approach to developing fuel channel inspection programs within OHN. The inspection programs are designed to balance the resources utilized (cost, outage time, and dose expenditure) with the benefits provided by the inspection data obtained (improved knowledge of component status, degradation mechanisms and rates, etc..). The CANDU community has yet to have a fuel channel operate for a full 30 year design life. Since research programs can not fully simulate reactor operating conditions, inspections become an essential feature of the life management strategy as the components age. Inspection programs often include activities designed to develop predictive capability for long term fuel channel behaviour and provide early warning of changes in behaviour. It should be noted that although this paper addresses the design of fuel channel inspection programs, the basic principles presented can be applied to the design of inspection programs for any major power plant component or system. (author)

A comparison of machine learning techniques for survival prediction in breast cancer.

Science.gov (United States)

Vanneschi, Leonardo; Farinaccio, Antonella; Mauri, Giancarlo; Antoniotti, Mauro; Provero, Paolo; Giacobini, Mario

2011-05-11

The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

A comparison of machine learning techniques for survival prediction in breast cancer

Directory of Open Access Journals (Sweden)

Vanneschi Leonardo

2011-05-01

Full Text Available Abstract Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data.

Perceptual Speed and Psychomotor Ability Predict Laparoscopic Skill Acquisition on a Simulator

NARCIS (Netherlands)

Groenier, Marleen; Groenier, Klaas H.; Miedema, Heleen A. T.; Broeders, Ivo A. M. J.

2015-01-01

OBJECTIVE: Performing minimally invasive surgery puts high demands on a surgeon's cognitive and psychomotor abilities. Assessment of these abilities can be used to predict a surgeon's learning curve, to create individualized training programs, and ultimately in selection programs for surgical

CERAPP: Collaborative estrogen receptor activity prediction project

DEFF Research Database (Denmark)

Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra

2016-01-01

). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. oBjectives: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project...... States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical......: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. conclusion: This project demonstrated...

Factors predicting health practitioners' awareness of UNHS program in Malaysian non-public hospitals.

Science.gov (United States)

Ismail, Abdussalaam Iyanda; Abdul Majid, Abdul Halim; Zakaria, Mohd Normani; Abdullah, Nor Azimah Chew; Hamzah, Sulaiman; Mukari, Siti Zamratol-Mai Sarah

2018-06-01

The current study aims to examine the effects of human resource (measured with the perception of health workers' perception towards UNHS), screening equipment, program layout and screening techniques on healthcare practitioners' awareness (measured with knowledge) of universal newborn hearing screening (UNHS) in Malaysian non-public hospitals. Via cross sectional approach, the current study collected data using a validated questionnaire to obtain information on the awareness of UNHS program among the health practitioners and to test the formulated hypotheses. 51, representing 81% response rate, out of 63 questionnaires distributed to the health professionals were returned and usable for statistical analysis. The survey instruments involving healthcare practitioners' awareness, human resource, program layout, screening instrument, and screening techniques instruments were adapted and scaled with 7-point Likert scale ranging from 1 (little) to 7 (many). Partial Least Squares (PLS) algorithm and bootstrapping techniques were employed to test the hypotheses of the study. With the result involving beta values, t-values and p-values (i.e. β=0.478, t=1.904, phealth practitioners. Likewise, program layout, human resource, screening technique and screening instrument explain 71% variance in health practitioners' awareness. Health practitioners' awareness is explained by program layout, human resource, and screening instrument with effect size (f2) of 0.065, 0.621, and 0.211 respectively, indicating that program layout, human resource, and screening instrument have small, large and medium effect size on health practitioners' awareness respectively. However, screening technique has zero effect on health practitioners' awareness, indicating the reason why T-statistics is not significant. Having started the UNHS program in 2003, non-public hospitals have more experienced and well-trained employees dealing with the screening tools and instrument, and the program layout is well

Operational budgeting using fuzzy goal programming

OpenAIRE

Saeed Mohammadi; Kamran Feizi; Ali Khatami Firouz Abadi

2013-01-01

Having an efficient budget normally has different advantages such as measuring the performance of various organizations, setting appropriate targets and promoting managers based on their achievements. However, any budgeting planning requires prediction of different cost components. There are various methods for budgeting planning such as incremental budgeting, program budgeting, zero based budgeting and performance budgeting. In this paper, we present a fuzzy goal programming to estimate oper...

Can Thrifty Gene(s or Predictive Fetal Programming for Thriftiness Lead to Obesity?

Directory of Open Access Journals (Sweden)

Ulfat Baig

2011-01-01

Full Text Available Obesity and related disorders are thought to have their roots in metabolic “thriftiness” that evolved to combat periodic starvation. The association of low birth weight with obesity in later life caused a shift in the concept from thrifty gene to thrifty phenotype or anticipatory fetal programming. The assumption of thriftiness is implicit in obesity research. We examine here, with the help of a mathematical model, the conditions for evolution of thrifty genes or fetal programming for thriftiness. The model suggests that a thrifty gene cannot exist in a stable polymorphic state in a population. The conditions for evolution of thrifty fetal programming are restricted if the correlation between intrauterine and lifetime conditions is poor. Such a correlation is not observed in natural courses of famine. If there is fetal programming for thriftiness, it could have evolved in anticipation of social factors affecting nutrition that can result in a positive correlation.

Development of object oriented program `SONSHO` for strength evaluation. Manual of Version 4.0 program

Energy Technology Data Exchange (ETDEWEB)

Hosogai, Hiromi [Joyo Industries Co. Ltd., Tokai, Ibaraki (Japan); Kasahara, Naoto

1998-07-01

Object Oriented Program `SONSHO` predicts creep fatigue damage factors based on Elevated Temperature Structural Design Guide for `Monju` and other various procedures from stress classification data obtained from structural analysis results. From view point of program implementation, it is required that external programs interface and frequent revise from update of material and creep fatigue evaluation methods. Object oriented approach was continuously introduced to improve these aspects of the program. Version 4.0 has the following new functions. (1) Material strength library was implemented as an independent program module based on Microsoft Active X control and 32bitDLL technologies, which can be accessed by general Windows programs. (2) Self instruction system `Wizard` enables manual less operation. (3) Microsoft common object model (COM) was adopted for program interface, and this program can communicate with Excel sheet data on memory. Sonsho Ver.4.0 can work on Windows 95 or Windows NT4.0. Microsoft Visual Basic 5.0 (Enterprose Edition) and Microsoft FORTRAN Power Station 4.0 were adopted for program. (author)

Improved Modeling and Prediction of Surface Wave Amplitudes

Science.gov (United States)

2017-05-31

AFRL-RV-PS- AFRL-RV-PS- TR-2017-0162 TR-2017-0162 IMPROVED MODELING AND PREDICTION OF SURFACE WAVE AMPLITUDES Jeffry L. Stevens, et al. Leidos...data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented...SUBTITLE Improved Modeling and Prediction of Surface Wave Amplitudes 5a. CONTRACT NUMBER FA9453-14-C-0225 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER

Application of 'SPICE' to predict temperature distribution in heat pipes

Energy Technology Data Exchange (ETDEWEB)

Li, H M; Liu, Y; Damodaran, M [Nanyang Technological Univ., Singapore (SG). School of Mechanical and Production Engineering

1991-11-01

This article presents a new alternative approach to predict temperature distribution in heat pipes. In this method, temperature distribution in a heat pipe, modelled as an analogous electrical circuit, is predicted by applying SPICE, a general-purpose circuit simulation program. SPICE is used to simulate electrical circuit designs before the prototype is assembled. Useful predictions are obtained for heat pipes with and without adiabatic sections and for heat pipes with various evaporator and condenser lengths. Comparison of the predicted results with experiments demonstrates fairly good agreement. It is also shown how interdisciplinary developments could be used appropriately. (author).

Application of an artificial intelligence program to therapy of high-risk surgical patients.

Science.gov (United States)

Patil, R S; Adibi, J; Shoemaker, W C

1996-11-01

We developed an artificial intelligence program from a large computerized database of hemodynamic and oxygen transport measurements together with prior studies defining survivors' values, outcome predictors, and a branched-chain decision tree. The artificial intelligence program was then tested on the data of 100 survivors and 100 nonsurvivors not used for the development of the program or other analyses. Using the predictor as a surrogate outcome measure, the therapy recommended by the program improved the predicted outcome 3.16% per therapeutic intervention while the actual therapy given increased outcome 1.86% in surviving patients; the artificial intelligence-recommended therapy improved outcome 7.9% in nonsurvivors, while the actual therapy given increased predicted outcome -0.29% in nonsurvivors (p < .05). There were fewer patients whose predicted outcome decreased after recommended treatment (14%) than after the actual therapy given (37%). Review of therapy recommended by the program did not reveal instances of inappropriate or potentially harmful recommendations.

Status of CHAP: composite HTGR analysis program

International Nuclear Information System (INIS)

Secker, P.A.; Gilbert, J.S.

1975-12-01

Development of an HTGR accident simulation program is in progress for the prediction of the overall HTGR plant transient response to various initiating events. The status of the digital computer program named CHAP (Composite HTGR Analysis Program) as of June 30, 1975, is given. The philosophy, structure, and capabilities of the CHAP code are discussed. Mathematical descriptions are given for those HTGR components that have been modeled. Component model validation and evaluation using auxiliary analysis codes are also discussed

Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree

Directory of Open Access Journals (Sweden)

S. Samadianfard

2017-01-01

Full Text Available Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23 presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21 used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13 proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16 used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11 evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities. Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned

Prediction of Lower Extremity Movement by Cyclograms

Directory of Open Access Journals (Sweden)

P. Kutilek

2012-01-01

Full Text Available Human gait is nowadays undergoing extensive analysis. Predictions of leg movements can be used for orthosis and prosthesis programming, and also for rehabilitation. Our work focuses on predicting human gait with the use of angle-angle diagrams, also called cyclograms. In conjunction with artificial intelligence, cyclograms offer a wide area of medical applications. We have identified cyclogram characteristics such as the slope and the area of the cyclogram for a neural network learning algorithm. Neural networks learned by cyclograms offer wide applications in prosthesis control systems.

Perceptual speed and psychomotor ability predict laparoscopic skill acquisition on a simulator

NARCIS (Netherlands)

Groenier, Marleen; Groenier, Klaas H; Miedema, Helene A.T.; Broeders, Ivo Adriaan Maria Johannes

2015-01-01

Objective Performing minimally invasive surgery puts high demands on a surgeon’s cognitive and psychomotor abilities. Assessment of these abilities can be used to predict a surgeon’s learning curve, to create individualized training programs, and ultimately in selection programs for surgical

Verification tests for GRAD, a computer program to predict nonuniform deformation and failure of Zr-2.5 wt percent Nb pressure tubes during a postulated loss-of-coolant accident

International Nuclear Information System (INIS)

Shewfelt, R.S.W.; Godin, D.P.

1985-03-01

During a postulated loss-of-coolant accident in a CANDU reactor, the temperature of the pressure tubes could rise sufficiently so that ballooning could occur. It is also likely that there would be a variation in temperature around the tube circumference, causing the deformation to be nonuniform. Since the deformation of the pressure tube controls how the core heat is transferred to the surrounding moderator, which is a large heat sink, a computer program, GRAD, has been developed to predict this nonuniform deformation. Numerous biaxial creep tests were done, where the temperature of internally pressurized sections of Zr-2.5 wt percent Nb pressure tubes were ramped to check the ability of GRAD to predict the resulting nonuniform deformation and possible tube failure. GRAD was successful in predicting the average transverse creep strain observed during the tests and the local transverse creep strain at the end of the tests. GRAD was also able to predict the failure time and average transverse creep strain at failure for all the specimens that failed

Stream Flow Prediction by Remote Sensing and Genetic Programming

Science.gov (United States)

Chang, Ni-Bin

2009-01-01

A genetic programming (GP)-based, nonlinear modeling structure relates soil moisture with synthetic-aperture-radar (SAR) images to present representative soil moisture estimates at the watershed scale. Surface soil moisture measurement is difficult to obtain over a large area due to a variety of soil permeability values and soil textures. Point measurements can be used on a small-scale area, but it is impossible to acquire such information effectively in large-scale watersheds. This model exhibits the capacity to assimilate SAR images and relevant geoenvironmental parameters to measure soil moisture.

The role of radiation damage analysis in the fusion program

International Nuclear Information System (INIS)

Doran, D.G.

1983-01-01

The objective of radiation damage analysis is the prediction of the performance of facility components exposed to a radiation environment. The US Magnetic Fusion Energy materials program includes an explicit damage analysis activity within the Damage Analysis and Fundamental Studies (DAFS) Program. Many of the papers in these Proceedings report work done directly or indirectly in support of the DAFS program. The emphasis of this program is on developing procedures, based on an understanding of damage mechanisms, for applying data obtained in diverse radiation environments to the prediction of component behavior in fusion devices. It is assumed that the Fusion Materials Irradiation Test Facility will be available in the late 1980s to test (and calibrate where necessary) correlation procedures to the high fluences expected in commercial reactors. (orig.)

RNA-SSPT: RNA Secondary Structure Prediction Tools.

Science.gov (United States)

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

Natural gas consumption prediction in Slovenian industry – a case study

Directory of Open Access Journals (Sweden)

Kovačič Miha

2016-09-01

Full Text Available In accordance with the regulations of the Energy Agency of the Republic of Slovenia, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted and the actually supplied quantities of natural gas. Yearly charges for these differences represent up to 2% of supplied natural gas costs. All the natural gas users, especially industry, have huge problems finding the proper method for efficient natural gas consumption prediction and, consequently, the decreasing of mentioned costs. In this study, prediction of the natural gas consumption in Štore Steel Ltd. (steel plant is presented. On the basis of production data, several models for natural gas consumption have been developed using linear regression, genetic programming and artificial neural network methods. The genetic programming approach outperformed linear regression and artificial neural networks.

Scoring function to predict solubility mutagenesis

Directory of Open Access Journals (Sweden)

Deutsch Christopher

2010-10-01

Full Text Available Abstract Background Mutagenesis is commonly used to engineer proteins with desirable properties not present in the wild type (WT protein, such as increased or decreased stability, reactivity, or solubility. Experimentalists often have to choose a small subset of mutations from a large number of candidates to obtain the desired change, and computational techniques are invaluable to make the choices. While several such methods have been proposed to predict stability and reactivity mutagenesis, solubility has not received much attention. Results We use concepts from computational geometry to define a three body scoring function that predicts the change in protein solubility due to mutations. The scoring function captures both sequence and structure information. By exploring the literature, we have assembled a substantial database of 137 single- and multiple-point solubility mutations. Our database is the largest such collection with structural information known so far. We optimize the scoring function using linear programming (LP methods to derive its weights based on training. Starting with default values of 1, we find weights in the range [0,2] so that predictions of increase or decrease in solubility are optimized. We compare the LP method to the standard machine learning techniques of support vector machines (SVM and the Lasso. Using statistics for leave-one-out (LOO, 10-fold, and 3-fold cross validations (CV for training and prediction, we demonstrate that the LP method performs the best overall. For the LOOCV, the LP method has an overall accuracy of 81%. Availability Executables of programs, tables of weights, and datasets of mutants are available from the following web page: http://www.wsu.edu/~kbala/OptSolMut.html.

A computer graphics program system for protein structure representation.

Science.gov (United States)

Ross, A M; Golub, E E

1988-01-01

We have developed a computer graphics program system for the schematic representation of several protein secondary structure analysis algorithms. The programs calculate the probability of occurrence of alpha-helix, beta-sheet and beta-turns by the method of Chou and Fasman and assign unique predicted structure to each residue using a novel conflict resolution algorithm based on maximum likelihood. A detailed structure map containing secondary structure, hydrophobicity, sequence identity, sequence numbering and the location of putative N-linked glycosylation sites is then produced. In addition, helical wheel diagrams and hydrophobic moment calculations can be performed to further analyze the properties of selected regions of the sequence. As they require only structure specification as input, the graphics programs can easily be adapted for use with other secondary structure prediction schemes. The use of these programs to analyze protein structure-function relationships is described and evaluated. PMID:2832829

Collaboratory for the Study of Earthquake Predictability

Science.gov (United States)

Schorlemmer, D.; Jordan, T. H.; Zechar, J. D.; Gerstenberger, M. C.; Wiemer, S.; Maechling, P. J.

2006-12-01

Earthquake prediction is one of the most difficult problems in physical science and, owing to its societal implications, one of the most controversial. The study of earthquake predictability has been impeded by the lack of an adequate experimental infrastructure---the capability to conduct scientific prediction experiments under rigorous, controlled conditions and evaluate them using accepted criteria specified in advance. To remedy this deficiency, the Southern California Earthquake Center (SCEC) is working with its international partners, which include the European Union (through the Swiss Seismological Service) and New Zealand (through GNS Science), to develop a virtual, distributed laboratory with a cyberinfrastructure adequate to support a global program of research on earthquake predictability. This Collaboratory for the Study of Earthquake Predictability (CSEP) will extend the testing activities of SCEC's Working Group on Regional Earthquake Likelihood Models, from which we will present first results. CSEP will support rigorous procedures for registering prediction experiments on regional and global scales, community-endorsed standards for assessing probability-based and alarm-based predictions, access to authorized data sets and monitoring products from designated natural laboratories, and software to allow researchers to participate in prediction experiments. CSEP will encourage research on earthquake predictability by supporting an environment for scientific prediction experiments that allows the predictive skill of proposed algorithms to be rigorously compared with standardized reference methods and data sets. It will thereby reduce the controversies surrounding earthquake prediction, and it will allow the results of prediction experiments to be communicated to the scientific community, governmental agencies, and the general public in an appropriate research context.

Prediction of drop time and impact velocity of rod cluster control assembly

International Nuclear Information System (INIS)

Choi, Kee Sung; Yim, Jeong Sik; Kim, Il Kon; Kim, Kyu Tae

1992-01-01

This paper deals with the drop modelling of rod cluster control assembly(RCCA) and the prediction of drop time and impact velocity of RCCA at scram event. On the scram, RCCA, dropping into the guide thimble of fuel assembly by the gravity, is subject to retarding forces such as hydraulic resistance, mechanical friction and buoyancy. Considering these retarding forces RCCA dynamic equation is derived and computerized it to solve the equation in conjunction with fluid equation which is coupled with the motion of the RCCA. Because the equation is nonlinear, coupled with fluid equations, the program is written in FORTRAN using numerical method in order to calculate the drop distance and velocity with time increment. To verify the program, its results are compared with those of other fuel vendors. Predicting identical tendency as other fuel vendors and the deviation is insignificant in values this program is expected to be used for predicting the drop time and impact velocity of RCCA when the parameters affecting the control rod drop time and impact velocity changes are occurred

Evaluation of nuclear power plant environmental impact prediction, based on monitoring programs. Summary and recommendations

International Nuclear Information System (INIS)

Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

1977-02-01

An evaluation of the effectivenss of non-radiological environmental monitoring programs is presented. The monitoring programs for Monticello, Haddam Neck, and Millstone Nuclear Generating Plants are discussed. Recommendations for improvements in monitoring programs are presented

IRSS: a web-based tool for automatic layout and analysis of IRES secondary structure prediction and searching system in silico

Directory of Open Access Journals (Sweden)

Hong Jun-Jie

2009-05-01

Full Text Available Abstract Background Internal ribosomal entry sites (IRESs provide alternative, cap-independent translation initiation sites in eukaryotic cells. IRES elements are important factors in viral genomes and are also useful tools for bi-cistronic expression vectors. Most existing RNA structure prediction programs are unable to deal with IRES elements. Results We designed an IRES search system, named IRSS, to obtain better results for IRES prediction. RNA secondary structure prediction and comparison software programs were implemented to construct our two-stage strategy for the IRSS. Two software programs formed the backbone of IRSS: the RNAL fold program, used to predict local RNA secondary structures by minimum free energy method; and the RNA Align program, used to compare predicted structures. After complete viral genome database search, the IRSS have low error rate and up to 72.3% sensitivity in appropriated parameters. Conclusion IRSS is freely available at this website http://140.135.61.9/ires/. In addition, all source codes, precompiled binaries, examples and documentations are downloadable for local execution. This new search approach for IRES elements will provide a useful research tool on IRES related studies.

Preliminary evaluation of the BIODOSE computer program

International Nuclear Information System (INIS)

Bonner, N.A.; Ng, Y.C.

1979-09-01

The BIODOSE computer program simulates the environmental transport of radionuclides released to surface water and predicts the dosage to humans. We have evaluated the program for its suitability to the needs of the Nuclear Regulatory Commission Waste Management Program. In particular, it is an evaluation to determine whether BIODOSE models account for the significant pathways and mechanisms resulting in radiological doses to man. In general, BIODOSE is a satisfactory code for converting radionuclide releases to the aqueous environment into doses to man

Computational prediction of chemical reactions: current status and outlook.

Science.gov (United States)

Engkvist, Ola; Norrby, Per-Ola; Selmi, Nidhal; Lam, Yu-Hong; Peng, Zhengwei; Sherer, Edward C; Amberg, Willi; Erhard, Thomas; Smyth, Lynette A

2018-06-01

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quality Assurance Program Plan for the Waste Isolation Pilot Plant Experimental-Waste Characterization Program

International Nuclear Information System (INIS)

1991-01-01

This Quality Assurance Program Plan (QAPP) identifies the quality of data necessary to meet the specific objectives associated with the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) Experimental-Waste Characterization Program (the Program). DOE plans to conduct experiments in the WIPP during a Test Phase of approximately 5 years. These experiments will be conducted to reduce the uncertainties associated with the prediction of several processes (e.g., gas generation) that may influence repository performance. The results of the experiments will be used to assess the ability of the WIPP to meet regulatory requirements for the long-term protection of human health and the environment from the disposal of TRU wastes. 37 refs., 25 figs., 18 tabs

Rock mechanics in the National Waste Terminal Storage Program

International Nuclear Information System (INIS)

Monsees, J.E.; Wigley, M.R.

1982-01-01

The overall objective of the rock mechanics program of the Office of Nuclear Waste Isolation is to predict the response of a rock mass hosting a waste repository during its construction, operation, and postoperational phases. The operational phase is expected to be 50 to 100 yr; the postoperational phase will last until the repository no longer poses any potential hazard to the biosphere, a period that may last several thousand years. The rock mechanics program is concerned with near-field effects on mine stability, as well as far-field effects relative to the overall integrity of the geologic waste isolation system. To accomplish these objectives, the rock mechanics program has established interactive studies in numerical simulation, laboratory testing, and field testing. The laboratory and field investigations provide input to the numerical simulations and give an opportunity for verification and validation of the predictive capabilities of the computer codes. Ultimately the computer codes will be used to predict the response of the geologic system to the development of a repository. 3 references, 5 figures

A nonproprietary, nonsecret program for calculating Stirling cryocoolers

Science.gov (United States)

Martini, W. R.

1985-01-01

A design program for an integrated Stirling cycle cryocooler was written on an IBM-PC computer. The program is easy to use and shows the trends and itemizes the losses. The calculated results were compared with some measured performance values. The program predicts somewhat optimistic performance and needs to be calibrated more with experimental measurements. Adding a multiplier to the friction factor can bring the calculated rsults in line with the limited test results so far available. The program is offered as a good framework on which to build a truly useful design program for all types of cryocoolers.

Wing Leading Edge RCC Rapid Response Damage Prediction Tool (IMPACT2)

Science.gov (United States)

Clark, Robert; Cottter, Paul; Michalopoulos, Constantine

2013-01-01

This rapid response computer program predicts Orbiter Wing Leading Edge (WLE) damage caused by ice or foam impact during a Space Shuttle launch (Program "IMPACT2"). The program was developed after the Columbia accident in order to assess quickly WLE damage due to ice, foam, or metal impact (if any) during a Shuttle launch. IMPACT2 simulates an impact event in a few minutes for foam impactors, and in seconds for ice and metal impactors. The damage criterion is derived from results obtained from one sophisticated commercial program, which requires hours to carry out simulations of the same impact events. The program was designed to run much faster than the commercial program with prediction of projectile threshold velocities within 10 to 15% of commercial-program values. The mathematical model involves coupling of Orbiter wing normal modes of vibration to nonlinear or linear springmass models. IMPACT2 solves nonlinear or linear impact problems using classical normal modes of vibration of a target, and nonlinear/ linear time-domain equations for the projectile. Impact loads and stresses developed in the target are computed as functions of time. This model is novel because of its speed of execution. A typical model of foam, or other projectile characterized by material nonlinearities, impacting an RCC panel is executed in minutes instead of hours needed by the commercial programs. Target damage due to impact can be assessed quickly, provided that target vibration modes and allowable stress are known.

U.S. aerospace industry opinion of the effect of computer-aided prediction-design technology on future wind-tunnel test requirements for aircraft development programs

Science.gov (United States)

Treon, S. L.

1979-01-01

A survey of the U.S. aerospace industry in late 1977 suggests that there will be an increasing use of computer-aided prediction-design technology (CPD Tech) in the aircraft development process but that, overall, only a modest reduction in wind-tunnel test requirements from the current level is expected in the period through 1995. Opinions were received from key spokesmen in 23 of the 26 solicited major companies or corporate divisions involved in the design and manufacture of nonrotary wing aircraft. Development programs for nine types of aircraft related to test phases and wind-tunnel size and speed range were considered.

An assessment of the validity of inelastic design analysis methods by comparisons of predictions with test results

International Nuclear Information System (INIS)

Corum, J.M.; Clinard, J.A.; Sartory, W.K.

1976-01-01

The use of computer programs that employ relatively complex constitutive theories and analysis procedures to perform inelastic design calculations on fast reactor system components introduces questions of validation and acceptance of the analysis results. We may ask ourselves, ''How valid are the answers.'' These questions, in turn, involve the concepts of verification of computer programs as well as qualification of the computer programs and of the underlying constitutive theories and analysis procedures. This paper addresses the latter - the qualification of the analysis methods for inelastic design calculations. Some of the work underway in the United States to provide the necessary information to evaluate inelastic analysis methods and computer programs is described, and typical comparisons of analysis predictions with inelastic structural test results are presented. It is emphasized throughout that rather than asking ourselves how valid, or correct, are the analytical predictions, we might more properly question whether or not the combination of the predictions and the associated high-temperature design criteria leads to an acceptable level of structural integrity. It is believed that in this context the analysis predictions are generally valid, even though exact correlations between predictions and actual behavior are not obtained and cannot be expected. Final judgment, however, must be reserved for the design analyst in each specific case. (author)

Real-Time Optimization for Economic Model Predictive Control

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca

2012-01-01

In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...

Input-constrained model predictive control via the alternating direction method of multipliers

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Frison, Gianluca; Andersen, Martin S.

2014-01-01

This paper presents an algorithm, based on the alternating direction method of multipliers, for the convex optimal control problem arising in input-constrained model predictive control. We develop an efficient implementation of the algorithm for the extended linear quadratic control problem (LQCP......) with input and input-rate limits. The algorithm alternates between solving an extended LQCP and a highly structured quadratic program. These quadratic programs are solved using a Riccati iteration procedure, and a structure-exploiting interior-point method, respectively. The computational cost per iteration...... is quadratic in the dimensions of the controlled system, and linear in the length of the prediction horizon. Simulations show that the approach proposed in this paper is more than an order of magnitude faster than several state-of-the-art quadratic programming algorithms, and that the difference in computation...

Genomic Prediction of Barley Hybrid Performance

Directory of Open Access Journals (Sweden)

Norman Philipp

2016-07-01

Full Text Available Hybrid breeding in barley ( L. offers great opportunities to accelerate the rate of genetic improvement and to boost yield stability. A crucial requirement consists of the efficient selection of superior hybrid combinations. We used comprehensive phenotypic and genomic data from a commercial breeding program with the goal of examining the potential to predict the hybrid performances. The phenotypic data were comprised of replicated grain yield trials for 385 two-way and 408 three-way hybrids evaluated in up to 47 environments. The parental lines were genotyped using a 3k single nucleotide polymorphism (SNP array based on an Illumina Infinium assay. We implemented ridge regression best linear unbiased prediction modeling for additive and dominance effects and evaluated the prediction ability using five-fold cross validations. The prediction ability of hybrid performances based on general combining ability (GCA effects was moderate, amounting to 0.56 and 0.48 for two- and three-way hybrids, respectively. The potential of GCA-based hybrid prediction requires that both parental components have been evaluated in a hybrid background. This is not necessary for genomic prediction for which we also observed moderate cross-validated prediction abilities of 0.51 and 0.58 for two- and three-way hybrids, respectively. This exemplifies the potential of genomic prediction in hybrid barley. Interestingly, prediction ability using the two-way hybrids as training population and the three-way hybrids as test population or vice versa was low, presumably, because of the different genetic makeup of the parental source populations. Consequently, further research is needed to optimize genomic prediction approaches combining different source populations in barley.

Flow-covariate prediction of stream pesticide concentrations.

Science.gov (United States)

Mosquin, Paul L; Aldworth, Jeremy; Chen, Wenlin

2018-01-01

Potential peak functions (e.g., maximum rolling averages over a given duration) of annual pesticide concentrations in the aquatic environment are important exposure parameters (or target quantities) for ecological risk assessments. These target quantities require accurate concentration estimates on nonsampled days in a monitoring program. We examined stream flow as a covariate via universal kriging to improve predictions of maximum m-day (m = 1, 7, 14, 30, 60) rolling averages and the 95th percentiles of atrazine concentration in streams where data were collected every 7 or 14 d. The universal kriging predictions were evaluated against the target quantities calculated directly from the daily (or near daily) measured atrazine concentration at 32 sites (89 site-yr) as part of the Atrazine Ecological Monitoring Program in the US corn belt region (2008-2013) and 4 sites (62 site-yr) in Ohio by the National Center for Water Quality Research (1993-2008). Because stream flow data are strongly skewed to the right, 3 transformations of the flow covariate were considered: log transformation, short-term flow anomaly, and normalized Box-Cox transformation. The normalized Box-Cox transformation resulted in predictions of the target quantities that were comparable to those obtained from log-linear interpolation (i.e., linear interpolation on the log scale) for 7-d sampling. However, the predictions appeared to be negatively affected by variability in regression coefficient estimates across different sample realizations of the concentration time series. Therefore, revised models incorporating seasonal covariates and partially or fully constrained regression parameters were investigated, and they were found to provide much improved predictions in comparison with those from log-linear interpolation for all rolling average measures. Environ Toxicol Chem 2018;37:260-273. © 2017 SETAC. © 2017 SETAC.

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

Science.gov (United States)

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

Students' Midprogram Content Area Performance as a Predictor of End-of-Program NCLEX Readiness.

Science.gov (United States)

Brussow, Jennifer A; Dunham, Michelle

2017-12-22

Many programs have implemented end-of-program predictive testing to identify students at risk of NCLEX-RN failure. Unfortunately, for many students, end-of-program testing comes too late. Regression and relative importance analysis were used to explore relationships between 9 content area assessments and an end-of-program assessment shown to be predictive of NCLEX-RN success. Results indicate that scores on assessments for content areas such as medical surgical nursing and care of children are predictive of end-of-program test scores, suggesting that instructors should provide remediation at the first sign of lagging performance.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in anyway or used commercially without permission from the journal.

Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Shen Yang; Bax, Ad, E-mail: bax@nih.gov [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)

2013-07-15

A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, {>=}90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed ({phi}, {psi}) torsion angles of ca 12 Masculine-Ordinal-Indicator . TALOS-N also reports sidechain {chi}{sup 1} rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.

Ethical dilemmas in genetic testing: examples from the Cuban program for predictive diagnosis of hereditary ataxias.

Science.gov (United States)

Mariño, Tania Cruz; Armiñán, Rubén Reynaldo; Cedeño, Humberto Jorge; Mesa, José Miguel Laffita; Zaldivar, Yanetza González; Rodríguez, Raúl Aguilera; Santos, Miguel Velázquez; Mederos, Luis Enrique Almaguer; Herrera, Milena Paneque; Pérez, Luis Velázquez

2011-06-01

Predictive testing protocols are intended to help patients affected with hereditary conditions understand their condition and make informed reproductive choices. However, predictive protocols may expose clinicians and patients to ethical dilemmas that interfere with genetic counseling and the decision making process. This paper describes ethical dilemmas in a series of five cases involving predictive testing for hereditary ataxias in Cuba. The examples herein present evidence of the deeply controversial situations faced by both individuals at risk and professionals in charge of these predictive studies, suggesting a need for expanded guidelines to address such complexities.

Student Use of Physics to Make Sense of Incomplete but Functional VPython Programs in a Lab Setting

Science.gov (United States)

Weatherford, Shawn A.

2011-12-01

Computational activities in Matter & Interactions, an introductory calculus-based physics course, have the instructional goal of providing students with the experience of applying the same set of a small number of fundamental principles to model a wide range of physical systems. However there are significant instructional challenges for students to build computer programs under limited time constraints, especially for students who are unfamiliar with programming languages and concepts. Prior attempts at designing effective computational activities were successful at having students ultimately build working VPython programs under the tutelage of experienced teaching assistants in a studio lab setting. A pilot study revealed that students who completed these computational activities had significant difficultly repeating the exact same tasks and further, had difficulty predicting the animation that would be produced by the example program after interpreting the program code. This study explores the interpretation and prediction tasks as part of an instructional sequence where students are asked to read and comprehend a functional, but incomplete program. Rather than asking students to begin their computational tasks with modifying program code, we explicitly ask students to interpret an existing program that is missing key lines of code. The missing lines of code correspond to the algebraic form of fundamental physics principles or the calculation of forces which would exist between analogous physical objects in the natural world. Students are then asked to draw a prediction of what they would see in the simulation produced by the VPython program and ultimately run the program to evaluate the students' prediction. This study specifically looks at how the participants use physics while interpreting the program code and creating a whiteboard prediction. This study also examines how students evaluate their understanding of the program and modification goals at the

Assessing participation in community-based physical activity programs in Brazil.

Science.gov (United States)

Reis, Rodrigo S; Yan, Yan; Parra, Diana C; Brownson, Ross C

2014-01-01

This study aimed to develop and validate a risk prediction model to examine the characteristics that are associated with participation in community-based physical activity programs in Brazil. We used pooled data from three surveys conducted from 2007 to 2009 in state capitals of Brazil with 6166 adults. A risk prediction model was built considering program participation as an outcome. The predictive accuracy of the model was quantified through discrimination (C statistic) and calibration (Brier score) properties. Bootstrapping methods were used to validate the predictive accuracy of the final model. The final model showed sex (women: odds ratio [OR] = 3.18, 95% confidence interval [CI] = 2.14-4.71), having less than high school degree (OR = 1.71, 95% CI = 1.16-2.53), reporting a good health (OR = 1.58, 95% CI = 1.02-2.24) or very good/excellent health (OR = 1.62, 95% CI = 1.05-2.51), having any comorbidity (OR = 1.74, 95% CI = 1.26-2.39), and perceiving the environment as safe to walk at night (OR = 1.59, 95% CI = 1.18-2.15) as predictors of participation in physical activity programs. Accuracy indices were adequate (C index = 0.778, Brier score = 0.031) and similar to those obtained from bootstrapping (C index = 0.792, Brier score = 0.030). Sociodemographic and health characteristics as well as perceptions of the environment are strong predictors of participation in community-based programs in selected cities of Brazil.

No Future Without the Past? Predicting Churn in the Face of Customer Privacy

NARCIS (Netherlands)

Holtrop, Niels; Wieringa, Jakob; Gijsenberg, Maarten; Verhoef, Pieter C

For customer-centric firms, churn prediction plays a central role in churn management programs. Methodological advances have emphasized the use of customer panel data to model the dynamic evolution of a customer base to improve churn predictions. However, pressure from policy makers and the public

No Future Without the Past? Predicting Churn in the Face of Customer Privacy

NARCIS (Netherlands)

Holtrop, Niels; Wieringa, J.E.; Gijsenberg, M.J.; Verhoef, P.C.

2017-01-01

For customer-centric firms, churn prediction plays a central role in churn management programs. Methodological advances have emphasized the use of customer panel data to model the dynamic evolution of a customer base to improve churn predictions. However, pressure from policy makers and the public

Bite weight prediction from acoustic recognition of chewing

NARCIS (Netherlands)

Amft, O.D.; Kusserow, M.; Tröster, G.

2009-01-01

Automatic dietary monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This paper presents an approach to predict weight of individual bites taken. We utilize a pattern recognition procedure to spot chewing cycles and food type in

Predicting Study Abroad Intentions Based on the Theory of Planned Behavior

Science.gov (United States)

Schnusenberg, Oliver; de Jong, Pieter; Goel, Lakshmi

2012-01-01

The emphasis on study abroad programs is growing in the academic context as U.S. based universities seek to incorporate a global perspective in education. Using a model that has underpinnings in the theory of planned behavior (TPB), we predict students' intention to participate in short-term study abroad program. We use TPB to identify behavioral,…

MDOT Pavement Management System : Prediction Models and Feedback System

Science.gov (United States)

2000-10-01

As a primary component of a Pavement Management System (PMS), prediction models are crucial for one or more of the following analyses: : maintenance planning, budgeting, life-cycle analysis, multi-year optimization of maintenance works program, and a...

Program Director Participation in a Leadership and Management Skills Fellowship and Characteristics of Program Quality.

Science.gov (United States)

Carek, Peter J; Mims, Lisa D; Conry, Colleen M; Maxwell, Lisa; Greenwood, Vicki; Pugno, Perry A

2015-01-01

The association between a residency program director completing a leadership and management skills fellowship and characteristics of quality and innovation of his/her residency program has not been studied. Therefore, the aim of this study is to examine the association between a residency program director's completion of a specific fellowship addressing these skills (National Institute for Program Director Development or NIPDD) and characteristics of quality and innovation of the program they direct. Using information from the American Academy of Family Physicians (AAFP), National Resident Matching Program (NRMP) and FREIDA® program characteristics were obtained. Descriptive statistics were used to summarize the data. The relationship between programs with a NIPDD graduate as director and program quality measures and indicators of innovation was analyzed using both chi square and logistic regression. Initial analyses showed significant associations between the NIPDD graduate status of a program director and regional location, mean years of program director tenure, and the program's 5-year aggregate ABFM board pass rate from 2007--2011. After grouping the programs into tertiles, the regression model showed significant positive associations with programs offering international experiences and being a NIPDD graduate. Program director participation in a fellowship addressing leadership and management skills (ie, NIPDD) was found to be associated with higher pass rates of new graduates on a Board certification examination and predictive of programs being in the upper tertile of programs in terms of Board pass rates.

Mathematical Modelling of a Friction Stir Welding Process to Predict the Joint Strength of Two Dissimilar Aluminium Alloys Using Experimental Data and Genetic Programming

Directory of Open Access Journals (Sweden)

Mohammed Yunus

2018-01-01

Full Text Available Friction stir welding (FSW is the most popular and efficient method of solid-state joining for similar as well as dissimilar metals and alloys. It is mostly used in applications for aerospace, rail, automotive, and marine industries. Many researchers are currently working with different perspectives on this FSW process for various combinations of materials. The general input process parameters are the thickness of the plate, axial load, rotational speed, welding speed, and tilt angle. The output parameters are joint hardness, % of elongation, and impact and yield strengths. Genetic programming (GP is a relatively new method of evolutionary computing with the principal advantage of this approach being to evaluate efficacious predictive mathematical models or equations without any prior assumption regarding the possible form of the functional relationship. This paper both defines and illustrates how GP can be applied to the FSW process to derive precise relationships between the output and input parameters in order to obtain a generalized prediction model. A GP model will assist engineers in quantifying the performance of FSW, and the results from this study can then be utilized to estimate future requirements based on the historical data to provide a robust solution. The obtained results from the GP models showed good agreement with experimental and target data at an average prediction error of 0.72%.

Admitting At-Risk Students into a Principal Preparation Program: Predicting Success.

Science.gov (United States)

Malone, Bobby G.; Nelson, Jacquelyn S.; Nelson, C. Van

2001-01-01

Study of graduation rates of at-risk students admitted to a master's degree program at a doctoral-degree-granting university found that the best predictor of degree completion was the product of the undergraduate GPA multiplied by the GRE Verbal score. (Contains 41 references.)

Performance of HIV Prevention of Mother-To-Child Transmission Programs in Sub-Saharan Africa: Longitudinal Assessment of 64 Nevirapine-Based Programs Implemented in 25 Countries, 2000-2011.

Directory of Open Access Journals (Sweden)

Joël Ladner

Full Text Available To evaluate the performance and to identify predictive factors of performance in prevention of mother-to-child HIV transmission programs (PMTCT in sub-Saharan African countries.From 2000 to 2011, PMTCT programs included in the Viramune Donation Programme (VDP were prospectively followed. Each institution included in the VDP provided data on program implementation, type of management institution, number of PMTCT sites, key programs outputs (HIV counseling and testing, NVP regimens received by mothers and newborns. Nevirapine Coverage Ratio (NCR, defined as the number of women who should have received nevirapine (observed HIV prevalence x number of women in antenatal care, was used to measure performance. Included programs were followed every six months through progress reports.A total of 64 programs in 25 sub-Saharan African countries were included. The mean program follow-up was 48.0 months (SD = 24.5; 20,084,490 women attended in antenatal clinics were included. The overall mean NCR was 0.52 (SD = 0.25, with an increase from 0.37 to 0.57 between the first and last progress reports (p<.0001; NCR increased by 3.26% per year-program. Between the first and the last report, the number of women counseled and tested increased from 64.3% to 86.0% (p<.0001, the number of women post-counseled from 87.5% to 91.3% (p = 0.08. After mixed linear regression analysis, type of responsible institution, number of women attended in ANC, and program initiation in 2005-2006 were significant predictive factors associated with the NCR. The effect of the time period increased from earlier to later periods.A longitudinal assessment of large PMTCT programs shows that scaling-up of programs was increased in sub-Saharan African countries. The PMTCT coverage increased throughout the study period, especially after 2006. Performance may be better for programs with a small or medium number of women attended in ANC. Identification of factors that predict PMTCT program

Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

Directory of Open Access Journals (Sweden)

Safiyullah Ferozkhan

2016-01-01

Full Text Available Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM. The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

A nomogram to predict the probability of passing the American Board of Internal Medicine examination

Directory of Open Access Journals (Sweden)

Andrei Brateanu

2012-10-01

Full Text Available Background : Although the American Board of Internal Medicine (ABIM certification is valued as a reflection of physicians’ experience, education, and expertise, limited methods exist to predict performance in the examination. Purpose : The objective of this study was to develop and validate a predictive tool based on variables common to all residency programs, regarding the probability of an internal medicine graduate passing the ABIM certification examination. Methods : The development cohort was obtained from the files of the Cleveland Clinic internal medicine residents who began training between 2004 and 2008. A multivariable logistic regression model was built to predict the ABIM passing rate. The model was represented as a nomogram, which was internally validated with bootstrap resamples. The external validation was done retrospectively on a cohort of residents who graduated from two other independent internal medicine residency programs between 2007 and 2011. Results : Of the 194 Cleveland Clinic graduates used for the nomogram development, 175 (90.2% successfully passed the ABIM certification examination. The final nomogram included four predictors: In-Training Examination (ITE scores in postgraduate year (PGY 1, 2, and 3, and the number of months of overnight calls in the last 6 months of residency. The nomogram achieved a concordance index (CI of 0.98 after correcting for over-fitting bias and allowed for the determination of an estimated probability of passing the ABIM exam. Of the 126 graduates from two other residency programs used for external validation, 116 (92.1% passed the ABIM examination. The nomogram CI in the external validation cohort was 0.94, suggesting outstanding discrimination. Conclusions : A simple user-friendly predictive tool, based on readily available data, was developed to predict the probability of passing the ABIM exam for internal medicine residents. This may guide program directors’ decision

Updated climatological model predictions of ionospheric and HF propagation parameters

International Nuclear Information System (INIS)

Reilly, M.H.; Rhoads, F.J.; Goodman, J.M.; Singh, M.

1991-01-01

The prediction performances of several climatological models, including the ionospheric conductivity and electron density model, RADAR C, and Ionospheric Communications Analysis and Predictions Program, are evaluated for different regions and sunspot number inputs. Particular attention is given to the near-real-time (NRT) predictions associated with single-station updates. It is shown that a dramatic improvement can be obtained by using single-station ionospheric data to update the driving parameters for an ionospheric model for NRT predictions of f(0)F2 and other ionospheric and HF circuit parameters. For middle latitudes, the improvement extends out thousands of kilometers from the update point to points of comparable corrected geomagnetic latitude. 10 refs

Steam generator tube integrity program

International Nuclear Information System (INIS)

Dierks, D.R.; Shack, W.J.; Muscara, J.

1996-01-01

A new research program on steam generator tubing degradation is being sponsored by the U.S. Nuclear Regulatory Commission (NRC) at Argonne National Laboratory. This program is intended to support a performance-based steam generator tube integrity rule. Critical areas addressed by the program include evaluation of the processes used for the in-service inspection of steam generator tubes and recommendations for improving the reliability and accuracy of inspections; validation and improvement of correlations for evaluating integrity and leakage of degraded steam generator tubes, and validation and improvement of correlations and models for predicting degradation in steam generator tubes as aging occurs. The studies will focus on mill-annealed Alloy 600 tubing, however, tests will also be performed on replacement materials such as thermally-treated Alloy 600 or 690. An overview of the technical work planned for the program is given

Prediction of velocity and attitude of a yacht sailing upwind by computational fluid dynamics

OpenAIRE

Lee, Heebum; Park, Mi Yeon; Park, Sunho; Rhee, Shin Hyung

2016-01-01

One of the most important factors in sailing yacht design is accurate velocity prediction. Velocity prediction programs (VPP's) are widely used to predict velocity of sailing yachts. VPP's, which are primarily based on experimental data and experience of long years, however suffer limitations when applied in realistic conditions. Thus, in the present study, a high fidelity velocity prediction method using computational fluid dynamics (CFD) was proposed. Using the developed method, velocity an...

Sea Ice Prediction Has Easy and Difficult Years

Science.gov (United States)

Hamilton, Lawrence C.; Bitz, Cecilia M.; Blanchard-Wrigglesworth, Edward; Cutler, Matthew; Kay, Jennifer; Meier, Walter N.; Stroeve, Julienne; Wiggins, Helen

2014-01-01

Arctic sea ice follows an annual cycle, reaching its low point in September each year. The extent of sea ice remaining at this low point has been trending downwards for decades as the Arctic warms. Around the long-term downward trend, however, there is significant variation in the minimum extent from one year to the next. Accurate forecasts of yearly conditions would have great value to Arctic residents, shipping companies, and other stakeholders and are the subject of much current research. Since 2008 the Sea Ice Outlook (SIO) (http://www.arcus.org/search-program/seaiceoutlook) organized by the Study of Environmental Arctic Change (SEARCH) (http://www.arcus.org/search-program) has invited predictions of the September Arctic sea ice minimum extent, which are contributed from the Arctic research community. Individual predictions, based on a variety of approaches, are solicited in three cycles each year in early June, July, and August. (SEARCH 2013).

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

Science.gov (United States)

Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

2017-08-28

The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical

MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing

DEFF Research Database (Denmark)

Lindgreen, Stinus; Gardner, Paul P; Krogh, Anders

2007-01-01

function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/......MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore......, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction...

DARTAB: a program to combine airborne radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of predicted health impacts

International Nuclear Information System (INIS)

Begovich, C.L.; Eckerman, K.F.; Schlatter, E.C.; Ohr, S.Y.; Chester, R.O.

1981-08-01

The DARTAB computer code combines radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of the predicted impact of radioactive airborne effluents. DARTAB is independent of the environmental transport code used to generate the environmental exposure data and the codes used to produce the dosimetric and health effects data. Therefore human dose and risk calculations need not be added to every environmental transport code. Options are included in DARTAB to permit the user to request tabulations by various topics (e.g., cancer site, exposure pathway, etc.) to facilitate characterization of the human health impacts of the effluents. The DARTAB code was written at ORNL for the US Environmental Protection Agency, Office of Radiation Programs

The Coastal Ocean Prediction Systems program: Understanding and managing our coastal ocean

International Nuclear Information System (INIS)

1990-01-01

This document is a compilation of summaries of papers presented at the Coastal Ocean Prediction Systems workshop. Topics include; marine forecasting, regulatory agencies and regulations, research and application models, research and operational observing, oceanic and atmospheric data assimilation, and coastal physical processes

An EMD-ANN based prediction methodology for DR driven smart household load demand

NARCIS (Netherlands)

Tascikaraoglu, A.; Paterakis, N.G.; Catalaõ, J.P.S.; Erdinç, O.; Bakirtzis, A.G.

2015-01-01

This study proposes a model for the prediction of smart household load demand influenced by a dynamic pricing demand response (DR) program. Price-based DR programs have a considerable impact on household demand pattern due to the expected choice of customers or their home energy management systems

Predicting Community College Outcomes: Does High School CTE Participation Have a Significant Effect?

Science.gov (United States)

Dietrich, Cecile; Lichtenberger, Eric; Kamalludeen, Rosemaliza

2016-01-01

This study explored the relative importance of participation in high school career and technical education (CTE) programs in predicting community college outcomes. A hierarchical generalized linear model (HGLM) was used to predict community college outcome attainment among a random sample of direct community college entrants. Results show that…

Darlington steam generator life assurance program

International Nuclear Information System (INIS)

Jelinski, E.; Dymarski, M.; Maruska, C.; Cartar, E.

1995-01-01

The Darlington Nuclear Generating Station belonging to Ontario Hydro is one of the most modern and advanced nuclear generating stations in the world. Four reactor units each generate 881 net MW, enough to provide power to a major city, and representing approximately 20% of the Ontario grid. The nuclear generating capacity in Ontario represents approximately 60% of the grid. In order to look after this major asset, many proactive preventative and predictive maintenance programs are being put in place. The steam generators are a major component in any power plant. World wide experience shows that nuclear steam generators require specialized attention to ensure reliable operation over the station life. This paper describes the Darlington steam generator life assurance program in terms of degradation identification, monitoring and management. The requirements for chemistry control, surveillance of process parameters, surveillance of inspection parameters, and the integration of preventative and predictive maintenance programs such as water lancing, chemical cleaning, RIHT monitoring, and other diagnostics to enhance our understanding of life management issues are identified and discussed. We conclude that we have advanced proactive activities to avoid and to minimize many of the problems affecting other steam generators. An effective steam generator maintenance program must expand the knowledge horizon to understand life limiting processes and to analyze and synthesize observations with theory. (author)

Does the MBA Experience Support Diversity? Demographic Effects on Program Satisfaction

Science.gov (United States)

Arbaugh, J. B.; Bento, Regina; Hwang, Alvin

2010-01-01

Using data provided by graduates from 128 MBA programs, we examined the extent to which age, gender, and ethnicity predicted student perceptions of the MBA experience. We found that women and minorities were more likely to see program costs and the availability of financial support as significant factors in their program enrollment decisions than…

Using NCAP to predict RFI effects in linear bipolar integrated circuits

Science.gov (United States)

Fang, T.-F.; Whalen, J. J.; Chen, G. K. C.

1980-11-01

Applications of the Nonlinear Circuit Analysis Program (NCAP) to calculate RFI effects in electronic circuits containing discrete semiconductor devices have been reported upon previously. The objective of this paper is to demonstrate that the computer program NCAP also can be used to calcuate RFI effects in linear bipolar integrated circuits (IC's). The IC's reported upon are the microA741 operational amplifier (op amp) which is one of the most widely used IC's, and a differential pair which is a basic building block in many linear IC's. The microA741 op amp was used as the active component in a unity-gain buffer amplifier. The differential pair was used in a broad-band cascode amplifier circuit. The computer program NCAP was used to predict how amplitude-modulated RF signals are demodulated in the IC's to cause undesired low-frequency responses. The predicted and measured results for radio frequencies in the 0.050-60-MHz range are in good agreement.

A model to predict the power output from wind farms

Energy Technology Data Exchange (ETDEWEB)

Landberg, L. [Riso National Lab., Roskilde (Denmark)

1997-12-31

This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

Prediction of LOFT core fluid conditions during blowdown and refill

International Nuclear Information System (INIS)

Grush, W.H.; White, J.R.

1978-01-01

One of the primary objectives of the LOFT (Loss-of-Fluid Test) Program is to provide data required to evaluate and improve the analytical methods currently used to predict the LOCA (Loss-of-Coolant Accident) response of large pressurized water reactors. The purpose of the paper is to describe the computer modeling methods used in predicting the fluid conditions in the LOFT core during the blowdown and refill phases of a nuclear LOCE (Loss-of-Coolant Experiment). Prediction results for a LOFT nonnuclear isothermal LOCE are compared to the experimental data to illustrate the validity of the modeling choices

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

Science.gov (United States)

Hidalgo, J Ignacio; Colmenar, J Manuel; Kronberger, Gabriel; Winkler, Stephan M; Garnica, Oscar; Lanchares, Juan

2017-08-08

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

Energy prediction using spatiotemporal pattern networks

Energy Technology Data Exchange (ETDEWEB)

Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun; Henze, Gregor P.; Sarkar, Soumik

2017-11-01

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

Prediction of Milk Quality Parameters Using Vibrational Spectroscopy and Chemometrics

DEFF Research Database (Denmark)

Eskildsen, Carl Emil Aae

fatty acids, protein fractions and coagulation properties from Fourier transform infrared measurements. This thesis shows how such predictions are trapped in a cage of covariance with major milk constituents like total fat and protein content. The prediction models for detailed milk composition...... are not based on causal relationships and this may seriously compromise calibration robustness. It is not recommended to implement indirect models for detailed milk composition in milk recording or breeding programs as such model are providing information on, for example, total protein rather than the specific...... protein fractions. If Fourier transform infrared based models on detailed milk composition are to be implemented in, for example, breeding programs it is recommended to decompose, for example, the individual fatty acids into functional groups, such as methyl, methylene, olefinic and carboxylic groups...

Atmospheric Radiation Measurement Program Plan

International Nuclear Information System (INIS)

1990-02-01

In order to understand energy's role in anthropogenic global climate change, significant reliance is being placed on General Circulation Models (GCMs). A major goal of the Department is to foster the development of GCMs capable of predicting the timing and magnitude of greenhouse gas-induced global warming and the regional effects of such warming. DOE research has revealed that cloud radiative feedback is the single most important effect determining the magnitude of possible climate responses to human activity. However, cloud radiative forcing and feedbacks are not understood at the levels needed for reliable climate prediction. The Atmospheric Radiation Measurement (ARM) Program will contribute to the DOE goal by improving the treatment of cloud radiative forcing and feedbacks in GCMs. Two issues will be addressed: the radiation budget and its spectral dependence and the radiative and other properties of clouds. Understanding cloud properties and how to predict them is critical because cloud properties may very well change as climate changes. The experimental objective of the ARM Program is to characterize empirically the radiative processes in the Earth's atmosphere with improved resolution and accuracy. A key to this characterization is the effective treatment of cloud formation and cloud properties in GCMs. Through this characterization of radiative properties, it will be possible to understand both the forcing and feedback effects. GCM modelers will then be able to better identify the best approaches to improved parameterizations of radiative transfer effects. This is expected to greatly improve the accuracy of long-term, GCM predictions and the efficacy of those predictions at the important regional scale, as the research community and DOE attempt to understand the effects of greenhouse gas emissions on the Earth's climate. 153 refs., 24 figs., 6 tabs

The nature and use of prediction skills in a biological computer simulation

Science.gov (United States)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.

The Effectiveness of Parenting Programs: A Review of Campbell Reviews

Science.gov (United States)

Barlow, Jane; Coren, Esther

2018-01-01

Parenting practices predict important outcomes for children, and parenting programs are potentially effective means of supporting parents to promote optimal outcomes for children. This review summarizes findings of systematic reviews of parenting programs published in the Campbell Library. Six reviews evaluated the effectiveness of a range of…

Model Predictive Control of a Nonlinear System with Known Scheduling Variable

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2012-01-01

Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

Predicting Cost and Schedule Growth for Military and Civil Space Systems

National Research Council Canada - National Science Library

Rusnock, Christina F

2008-01-01

... for predicting cost and schedule growth. The analysis consists of logistic and multiple regression to assess 21 Department of Defense and 71 National Aeronautics and Space Administration (NASA) space programs...

Appropriate Combination of Artificial Intelligence and Algorithms for Increasing Predictive Accuracy Management

Directory of Open Access Journals (Sweden)

Shahram Gilani Nia

2010-03-01

Full Text Available In this paper a simple and effective expert system to predict random data fluctuation in short-term period is established. Evaluation process includes introducing Fourier series, Markov chain model prediction and comparison (Gray combined with the model prediction Gray- Fourier- Markov that the mixed results, to create an expert system predicted with artificial intelligence, made this model to predict the effectiveness of random fluctuation in most data management programs to increase. The outcome of this study introduced artificial intelligence algorithms that help detect that the computer environment to create a system that experts predict the short-term and unstable situation happens correctly and accurately predict. To test the effectiveness of the algorithm presented studies (Chen Tzay len,2008, and predicted data of tourism demand for Iran model is used. Results for the two countries show output model has high accuracy.

Application of numerical analysis technique to make up for pipe wall thinning prediction program

International Nuclear Information System (INIS)

Hwang, Kyeong Mo; Jin, Tae Eun; Park, Won; Oh, Dong Hoon

2009-01-01

Flow Accelerated Corrosion (FAC) leads to wall thinning of steel piping exposed to flowing water or wet steam. Experience has shown that FAC damage to piping at fossil and nuclear plants can lead to costly outages and repairs and can affect plant reliability and safety. CHEWORKS have been utilized in domestic nuclear plants as a predictive tool to assist FAC engineers in planning inspections and evaluating the inspection data to prevent piping failures caused by FAC. However, CHECWORKS may be occasionally left out local susceptible portions owing to predicting FAC damage by pipeline group after constructing a database for all secondary side piping in nuclear plants. This paper describes the methodologies that can complement CHECWORKS and the verifications of the CHECWORKS prediction results in terms of numerical analysis. FAC susceptible locations based on CHECWORKS for the two pipeline groups of a nuclear plant was compared with those of numerical analysis based on FLUENT.

Development of predictive weather scenarios for early prediction of rice yield in South Korea

Science.gov (United States)

Shin, Y.; Cho, J.; Jung, I.

2017-12-01

International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.

Sustained Implementation Support Scale: Validation of a Measure of Program Characteristics and Workplace Functioning for Sustained Program Implementation.

Science.gov (United States)

Hodge, Lauren M; Turner, Karen M T; Sanders, Matthew R; Filus, Ania

2017-07-01

An evaluation measure of enablers and inhibitors to sustained evidence-based program (EBP) implementation may provide a useful tool to enhance organizations' capacity. This paper outlines preliminary validation of such a measure. An expert informant and consumer feedback approach was used to tailor constructs from two existing measures assessing key domains associated with sustained implementation. Validity and reliability were evaluated for an inventory composed of five subscales: Program benefits, Program burden, Workplace support, Workplace cohesion, and Leadership style. Exploratory and confirmatory factor analysis with a sample of 593 Triple P-Positive Parenting Program-practitioners led to a 28-item scale with good reliability and good convergent, discriminant, and predictive validity. Practitioners sustaining implementation at least 3 years post-training were more likely to have supervision/peer support, reported higher levels of program benefit, workplace support, and positive leadership style, and lower program burden compared to practitioners who were non-sustainers.

Indonesian Stock Prediction using Support Vector Machine (SVM

Directory of Open Access Journals (Sweden)

Santoso Murtiyanto

2018-01-01

Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

Interpreting Disruption Prediction Models to Improve Plasma Control

Science.gov (United States)

Parsons, Matthew

2017-10-01

In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Science.gov (United States)

Lei, Yaguo; Li, Naipeng; Guo, Liang; Li, Ningbo; Yan, Tao; Lin, Jing

2018-05-01

Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes, i.e., data acquisition, health indicator (HI) construction, health stage (HS) division, and RUL prediction. Over recent years, a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process, i.e., RUL prediction. However, there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap, this paper provides a review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction. First, in data acquisition, several prognostic datasets widely used in academic literature are introduced systematically. Then, commonly used HI construction approaches and metrics are discussed. After that, the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards, the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally, the paper provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

Evolutionary modeling and prediction of non-coding RNAs in Drosophila.

Directory of Open Access Journals (Sweden)

Robert K Bradley

2009-08-01

Full Text Available We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3' end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA.

Elements of a total MOV maintenance program

International Nuclear Information System (INIS)

Lavallee, W.L.

1989-01-01

Establishing a good preventive and predictive maintenance program for motor-operated valves (MOVs) is an especially challenging task for nuclear power plants. Because of the sheer number of MOVs involved, all with somewhat different characteristics and requirements, and the extremely critical functions that some of the MOVs perform, the maintenance program quickly becomes a major exercise in coordination. This paper outlines a three-phase approach to achieving a comprehensive MOV maintenance program. It is based on experience in assisting nuclear plants with their MOV programs and encountering many of the pitfalls that can hamper these programs. The three phases include up-front engineering preparation, field diagnostic testing, and maintenance follow-up and trending. Each of these phases is discussed, and a flowchart describing the individual elements of each phase is provided

A quantitative analysis of factors that influence and predict students' intention to major in and complete an undergraduate program in STEM or non-STEM

Science.gov (United States)

Yang, Xuemei

2005-11-01

The goal of this study was to explore and understand the factors that influence students' intention to major in and complete an undergraduate program in a science, technology, engineering, or mathematics (STEM) discipline, in a non-STEM field, and how students' gender directly and indirectly affects their success in college. A quantitative study of three thousand four (3004) ACT-tested students who entered a Midwestern, land-grant university as freshmen in fall, 1999 was conducted based on their ACT Assessment information and their enrollment and graduation status after five years. A wide variety of variables were considered and logistic regression, factor analysis, and path analysis were used to analyze the data. The results show that students who intended to major in or completed STEM programs generally have better academic qualifications than their counterparts who intended to major in non-STEM fields. Students who intended to major in or completed STEM programs came from lower income families and smaller communities than those who intended to major in or graduated from non-STEM programs. In this study, gender's direct effect on students' college achievement is eleven times the total of gender's indirect effects through several major factors for students in both STEM fields and non-STEM fields. Perhaps nature has favored females when students' achievement is measured as their college GPA. The results also show that the overall high dropout rate is strongly associated with students' inadequate preparation in high school and family income. Out-of-school accomplishment in community service is a negative influence on their completion of a college degree. ACT scores are not necessary for prediction of college graduation.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

Directory of Open Access Journals (Sweden)

Alencar Xavier

2016-08-01

Full Text Available Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr. We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans.

Science.gov (United States)

Xavier, Alencar; Muir, William M; Rainey, Katy Martin

2016-08-09

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. Copyright © 2016 Xavie et al.

Closing in on the C. elegans ORFeome by cloning TWINSCAN predictions

DEFF Research Database (Denmark)

Wei, Chaochun; Lamesch, Philippe; Arumugam, Manimozhiyan

2005-01-01

The genome of Caenorhabditis elegans was the first animal genome to be sequenced. Although considerable effort has been devoted to annotating it, the standard WormBase annotation contains thousands of predicted genes for which there is no cDNA or EST evidence. We hypothesized that a more complete...... experimental annotation could be obtained by creating a more accurate gene-prediction program and then amplifying and sequencing predicted genes. Our approach was to adapt the TWINSCAN gene prediction system to C. elegans and C. briggsae and to improve its splice site and intron-length models. The resulting...... be significantly increased by replacing its partially curated predicted genes with TWINSCAN predictions. The technology described in this study will continue to drive the C. elegans ORFeome toward completion and contribute to the annotation of the three Caenorhabditis species currently being sequenced. The results...

Genomic Prediction of Manganese Efficiency in Winter Barley

Directory of Open Access Journals (Sweden)

Florian Leplat

2016-07-01

Full Text Available Manganese efficiency is a quantitative abiotic stress trait controlled by several genes each with a small effect. Manganese deficiency leads to yield reduction in winter barley ( L.. Breeding new cultivars for this trait remains difficult because of the lack of visual symptoms and the polygenic features of the trait. Hence, Mn efficiency is a potential suitable trait for a genomic selection (GS approach. A collection of 248 winter barley varieties was screened for Mn efficiency using Chlorophyll (Chl fluorescence in six environments prone to induce Mn deficiency. Two models for genomic prediction were implemented to predict future performance and breeding value of untested varieties. Predictions were obtained using multivariate mixed models: best linear unbiased predictor (BLUP and genomic best linear unbiased predictor (G-BLUP. In the first model, predictions were based on the phenotypic evaluation, whereas both phenotypic and genomic marker data were included in the second model. Accuracy of predicting future phenotype, , and accuracy of predicting true breeding values, , were calculated and compared for both models using six cross-validation (CV schemes; these were designed to mimic plant breeding programs. Overall, the CVs showed that prediction accuracies increased when using the G-BLUP model compared with the prediction accuracies using the BLUP model. Furthermore, the accuracies [] of predicting breeding values were more accurate than accuracy of predicting future phenotypes []. The study confirms that genomic data may enhance the prediction accuracy. Moreover it indicates that GS is a suitable breeding approach for quantitative abiotic stress traits.

Predicting performance at medical school: can we identify at-risk students?

Directory of Open Access Journals (Sweden)

Shaban S

2011-05-01

Full Text Available Sami Shaban, Michelle McLeanDepartment of Medical Education, Faculty of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab EmiratesBackground: The purpose of this study was to examine the predictive potential of multiple indicators (eg, preadmission scores, unit, module and clerkship grades, course and examination scores on academic performance at medical school, with a view to identifying students at risk.Methods: An analysis was undertaken of medical student grades in a 6-year medical school program at the Faculty of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates, over the past 14 years.Results: While high school scores were significantly (P < 0.001 correlated with the final integrated examination, predictability was only 6.8%. Scores for the United Arab Emirates university placement assessment (Common Educational Proficiency Assessment were only slightly more promising as predictors with 14.9% predictability for the final integrated examination. Each unit or module in the first four years was highly correlated with the next unit or module, with 25%–60% predictability. Course examination scores (end of years 2, 4, and 6 were significantly correlated (P < 0.001 with the average scores in that 2-year period (59.3%, 64.8%, and 55.8% predictability, respectively. Final integrated examination scores were significantly correlated (P < 0.001 with National Board of Medical Examiners scores (35% predictability. Multivariate linear regression identified key grades with the greatest predictability of the final integrated examination score at three stages in the program.Conclusion: This study has demonstrated that it may be possible to identify “at-risk” students relatively early in their studies through continuous data archiving and regular analysis. The data analysis techniques used in this study are not unique to this institution.Keywords: at-risk students, grade

Life prediction methodology for ceramic components of advanced heat engines. Phase 1: Volume 1, Final report

Energy Technology Data Exchange (ETDEWEB)

Cuccio, J.C.; Brehm, P.; Fang, H.T. [Allied-Signal Aerospace Co., Phoenix, AZ (United States). Garrett Engine Div.] [and others

1995-03-01

Emphasis of this program is to develop and demonstrate ceramics life prediction methods, including fast fracture, stress rupture, creep, oxidation, and nondestructive evaluation. Significant advancements were made in these methods and their predictive capabilities successfully demonstrated.

Enhancing Accuracy of Sediment Total Load Prediction Using Evolutionary Algorithms (Case Study: Gotoorchay River

Directory of Open Access Journals (Sweden)

K. Roshangar

2016-09-01

Full Text Available Introduction: Exact prediction of transported sediment rate by rivers in water resources projects is of utmost importance. Basically erosion and sediment transport process is one of the most complexes hydrodynamic. Although different studies have been developed on the application of intelligent models based on neural, they are not widely used because of lacking explicitness and complexity governing on choosing and architecting of proper network. In this study, a Genetic expression programming model (as an important branches of evolutionary algorithems for predicting of sediment load is selected and investigated as an intelligent approach along with other known classical and imperical methods such as Larsen´s equation, Engelund-Hansen´s equation and Bagnold´s equation. Materials and Methods: In this study, in order to improve explicit prediction of sediment load of Gotoorchay, located in Aras catchment, Northwestern Iran latitude: 38°24´33.3˝ and longitude: 44°46´13.2˝, genetic programming (GP and Genetic Algorithm (GA were applied. Moreover, the semi-empirical models for predicting of total sediment load and rating curve have been used. Finally all the methods were compared and the best ones were introduced. Two statistical measures were used to compare the performance of the different models, namely root mean square error (RMSE and determination coefficient (DC. RMSE and DC indicate the discrepancy between the observed and computed values. Results and Discussions: The statistical characteristics results obtained from the analysis of genetic programming method for both selected model groups indicated that the model 4 including the only discharge of the river, relative to other studied models had the highest DC and the least RMSE in the testing stage (DC= 0.907, RMSE= 0.067. Although there were several parameters applied in other models, these models were complicated and had weak results of prediction. Our results showed that the model 9

Lazy Spilling for a Time-Predictable Stack Cache: Implementation and Analysis

DEFF Research Database (Denmark)

Abbaspourseyedi, Sahar; Jordan, Alexander; Brandner, Florian

2014-01-01

The growing complexity of modern computer architectures increasingly complicates the prediction of the run-time behavior of software. For real-time systems, where a safe estimation of the program's worst-case execution time is needed, time-predictable computer architectures promise to resolve......, we show that lazy spilling can be analyzed with little extra effort, which benefits the worst-case spilling behavior that is relevant for a real-time system....

Tools for Predicting Cleaning Efficiency in the LHC

CERN Document Server

Assmann, R W; Brugger, M; Hayes, M; Jeanneret, J B; Kain, V; Kaltchev, D I; Schmidt, F

2003-01-01

The computer codes SIXTRACK and DIMAD have been upgraded to include realistic models of proton scattering in collimator jaws, mechanical aperture restrictions, and time-dependent fields. These new tools complement long-existing simplified linear tracking programs used up to now for tracking with collimators. Scattering routines from STRUCT and K2 have been compared with one another and the results have been cross-checked to the FLUKA Monte Carlo package. A systematic error is assigned to the predictions of cleaning efficiency. Now, predictions of the cleaning efficiency are possible with a full LHC model, including chromatic effects, linear and nonlinear errors, beam-beam kicks and associated diffusion, and time-dependent fields. The beam loss can be predicted around the ring, both for regular and irregular beam losses. Examples are presented.

Thermomechanical fatigue life prediction of high temperature components

Energy Technology Data Exchange (ETDEWEB)

Seifert, Thomas; Hartrott, Philipp von; Riedel, Hermann; Siegele, Dieter [Fraunhofer-Inst. fuer Werkstoffmechanik (IWM), Freiburg (Germany)

2009-07-01

The aim of the work described in this paper is to provide a computational method for fatigue life prediction of high temperature components, in which the time and temperature dependent fatigue crack growth is a relevant damage mechanism. The fatigue life prediction is based on a law for microcrack growth and a fracture mechanics estimate of the cyclic crack tip opening displacement. In addition, a powerful model for nonisothermal cyclic plasticity is employed, and an efficient laboratory test procedure is proposed for the determination of the model parameters. The models are efficiently implemented into finite element programs and are used to predict the fatigue life of a cast iron exhaust manifold and a notch in the perimeter of a turbine rotor made of a ferritic/martensitic 10%-chromium steel. (orig.)

Improving the Accuracy of a Heliocentric Potential (HCP Prediction Model for the Aviation Radiation Dose

Directory of Open Access Journals (Sweden)

Junga Hwang

2016-12-01

Full Text Available The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs, flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA. However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015. In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1 real-time daily sunspot assessments, (2 predictions of the daily HCP by our prediction algorithm, and (3 calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

Seismic Safety Program: Ground motion and structural response

Energy Technology Data Exchange (ETDEWEB)

1993-05-01

In 1964, John A. Blume & Associates Research Division (Blume) began a broad-range structural response program to assist the Nevada Operations Office of the US Atomic Energy Commission (AEC) in ensuring the continued safe conduct of underground nuclear detonation testing at the Nevada Test Site (NTS) and elsewhere. Blume`s long experience in earthquake engineering provided a general basis for the program, but much more specialized knowledge was required for the AEC`s purposes. Over the next 24 years Blume conducted a major research program to provide essential understanding of the detailed nature of the response of structures to dynamic loads such as those imposed by seismic wave propagation. The program`s results have been embodied in a prediction technology which has served to provide reliable advanced knowledge of the probable effects of seismic ground motion on all kinds of structures, for use in earthquake engineering and in building codes as well as for the continuing needs of the US Department of Energy`s Nevada Operations Office (DOE/NV). This report is primarily an accounting of the Blume work, beginning with the setting in 1964 and the perception of the program needs as envisioned by Dr. John A. Blume. Subsequent chapters describe the structural response program in detail and the structural prediction procedures which resulted; the intensive data acquisition program which, as is discussed at some length, relied heavily on the contributions of other consultant-contractors in the DOE/NV Seismic Safety Support Program; laboratory and field studies to provide data on building elements and structures subjected to dynamic loads from sources ranging from testing machines to earthquakes; structural response activities undertaken for testing at the NTS and for off-NTS underground nuclear detonations; and concluding with an account of corollary studies including effects of natural forces and of related studies on building response.

RNAstructure: software for RNA secondary structure prediction and analysis.

Science.gov (United States)

Reuter, Jessica S; Mathews, David H

2010-03-15

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

Use of computer-assisted prediction of toxic effects of chemical substances

International Nuclear Information System (INIS)

Simon-Hettich, Brigitte; Rothfuss, Andreas; Steger-Hartmann, Thomas

2006-01-01

The current revision of the European policy for the evaluation of chemicals (REACH) has lead to a controversy with regard to the need of additional animal safety testing. To avoid increases in animal testing but also to save time and resources, alternative in silico or in vitro tests for the assessment of toxic effects of chemicals are advocated. The draft of the original document issued in 29th October 2003 by the European Commission foresees the use of alternative methods but does not give further specification on which methods should be used. Computer-assisted prediction models, so-called predictive tools, besides in vitro models, will likely play an essential role in the proposed repertoire of 'alternative methods'. The current discussion has urged the Advisory Committee of the German Toxicology Society to present its position on the use of predictive tools in toxicology. Acceptable prediction models already exist for those toxicological endpoints which are based on well-understood mechanism, such as mutagenicity and skin sensitization, whereas mechanistically more complex endpoints such as acute, chronic or organ toxicities currently cannot be satisfactorily predicted. A potential strategy to assess such complex toxicities will lie in their dissection into models for the different steps or pathways leading to the final endpoint. Integration of these models should result in a higher predictivity. Despite these limitations, computer-assisted prediction tools already today play a complementary role for the assessment of chemicals for which no data is available or for which toxicological testing is impractical due to the lack of availability of sufficient compounds for testing. Furthermore, predictive tools offer support in the screening and the subsequent prioritization of compound for further toxicological testing, as expected within the scope of the European REACH program. This program will also lead to the collection of high-quality data which will broaden the

Prediction of quantitative intrathoracic fluid volume to diagnose pulmonary oedema using LabVIEW.

Science.gov (United States)

Urooj, Shabana; Khan, M; Ansari, A Q; Lay-Ekuakille, Aimé; Salhan, Ashok K

2012-01-01

Pulmonary oedema is a life-threatening disease that requires special attention in the area of research and clinical diagnosis. Computer-based techniques are rarely used to quantify the intrathoracic fluid volume (IFV) for diagnostic purposes. This paper discusses a software program developed to detect and diagnose pulmonary oedema using LabVIEW. The software runs on anthropometric dimensions and physiological parameters, mainly transthoracic electrical impedance (TEI). This technique is accurate and faster than existing manual techniques. The LabVIEW software was used to compute the parameters required to quantify IFV. An equation relating per cent control and IFV was obtained. The results of predicted TEI and measured TEI were compared with previously reported data to validate the developed program. It was found that the predicted values of TEI obtained from the computer-based technique were much closer to the measured values of TEI. Six new subjects were enrolled to measure and predict transthoracic impedance and hence to quantify IFV. A similar difference was also observed in the measured and predicted values of TEI for the new subjects.

77 FR 66025 - Program Access Rules

Science.gov (United States)

2012-10-31

... distribution market if the prohibition were lifted.'' Accordingly, we rely on ``economic theory and predictive... incentive and the ability to harm competition and diversity in the distribution of video programming by entering into exclusive contracts. We undertake the same analysis here. Below, we consider the ``incentive...

Availability program: Phase I report

International Nuclear Information System (INIS)

Thomson, S.L.; Dabiri, A.; Keeton, D.C.; Riemer, B.W.; Waganer, L.M.

1985-05-01

An Availability Working Group was formed within the Office of Fusion Energy in March 1984 to consider the establishment of an availability program for magnetic fusion. The scope of this program is defined to include the development of (1) a comprehensive data base, (2) empirical correlations, and (3) analytical methods for application to fusion facilities and devices. The long-term goal of the availability program is to develop a validated, integrated methodology that will provide (1) projections of plant availability and (2) input to design decisions on maintainability and system reliability requirements. The Phase I study group was commissioned to assess the status of work in progress that is relevant to the availability program. The scope of Phase I included surveys of existing data and data collection programs at operating fusion research facilities, the assessment of existing computer models to calculate system availability, and the review of methods to predict and correlate data on component failure and maintenance. The results of these investigations are reported to the Availability Working Group in this document

Competition and rural primary care programs.

Science.gov (United States)

Ricketts, T C

1990-04-01

Rural primary care programs were established in areas where there was thought to be no competition for patients. However, evidence from site visits and surveys of a national sample of subsidized programs revealed a pattern of competitive responses by the clinics. In this study of 193 rural primary care programs, mail and telephone surveys produced uniform data on the organization, operation, finances, and utilization of a representative sample of clinics. The programs were found to compete in terms of: (1) price, (2) service mix, (3) staff availability, (4) structural accessibility, (5) outreach, and (6) targeting a segment of the market. The competitive strategies employed by the clinics had consequences that affected their productivity and financial stability. The strategies were related to the perceived missions of the programs, and depended heavily upon the degree of isolation of the program and the targeting of the services. The competitive strategy chosen by a particular program could not be predicted based on service area population and apparent competitors in the service area. The goals and objectives of the programs had more to do with their competitive responses than with market characteristics. Moreover, the chosen strategies may not meet the demands of those markets.

Mentorship Programs in Radiation Oncology Residency Training Programs: A Critical Unmet Need

Energy Technology Data Exchange (ETDEWEB)

Dhami, Gurleen; Gao, Wendy; Gensheimer, Michael F. [Department of Radiation Oncology, University of Washington, Seattle, Washington (United States); Trister, Andrew D. [Sage Bionetworks, Seattle, Washington (United States); Kane, Gabrielle [Department of Radiation Oncology, University of Washington, Seattle, Washington (United States); Zeng, Jing, E-mail: jzeng13@uw.edu [Department of Radiation Oncology, University of Washington, Seattle, Washington (United States)

2016-01-01

Purpose: To conduct a nationwide survey to evaluate the current status of resident mentorship in radiation oncology. Methods and Materials: An anonymous electronic questionnaire was sent to all residents and recent graduates at US Accreditation Council for Graduate Medical Education–accredited radiation oncology residency programs, identified in the member directory of the Association of Residents in Radiation Oncology. Factors predictive of having a mentor and satisfaction with the mentorship experience were identified using univariate and multivariate analyses. Results: The survey response rate was 25%, with 85% of respondents reporting that mentorship plays a critical role in residency training, whereas only 53% had a current mentor. Larger programs (≥10 faculty, P=.004; and ≥10 residents, P<.001) were more likely to offer a formal mentorship program, which makes it more likely for residents to have an active mentor (88% vs 44%). Residents in a formal mentoring program reported being more satisfied with the overall mentorship experience (univariate odds ratio 8.77, P<.001; multivariate odds ratio 5, P<.001). On multivariate analysis, women were less likely to be satisfied with the mentorship experience. Conclusions: This is the first survey focusing on the status of residency mentorship in radiation oncology. Our survey highlights the unmet need for mentorship in residency programs.

BEHAVE: fire behavior prediction and fuel modeling system--FUEL subsystem

Science.gov (United States)

Robert E. Burgan; Richard C. Rothermel

1984-01-01

This manual documents the fuel modeling procedures of BEHAVE--a state-of-the-art wildland fire behavior prediction system. Described are procedures for collecting fuel data, using the data with the program, and testing and adjusting the fuel model.

XTALOPT: An open-source evolutionary algorithm for crystal structure prediction

Science.gov (United States)

Lonie, David C.; Zurek, Eva

2011-02-01

The implementation and testing of XTALOPT, an evolutionary algorithm for crystal structure prediction, is outlined. We present our new periodic displacement (ripple) operator which is ideally suited to extended systems. It is demonstrated that hybrid operators, which combine two pure operators, reduce the number of duplicate structures in the search. This allows for better exploration of the potential energy surface of the system in question, while simultaneously zooming in on the most promising regions. A continuous workflow, which makes better use of computational resources as compared to traditional generation based algorithms, is employed. Various parameters in XTALOPT are optimized using a novel benchmarking scheme. XTALOPT is available under the GNU Public License, has been interfaced with various codes commonly used to study extended systems, and has an easy to use, intuitive graphical interface. Program summaryProgram title:XTALOPT Catalogue identifier: AEGX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 36 849 No. of bytes in distributed program, including test data, etc.: 1 149 399 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux Classification: 7.7 External routines: QT [2], OpenBabel [3], AVOGADRO [4], SPGLIB [8] and one of: VASP [5], PWSCF [6], GULP [7]. Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum on their potential energy surface. Our evolutionary algorithm, XTALOPT, is freely

What do reversible programs compute?

DEFF Research Database (Denmark)

Axelsen, Holger Bock; Glück, Robert

2011-01-01

Reversible computing is the study of computation models that exhibit both forward and backward determinism. Understanding the fundamental properties of such models is not only relevant for reversible programming, but has also been found important in other fields, e.g., bidirectional model...... transformation, program transformations such as inversion, and general static prediction of program properties. Historically, work on reversible computing has focussed on reversible simulations of irreversible computations. Here, we take the viewpoint that the property of reversibility itself should...... are not strictly classically universal, but that they support another notion of universality; we call this RTM-universality. Thus, even though the RTMs are sub-universal in the classical sense, they are powerful enough as to include a self-interpreter. Lifting this to other computation models, we propose r...

SCRAM: a program for calculating scram times

International Nuclear Information System (INIS)

Bourquin, R.D.; Birney, K.R.

1975-01-01

Prediction of scram times is one facet of design analysis for control rod assemblies. Parameters for the entire control rod sub-system must be considered in such analyses and experimental verification is used when it is available. The SCRAM computer program was developed for design analysis of improved control rod assemblies for the Fast Flux Test Facility (FFTF). A description of the evolution of the program from basic equations to a functional design analysis tool is presented

GMAT versus Alternatives: Predictive Validity Evidence from Central Europe and the Middle East

Science.gov (United States)

Koys, Daniel

2010-01-01

The author found that the GPA at the end of the MBA program is most accurately predicted by the Graduate Management Admission Test (GMAT) and the Test of English as a Foreign Language (TOEFL). MBA GPA is also predicted, though less accurately, by the Scholastic Level Exam, a mathematics test, undergraduate GPA, and previous career progression. If…

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer.

Science.gov (United States)

Mino-Kenudson, Mari

2017-10-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC.

Terra Nova Environmental effects monitoring program

International Nuclear Information System (INIS)

Williams, U.; Murdoch, M.

2000-01-01

Elements of the environmental effects monitoring program in the Terra Nova oil field, about 350 km east-southeast of St. John's, Newfoundland, are described. This oilfield is being developed using a floating production storage and offloading (FPSO) facility. A total of 24 wells are expected to be drilled through seven subsea templates located in four glory holes to protect them from icebergs. Subsea installations will be linked to the FPSO by trenched flowlines connected to flexible risers. The FPSO will offload to shuttle tankers. First oil is expected in 2001. The environmental effects monitoring program will be conducted annually for the first two years beginning in 2000. Subsequent scheduling will be determined after a review of monitoring data collected during the first three years. Input to the design of the monitoring program was provided by all stakeholders, i. e. owners, local public, government agencies and regional and international experts. A model was developed linking project discharges and possible effects to the environment, including marine resources in the area, and the information derived from these activities was used to generate a set of predictions and hypotheses to be tested in the monitoring program. The monitoring program will use two spatial models: a regression or gradient design and a control-impact design. The gradient design will monitor water column and sediment chemistry, sediment toxicity and benthic invertebrate communities. The control-impact design will be used to monitor larger and more mobile fish or shellfish. The evaluated results will serve as the basis for determining impact predictions and to provide information to allow for decisions pertaining to the protection of the marine environment

Infrared thermography program at Darlington NGD

International Nuclear Information System (INIS)

Speer, B.

1997-01-01

Infrared thermography is a proven predictive maintenance tool for improving equipment reliability and reducing maintenance costs. It has been identified as one of the maintenance technologies that could contribute to the reduction of OHN forced incapability factor. At Darlington NGD a program has been established by combining OHN and Nuclear Maintenance Applications Center (NMAC) operating experience. This presentation outlines the development and implementation of this program. The main points are: roles and responsibilities, equipment selection, software requirements, manpower level, inspection equipment, training and a cost/benefit review. (author)

[Predictive ocular motor control in Parkinson's disease].

Science.gov (United States)

Ying, Li; Liu, Zhen-Guo; Chen, Wei; Gan, Jing; Wang, Wen-An

2008-02-19

To investigate the changes of predictive ocular motor function in the patients with Parkinson's disease (PD), and to discuss its clinical value. Videonystagmography (VNG) was used to examine 24 patients with idiopathic Parkinson's disease, 15 males and 9 females, aged 61 +/- 6 (50-69), and 24 sex and age-matched healthy control subjects on random ocular saccade (with the target moving at random intervals to random positions) and predictive ocular saccade (with the 1.25-second light target moving 10 degrees right or left from the center). In the random ocular saccade program, the latency of saccade of the PD patients was 284 ms +/- 58 ms, significantly longer than that of the healthy controls (236 ms +/- 37 ms, P = 0.003). In the predictive ocular saccade pattern, the latency of saccades the PD patients was 150 ms +/- 138 ms, significantly longer than that of the healthy controls (59 ms +/- 102 ms, P = 0.002). The appearance rate of predictive saccades (with the latency of saccade <80 ms) in the PD group was 21%, significantly lower than that in the control group (31%, P = 0.003). There is dysfunction of predictive ocular motor control in the PD patients, and the cognitive function may be impaired at the early stage of PD.

Prediction of deformations during gas-tungsten-arc stationary welds

International Nuclear Information System (INIS)

Duncan, D.B.; Giedt, W.H.

1980-10-01

Local temperature measurements on the heated and unheated surfaces, and strain measurements on the unheated surfaces of unrestrained circular weld specimens of annealed and cold-rolled Nitronic 40 stainless steel during stationary welding, are compared with values predicted from finite-element programs for temperature and strain variations. Experimental and predicted temperature histories agree within 10%. Predicted and measured hoop strain profiles (using a moire fringe technique), for the unheated surface are compared, showing significant deviations near the central region. Transient deflection measurements of the unheated specimen surfaces show good agreement with theory during the period the arc is operating. Close agreement in deflection behavior was observed during the cooling portion of the weld cycle for the annealed specimen, whereas substantial deviations occurred for the cold-rolled specimens

Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury

Directory of Open Access Journals (Sweden)

Gary E Strangman

2010-10-01

Full Text Available Cognitive deficits following traumatic brain injury (TBI commonly include difficulties with memory, attention, and executive dysfunction. These deficits are amenable to cognitive rehabilitation, but optimally selecting rehabilitation programs for individual patients remains a challenge. Recent methods for quantifying regional brain morphometry allow for automated quantification of tissue volumes in numerous distinct brain structures. We hypothesized that such quantitative structural information could help identify individuals more or less likely to benefit from memory rehabilitation. Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning. They then participated in a 12 session memory rehabilitation program emphasizing internal memory strategies (I-MEMS. Primary outcome measures (HVLT, RBMT were collected at the time of the MRI scan, immediately following therapy, and again at one month post-therapy. Regional brain volumes were used to predict outcome, adjusting for standard predictors (e.g., injury severity, age, education, pretest scores. We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex. The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable. We conclude that specific cerebral networks including these regions may contribute to learning during I-MEMS rehabilitation, and suggest that morphometric measures may provide substantial predictive value for rehabilitation outcome in other cognitive interventions as well.

User's Manual for SPECTROM-41: a Finite-Element Heat Transfer Program

International Nuclear Information System (INIS)

Svalstad, D.K.

1983-06-01

This User's Manual addresses SPECTROM-41: A Finite Element Heat Transfer Computer Program. The user is introduced to the program's capabilities and operation, with required user input outlined in detail. Example problems are included to illustrate the use of the various program features, and included to illustrate the use of the various program features, and analytical solutions are presented for four of the examples to provide a measure of program accuracy. Past and ongoing comparative benchmark analyses are highlighted to provide the user with an indication of how SPECTROM-41 predictions compare with other available heat transfer programs

Comparative Evaluation of Some Crop Yield Prediction Models ...

African Journals Online (AJOL)

A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

Microstructure-based approach for predicting crack initiation and early growth in metals.

Energy Technology Data Exchange (ETDEWEB)

Cox, James V.; Emery, John M.; Brewer, Luke N.; Reedy, Earl David, Jr.; Puskar, Joseph David; Bartel, Timothy James; Dingreville, Remi P. M.; Foulk, James W., III; Battaile, Corbett Chandler; Boyce, Brad Lee

2009-09-01

Fatigue cracking in metals has been and is an area of great importance to the science and technology of structural materials for quite some time. The earliest stages of fatigue crack nucleation and growth are dominated by the microstructure and yet few models are able to predict the fatigue behavior during these stages because of a lack of microstructural physics in the models. This program has developed several new simulation tools to increase the microstructural physics available for fatigue prediction. In addition, this program has extended and developed microscale experimental methods to allow the validation of new microstructural models for deformation in metals. We have applied these developments to fatigue experiments in metals where the microstructure has been intentionally varied.

Alberta Stroke Program Early CT Score-Time Score Predicts Outcome after Endovascular Therapy in Patients with Acute Ischemic Stroke: A Retrospective Single-Center Study.

Science.gov (United States)

Todo, Kenichi; Sakai, Nobuyuki; Kono, Tomoyuki; Hoshi, Taku; Imamura, Hirotoshi; Adachi, Hidemitsu; Yamagami, Hiroshi; Kohara, Nobuo

2018-04-01

Clinical outcomes after successful endovascular therapy in patients with acute ischemic stroke are associated with several factors including onset-to-reperfusion time (ORT), the National Institute of Health Stroke Scale (NIHSS) score, and the Alberta Stroke Program Early CT Score (ASPECTS). The NIHSS-time score, calculated as follows: [NIHSS score] × [onset-to-treatment time (h)] or [NIHSS score] × [ORT (h)], has been reported to predict clinical outcomes after intravenous recombinant tissue plasminogen activator therapy and endovascular therapy for acute stroke. The objective of the current study was to assess whether the combination of the ASPECTS and the ORT can predict the outcomes after endovascular therapy. The charts of 117 consecutive ischemic stroke patients with successful reperfusion after endovascular therapy were retrospectively reviewed. We analyzed the association of ORT, ASPECTS, and ASPECTS-time score with clinical outcome. ASPECTS-time score was calculated as follows: [11 - ASPECTS] × [ORT (h)]. Rates of good outcome for patients with ASPECTS-time scores of tertile values, scores 5.67 or less, scores greater than 5.67 to 10.40 or less, and scores greater than 10.40, were 66.7%, 56.4%, and 33.3%, respectively (P < .05). Ordinal logistic regression analysis showed that the ASPECTS-time score (per category increase) was an independent predictor for better outcome (common odds ratio: .374; 95% confidence interval: .150-0.930; P < .05). A lower ASPECTS-time score may predict better clinical outcomes after endovascular treatment. Copyright © 2018 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Environmental qualification program for Wolsong project

International Nuclear Information System (INIS)

Duggal, A.; Johal, H.; Yee, F.; Suh, S.K.

1995-01-01

The Wolsong EQ Program is a process that begins at the design concept stage and continues throughout the operating life of the station. As all components may not have a 30 year service life without periodic maintenance, the EQ Program becomes an important management tool for the owner of the plant. First, the environmental conditions are predicted for the postulated events. Next, suitably qualified equipment is specified and procured. Then the equipment is installed according to specific instructions. Finally, by means of ongoing maintenance and replacement of parts, the qualification of the equipment is maintained during the operating life of the plant. Proper documentation and traceability is required at all stages of the program. As defined in the Wolsong Project Environmental Qualification Design Guide a comprehensive Environmental Qualification (EQ) Program ensures that safety related equipment located in an area in which a harsh environment could occur, can function when required for the life of the station . This program was implemented at the beginning of the Wolsong project. Using this program, components/equipment are qualified prior to installation and a maintenance program is established to keep equipment 'qualified' throughout the station life

Predicting wildfire ignitions, escapes, and large fire activity using Predictive Service’s 7-Day Fire Potential Outlook in the western USA

Science.gov (United States)

Karin L. Riley; Crystal Stonesifer; Haiganoush Preisler; Dave Calkin

2014-01-01

Can fire potential forecasts assist with pre-positioning of fire suppression resources, which could result in a cost savings to the United States government? Here, we present a preliminary assessment of the 7-Day Fire Potential Outlook forecasts made by the Predictive Services program. We utilized historical fire occurrence data and archived forecasts to assess how...

Transforming Dissatisfaction with Services into Self-Determination: A Social Psychological Perspective on Community Program Effectiveness.

Science.gov (United States)

Macias, Cathaleene; Aronson, Elliot; Hargreaves, William; Weary, Gifford; Barreira, Paul J; Harvey, John; Rodican, Charles F; Bickman, Leonard; Fisher, William

2009-08-01

A field study of supported employment for adults with mental illness (N=174) provided an experimental test of cognitive dissonance theory. We predicted that most work-interested individuals randomly assigned to a non-preferred program would reject services and lower their work aspirations. However, individuals who chose to pursue employment through a non-preferred program were expected to resolve this dissonance through favorable service evaluations and strong efforts to succeed at work. Significant work interest-by-service preference interactions supported these predictions. Over two years, participants interested in employment who obtained work through a non-preferred program stayed employed a median of 362 days versus 108 days for those assigned to a preferred program, and participants who obtained work through a non-preferred program had higher service satisfaction.

Crystal structure prediction of flexible molecules using parallel genetic algorithms with a standard force field.

Science.gov (United States)

Kim, Seonah; Orendt, Anita M; Ferraro, Marta B; Facelli, Julio C

2009-10-01

This article describes the application of our distributed computing framework for crystal structure prediction (CSP) the modified genetic algorithms for crystal and cluster prediction (MGAC), to predict the crystal structure of flexible molecules using the general Amber force field (GAFF) and the CHARMM program. The MGAC distributed computing framework includes a series of tightly integrated computer programs for generating the molecule's force field, sampling crystal structures using a distributed parallel genetic algorithm and local energy minimization of the structures followed by the classifying, sorting, and archiving of the most relevant structures. Our results indicate that the method can consistently find the experimentally known crystal structures of flexible molecules, but the number of missing structures and poor ranking observed in some crystals show the need for further improvement of the potential. Copyright 2009 Wiley Periodicals, Inc.

The organization of professional predictions on the development of automation for stope equipment

Energy Technology Data Exchange (ETDEWEB)

Kanygin, U.M.; Markashov, V.E.; Pashchevskii, U.G.

1980-01-01

The problems of organizing and conducting experimental predictions on the development of automation for stope equipment are examined. Professional evaluations are developed, and the order for processing the results is given, together with a calculation program for use with the ES-1020 computer. Several results from predictive studies of the development of automation for use with stope equipment are given.

Predicting Employment Outcomes for Consumers in Community College Short-Term Training Programs

Science.gov (United States)

Flannery, K. Brigid; Benz, Michael R.; Yovanoff, Paul; Kato, Mary McGrath; Lindstrom, Lauren

2011-01-01

Postsecondary education has been linked to improved access to employment opportunities for individuals with and without disabilities. The purpose of this study was to determine factors associated with increased employment outcomes for Vocational Rehabilitation consumers enrolled in community college short term occupational skill training programs.…

Prediction Models for Dynamic Demand Response

Energy Technology Data Exchange (ETDEWEB)

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

2015-11-02

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

The 136 MHZ/400 MHz earth station antenna-noise temperature prediction program for RAE-B

Science.gov (United States)

Taylor, R. E.; Fee, J. J.; Chin, M.

1972-01-01

A simulation study was undertaken to determine the 136 MHz and 400 MHz noise temperature of the ground network antennas which will track the RAE-B satellite during data transmission periods. Since the noise temperature of the antenna effectively sets the signal-to-noise ratio of the received signal, a knowledge of SNR will be helpful in locating the optimum time windows for data transmission during low noise periods. Antenna noise temperatures will be predicted for selected earth-based ground stations which will support RAE-B. Telemetry data acquisition will be at 400 MHz; tracking support at 136 MHz will be provided by the Goddard Range and Range Rate (RARR) stations. The antenna-noise temperature predictions will include the effects of galactic-brightness temperature, the sun, and the brightest radio stars. Predictions will cover the ten-month period from March 1, 1973 to December 31, 1973.

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

Science.gov (United States)

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

2018-06-01

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

Advanced maintenance research programs

International Nuclear Information System (INIS)

Marston, T.U.; Gelhaus, F.; Burke, R.

1985-01-01

The purpose of this paper is to provide the reader with an idea of the advanced maintenance research program at the Electric Power Research Institute (EPRI). A brief description of the maintenance-related activities is provided as a foundation for the advanced maintenance research projects. The projects can be divided into maintenance planning, preventive maintenance program development and implementation, predictive (or conditional) maintenance, and innovative maintenance techniques. The projects include hardware and software development, human factors considerations, and technology promotion and implementation. The advanced concepts include: the incorporation of artificial intelligence into outage planning; turbine and pump maintenance; rotating equipment monitoring and diagnostics with the aid of expert systems; and the development of mobile robots for nuclear power plant maintenance

Prediction of velocity and attitude of a yacht sailing upwind by computational fluid dynamics

Directory of Open Access Journals (Sweden)

Heebum Lee

2016-01-01

Full Text Available One of the most important factors in sailing yacht design is accurate velocity prediction. Velocity prediction programs (VPP's are widely used to predict velocity of sailing yachts. VPP's, which are primarily based on experimental data and experience of long years, however suffer limitations when applied in realistic conditions. Thus, in the present study, a high fidelity velocity prediction method using computational fluid dynamics (CFD was proposed. Using the developed method, velocity and attitude of a 30 feet sloop yacht, which was developed by Korea Research Institute of Ship and Ocean (KRISO and termed KORDY30, were predicted in upwind sailing condition.

Smoking cessation results in a clinical lung cancer screening program.

Science.gov (United States)

Borondy Kitts, Andrea K; McKee, Andrea B; Regis, Shawn M; Wald, Christoph; Flacke, Sebastian; McKee, Brady J

2016-07-01

Lung cancer screening may provide a "teachable moment" for promoting smoking cessation. This study assessed smoking cessation and relapse rates among individuals undergoing follow-up low-dose chest computed tomography (CT) in a clinical CT lung screening program and assessed the influence of initial screening results on smoking behavior. Self-reported smoking status for individuals enrolled in a clinical CT lung screening program undergoing a follow-up CT lung screening exam between 1st February, 2014 and 31st March, 2015 was retrospectively reviewed and compared to self-reported smoking status using a standardized questionnaire at program entry. Point prevalence smoking cessation and relapse rates were calculated across the entire population and compared with exam results. All individuals undergoing screening fulfilled the National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology: Lung Cancer Screening v1.2012(®) high-risk criteria and had an order for CT lung screening. A total of 1,483 individuals underwent a follow-up CT lung screening exam during the study interval. Smoking status at time of follow-up exam was available for 1,461/1,483 (98.5%). A total of 46% (678/1,461) were active smokers at program entry. The overall point prevalence smoking cessation and relapse rates were 20.8% and 9.3%, respectively. Prior positive screening exam results were not predictive of smoking cessation (OR 1.092; 95% CI, 0.715-1.693) but were predictive of reduced relapse among former smokers who had stopped smoking for 2 years or less (OR 0.330; 95% CI, 0.143-0.710). Duration of program enrollment was predictive of smoking cessation (OR 0.647; 95% CI, 0.477-0.877). Smoking cessation and relapse rates in a clinical CT lung screening program rates are more favorable than those observed in the general population. Duration of participation in the screening program correlated with increased smoking cessation rates. A positive exam result correlated with reduced

Development of Performance Analysis Program for an Axial Compressor with Meanline Analysis

International Nuclear Information System (INIS)

Park, Jun Young; Park, Moo Ryong; Choi, Bum Suk; Song, Je Wook

2009-01-01

Axial-flow compressor is one of the most important parts of gas turbine units with axial turbine and combustor. Therefore, precise prediction of performance is very important for development of new compressor or modification of existing one. Meanline analysis is a simple, fast and powerful method for performance prediction of axial-flow compressors with different geometries. So, Meanline analysis is frequently used in preliminary design stage and performance analysis for given geometry data. Much correlations for meanline analysis have been developed theoretically and experimentally for estimating various types of losses and flow deviation angle for long time. In present study, meanline analysis program was developed to estimate compressor losses, incidence angles, deviation angles, stall and surge conditions with many correlations. Performance prediction of one stage axial compressors is conducted with this meanline analysis program. The comparison between experimental and numerical results show a good agreement. This meanline analysis program can be used for various types of single stage axial-flow compressors with different geometries, as well as multistage axial-flow compressors

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

Directory of Open Access Journals (Sweden)

Benjamin Stich

2018-03-01

Full Text Available Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP, BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY, and tuber yield (TY of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.

Flammability Assessment Methodology Program Phase I: Final Report

Energy Technology Data Exchange (ETDEWEB)

C. A. Loehr; S. M. Djordjevic; K. J. Liekhus; M. J. Connolly

1997-09-01

The Flammability Assessment Methodology Program (FAMP) was established to investigate the flammability of gas mixtures found in transuranic (TRU) waste containers. The FAMP results provide a basis for increasing the permissible concentrations of flammable volatile organic compounds (VOCs) in TRU waste containers. The FAMP results will be used to modify the ''Safety Analysis Report for the TRUPACT-II Shipping Package'' (TRUPACT-II SARP) upon acceptance of the methodology by the Nuclear Regulatory Commission. Implementation of the methodology would substantially increase the number of drums that can be shipped to the Waste Isolation Pilot Plant (WIPP) without repackaging or treatment. Central to the program was experimental testing and modeling to predict the gas mixture lower explosive limit (MLEL) of gases observed in TRU waste containers. The experimental data supported selection of an MLEL model that was used in constructing screening limits for flammable VOC and flammable gas concentrations. The MLEL values predicted by the model for individual drums will be utilized to assess flammability for drums that do not meet the screening criteria. Finally, the predicted MLEL values will be used to derive acceptable gas generation rates, decay heat limits, and aspiration time requirements for drums that do not pass the screening limits. The results of the program demonstrate that an increased number of waste containers can be shipped to WIPP within the flammability safety envelope established in the TRUPACT-II SARP.

First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions

Energy Technology Data Exchange (ETDEWEB)

Ceder, Gerbrand [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials and Engineering

2018-01-28

Novel materials are often the enabler for new energy technologies. In ab-initio computational materials science, method are developed to predict the behavior of materials starting from the laws of physics, so that properties can be predicted before compounds have to be synthesized and tested. As such, a virtual materials laboratory can be constructed, saving time and money. The objectives of this program were to develop first-principles theory to predict the structure and thermodynamic stability of materials. Since its inception the program focused on the development of the cluster expansion to deal with the increased complexity of complex oxides. This research led to the incorporation of vibrational degrees of freedom in ab-initio thermodynamics, developed methods for multi-component cluster expansions, included the explicit configurational degrees of freedom of localized electrons, developed the formalism for stability in aqueous environments, and culminated in the first ever approach to produce exact ground state predictions of the cluster expansion. Many of these methods have been disseminated to the larger theory community through the Materials Project, pymatgen software, or individual codes. We summarize three of the main accomplishments.

Using Genetic Distance to Infer the Accuracy of Genomic Prediction.

Directory of Open Access Journals (Sweden)

Marco Scutari

2016-09-01

Full Text Available The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

Range prediction for electric vehicles; Reichweitenprognose fuer Elektromobile

Energy Technology Data Exchange (ETDEWEB)

Conradi, Peter [All4IP Technologies GmbH and Co.KG, Darmstadt (Germany)

2012-06-15

The range of electric vehicles varies strongly in dependency of a number of external factors. To be able to make an exact dynamic prediction of the remaining range during the journey, All4IP Technologies developed a special software that can access the CAN bus. The App, programmed for iOS and Android operating systems considers even the topology of the area. (orig.)

LWR-PV Surveillance Dosimetry Improvement Program review graphics

International Nuclear Information System (INIS)

McElroy, W.N.; Gold, R.; Gutherie, G.L.

1979-10-01

A primary objective of the multilaboratory program is to prepare an updated and improved set of dosimetry, damage correlation, and the associated reactor analysis ASTM standards for LWR-PV irradiation surveillance programs. Supporting this objective are a series of analytical and experimental validation and calibration studies in Benchmark Neutron Fields, reactor Test Regions, and operating power reactor Surveillance Positions. These studies will establish and certify the precision and accuracy of the measurement and predictive methods which are recommended for use in these standards. Consistent and accurate measurement and data analysis techniques and methods, therefore, will have been developed and validated along with guidelines for required neutron field calculations that are used to (1) correlate changes in material properties with the characteristics of the neutron radiation field and (2) predict pressure vessel steel toughness and embrittlement from power reactor surveillance data

Genomic Prediction in Barley

DEFF Research Database (Denmark)

Edriss, Vahid; Cericola, Fabio; Jensen, Jens D

2015-01-01

to next generation. The main goal of this study was to see the potential of using genomic prediction in a commercial Barley breeding program. The data used in this study was from Nordic Seed company which is located in Denmark. Around 350 advanced lines were genotyped with 9K Barely chip from Illumina....... Traits used in this study were grain yield, plant height and heading date. Heading date is number days it takes after 1st June for plant to head. Heritabilities were 0.33, 0.44 and 0.48 for yield, height and heading, respectively for the average of nine plots. The GBLUP model was used for genomic...

Predicting short-term weight loss using four leading health behavior change theories

Directory of Open Access Journals (Sweden)

Barata José T

2007-04-01

Full Text Available Abstract Background This study was conceived to analyze how exercise and weight management psychosocial variables, derived from several health behavior change theories, predict weight change in a short-term intervention. The theories under analysis were the Social Cognitive Theory, the Transtheoretical Model, the Theory of Planned Behavior, and Self-Determination Theory. Methods Subjects were 142 overweight and obese women (BMI = 30.2 ± 3.7 kg/m2; age = 38.3 ± 5.8y, participating in a 16-week University-based weight control program. Body weight and a comprehensive psychometric battery were assessed at baseline and at program's end. Results Weight decreased significantly (-3.6 ± 3.4%, p Conclusion The present models were able to predict 20–30% of variance in short-term weight loss and changes in weight management self-efficacy accounted for a large share of the predictive power. As expected from previous studies, exercise variables were only moderately associated with short-term outcomes; they are expected to play a larger explanatory role in longer-term results.

Assessment of military population-based psychological resilience programs.

Science.gov (United States)

Morgan, Brenda J; Bibb, Sandra C Garmon

2011-09-01

Active duty service members' (ADSMs) seemingly poor adaptability to traumatic stressors is a risk to force health. Enhancing the psychological resilience of ADSMs has become a key focus of Department of Defense (DoD) leaders and the numbers of military programs for enhancing psychological resilience have increased. The purpose of this article is to describe the results of an assessment conducted to determine comprehensiveness of current psychological resilience building programs that target ADSMs. A modified six-step, population-based needs assessment was used to evaluate resilience programs designed to meet the psychological needs of the ADSM population. The assessment results revealed a gap in published literature regarding program outcomes. DoD leaders may benefit from targeted predictive research that assesses program effectiveness outcomes. The necessity of including preventive, evidence-based interventions in new programs, such as positive emotion interventions shown to enhance psychological resilience in civilian samples, is also recommended.

Improving the accuracy of protein secondary structure prediction using structural alignment

Directory of Open Access Journals (Sweden)

Gallin Warren J

2006-06-01

Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

Development and status of data quality assurance program at NASA Langley research center: Toward national standards

Science.gov (United States)

Hemsch, Michael J.

1996-01-01

As part of a continuing effort to re-engineer the wind tunnel testing process, a comprehensive data quality assurance program is being established at NASA Langley Research Center (LaRC). The ultimate goal of the program is routing provision of tunnel-to-tunnel reproducibility with total uncertainty levels acceptable for test and evaluation of civilian transports. The operational elements for reaching such levels of reproducibility are: (1) statistical control, which provides long term measurement uncertainty predictability and a base for continuous improvement, (2) measurement uncertainty prediction, which provides test designs that can meet data quality expectations with the system's predictable variation, and (3) national standards, which provide a means for resolving tunnel-to-tunnel differences. The paper presents the LaRC design for the program and discusses the process of implementation.

Prediction of electric energy consumption in Cuba for the period 2000-2015

International Nuclear Information System (INIS)

Garcia Rodirguez, B

1999-01-01

This paper consists on a prediction of the growth in electric energy consumption in Cuba, for the period 2000-2015 and with respect to 1990, it also considers the specific features of the National Electroenergetic System. Validated Guidelines in accordance with the Delphi method, which incorporates the basis characteristics considered by international programs for these predictions, were used for this purpose. From the analysis of the behaviour in power consumption of the different consumers and of the expected changes in them according to the expected scenarios, a prediction on the growth in the demand of electric energy is made

Safety-critical Java on a time-predictable processor

DEFF Research Database (Denmark)

Korsholm, Stephan E.; Schoeberl, Martin; Puffitsch, Wolfgang

2015-01-01

For real-time systems the whole execution stack needs to be time-predictable and analyzable for the worst-case execution time (WCET). This paper presents a time-predictable platform for safety-critical Java. The platform consists of (1) the Patmos processor, which is a time-predictable processor......; (2) a C compiler for Patmos with support for WCET analysis; (3) the HVM, which is a Java-to-C compiler; (4) the HVM-SCJ implementation which supports SCJ Level 0, 1, and 2 (for both single and multicore platforms); and (5) a WCET analysis tool. We show that real-time Java programs translated to C...... and compiled to a Patmos binary can be analyzed by the AbsInt aiT WCET analysis tool. To the best of our knowledge the presented system is the second WCET analyzable real-time Java system; and the first one on top of a RISC processor....

Predicting Handwriting Legibility in Taiwanese Elementary School Children.

Science.gov (United States)

Lee, Tzu-I; Howe, Tsu-Hsin; Chen, Hao-Ling; Wang, Tien-Ni

This study investigates handwriting characteristics and potential predictors of handwriting legibility among typically developing elementary school children in Taiwan. Predictors of handwriting legibility included visual-motor integration (VMI), visual perception (VP), eye-hand coordination (EHC), and biomechanical characteristics of handwriting. A total of 118 children were recruited from an elementary school in Taipei, Taiwan. A computerized program then assessed their handwriting legibility. The biomechanics of handwriting were assessed using a digitizing writing tablet. The children's VMI, VP, and EHC were assessed using the Beery-Buktenica Developmental Test of Visual-Motor Integration. Results indicated that predictive factors of handwriting legibility varied in different age groups. VMI predicted handwriting legibility for first-grade students, and EHC and stroke force predicted handwriting legibility for second-grade students. Kinematic factors such as stroke velocity were the only predictor for children in fifth and sixth grades. Copyright © 2016 by the American Occupational Therapy Association, Inc.

Ensemble approach combining multiple methods improves human transcription start site prediction

LENUS (Irish Health Repository)

Dineen, David G

2010-11-30

Abstract Background The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. Results We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier (\\'Profisi Ensemble\\') using predictions from 7 programs, along with 2 other data sources. Support vector machines using \\'full\\' and \\'reduced\\' data sets are combined in an either\\/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. Conclusions Supervised learning methods are a useful way to combine predictions from diverse sources.

Prediction of impurity spectral emission in plasmas

International Nuclear Information System (INIS)

Gordon, H.; Summers, H.P.

1985-01-01

This paper summarises the development of a set of general purpose computational procedures for the prediction of spectral emission from plasmas, with emphasis on fusion plasmas. The first stage was concerned with the calculation of populations of low levels of impurity ions in a statistical balance approximation in thermal plasmas of arbitrary electron and proton temperatures and densities. This was merged with associated calculations of ionisation stage abundances in equilibrium, time dependent and spatially inhomogeneous conditions to yield spectrum line emissivities of direct relevance for comparative and diagnostic studies of observed spectra. The integrated computer program package draws upon sets or basic atomic data. In the present work the compilation of this basic data is adressed. A set of computer programs has beeen developed and used to convert systematically atomic rate data, drawn from the literature, to standard forms and parameter ranges. Regularities in this data along isoelectronic sequences are exploited to infer rates for an arbitrary ion from a set of representative data (termed the 'general Z' database). From this, the input for the spectral prediction codes above is generated. Presently data in the H, He, Li and Be isoelectronic sequences is prepared. The operation of the procedures is illustrated. (orig.)

A stepwise model to predict monthly streamflow

Science.gov (United States)

Mahmood Al-Juboori, Anas; Guven, Aytac

2016-12-01

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

Environmental research program. 1995 Annual report

Energy Technology Data Exchange (ETDEWEB)

Brown, N.J.

1996-06-01

The objective of the Environmental Research Program is to enhance the understanding of, and mitigate the effects of pollutants on health, ecological systems, global and regional climate, and air quality. The program is multidisciplinary and includes fundamental research and development in efficient and environmentally benign combustion, pollutant abatement and destruction, and novel methods of detection and analysis of criteria and noncriteria pollutants. This diverse group conducts investigations in combustion, atmospheric and marine processes, flue-gas chemistry, and ecological systems. Combustion chemistry research emphasizes modeling at microscopic and macroscopic scales. At the microscopic scale, functional sensitivity analysis is used to explore the nature of the potential-to-dynamics relationships for reacting systems. Rate coefficients are estimated using quantum dynamics and path integral approaches. At the macroscopic level, combustion processes are modelled using chemical mechanisms at the appropriate level of detail dictated by the requirements of predicting particular aspects of combustion behavior. Parallel computing has facilitated the efforts to use detailed chemistry in models of turbulent reacting flow to predict minor species concentrations.

Rotor Wake/Stator Interaction Noise Prediction Code Technical Documentation and User's Manual

Science.gov (United States)

Topol, David A.; Mathews, Douglas C.

2010-01-01

This report documents the improvements and enhancements made by Pratt & Whitney to two NASA programs which together will calculate noise from a rotor wake/stator interaction. The code is a combination of subroutines from two NASA programs with many new features added by Pratt & Whitney. To do a calculation V072 first uses a semi-empirical wake prediction to calculate the rotor wake characteristics at the stator leading edge. Results from the wake model are then automatically input into a rotor wake/stator interaction analytical noise prediction routine which calculates inlet aft sound power levels for the blade-passage-frequency tones and their harmonics, along with the complex radial mode amplitudes. The code allows for a noise calculation to be performed for a compressor rotor wake/stator interaction, a fan wake/FEGV interaction, or a fan wake/core stator interaction. This report is split into two parts, the first part discusses the technical documentation of the program as improved by Pratt & Whitney. The second part is a user's manual which describes how input files are created and how the code is run.

PRETTA：A COMPUTER PROGRAM FOR PWR PRESSURIZER’S TRANSIENT THERMODYNAMICS

Institute of Scientific and Technical Information of China (English)

阿谢德; 徐济鋆

2001-01-01

A computer program PRETTA “Pressurizer Transient Thermodynamics Analysis” was developed for the prediction of pressurizer under transient conditions. It is based on the solution of the conservation laws of heat and mass applied to the three separate and non equilibrium thermodynamic regions. In the program all of the important thermal-hydraulics phenomena occurring in the pressurizer: stratification of the hot water and incoming cold water, bulk flashing and condensation, wall condensation, and interfacial heat and mass transfer have been considered. The bubble rising and rain-out models are developed to describe bulk flashing and condensation, respectively. To obtain the wall condensation rate, a one-dimensional heat conduction equation is solved by the pivoting method. The presented computer program will predict the pressure-time behavior of a PWR pressurizer during a variety of transients. The results obtained from the proposed mathematical model are in good agreement with available data on the CHASHMA nuclear power plant's pressurizer performance.

Consensus models to predict endocrine disruption for all ...

Science.gov (United States)

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

Theoretical and algorithmic advances in multi-parametric programming and control

KAUST Repository

Pistikopoulos, Efstratios N.; Dominguez, Luis; Panos, Christos; Kouramas, Konstantinos; Chinchuluun, Altannar

2012-01-01

This paper presents an overview of recent theoretical and algorithmic advances, and applications in the areas of multi-parametric programming and explicit/multi-parametric model predictive control (mp-MPC). In multi-parametric programming, advances include areas such as nonlinear multi-parametric programming (mp-NLP), bi-level programming, dynamic programming and global optimization for multi-parametric mixed-integer linear programming problems (mp-MILPs). In multi-parametric/explicit MPC (mp-MPC), advances include areas such as robust multi-parametric control, multi-parametric nonlinear MPC (mp-NMPC) and model reduction in mp-MPC. A comprehensive framework for multi-parametric programming and control is also presented. Recent applications include a hydrogen storage device, a fuel cell power generation system, an unmanned autonomous vehicle (UAV) and a hybrid pressure swing adsorption (PSA) system. © 2012 Springer-Verlag.

Theoretical and algorithmic advances in multi-parametric programming and control

KAUST Repository

Pistikopoulos, Efstratios N.

2012-04-21

This paper presents an overview of recent theoretical and algorithmic advances, and applications in the areas of multi-parametric programming and explicit/multi-parametric model predictive control (mp-MPC). In multi-parametric programming, advances include areas such as nonlinear multi-parametric programming (mp-NLP), bi-level programming, dynamic programming and global optimization for multi-parametric mixed-integer linear programming problems (mp-MILPs). In multi-parametric/explicit MPC (mp-MPC), advances include areas such as robust multi-parametric control, multi-parametric nonlinear MPC (mp-NMPC) and model reduction in mp-MPC. A comprehensive framework for multi-parametric programming and control is also presented. Recent applications include a hydrogen storage device, a fuel cell power generation system, an unmanned autonomous vehicle (UAV) and a hybrid pressure swing adsorption (PSA) system. © 2012 Springer-Verlag.

Recommendations for strengthening the infrared technology component of any condition monitoring program

Science.gov (United States)

Nicholas, Jack R., Jr.; Young, R. K.

1999-03-01

This presentation provides insights of a long term 'champion' of many condition monitoring technologies and a Level III infra red thermographer. The co-authors present recommendations based on their observations of infra red and other components of predictive, condition monitoring programs in manufacturing, utility and government defense and energy activities. As predictive maintenance service providers, trainers, informal observers and formal auditors of such programs, the co-authors provide a unique perspective that can be useful to practitioners, managers and customers of advanced programs. Each has over 30 years experience in the field of machinery operation, maintenance, and support the origins of which can be traced to and through the demanding requirements of the U.S. Navy nuclear submarine forces. They have over 10 years each of experience with programs in many different countries on 3 continents. Recommendations are provided on the following: (1) Leadership and Management Support (For survival); (2) Life Cycle View (For establishment of a firm and stable foundation for a program); (3) Training and Orientation (For thermographers as well as operators, managers and others); (4) Analyst Flexibility (To innovate, explore and develop their understanding of machinery condition); (5) Reports and Program Justification (For program visibility and continued expansion); (6) Commitment to Continuous Improvement of Capability and Productivity (Through application of updated hardware and software); (7) Mutual Support by Analysts (By those inside and outside of the immediate organization); (8) Use of Multiple Technologies and System Experts to Help Define Problems (Through the use of correlation analysis of data from up to 15 technologies. An example correlation analysis table for AC and DC motors is provided.); (9) Root Cause Analysis (Allows a shift from reactive to proactive stance for a program); (10) Master Equipment Identification and Technology Application (To

Prediction of dementia in primary care patients.

Directory of Open Access Journals (Sweden)

Frank Jessen

Full Text Available BACKGROUND: Current approaches for AD prediction are based on biomarkers, which are however of restricted availability in primary care. AD prediction tools for primary care are therefore needed. We present a prediction score based on information that can be obtained in the primary care setting. METHODOLOGY/PRINCIPAL FINDINGS: We performed a longitudinal cohort study in 3.055 non-demented individuals above 75 years recruited via primary care chart registries (Study on Aging, Cognition and Dementia, AgeCoDe. After the baseline investigation we performed three follow-up investigations at 18 months intervals with incident dementia as the primary outcome. The best set of predictors was extracted from the baseline variables in one randomly selected half of the sample. This set included age, subjective memory impairment, performance on delayed verbal recall and verbal fluency, on the Mini-Mental-State-Examination, and on an instrumental activities of daily living scale. These variables were aggregated to a prediction score, which achieved a prediction accuracy of 0.84 for AD. The score was applied to the second half of the sample (test cohort. Here, the prediction accuracy was 0.79. With a cut-off of at least 80% sensitivity in the first cohort, 79.6% sensitivity, 66.4% specificity, 14.7% positive predictive value (PPV and 97.8% negative predictive value of (NPV for AD were achieved in the test cohort. At a cut-off for a high risk population (5% of individuals with the highest risk score in the first cohort the PPV for AD was 39.1% (52% for any dementia in the test cohort. CONCLUSIONS: The prediction score has useful prediction accuracy. It can define individuals (1 sensitively for low cost-low risk interventions, or (2 more specific and with increased PPV for measures of prevention with greater costs or risks. As it is independent of technical aids, it may be used within large scale prevention programs.

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.

Development of an effective valve packing program

Energy Technology Data Exchange (ETDEWEB)

Hart, K.A.

1996-12-01

Current data now shows that graphite valve packing installed within the guidance of a controlled program produces not only reliable stem sealing but predictable running loads. By utilizing recent technological developments in valve performance monitoring for both MOV`s and AOV`s, valve packing performance can be enhanced while reducing maintenance costs. Once known, values are established for acceptable valve packing loads, the measurement of actual valve running loads via the current MOV/AOV diagnostic techniques can provide indication of future valve stem sealing problems, improper valve packing installation or identify the opportunity for valve packing program improvements. At times the full benefit of these advances in material and predictive technology remain under utilized due to simple past misconceptions associated with valve packing. This paper will explore the basis for these misconceptions, provide general insight into the current understanding of valve packing and demonstrate how with this new understanding and current valve diagnostic equipment the key aspects required to develop an effective, quality valve packing program fit together. The cost and operational benefits provided by this approach can be significant impact by the: elimination of periodic valve repacking, reduction of maintenance costs, benefits of leak-free valve operation, justification for reduced Post Maintenance Test Requirements, reduced radiation exposure, improved plant appearance.

Advanced condition monitoring program for turbine system

International Nuclear Information System (INIS)

Ono, Shigetoshi

2015-01-01

It is important for utilities to achieve a stable operation in nuclear power plants. To achieve it, plant anomalies that affect a stable operation must be found out and eliminated. Therefore, the advanced condition monitoring program was developed. In this program, a sophisticated heat balance model based on the actual plant data is adopted to identify plant anomalies at an incipient stage and the symptoms of plant anomalies are found by heat balance changes from the model calculation. The model calculation results have shown precise prediction for actual plant parameters. Moreover, this program has the diagnostic engine that helps operators derive the cause of plant anomalies. By using this monitoring program, the component reliability in the turbine system can be periodically monitored and assessed, and as a result the stable operation of nuclear power plants can be achieved. (author)

Frequency Domain Computer Programs for Prediction and Analysis of Rail Vehicle Dynamics : Volume 1. Technical Report

Science.gov (United States)

1975-12-01

Frequency domain computer programs developed or acquired by TSC for the analysis of rail vehicle dynamics are described in two volumes. Volume I defines the general analytical capabilities required for computer programs applicable to single rail vehi...

Predicting Metabolic Syndrome Using the Random Forest Method

Directory of Open Access Journals (Sweden)

Apilak Worachartcheewan

2015-01-01

Full Text Available Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III criteria. The Random Forest (RF method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females. RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.

GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

DEFF Research Database (Denmark)

Swenson, M Shel; Anderson, Joshua; Ash, Andrew

2012-01-01

achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage...

Dynamical Predictability of Monthly Means.

Science.gov (United States)

Shukla, J.

1981-12-01

perturbations. The 30-day means for days 16-46 over certain areas are also significantly different from the valances due to random perturbations.These results suggest that the evolution of long waves remains sufficiently predictable at least up to one month, and possibly up to 45 days, so that the combined effects of their own nonpredictability and their depredictabilization by synoptic-scale instabilities is not large enough to degrade the dynamical prediction of monthly means. The Northern Hemisphere appears to be more predictable than the Southern Hemisphere.It is noteworthy that the lack of predictability for the second month is not because the model simulations relax to the same model state but because of very large departures in the simulated model states. This suggests that, with improvements in model resolution and physical parameterizations, there is potential for extending the predictability of time averages even beyond one month.Here, we have examined only the dynamical predictability, because the boundary conditions are identical in all the integrations. Based on these results, and the possibility of additional predictability due to the influence of persistent anomalies of sea surface temperature, sea ice, snow and soil moisture, it is suggested that there is sufficient physical basis to undertake a systematic program to establish the feasibility of predicting monthly means by numerical integrations of realistic dynamical models.

Broadband Fan Noise Prediction System for Turbofan Engines. Volume 2; BFaNS User's Manual and Developer's Guide

Science.gov (United States)

Morin, Bruce L.

2010-01-01

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the second volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User s Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running BFaNS. It concludes with technical documentation of the BFaNS computer program.

A neuro-fuzzy model for prediction of the indoor temperature in typical Australian residential buildings

Energy Technology Data Exchange (ETDEWEB)

Alasha' ary, Haitham; Moghtaderi, Behdad; Page, Adrian; Sugo, Heber [Priority Research Centre for Energy, Chemical Engineering, School of Engineering, Faculty of Engineering and Built Environment, the University of Newcastle, Callaghan, Newcastle, NSW 2308 (Australia)

2009-07-15

The Masonry Research Group at The University of Newcastle, Australia has embarked on an extensive research program to study the thermal performance of common walling systems in Australian residential buildings by studying the thermal behaviour of four representative purpose-built thermal test buildings (referred to as 'test modules' or simply 'modules' hereafter). The modules are situated on the university campus and are constructed from brick veneer (BV), cavity brick (CB) and lightweight (LW) constructions. The program of study has both experimental and analytical strands, including the use of a neuro-fuzzy approach to predict the thermal behaviour. The latter approach employs an experimental adaptive neuro-fuzzy inference system (ANFIS) which is used in this study to predict the room (indoor) temperatures of the modules under a range of climatic conditions pertinent to Newcastle (NSW, Australia). The study shows that this neuro-fuzzy model is capable of accurately predicting the room temperature of such buildings; thus providing a potential computationally efficient and inexpensive predictive tool for the more effective thermal design of housing. (author)

Predicting performance using background characteristics of international medical graduates in an inner-city university-affiliated Internal Medicine residency training program

Directory of Open Access Journals (Sweden)

Akhuetie Jane

2009-07-01

years and USMLE step I & step II clinical skills scores were 85 (IQR: 80–88 & 82 (IQR: 79–87 respectively. The median aggregate CBE scores during training were: PG1 5.8 (IQR: 5.6–6.3; PG2 6.3 (IQR 6–6.8 & PG3 6.7 (IQR: 6.7 – 7.1. 25% of our residents scored consistently above US national median ITE scores in all 3 years of training and 16% pursued a fellowship. Younger residents had higher aggregate annual CBE score than the program median (p Conclusion Background IMG features namely, age and USMLE scores predict performance evaluation and in-training examination scores during residency training. In addition enhanced research activities during residency training could facilitate fellowship goals among interested IMGs.

Fisher: a program for the detection of H/ACA snoRNAs using MFE secondary structure prediction and comparative genomics - assessment and update.

Science.gov (United States)

Freyhult, Eva; Edvardsson, Sverker; Tamas, Ivica; Moulton, Vincent; Poole, Anthony M

2008-07-21

The H/ACA family of small nucleolar RNAs (snoRNAs) plays a central role in guiding the pseudouridylation of ribosomal RNA (rRNA). In an effort to systematically identify the complete set of rRNA-modifying H/ACA snoRNAs from the genome sequence of the budding yeast, Saccharomyces cerevisiae, we developed a program - Fisher - and previously presented several candidate snoRNAs based on our analysis 1. In this report, we provide a brief update of this work, which was aborted after the publication of experimentally-identified snoRNAs 2 identical to candidates we had identified bioinformatically using Fisher. Our motivation for revisiting this work is to report on the status of the candidate snoRNAs described in 1, and secondly, to report that a modified version of Fisher together with the available multiple yeast genome sequences was able to correctly identify several H/ACA snoRNAs for modification sites not identified by the snoGPS program 3. While we are no longer developing Fisher, we briefly consider the merits of the Fisher algorithm relative to snoGPS, which may be of use for workers considering pursuing a similar search strategy for the identification of small RNAs. The modified source code for Fisher is made available as supplementary material. Our results confirm the validity of using minimum free energy (MFE) secondary structure prediction to guide comparative genomic screening for RNA families with few sequence constraints.

Program, policy, and price interventions for tobacco control: quantifying the return on investment of a state tobacco control program.

Science.gov (United States)

Dilley, Julia A; Harris, Jeffrey R; Boysun, Michael J; Reid, Terry R

2012-02-01

We examined health effects associated with 3 tobacco control interventions in Washington State: a comprehensive state program, a state policy banning smoking in public places, and price increases. We used linear regression models to predict changes in smoking prevalence and specific tobacco-related health conditions associated with the interventions. We estimated dollars saved over 10 years (2000-2009) by the value of hospitalizations prevented, discounting for national trends. Smoking declines in the state exceeded declines in the nation. Of the interventions, the state program had the most consistent and largest effect on trends for heart disease, cerebrovascular disease, respiratory disease, and cancer. Over 10 years, implementation of the program was associated with prevention of nearly 36,000 hospitalizations, at a value of about $1.5 billion. The return on investment for the state program was more than $5 to $1. The combined program, policy, and price interventions resulted in reductions in smoking and related health effects, while saving money. Public health and other leaders should continue to invest in tobacco control, including comprehensive programs.

Faculty Salary as a Predictor of Student Outgoing Salaries from MBA Programs

Science.gov (United States)

Hamlen, Karla R.; Hamlen, William A.

2016-01-01

The authors' purpose was to investigate the predictive value of faculty salaries on outgoing salaries of master of business administration (MBA) students when controlling for other student and program variables. Data were collected on 976 MBA programs using Barron's "Guide to Graduate Business Schools" over the years 1988-2005 and the…

How Predictive Analytics and Choice Architecture Can Improve Student Success

Science.gov (United States)

Denley, Tristan

2014-01-01

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

Predicting Drug Court Treatment Completion Using the MMPI-2-RF

Science.gov (United States)

Mattson, Curtis; Powers, Bradley; Halfaker, Dale; Akeson, Steven; Ben-Porath, Yossef

2012-01-01

We examined the ability of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) substantive scales to predict Drug Court treatment completion in a sample of individuals identified as being at risk for failure to complete the program. Higher scores on MMPI-2-RF scales…

Program listing for heat-pump seasonal-performance model (SPM). [CNHSPM

Energy Technology Data Exchange (ETDEWEB)

1982-06-30

The computer program CNHSPM is listed which predicts heat pump seasonal energy consumption (including defrost, cyclic degradation, and supplementary heat) using steady state rating point performance and binned weather data. (LEW)

A Novel Exercise Thermophysiology Comfort Prediction Model with Fuzzy Logic

Directory of Open Access Journals (Sweden)

Nan Jia

2016-01-01

Full Text Available Participation in a regular exercise program can improve health status and contribute to an increase in life expectancy. However, exercise accidents like dehydration, exertional heatstroke, syncope, and even sudden death exist. If these accidents can be analyzed or predicted before they happen, it will be beneficial to alleviate or avoid uncomfortable or unacceptable human disease. Therefore, an exercise thermophysiology comfort prediction model is needed. In this paper, coupling the thermal interactions among human body, clothing, and environment (HCE as well as the human body physiological properties, a human thermophysiology regulatory model is designed to enhance the human thermophysiology simulation in the HCE system. Some important thermal and physiological performances can be simulated. According to the simulation results, a human exercise thermophysiology comfort prediction method based on fuzzy inference system is proposed. The experiment results show that there is the same prediction trend between the experiment result and simulation result about thermophysiology comfort. At last, a mobile application platform for human exercise comfort prediction is designed and implemented.

Overview of the Novel Intelligent JAXA Active Rotor Program

Science.gov (United States)

Saito, Shigeru; Kobiki, Noboru; Tanabe, Yasutada; Johnson, Wayne; Yamauchi, Gloria K.; Young, Larry A.

2010-01-01

The Novel Intelligent JAXA Active Rotor (NINJA Rotor) program is a cooperative effort between JAXA and NASA, involving a test of a JAXA pressure-instrumented, active-flap rotor in the 40- by 80-Foot Wind Tunnel at Ames Research Center. The objectives of the program are to obtain an experimental database of a rotor with active flaps and blade pressure instrumentation, and to use that data to develop analyses to predict the aerodynamic and aeroacoustic performance of rotors with active flaps. An overview of the program is presented, including a description of the rotor and preliminary pretest calculations.

Current Trends in Communication Graduate Degrees: Survey of Communications, Advertising, PR, and IMC Graduate Programs

Science.gov (United States)

Quesenberry, Keith A.; Coolsen, Michael K.; Wilkerson, Kristen

2015-01-01

A survey of 61 master's degree advertising programs reveals significant trends in program titles, curriculum design, course delivery, and students served. The results provide insight for current and planned master's degree programs as research predicts a continued increase in demand for master's education over the next decade. Survey results are…

Development and implementation of the heavy water program at Bruce Power

International Nuclear Information System (INIS)

Davloor, R.; Bourassa, C.

2014-01-01

Bruce Power operates 8 pressurized heavy water reactor units requiring more than 6000 mega grams (Mg) of heavy water. A Heavy Water Management Program that has been developed to administer this asset over the past 3 years. Through a corporate management system the Program provides governance, oversight and support to the stations. It is implemented through organizational structure, program and procedure documents and an information management system that provides benchmarked metrics, business intelligence and analytics for decision making and prediction. The program drives initiatives such as major maintenance activities, capital programs, detritiation strategies and ensures heavy water systems readiness for outages and rehabilitation of units. (author)

Development and implementation of the heavy water program at Bruce Power

Energy Technology Data Exchange (ETDEWEB)

Davloor, R.; Bourassa, C., E-mail: ram.davloor@brucepower.com, E-mail: carl.bourassa@brucepower.com [Bruce Power, Tiverton, ON (Canada)

2014-07-01

Bruce Power operates 8 pressurized heavy water reactor units requiring more than 6000 mega grams (Mg) of heavy water. A Heavy Water Management Program that has been developed to administer this asset over the past 3 years. Through a corporate management system the Program provides governance, oversight and support to the stations. It is implemented through organizational structure, program and procedure documents and an information management system that provides benchmarked metrics, business intelligence and analytics for decision making and prediction. The program drives initiatives such as major maintenance activities, capital programs, detritiation strategies and ensures heavy water systems readiness for outages and rehabilitation of units. (author)

Genomic prediction applied to high-biomass sorghum for bioenergy production.

Science.gov (United States)

de Oliveira, Amanda Avelar; Pastina, Maria Marta; de Souza, Vander Filipe; da Costa Parrella, Rafael Augusto; Noda, Roberto Willians; Simeone, Maria Lúcia Ferreira; Schaffert, Robert Eugene; de Magalhães, Jurandir Vieira; Damasceno, Cynthia Maria Borges; Margarido, Gabriel Rodrigues Alves

2018-01-01

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum ( Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesCπ, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.

Development of a computing program for prediction of wind power for midsize and wide grid areas. Final report; Entwicklung eines Rechenmodells zur Vorhersage der Windleistung fuer mittlere und grosse Versorgungsgebiete. Abschlussbericht

Energy Technology Data Exchange (ETDEWEB)

Reeder, L.; Rohrig, K.; Ernst, B.; Schorn, P.; Bettels, B.

2002-06-30

In co-operation with partners out of industry and research a machine program was developed predicting the output of wind power plants. Three attributes should be realised by this prediction tool: Short computing time, usable for various grid regions, high reliability. Therewith the transmission system operators get a tool for reducing the amount of control energy which is needed to ensure the balance between power generation and consumption in their networks. This prediction tool for up to two days was developed exemplary for the northern grid area of the transmission system operator 'E.ON Netz GmbH' (ENE). The wind power prediction is based on numerical weather forecast from the German weather service (Deutscher Wetterdienst). The weather forecast is given for 16 representative sites within the ENE-area. The meso-scale model KLIMM (Klima Model Mainz) was used to calculate the meteorological variables near to the wind farms, which are connected to the one transformer substation belonging to one representative place. Therefor KLIMM is fed with the weather forecast given for one limited location in the representative sites. The transformation of the meteorological variables to the output of wind power plants at the representative site is done by Neural Networks. These Neural Networks have been trained with corresponding measurements. Using an existing online-model the total wind power for the whole ENE-area will be calculated from the individual wind power of the representative sites. The Evaluation of the prediction- and measured data from 2001 shows comparing with reference-models, that the prediction-model evolved in the project lead to very good results. (orig.)

Computation of piecewise affine terminal cost functions for model predictive control

NARCIS (Netherlands)

Brunner, F.D.; Lazar, M.; Allgöwer, F.; Fränzle, Martin; Lygeros, John

2014-01-01

This paper proposes a method for the construction of piecewise affine terminal cost functions for model predictive control (MPC). The terminal cost function is constructed on a predefined partition by solving a linear program for a given piecewise affine system, a stabilizing piecewise affine

Making detailed predictions makes (some) predictions worse

Science.gov (United States)

Kelly, Theresa F.

In this paper, we investigate whether making detailed predictions about an event makes other predictions worse. Across 19 experiments, 10,895 participants, and 415,960 predictions about 724 professional sports games, we find that people who made detailed predictions about sporting events (e.g., how many hits each baseball team would get) made worse predictions about more general outcomes (e.g., which team would win). We rule out that this effect is caused by inattention or fatigue, thinking too hard, or a differential reliance on holistic information about the teams. Instead, we find that thinking about game-relevant details before predicting winning teams causes people to give less weight to predictive information, presumably because predicting details makes information that is relatively useless for predicting the winning team more readily accessible in memory and therefore incorporated into forecasts. Furthermore, we show that this differential use of information can be used to predict what kinds of games will and will not be susceptible to the negative effect of making detailed predictions.

Operational budgeting using fuzzy goal programming

Directory of Open Access Journals (Sweden)

Saeed Mohammadi

2013-10-01

Full Text Available Having an efficient budget normally has different advantages such as measuring the performance of various organizations, setting appropriate targets and promoting managers based on their achievements. However, any budgeting planning requires prediction of different cost components. There are various methods for budgeting planning such as incremental budgeting, program budgeting, zero based budgeting and performance budgeting. In this paper, we present a fuzzy goal programming to estimate operational budget. The proposed model uses fuzzy triangular as well as interval number to estimate budgeting expenses. The proposed study of this paper is implemented for a real-world case study in province of Qom, Iran and the results are analyzed.

Predictive analytics can support the ACO model.

Science.gov (United States)

Bradley, Paul

2012-04-01

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

Vision and Vestibular System Dysfunction Predicts Prolonged Concussion Recovery in Children.

Science.gov (United States)

Master, Christina L; Master, Stephen R; Wiebe, Douglas J; Storey, Eileen P; Lockyer, Julia E; Podolak, Olivia E; Grady, Matthew F

2018-03-01

Up to one-third of children with concussion have prolonged symptoms lasting beyond 4 weeks. Vision and vestibular dysfunction is common after concussion. It is unknown whether such dysfunction predicts prolonged recovery. We sought to determine which vision or vestibular problems predict prolonged recovery in children. A retrospective cohort of pediatric patients with concussion. A subspecialty pediatric concussion program. Four hundred thirty-two patient records were abstracted. Presence of vision or vestibular dysfunction upon presentation to the subspecialty concussion program. The main outcome of interest was time to clinical recovery, defined by discharge from clinical follow-up, including resolution of acute symptoms, resumption of normal physical and cognitive activity, and normalization of physical examination findings to functional levels. Study subjects were 5 to 18 years (median = 14). A total of 378 of 432 subjects (88%) presented with vision or vestibular problems. A history of motion sickness was associated with vestibular dysfunction. Younger age, public insurance, and presence of headache were associated with later presentation for subspecialty concussion care. Vision and vestibular problems were associated within distinct clusters. Provocable symptoms with vestibulo-ocular reflex (VOR) and smooth pursuits and abnormal balance and accommodative amplitude (AA) predicted prolonged recovery time. Vision and vestibular problems predict prolonged concussion recovery in children. A history of motion sickness may be an important premorbid factor. Public insurance status may represent problems with disparities in access to concussion care. Vision assessments in concussion must include smooth pursuits, saccades, near point of convergence (NPC), and accommodative amplitude (AA). A comprehensive, multidomain assessment is essential to predict prolonged recovery time and enable active intervention with specific school accommodations and targeted rehabilitation.

Energy Consumption and Indoor Environment Predicted by a Combination of Computational Fluid Dynamics and Building Energy Performance Simulation

DEFF Research Database (Denmark)

Nielsen, Peter Vilhelm

2003-01-01

An interconnection between a building energy performance simulation program and a Computational Fluid Dynamics program (CFD) for room air distribution is introduced for improvement of the predictions of both the energy consumption and the indoor environment.The article describes a calculation...

Two stage neural network modelling for robust model predictive control.

Science.gov (United States)

Patan, Krzysztof

2018-01-01

The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery

DEFF Research Database (Denmark)

Hickey, John M.; Chiurugwi, Tinashe; Mackay, Ian

2017-01-01

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human...... that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying...... use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform...

International Space Station Bacteria Filter Element Post-Flight Testing and Service Life Prediction

Science.gov (United States)

Perry, J. L.; von Jouanne, R. G.; Turner, E. H.

2003-01-01

The International Space Station uses high efficiency particulate air (HEPA) filters to remove particulate matter from the cabin atmosphere. Known as Bacteria Filter Elements (BFEs), there are 13 elements deployed on board the ISS's U.S. Segment. The pre-flight service life prediction of 1 year for the BFEs is based upon performance engineering analysis of data collected during developmental testing that used a synthetic dust challenge. While this challenge is considered reasonable and conservative from a design perspective, an understanding of the actual filter loading is required to best manage the critical ISS Program resources. Thus testing was conducted on BFEs returned from the ISS to refine the service life prediction. Results from this testing and implications to ISS resource management are discussed. Recommendations for realizing significant savings to the ISS Program are presented.

A burnout prediction model based around char morphology

Energy Technology Data Exchange (ETDEWEB)

T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

2005-07-01

Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.

On the best learning algorithm for web services response time prediction

DEFF Research Database (Denmark)

Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

2013-01-01

In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

Learning Management System with Prediction Model and Course-Content Recommendation Module

Science.gov (United States)

Evale, Digna S.

2017-01-01

Aim/Purpose: This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background: It utilized five-year historical data to find patterns for predicting student performance in Java Programming to generate…

The 136 MHz/400 MHz earth station antenna-noise temperature prediction program documentation for RAE-B

Science.gov (United States)

Chin, M.

1972-01-01

A simulation study to determine the 136 MHz and 400 MHz noise temperature of the ground network antennas which will track the RAE-B satellite during data transmission periods is described. Since the noise temperature of the antenna effectively sets the signal-to-noise ratio (SNR) of the received signal, a knowledge of SNR will be helpful in locating the optimum time windows for data transmission during low-noise periods. Antenna-noise temperatures at 136 MHz and 400 MHz will be predicted for selected earth-based ground stations which will support RAE-B. The antenna-noise temperature predictions will include the effects of galactic-brightness temperature, the sun, and the brightest radio stars. Predictions will cover the ten-month period from March 1, 1973 to December 31, 1973. The RAE-B mission will be expecially susceptible to SNR degradation during the two eclipses of the Sun occurring in this period.

Prediksi Nilai Tukar Rupiah Terhadap US Dollar Menggunakan Metode Genetic Programming

Directory of Open Access Journals (Sweden)

Daneswara Jauhari

2016-12-01

Exchange currency rate has a wide influence in the economy of a country, both domestically or internationally. The importance of knowing the pattern of exchange rate against the IDR to USD could help the economic growth due to foreign trade involves the use of currencies of different countries. Therefore, we need an application that can predict the value of IDR against the USD in the future. In this research, the authors use genetic programming (GP method which produces solutions (chromosome that obtained from the evaluation of exchange rate and then this solution used as an approximation or prediction of currency exchange rate in the future. These solutions formed from the combination of the set terminal and the set of function that generated randomly. After testing by the number popsize and different iterations, it was found that the GP algorithm can predict the value of the rupiah against the US Dollar with a very good, judging from the value of Mean Absolute Percentage Error (MAPE generated by 0.08%. This research can be developed even better by adding terminal parameters and operating parameters so they can add variation calculation results. Keywords:  prediction, exchange currency rate, genetic programming, MAPE.

Broadband Fan Noise Prediction System for Turbofan Engines. Volume 1; Setup_BFaNS User's Manual and Developer's Guide

Science.gov (United States)

Morin, Bruce L.

2010-01-01

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the first volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User's Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running Setup_BFaNS. It concludes with technical documentation of the Setup_BFaNS computer program.

In-training factors predictive of choosing and sustaining a productive academic career path in neurological surgery.

Science.gov (United States)

Crowley, R Webster; Asthagiri, Ashok R; Starke, Robert M; Zusman, Edie E; Chiocca, E Antonio; Lonser, Russell R

2012-04-01

Factors during neurosurgical residency that are predictive of an academic career path and promotion have not been defined. To determine factors associated with selecting and sustaining an academic career in neurosurgery by analyzing in-training factors for all graduates of American College of Graduate Medical Education (ACGME)-accredited programs between 1985 and 1990. Neurological surgery residency graduates (between 1985 and 1990) from ACGME-approved training programs were analyzed to determine factors associated with choosing an academic career path and having academic success. Information was available for 717 of the 720 (99%) neurological surgery resident training graduates (678 male, 39 female). One hundred thirty-eight graduates (19.3%) held full-time academic positions. One hundred seven (14.9%) were professors and 35 (4.9%) were department chairs/chiefs. An academic career path/success was associated with more total (5.1 vs 1.9; P female trainees (2.6 vs 0.9 publications; P career but not predictive of becoming professor or chair/chief (P > .05). Defined in-training factors including number of total publications, number of first-author publications, and program size are predictive of residents choosing and succeeding in an academic career path.

Predicting academic performance and clinical competency for international dental students: seeking the most efficient and effective measures.

Science.gov (United States)

Stacey, D Graham; Whittaker, John M

2005-02-01

Measures used in the selection of international dental students to a U.S. D.D.S. program were examined to identify the grouping that most effectively and efficiently predicted academic performance and clinical competency. Archival records from the International Dental Program (IDP) at Loma Linda University provided data on 171 students who had trained in countries outside the United States. The students sought admission to the D.D.S. degree program, successful completion of which qualified them to sit for U.S. licensure. As with most dental schools, competition is high for admission to the D.D.S. program. The study's goal was to identify what measures contributed to a fair and accurate selection process for dental school applicants from other nations. Multiple regression analyses identified National Board Part II and dexterity measures as significant predictors of academic performance and clinical competency. National Board Part I, TOEFL, and faculty interviews added no significant additional help in predicting eventual academic performance and clinical competency.

Predicting efficacy of robot-aided rehabilitation in chronic stroke patients using an MRI-compatible robotic device.

Science.gov (United States)

Sergi, Fabrizio; Krebs, Hermano Igo; Groissier, Benjamin; Rykman, Avrielle; Guglielmelli, Eugenio; Volpe, Bruce T; Schaechter, Judith D

2011-01-01

We are investigating the neural correlates of motor recovery promoted by robot-mediated therapy in chronic stroke. This pilot study asked whether efficacy of robot-aided motor rehabilitation in chronic stroke could be predicted by a change in functional connectivity within the sensorimotor network in response to a bout of motor rehabilitation. To address this question, two stroke patients participated in a functional connectivity MRI study pre and post a 12-week robot-aided motor rehabilitation program. Functional connectivity was evaluated during three consecutive scans before the rehabilitation program: resting-state; point-to-point reaching movements executed by the paretic upper extremity (UE) using a newly developed MRI-compatible sensorized passive manipulandum; resting-state. A single resting-state scan was conducted after the rehabilitation program. Before the program, UE movement reduced functional connectivity between the ipsilesional and contralesional primary motor cortex. Reduced interhemispheric functional connectivity persisted during the second resting-state scan relative to the first and during the resting-state scan after the rehabilitation program. Greater reduction in interhemispheric functional connectivity during the resting-state was associated with greater gains in UE motor function induced by the 12-week robotic therapy program. These findings suggest that greater reduction in interhemispheric functional connectivity in response to a bout of motor rehabilitation may predict greater efficacy of the full rehabilitation program.

The in-training examination: an analysis of its predictive value on performance on the general pediatrics certification examination.

Science.gov (United States)

Althouse, Linda A; McGuinness, Gail A

2008-09-01

This study investigates the predictive validity of the In-Training Examination (ITE). Although studies have confirmed the predictive validity of ITEs in other medical specialties, no study has been done for general pediatrics. Each year, residents in accredited pediatric training programs take the ITE as a self-assessment instrument. The ITE is similar to the American Board of Pediatrics General Pediatrics Certifying Examination. First-time takers of the certifying examination over a 5-year period who took at least 1 ITE examination were included in the sample. Regression models analyzed the predictive value of the ITE. The predictive power of the ITE in the first training year is minimal. However, the predictive power of the ITE increases each year, providing the greatest power in the third year of training. Even though ITE scores provide information regarding the likelihood of passing the certification examination, the data should be used with caution, particularly in the first training year. Other factors also must be considered when predicting performance on the certification examination. This study continues to support the ITE as an assessment tool for program directors, as well as a means of providing residents with feedback regarding their acquisition of pediatric knowledge.

The NASA/industry Design Analysis Methods for Vibrations (DAMVIBS) program : Bell Helicopter Textron accomplishments

Science.gov (United States)

Cronkhite, James D.

1993-01-01

Accurate vibration prediction for helicopter airframes is needed to 'fly from the drawing board' without costly development testing to solve vibration problems. The principal analytical tool for vibration prediction within the U.S. helicopter industry is the NASTRAN finite element analysis. Under the NASA DAMVIBS research program, Bell conducted NASTRAN modeling, ground vibration testing, and correlations of both metallic (AH-1G) and composite (ACAP) airframes. The objectives of the program were to assess NASTRAN airframe vibration correlations, to investigate contributors to poor agreement, and to improve modeling techniques. In the past, there has been low confidence in higher frequency vibration prediction for helicopters that have multibladed rotors (three or more blades) with predominant excitation frequencies typically above 15 Hz. Bell's findings under the DAMVIBS program, discussed in this paper, included the following: (1) accuracy of finite element models (FEM) for composite and metallic airframes generally were found to be comparable; (2) more detail is needed in the FEM to improve higher frequency prediction; (3) secondary structure not normally included in the FEM can provide significant stiffening; (4) damping can significantly affect phase response at higher frequencies; and (5) future work is needed in the areas of determination of rotor-induced vibratory loads and optimization.

Trends in light water reactor dosimetry programs

International Nuclear Information System (INIS)

Rahn, F.J.; Serpan, C.Z.; Fabry, A.; McElroy, W.N.; Grundl, J.A.; Debrue, J.

1977-01-01

Dosimetry programs and techniques play an essential role in the continued assurance of the safety and reliability of components of light water reactors. Primary concern focuses on the neutron irradiation embrittlement of reactor pressure vessels and methods by which the integrity of a pressure vessel can be predicted and monitored throughout its service life. Research in these areas requires a closely coordinated program which integrates the elements of the calculational and material sciences, the development of advanced dosimetric techniques and the use of benchmarks and validation of these methods. The paper reviews the status of the various international efforts in the dosimetry area

CNTB program for the analysis of partially mixed containment atmospheres during depressurization events

International Nuclear Information System (INIS)

Landoni, J.A.

1979-07-01

This program describes the analytical models for the CNTB computer program, which is permanently filed in the archive library of the General Atomic (GA) San Diego Data Center under reference number THSD-2699. Developed during the last four years, this computer program has been successfully applied in its presented form to the type of containment atmosphere transients illustrated in this report. For example, the CNTB computer program is applicable (1) to the design basis depressurization accident (DBDA) to determine the effect of the partial mixing on the containment atmospheric peak pressure (known as nonmixing penalty) and (2) for Class 9 accidents, such as the loss of forced circulation (LOFC), for the AIPA Phase I studies. The capability of the CNTB computer program has been substantially improved over its precursor, the CONTEMPT-G computer program, to predict the thermodynamic behavior of the containment atmosphere during helium releases, assuming partial mixing of the original air with the effluent and to predict the amount of the environmental leaks under closed and open containment conditions. In addition, the CNTB computer program running times are considerably below the ones required for the CONTEMPT-G computer program. Computational solution of the variable parameters in the containment atmosphere is effected by an iterative technique, while the temperatures for its boundaries are obtained by finite differences. The CNTB computer program, written in FORTRAN V, has been implemented at GA on the UNIVAC 1110 computer

An algorithm to discover gene signatures with predictive potential

Directory of Open Access Journals (Sweden)

Hallett Robin M

2010-09-01

Full Text Available Abstract Background The advent of global gene expression profiling has generated unprecedented insight into our molecular understanding of cancer, including breast cancer. For example, human breast cancer patients display significant diversity in terms of their survival, recurrence, metastasis as well as response to treatment. These patient outcomes can be predicted by the transcriptional programs of their individual breast tumors. Predictive gene signatures allow us to correctly classify human breast tumors into various risk groups as well as to more accurately target therapy to ensure more durable cancer treatment. Results Here we present a novel algorithm to generate gene signatures with predictive potential. The method first classifies the expression intensity for each gene as determined by global gene expression profiling as low, average or high. The matrix containing the classified data for each gene is then used to score the expression of each gene based its individual ability to predict the patient characteristic of interest. Finally, all examined genes are ranked based on their predictive ability and the most highly ranked genes are included in the master gene signature, which is then ready for use as a predictor. This method was used to accurately predict the survival outcomes in a cohort of human breast cancer patients. Conclusions We confirmed the capacity of our algorithm to generate gene signatures with bona fide predictive ability. The simplicity of our algorithm will enable biological researchers to quickly generate valuable gene signatures without specialized software or extensive bioinformatics training.

Nuclear criticality predictability

International Nuclear Information System (INIS)

Briggs, J.B.

1999-01-01

As a result of lots of efforts, a large portion of the tedious and redundant research and processing of critical experiment data has been eliminated. The necessary step in criticality safety analyses of validating computer codes with benchmark critical data is greatly streamlined, and valuable criticality safety experimental data is preserved. Criticality safety personnel in 31 different countries are now using the 'International Handbook of Evaluated Criticality Safety Benchmark Experiments'. Much has been accomplished by the work of the ICSBEP. However, evaluation and documentation represents only one element of a successful Nuclear Criticality Safety Predictability Program and this element only exists as a separate entity, because this work was not completed in conjunction with the experimentation process. I believe; however, that the work of the ICSBEP has also served to unify the other elements of nuclear criticality predictability. All elements are interrelated, but for a time it seemed that communications between these elements was not adequate. The ICSBEP has highlighted gaps in data, has retrieved lost data, has helped to identify errors in cross section processing codes, and has helped bring the international criticality safety community together in a common cause as true friends and colleagues. It has been a privilege to associate with those who work so diligently to make the project a success. (J.P.N.)

Magnetic particle movement program to calculate particle paths in flow and magnetic fields

International Nuclear Information System (INIS)

Inaba, Toru; Sakazume, Taku; Yamashita, Yoshihiro; Matsuoka, Shinya

2014-01-01

We developed an analysis program for predicting the movement of magnetic particles in flow and magnetic fields. This magnetic particle movement simulation was applied to a capturing process in a flow cell and a magnetic separation process in a small vessel of an in-vitro diagnostic system. The distributions of captured magnetic particles on a wall were calculated and compared with experimentally obtained distributions. The calculations involved evaluating not only the drag, pressure gradient, gravity, and magnetic force in a flow field but also the friction force between the particle and the wall, and the calculated particle distributions were in good agreement with the experimental distributions. Friction force was simply modeled as static and kinetic friction forces. The coefficients of friction were determined by comparing the calculated and measured results. This simulation method for solving multiphysics problems is very effective at predicting the movements of magnetic particles and is an excellent tool for studying the design and application of devices. - Highlights: ●We developed magnetic particles movement program in flow and magnetic fields. ●Friction force on wall is simply modeled as static and kinetic friction force. ●This program was applied for capturing and separation of an in-vitro diagnostic system. ●Predicted particle distributions on wall were agreed with experimental ones. ●This method is very effective at predicting movements of magnetic particles

Measurement and analysis of P2P IPTV program resource.

Science.gov (United States)

Wang, Wenxian; Chen, Xingshu; Wang, Haizhou; Zhang, Qi; Wang, Cheng

2014-01-01

With the rapid development of P2P technology, P2P IPTV applications have received more and more attention. And program resource distribution is very important to P2P IPTV applications. In order to collect IPTV program resources, a distributed multi-protocol crawler is proposed. And the crawler has collected more than 13 million pieces of information of IPTV programs from 2009 to 2012. In addition, the distribution of IPTV programs is independent and incompact, resulting in chaos of program names, which obstructs searching and organizing programs. Thus, we focus on characteristic analysis of program resources, including the distributions of length of program names, the entropy of the character types, and hierarchy depth of programs. These analyses reveal the disorderly naming conventions of P2P IPTV programs. The analysis results can help to purify and extract useful information from chaotic names for better retrieval and accelerate automatic sorting of program and establishment of IPTV repository. In order to represent popularity of programs and to predict user behavior and popularity of hot programs over a period, we also put forward an analytical model of hot programs.

Measurement and Analysis of P2P IPTV Program Resource

Directory of Open Access Journals (Sweden)

Wenxian Wang

2014-01-01

Full Text Available With the rapid development of P2P technology, P2P IPTV applications have received more and more attention. And program resource distribution is very important to P2P IPTV applications. In order to collect IPTV program resources, a distributed multi-protocol crawler is proposed. And the crawler has collected more than 13 million pieces of information of IPTV programs from 2009 to 2012. In addition, the distribution of IPTV programs is independent and incompact, resulting in chaos of program names, which obstructs searching and organizing programs. Thus, we focus on characteristic analysis of program resources, including the distributions of length of program names, the entropy of the character types, and hierarchy depth of programs. These analyses reveal the disorderly naming conventions of P2P IPTV programs. The analysis results can help to purify and extract useful information from chaotic names for better retrieval and accelerate automatic sorting of program and establishment of IPTV repository. In order to represent popularity of programs and to predict user behavior and popularity of hot programs over a period, we also put forward an analytical model of hot programs.