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 fuzzy expert system for predicting the performance of switched reluctance motor

International Nuclear Information System (INIS)

Mirzaeian, B.; Moallem, M.; Lucas, Caro

2001-01-01

In this paper a fuzzy expert system for predicting the performance of a switched reluctance motor has been developed. The design vector consists of design parameters, and output performance variables are efficiency and torque ripple. An accurate analysis program based on Improved Magnetic Equivalent Circuit method has been used to generate the input-output data. These input-output data is used to produce the initial fuzzy rules for predicting the performance of Switched Reluctance Motor. The initial set of fuzzy rules with triangular membership functions has been devised using a table look-up scheme. The initial fuzzy rules have been optimized to a set of fuzzy rules with Gaussian membership functions using gradient descent training scheme. The performance prediction results for a 6/8, 4 kw, Switched Reluctance Motor shows good agreement with the results obtained from Improved Magnetic Equivalent Circuit method or Finite Element analysis. The developed fuzzy expert system can be used for fast prediction of motor performance in the optimal design process or on-line control schemes of Switched Reluctance motor

Strategies of high-performing paramedic educational programs.

Science.gov (United States)

Margolis, Gregg S; Romero, Gabriel A; Fernandez, Antonio R; Studnek, Jonathan R

2009-01-01

To identify the specific educational strategies used by paramedic educational programs that have attained consistently high success rates on the National Registry of Emergency Medical Technicians (NREMT) examination. NREMT data from 2003-2007 were analyzed to identify consistently high-performing paramedic educational programs. Representatives from 12 programs that have maintained a 75% first-attempt pass rate for at least four of five years and had more than 20 graduates per year were invited to participate in a focus group. Using the nominal group technique (NGT), participants were asked to answer the following question: "What are specific strategies that lead to a successful paramedic educational program?" All 12 emergency medical services (EMS) educational programs meeting the eligibility requirements participated. After completing the seven-step NGT process, 12 strategies were identified as leading to a successful paramedic educational program: 1) achieve and maintain national accreditation; 2) maintain high-level entry requirements and prerequisites; 3) provide students with a clear idea of expectations for student success; 4) establish a philosophy and foster a culture that values continuous review and improvement; 5) create your own examinations, lesson plans, presentations, and course materials using multiple current references; 6) emphasize emergency medical technician (EMT)-Basic concepts throughout the class; 7) use frequent case-based classroom scenarios; 8) expose students to as many prehospital advanced life support (ALS) patient contacts as possible, preferably where they are in charge; 9) create and administer valid examinations that have been through a review process (such as qualitative analysis); 10) provide students with frequent detailed feedback regarding their performance (such as formal examination reviews); 11) incorporate critical thinking and problem solving into all testing; and 12) deploy predictive testing with analysis prior to

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...

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 emergency diesel starting performance

International Nuclear Information System (INIS)

DeBey, T.M.

1989-01-01

The US Department of Energy effort to extend the operational lives of commercial nuclear power plants has examined methods for predicting the performance of specific equipment. This effort focuses on performance prediction as a means for reducing equipment surveillance, maintenance, and outages. Realizing these goals will result in nuclear plants that are more reliable, have lower maintenance costs, and have longer lives. This paper describes a monitoring system that has been developed to predict starting performance in emergency diesels. A prototype system has been built and tested on an engine at Sandia National Laboratories. 2 refs

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)

Parametric Study for MOV Performance Improvement Using PPM Program

Energy Technology Data Exchange (ETDEWEB)

Lee, Seungho; Seon, Juhyoung; Han, Bongsub [SOOSAN INDUSTRIES, Seoul (Korea, Republic of)

2016-10-15

Nuclear power plants mainly use Air Operated Valve(hereinafter referred to as AOV) and Motor Operator Valve(hereinafter referred to as MOV) for protecting system, blocking and controlling flow. Field test(static, dynamic test) results and performance prediction program are used to evaluate if MOV currently installed on nuclear power plants has the operational performance. The improvement of operating performance for Flexible Gate valve was confirmed on changing input variables of performance program(PPM). here are several methods through reviewing design basis, changes operating procedures and maintenance work of stem(or packing, etc.) to improve operating performance of MOV generally installed in the nuclear power plants. This study verified the changes of the MOV operating performance through the improvement of stem and hydraulic parts(seat, guide etc.). Especially, MOV operating performance was much greater improved when the Disk Seat Angle was decreasing. Generally, improvement work to minimize friction of seat, disk and guide is limited and dynamic diagnostic testing has to be performed with change in valve factor for improvement of hydraulic parts.

Caregiver Responsiveness to the Family Bereavement Program: What predicts responsiveness? What does responsiveness predict?

OpenAIRE

Schoenfelder, Erin N.; Sandler, Irwin N.; Millsap, Roger E.; Wolchik, Sharlene A.; Berkel, Cady; Ayers, Timothy S.

2013-01-01

The study developed a multi-dimensional measure to assess participant responsiveness to a preventive intervention, and applied this measure to study how participant baseline characteristics predict responsiveness and how responsiveness predicts program outcomes. The study was conducted with caregivers who participated in the parenting-focused component of the Family Bereavement Program (FBP), a prevention program for families that have experienced parental death. The sample consisted of 89 ca...

Complexity factors and prediction of performance

International Nuclear Information System (INIS)

Braarud, Per Oeyvind

1998-03-01

Understanding of what makes a control room situation difficult to handle is important when studying operator performance, both with respect to prediction as well as improvement of the human performance. A factor analytic approach identified eight factors from operators' answers to an 39 item questionnaire about complexity of the operator's task in the control room. A Complexity Profiling Questionnaire was developed, based on the factor analytic results from the operators' conception of complexity. The validity of the identified complexity factors was studied by prediction of crew performance and prediction of plant performance from ratings of the complexity of scenarios. The scenarios were rated by both process experts and the operators participating in the scenarios, using the Complexity Profiling Questionnaire. The process experts' complexity ratings predicted both crew performance and plant performance, while the operators' rating predicted plant performance only. The results reported are from initial studies of complexity, and imply a promising potential for further studies of the concept. The approach used in the study as well as the reported results are discussed. A chapter about the structure of the conception of complexity, and a chapter about further research conclude the report. (author)

The Use of Linear Programming for Prediction.

Science.gov (United States)

Schnittjer, Carl J.

The purpose of the study was to develop a linear programming model to be used for prediction, test the accuracy of the predictions, and compare the accuracy with that produced by curvilinear multiple regression analysis. (Author)

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

Can Medical School Performance Predict Residency Performance? Resident Selection and Predictors of Successful Performance in Obstetrics and Gynecology

Science.gov (United States)

Stohl, Hindi E.; Hueppchen, Nancy A.; Bienstock, Jessica L.

2010-01-01

Background During the evaluation process, Residency Admissions Committees typically gather data on objective and subjective measures of a medical student's performance through the Electronic Residency Application Service, including medical school grades, standardized test scores, research achievements, nonacademic accomplishments, letters of recommendation, the dean's letter, and personal statements. Using these data to identify which medical students are likely to become successful residents in an academic residency program in obstetrics and gynecology is difficult and to date, not well studied. Objective To determine whether objective information in medical students' applications can help predict resident success. Method We performed a retrospective cohort study of all residents who matched into the Johns Hopkins University residency program in obstetrics and gynecology between 1994 and 2004 and entered the program through the National Resident Matching Program as a postgraduate year-1 resident. Residents were independently evaluated by faculty and ranked in 4 groups according to perceived level of success. Applications from residents in the highest and lowest group were abstracted. Groups were compared using the Fisher exact test and the Student t test. Results Seventy-five residents met inclusion criteria and 29 residents were ranked in the highest and lowest quartiles (15 in highest, 14 in lowest). Univariate analysis identified no variables as consistent predictors of resident success. Conclusion In a program designed to train academic obstetrician-gynecologists, objective data from medical students' applications did not correlate with successful resident performance in our obstetrics-gynecology residency program. We need to continue our search for evaluation criteria that can accurately and reliably select the medical students that are best fit for our specialty. PMID:21976076

Establishing a predictive maintenance program at the Hanford Site

International Nuclear Information System (INIS)

Winslow, R.W.

1994-05-01

This document contains information about a new Predictive Maintenance Program being developed and implemented at the Hanford Reservation. Details of the document include: background on persons developing the program, history of predictive maintenance, implementation of new program, engineering task analysis, network development and new software, issues to be resolved, benefits expected, and appendix gives information about the symposium from which this paper is based

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.

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.

Safety performance indicators program

International Nuclear Information System (INIS)

Vidal, Patricia G.

2004-01-01

In 1997 the Nuclear Regulatory Authority (ARN) initiated a program to define and implement a Safety Performance Indicators System for the two operating nuclear power plants, Atucha I and Embalse. The objective of the program was to incorporate a set of safety performance indicators to be used as a new regulatory tool providing an additional view of the operational performance of the nuclear power plants, improving the ability to detect degradation on safety related areas. A set of twenty-four safety performance indicators was developed and improved throughout pilot implementation initiated in July 1998. This paper summarises the program development, the main criteria applied in each stage and the results obtained. (author)

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

Directory of Open Access Journals (Sweden)

Amy S. Nowacki

2013-08-01

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

Does resident ranking during recruitment accurately predict subsequent performance as a surgical resident?

Science.gov (United States)

Fryer, Jonathan P; Corcoran, Noreen; George, Brian; Wang, Ed; Darosa, Debra

2012-01-01

While the primary goal of ranking applicants for surgical residency training positions is to identify the candidates who will subsequently perform best as surgical residents, the effectiveness of the ranking process has not been adequately studied. We evaluated our general surgery resident recruitment process between 2001 and 2011 inclusive, to determine if our recruitment ranking parameters effectively predicted subsequent resident performance. We identified 3 candidate ranking parameters (United States Medical Licensing Examination [USMLE] Step 1 score, unadjusted ranking score [URS], and final adjusted ranking [FAR]), and 4 resident performance parameters (American Board of Surgery In-Training Examination [ABSITE] score, PGY1 resident evaluation grade [REG], overall REG, and independent faculty rating ranking [IFRR]), and assessed whether the former were predictive of the latter. Analyses utilized Spearman correlation coefficient. We found that the URS, which is based on objective and criterion based parameters, was a better predictor of subsequent performance than the FAR, which is a modification of the URS based on subsequent determinations of the resident selection committee. USMLE score was a reliable predictor of ABSITE scores only. However, when we compared our worst residence performances with the performances of the other residents in this evaluation, the data did not produce convincing evidence that poor resident performances could be reliably predicted by any of the recruitment ranking parameters. Finally, stratifying candidates based on their rank range did not effectively define a ranking cut-off beyond which resident performance would drop off. Based on these findings, we recommend surgery programs may be better served by utilizing a more structured resident ranking process and that subsequent adjustments to the rank list generated by this process should be undertaken with caution. Copyright © 2012 Association of Program Directors in Surgery

Mining Behavior Based Safety Data to Predict Safety Performance

Energy Technology Data Exchange (ETDEWEB)

Jeffrey C. Joe

2010-06-01

The Idaho National Laboratory (INL) operates a behavior based safety program called Safety Observations Achieve Results (SOAR). This peer-to-peer observation program encourages employees to perform in-field observations of each other's work practices and habits (i.e., behaviors). The underlying premise of conducting these observations is that more serious accidents are prevented from occurring because lower level “at risk” behaviors are identified and corrected before they can propagate into culturally accepted “unsafe” behaviors that result in injuries or fatalities. Although the approach increases employee involvement in safety, the premise of the program has not been subject to sufficient empirical evaluation. The INL now has a significant amount of SOAR data on these lower level “at risk” behaviors. This paper describes the use of data mining techniques to analyze these data to determine whether they can predict if and when a more serious accident will occur.

Database principles programming performance

CERN Document Server

O'Neil, Patrick

2014-01-01

Database: Principles Programming Performance provides an introduction to the fundamental principles of database systems. This book focuses on database programming and the relationships between principles, programming, and performance.Organized into 10 chapters, this book begins with an overview of database design principles and presents a comprehensive introduction to the concepts used by a DBA. This text then provides grounding in many abstract concepts of the relational model. Other chapters introduce SQL, describing its capabilities and covering the statements and functions of the programmi

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

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.

Single-leg squats can predict leg alignment in dancers performing ballet movements in "turnout".

Science.gov (United States)

Hopper, Luke S; Sato, Nahoko; Weidemann, Andries L

2016-01-01

The physical assessments used in dance injury surveillance programs are often adapted from the sports and exercise domain. Bespoke physical assessments may be required for dance, particularly when ballet movements involve "turning out" or external rotation of the legs beyond that typically used in sports. This study evaluated the ability of the traditional single-leg squat to predict the leg alignment of dancers performing ballet movements with turnout. Three-dimensional kinematic data of dancers performing the single-leg squat and five ballet movements were recorded and analyzed. Reduction of the three-dimensional data into a one-dimensional variable incorporating the ankle, knee, and hip joint center positions provided the strongest predictive model between the single-leg squat and the ballet movements. The single-leg squat can predict leg alignment in dancers performing ballet movements, even in "turned out" postures. Clinicians should pay careful attention to observational positioning and rating criteria when assessing dancers performing the single-leg squat.

The Real World Significance of Performance Prediction

Science.gov (United States)

Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

2012-01-01

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…

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...

Performance prediction of mechanical excavators from linear cutter tests on Yucca Mountain welded tuffs

International Nuclear Information System (INIS)

Gertsch, R.; Ozdemir, L.

1992-09-01

The performances of mechanical excavators are predicted for excavations in welded tuff. Emphasis is given to tunnel boring machine evaluations based on linear cutting machine test data obtained on samples of Topopah Spring welded tuff. The tests involve measurement of forces as cutters are applied to the rock surface at certain spacing and penetrations. Two disc and two point-attack cutters representing currently available technology are thus evaluated. The performance predictions based on these direct experimental measurements are believed to be more accurate than any previous values for mechanical excavation of welded tuff. The calculations of performance are predicated on minimizing the amount of energy required to excavate the welded tuff. Specific energy decreases with increasing spacing and penetration, and reaches its lowest at the widest spacing and deepest penetration used in this test program. Using the force, spacing, and penetration data from this experimental program, the thrust, torque, power, and rate of penetration are calculated for several types of mechanical excavators. The results of this study show that the candidate excavators will require higher torque and power than heretofore estimated

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

Hybrid Corporate Performance Prediction Model Considering Technical Capability

Directory of Open Access Journals (Sweden)

Joonhyuck Lee

2016-07-01

Full Text Available Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.

Constraint Logic Programming approach to protein structure prediction

Directory of Open Access Journals (Sweden)

Fogolari Federico

2004-11-01

Full Text Available Abstract Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Constraint Logic Programming approach to protein structure prediction.

Science.gov (United States)

Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

2004-11-30

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Predictive Performance Tuning of OpenACC Accelerated Applications

KAUST Repository

Siddiqui, Shahzeb

2014-05-04

Graphics Processing Units (GPUs) are gradually becoming mainstream in supercomputing as their capabilities to significantly accelerate a large spectrum of scientific applications have been clearly identified and proven. Moreover, with the introduction of high level programming models such as OpenACC [1] and OpenMP 4.0 [2], these devices are becoming more accessible and practical to use by a larger scientific community. However, performance optimization of OpenACC accelerated applications usually requires an in-depth knowledge of the hardware and software specifications. We suggest a prediction-based performance tuning mechanism [3] to quickly tune OpenACC parameters for a given application to dynamically adapt to the execution environment on a given system. This approach is applied to a finite difference kernel to tune the OpenACC gang and vector clauses for mapping the compute kernels into the underlying accelerator architecture. Our experiments show a significant performance improvement against the default compiler parameters and a faster tuning by an order of magnitude compared to the brute force search tuning.

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...

Genome-Wide Prediction of the Performance of Three-Way Hybrids in Barley

Directory of Open Access Journals (Sweden)

Zuo Li

2017-03-01

Full Text Available Predicting the grain yield performance of three-way hybrids is challenging. Three-way crosses are relevant for hybrid breeding in barley ( L. and maize ( L. adapted to East Africa. The main goal of our study was to implement and evaluate genome-wide prediction approaches of the performance of three-way hybrids using data of single-cross hybrids for a scenario in which parental lines of the three-way hybrids originate from three genetically distinct subpopulations. We extended the ridge regression best linear unbiased prediction (RRBLUP and devised a genomic selection model allowing for subpopulation-specific marker effects (GSA-RRBLUP: general and subpopulation-specific additive RRBLUP. Using an empirical barley data set, we showed that applying GSA-RRBLUP tripled the prediction ability of three-way hybrids from 0.095 to 0.308 compared with RRBLUP, modeling one additive effect for all three subpopulations. The experimental findings were further substantiated with computer simulations. Our results emphasize the potential of GSA-RRBLUP to improve genome-wide hybrid prediction of three-way hybrids for scenarios of genetically diverse parental populations. Because of the advantages of the GSA-RRBLUP model in dealing with hybrids from different parental populations, it may also be a promising approach to boost the prediction ability for hybrid breeding programs based on genetically diverse heterotic groups.

Single-leg squats can predict leg alignment in dancers performing ballet movements in “turnout”

Science.gov (United States)

Hopper, Luke S; Sato, Nahoko; Weidemann, Andries L

2016-01-01

The physical assessments used in dance injury surveillance programs are often adapted from the sports and exercise domain. Bespoke physical assessments may be required for dance, particularly when ballet movements involve “turning out” or external rotation of the legs beyond that typically used in sports. This study evaluated the ability of the traditional single-leg squat to predict the leg alignment of dancers performing ballet movements with turnout. Three-dimensional kinematic data of dancers performing the single-leg squat and five ballet movements were recorded and analyzed. Reduction of the three-dimensional data into a one-dimensional variable incorporating the ankle, knee, and hip joint center positions provided the strongest predictive model between the single-leg squat and the ballet movements. The single-leg squat can predict leg alignment in dancers performing ballet movements, even in “turned out” postures. Clinicians should pay careful attention to observational positioning and rating criteria when assessing dancers performing the single-leg squat. PMID:27895518

Performance-based planning and programming guidebook.

Science.gov (United States)

2013-09-01

"Performance-based planning and programming (PBPP) refers to the application of performance management principles within the planning and programming processes of transportation agencies to achieve desired performance outcomes for the multimodal tran...

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.

Containment performance improvement program

International Nuclear Information System (INIS)

Beckner, W.; Mitchell, J.; Soffer, L.; Chow, E.; Lane, J.; Ridgely, J.

1990-01-01

The Containment Performance Improvement (CPI) program has been one of the main elements in the US Nuclear Regulatory Commission's (NRC's) integrated approach to closure of severe accident issues for US nuclear power plants. During the course of the program, results from various probabilistic risk assessment (PRA) studies and from severe accident research programs for the five US containment types have been examined to identify significant containment challenges and to evaluate potential improvements. The five containment types considered are: the boiling water reactor (BMR) Mark I containment, the BWR Mark II containment, the BWR Mark III containment, the pressurized water reactor (PWR) ice condenser containment, and the PWR dry containments (including both subatmospheric and large subtypes). The focus of the CPI program has been containment performance and accident mitigation, however, insights are also being obtained in the areas of accident prevention and accident management

Prediction of the thermohydraulic performance of porous-media reservoirs for compressed-air energy storage

Energy Technology Data Exchange (ETDEWEB)

Wiles, L.E.; McCann, R.A.

1981-09-01

The numerical modeling capability that has been developed at the Pacific Northwest Laboratory (PNL) for the prediction of the thermohydraulic performance of porous media reservoirs for compressed air energy storage (CAES) is described. The capability of the numerical models was demonstrated by application to a variety of parametric analyses and the support analyses for the CAES porous media field demonstration program. The demonstration site analyses include calculations for the displacement of aquifer water to develop the air storage zone, the potential for water coning, thermal development in the reservoir, and the dehydration of the near-wellbore region. Unique features of the demonstration site reservoir that affect the thermohydraulic performance are identified and contrasted against the predicted performance for conditions that would be considered more typical of a commercial CAES site.

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

A statistical model for predicting muscle performance

Science.gov (United States)

Byerly, Diane Leslie De Caix

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

P3T+: A Performance Estimator for Distributed and Parallel Programs

Directory of Open Access Journals (Sweden)

T. Fahringer

2000-01-01

Full Text Available Developing distributed and parallel programs on today's multiprocessor architectures is still a challenging task. Particular distressing is the lack of effective performance tools that support the programmer in evaluating changes in code, problem and machine sizes, and target architectures. In this paper we introduce P3T+ which is a performance estimator for mostly regular HPF (High Performance Fortran programs but partially covers also message passing programs (MPI. P3T+ is unique by modeling programs, compiler code transformations, and parallel and distributed architectures. It computes at compile-time a variety of performance parameters including work distribution, number of transfers, amount of data transferred, transfer times, computation times, and number of cache misses. Several novel technologies are employed to compute these parameters: loop iteration spaces, array access patterns, and data distributions are modeled by employing highly effective symbolic analysis. Communication is estimated by simulating the behavior of a communication library used by the underlying compiler. Computation times are predicted through pre-measured kernels on every target architecture of interest. We carefully model most critical architecture specific factors such as cache lines sizes, number of cache lines available, startup times, message transfer time per byte, etc. P3T+ has been implemented and is closely integrated with the Vienna High Performance Compiler (VFC to support programmers develop parallel and distributed applications. Experimental results for realistic kernel codes taken from real-world applications are presented to demonstrate both accuracy and usefulness of P3T+.

Comparisons of Faulting-Based Pavement Performance Prediction Models

Directory of Open Access Journals (Sweden)

Weina Wang

2017-01-01

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

R high performance programming

CERN Document Server

Lim, Aloysius

2015-01-01

This book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.

Evaluation on the model of performance predictions for on-line monitoring system for combined-cycle power plant

International Nuclear Information System (INIS)

Kim, Si Moon

2002-01-01

This paper presents the simulation model developed to predict design and off-design performance of an actual combined cycle power plant(S-Station in Korea), which would be running combined with on-line performance monitoring system in an on-line real-time fashion. The first step in thermal performance analysis is to build an accurate performance model of the power plant, in order to achieve this goal, GateCycle program has been employed in developing the model. This developed models predict design and off-design performance with a precision of one percent over a wide range of operating conditions so that on-line real-time performance monitoring can accurately establish both current performance and expected performance and also help the operator identify problems before they would be noticed

Performance predictions affect attentional processes of event-based prospective memory.

Science.gov (United States)

Rummel, Jan; Kuhlmann, Beatrice G; Touron, Dayna R

2013-09-01

To investigate whether making performance predictions affects prospective memory (PM) processing, we asked one group of participants to predict their performance in a PM task embedded in an ongoing task and compared their performance with a control group that made no predictions. A third group gave not only PM predictions but also ongoing-task predictions. Exclusive PM predictions resulted in slower ongoing-task responding both in a nonfocal (Experiment 1) and in a focal (Experiment 2) PM task. Only in the nonfocal task was the additional slowing accompanied by improved PM performance. Even in the nonfocal task, however, was the correlation between ongoing-task speed and PM performance reduced after predictions, suggesting that the slowing was not completely functional for PM. Prediction-induced changes could be avoided by asking participants to additionally predict their performance in the ongoing task. In sum, the present findings substantiate a role of metamemory for attention-allocation strategies of PM. Copyright © 2013 Elsevier Inc. All rights reserved.

Performance Demonstration Program Plan for the WIPP Experimental-Waste Characterization Program

International Nuclear Information System (INIS)

1991-02-01

The Performance Demonstration Program is designed to ensure that compliance with the Quality Assurance Objective, identified in the Quality Assurance Program Plan for the WIPP Experimental-Waste Characterization Program (QAPP), is achieved. This Program Plan is intended for use by the WPO to assess the laboratory support provided for the characterization of WIPP TRU waste by the storage/generator sites. Phase 0 of the Performance Demonstration Program encompasses the analysis of headspace gas samples for inorganic and organic components. The WPO will ensure the implementation of this plan by designating an independent organization to coordinate and provide technical oversight for the program (Program Coordinator). Initial program support, regarding the technical oversight and coordination functions, shall be provided by the USEPA-ORP. This plan identifies the criteria that will be used for the evaluation of laboratory performance, the responsibilities of the Program Coordinator, and the responsibilities of the participating laboratories. 5 tabs

Multivariate performance reliability prediction in real-time

International Nuclear Information System (INIS)

Lu, S.; Lu, H.; Kolarik, W.J.

2001-01-01

This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique

NRC performance indicator program

International Nuclear Information System (INIS)

Singh, R.N.

1987-01-01

The performance indicator development work of the US Nuclear Regulatory Commission (NRC) interoffice task group involved several major activities that included selection of candidate indicators for a trial program, data collection and review, validation of the trial indicators, display method development, interactions with the industry, and selection of an optimum set of indicators for the program. After evaluating 27 potential indicators against certain ideal attributes, the task group selected 17 for the trial program. The pertinent data for these indicators were then collected from 50 plants at 30 sites. The validation of the indicators consisted of two primary processes: logical validity and statistical analysis. The six indicators currently in the program are scrams, safety system actuations, significant events, safety system failures, forced outage rate, and equipment forced outages per 100 critical hours. A report containing data on the six performance indicators and some supplemental information is issued on a quarterly basis. The NRC staff is also working on refinements of existing indicators and development of additional indicators as directed by the commission

Experience in the application of erosion-corrosion prediction programs

International Nuclear Information System (INIS)

Castiella Villacampa, E.; Cacho Cordero, L.; Pascual Velazquez, A.; Casar Asuar, M.

1994-01-01

Recently the results of the Nuclear Regulatory Commission's follow-on programme relating to the application of erosion-corrosion supervision and control programs were published. The main problems encountered in their practical application are highlighted, namely those associated with prediction, calculation of minimum thickness acceptable by code, results analyses of the thicknesses measured using ultrasound technology, cases of incorrect substitution, etc. A number of power plants in Spain are currently using a computerised prediction and monitoring program for the erosion-corrosion phenomenon. The experience gained in the application of this program has been such that it has led to a number or benefits: an improvement in the application of the program, proof of its suitability to real situation, the establishment of a series of criteria relative to the inclusion or exclusion of consideration during data input, the monitoring of the phenomenon, selection of elements for inspection, etc. The report describes these areas, using typical examples as illustrations. (Author)

TBM performance prediction in Yucca Mountain welded tuff from linear cutter tests

International Nuclear Information System (INIS)

Gertsch, R.; Ozdemir, L.; Gertsch, L.

1992-01-01

This paper discusses performance prediction which were developed for tunnel boring machines operating in welded tuff for the construction of the experimental study facility and the potential nuclear waste repository at Yucca Mountain. The predictions were based on test data obtained from an extensive series of linear cutting tests performed on samples of Topopah String welded tuff from the Yucca Mountain Project site. Using the cutter force, spacing, and penetration data from the experimental program, the thrust, torque, power, and rate of penetration were estimated for a 25 ft diameter tunnel boring machine (TBM) operating in welded tuff. The result show that the Topopah Spring welded tuff (TSw2) can be excavated at relatively high rates of advance with state-of-the-art TBMs. The result also show, however, that the TBM torque and power requirements will be higher than estimated based on rock physical properties and past tunneling experience in rock formations of similar strength

Development of Wind Farm AEP Prediction Program Considering Directional Wake Effect

Energy Technology Data Exchange (ETDEWEB)

Yang, Kyoungboo; Cho, Kyungho; Huh, Jongchul [Jeju Nat’l Univ., Jeju (Korea, Republic of)

2017-07-15

For accurate AEP prediction in a wind farm, it is necessary to effectively calculate the wind speed reduction and the power loss due to the wake effect in each wind direction. In this study, a computer program for AEP prediction considering directional wake effect was developed. The results of the developed program were compared with the actual AEP of the wind farm and the calculation result of existing commercial software to confirm the accuracy of prediction. The applied equations are identical with those of commercial software based on existing theories, but there is a difference in the calculation process of the detection of the wake effect area in each wind direction. As a result, the developed program predicted to be less than 1% of difference to the actual capacity factor and showed more than 2% of better results compared with the existing commercial software.

Predicting High-Power Performance in Professional Cyclists.

Science.gov (United States)

Sanders, Dajo; Heijboer, Mathieu; Akubat, Ibrahim; Meijer, Kenneth; Hesselink, Matthijs K

2017-03-01

To assess if short-duration (5 to ~300 s) high-power performance can accurately be predicted using the anaerobic power reserve (APR) model in professional cyclists. Data from 4 professional cyclists from a World Tour cycling team were used. Using the maximal aerobic power, sprint peak power output, and an exponential constant describing the decrement in power over time, a power-duration relationship was established for each participant. To test the predictive accuracy of the model, several all-out field trials of different durations were performed by each cyclist. The power output achieved during the all-out trials was compared with the predicted power output by the APR model. The power output predicted by the model showed very large to nearly perfect correlations to the actual power output obtained during the all-out trials for each cyclist (r = .88 ± .21, .92 ± .17, .95 ± .13, and .97 ± .09). Power output during the all-out trials remained within an average of 6.6% (53 W) of the predicted power output by the model. This preliminary pilot study presents 4 case studies on the applicability of the APR model in professional cyclists using a field-based approach. The decrement in all-out performance during high-intensity exercise seems to conform to a general relationship with a single exponential-decay model describing the decrement in power vs increasing duration. These results are in line with previous studies using the APR model to predict performance during brief all-out trials. Future research should evaluate the APR model with a larger sample size of elite cyclists.

Overview of the NRC performance monitoring program

International Nuclear Information System (INIS)

Jordan, E.L.

1987-01-01

In response to the accident at Three Mile Island, the NRC developed the Systematic Assessment of Licensee Performance (SALP) Program to aid in the identification of those licensees that were more likely than others to have safety problems and to provide a rational basis for allocation of inspection resources. The NRC also has an ongoing program of screening and evaluating operating reactor event reports on a daily basis for promptly identifying safety problems. Although the SALP and event report evaluation programs have been successful in identifying potential performance problems, a concern developed recently about the adequacy and timeliness of NRC programs to detect poor or declining performance. The performance indicator program as approved by the commission is in the implementation phase. The program is expected to undergo refinements as new indicators are developed and experience is gained in the use of indicators

Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model

Directory of Open Access Journals (Sweden)

Yu-Ri Kim

2016-03-01

Full Text Available Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology, a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance. Furthermore, using the new prediction model, a related mobile application was also developed.

Iowa calibration of MEPDG performance prediction models.

Science.gov (United States)

2013-06-01

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

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.

Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

DEFF Research Database (Denmark)

Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

Predicting Expressive Dynamics in Piano Performances using Neural Networks

NARCIS (Netherlands)

van Herwaarden, Sam; Grachten, Maarten; de Haas, W. Bas

2014-01-01

This paper presents a model for predicting expressive accentuation in piano performances with neural networks. Using Restricted Boltzmann Machines (RBMs), features are learned from performance data, after which these features are used to predict performed loudness. During feature learning, data

Enabling Technology for Monitoring & Predicting Gas Turbine Health & Performance in COAL IGCC Powerplants

Energy Technology Data Exchange (ETDEWEB)

Kenneth A. Yackly

2004-09-30

The ''Enabling & Information Technology To Increase RAM for Advanced Powerplants'' program, by DOE request, has been re-directed, de-scoped to two tasks, shortened to a 2-year period of performance, and refocused to develop, validate and accelerate the commercial use of enabling materials technologies and sensors for Coal IGCC powerplants. The new program has been re-titled as ''Enabling Technology for Monitoring & Predicting Gas Turbine Health & Performance in IGCC Powerplants'' to better match the new scope. This technical progress report summarizes the work accomplished in the reporting period April 1, 2004 to August 31, 2004 on the revised Re-Directed and De-Scoped program activity. The program Tasks are: Task 1--IGCC Environmental Impact on high Temperature Materials: This first materials task has been refocused to address Coal IGCC environmental impacts on high temperature materials use in gas turbines and remains in the program. This task will screen material performance and quantify the effects of high temperature erosion and corrosion of hot gas path materials in Coal IGCC applications. The materials of interest will include those in current service as well as advanced, high-performance alloys and coatings. Task 2--Material In-Service Health Monitoring: This second task develops and demonstrates new sensor technologies to determine the in-service health of advanced technology Coal IGCC powerplants, and remains in the program with a reduced scope. Its focus is now on only two critical sensor need areas for advanced Coal IGCC gas turbines: (1) Fuel Quality Sensor for detection of fuel impurities that could lead to rapid component degradation, and a Fuel Heating Value Sensor to rapidly determine the fuel heating value for more precise control of the gas turbine, and (2) Infra-Red Pyrometer to continuously measure the temperature of gas turbine buckets, nozzles, and combustor hardware.

Influence of year-on-year performance on final degree classification in a chiropractic master's degree program.

Science.gov (United States)

Dewhurst, Philip; Rix, Jacqueline; Newell, David

2016-03-01

We explored if any predictors of success could be identified from end-of-year grades in a chiropractic master's program and whether these grades could predict final-year grade performance and year-on-year performance. End-of-year average grades and module grades for a single cohort of students covering all academic results for years 1-4 of the 2013 graduating class were used for this analysis. Analysis consisted of within-year correlations of module grades with end-of-year average grades, linear regression models for continuous data, and logistic regression models for predicting final degree classifications. In year 1, 140 students were enrolled; 85.7% of students completed the program 4 years later. End-of-year average grades for years 1-3 were correlated (Pearson r values ranging from .75 to .87), but the end-of-year grades for years 1-3 were poorly correlated with clinic internship performance. In linear regression, several modules were predictive of end-of-year average grades for each year. For year 1, logistic regression showed that the modules Physiology and Pharmacology and Investigative Imaging were predictive of year 1 performance (odds ratio [OR] = 1.15 and 0.9, respectively). In year 3, the modules Anatomy and Histopathology 3 and Problem Solving were predictors of the difference between a pass/merit or distinction final degree classification (OR = 1.06 and 1.12, respectively). Early academic performance is weakly correlated with final-year clinic internship performance. The modules of Anatomy and Histopathology year 3 and Problem Solving year 3 emerged more consistently than other modules as being associated with final-year classifications.

Performance Demonstration Program Management Plan

International Nuclear Information System (INIS)

2005-01-01

To demonstrate compliance with the Waste Isolation Pilot Plant (WIPP) waste characterization program, each testing and analytical facility performing waste characterization activities participates in the Performance Demonstration Program (PDP). The PDP serves as a quality control check against expected results and provides information about the quality of data generated in the characterization of waste destined for WIPP. Single blind audit samples are prepared and distributed by an independent organization to each of the facilities participating in the PDP. There are three elements within the PDP: analysis of simulated headspace gases, analysis of solids for Resource Conservation and Recovery Act (RCRA) constituents, and analysis for transuranic (TRU) radionuclides using nondestructive assay (NDA) techniques. Because the analysis for TRU radionuclides using NDA techniques involves both the counting of drums and standard waste boxes, four PDP plans are required to describe the activities of the three PDP elements. In accordance with these PDP plans, the reviewing and approving authority for PDP results and for the overall program is the CBFO PDP Appointee. The CBFO PDP Appointee is responsible for ensuring the implementation of each of these plans by concurring with the designation of the Program Coordinator and by providing technical oversight and coordination for the program. The Program Coordinator will designate the PDP Manager, who will coordinate the three elements of the PDP. The purpose of this management plan is to identify how the requirements applicable to the PDP are implemented during the management and coordination of PDP activities. The other participants in the program (organizations that perform site implementation and activities under CBFO contracts or interoffice work orders) are not covered under this management plan. Those activities are governed by the organization's quality assurance (QA) program and procedures or as otherwise directed by CBFO.

Reliable predictions of waste performance in a geologic repository

International Nuclear Information System (INIS)

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

1985-08-01

Establishing reliable estimates of long-term performance of a waste repository requires emphasis upon valid theories to predict performance. Predicting rates that radionuclides are released from waste packages cannot rest upon empirical extrapolations of laboratory leach data. Reliable predictions can be based on simple bounding theoretical models, such as solubility-limited bulk-flow, if the assumed parameters are reliably known or defensibly conservative. Wherever possible, performance analysis should proceed beyond simple bounding calculations to obtain more realistic - and usually more favorable - estimates of expected performance. Desire for greater realism must be balanced against increasing uncertainties in prediction and loss of reliability. Theoretical predictions of release rate based on mass-transfer analysis are bounding and the theory can be verified. Postulated repository analogues to simulate laboratory leach experiments introduce arbitrary and fictitious repository parameters and are shown not to agree with well-established theory. 34 refs., 3 figs., 2 tabs

Predictive validity of pre-admission assessments on medical student performance.

Science.gov (United States)

Dabaliz, Al-Awwab; Kaadan, Samy; Dabbagh, M Marwan; Barakat, Abdulaziz; Shareef, Mohammad Abrar; Al-Tannir, Mohamad; Obeidat, Akef; Mohamed, Ayman

2017-11-24

To examine the predictive validity of pre-admission variables on students' performance in a medical school in Saudi Arabia. In this retrospective study, we collected admission and college performance data for 737 students in preclinical and clinical years. Data included high school scores and other standardized test scores, such as those of the National Achievement Test and the General Aptitude Test. Additionally, we included the scores of the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) exams. Those datasets were then compared with college performance indicators, namely the cumulative Grade Point Average (cGPA) and progress test, using multivariate linear regression analysis. In preclinical years, both the National Achievement Test (p=0.04, B=0.08) and TOEFL (p=0.017, B=0.01) scores were positive predictors of cGPA, whereas the General Aptitude Test (p=0.048, B=-0.05) negatively predicted cGPA. Moreover, none of the pre-admission variables were predictive of progress test performance in the same group. On the other hand, none of the pre-admission variables were predictive of cGPA in clinical years. Overall, cGPA strongly predict-ed students' progress test performance (p<0.001 and B=19.02). Only the National Achievement Test and TOEFL significantly predicted performance in preclinical years. However, these variables do not predict progress test performance, meaning that they do not predict the functional knowledge reflected in the progress test. We report various strengths and deficiencies in the current medical college admission criteria, and call for employing more sensitive and valid ones that predict student performance and functional knowledge, especially in the clinical years.

Large-scale linear programs in planning and prediction.

Science.gov (United States)

2017-06-01

Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optim...

Predictive performance models and multiple task performance

Science.gov (United States)

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

1989-01-01

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

Single-leg squats can predict leg alignment in dancers performing ballet movements in “turnout”

Directory of Open Access Journals (Sweden)

Hopper LS

2016-11-01

Full Text Available Luke S Hopper,1 Nahoko Sato,2 Andries L Weidemann1 1Western Australian Academy of Performing Arts, Edith Cowan University, Mt Lawley, WA, Australia; 2Department of Physical Therapy, Nagoya Gakuin University, Seto, Japan Abstract: The physical assessments used in dance injury surveillance programs are often adapted from the sports and exercise domain. Bespoke physical assessments may be required for dance, particularly when ballet movements involve “turning out” or external rotation of the legs beyond that typically used in sports. This study evaluated the ability of the traditional single-leg squat to predict the leg alignment of dancers performing ballet movements with turnout. Three-dimensional kinematic data of dancers performing the single-leg squat and five ballet movements were recorded and analyzed. Reduction of the three-dimensional data into a one-dimensional variable incorporating the ankle, knee, and hip joint center positions provided the strongest predictive model between the single-leg squat and the ballet movements. The single-leg squat can predict leg alignment in dancers performing ballet movements, even in “turned out” postures. Clinicians should pay careful attention to observational positioning and rating criteria when assessing dancers performing the single-leg squat. Keywords: injury, motion capture, clinical assessment

Integrated predictive maintenance program vibration and lube oil analysis: Part I - history and the vibration program

Energy Technology Data Exchange (ETDEWEB)

Maxwell, H.

1996-12-01

This paper is the first of two papers which describe the Predictive Maintenance Program for rotating machines at the Palo Verde Nuclear Generating Station. The organization has recently been restructured and significant benefits have been realized by the interaction, or {open_quotes}synergy{close_quotes} between the Vibration Program and the Lube Oil Analysis Program. This paper starts with the oldest part of the program - the Vibration Program and discusses the evolution of the program to its current state. The {open_quotes}Vibration{close_quotes} view of the combined program is then presented.

Integrated predictive maintenance program vibration and lube oil analysis: Part I - history and the vibration program

International Nuclear Information System (INIS)

Maxwell, H.

1996-01-01

This paper is the first of two papers which describe the Predictive Maintenance Program for rotating machines at the Palo Verde Nuclear Generating Station. The organization has recently been restructured and significant benefits have been realized by the interaction, or open-quotes synergyclose quotes between the Vibration Program and the Lube Oil Analysis Program. This paper starts with the oldest part of the program - the Vibration Program and discusses the evolution of the program to its current state. The open-quotes Vibrationclose quotes view of the combined program is then presented

Cameco engineered tailings program: linking applied research with industrial processes for improved tailings performance

International Nuclear Information System (INIS)

Kotzer, T.G.

2010-01-01

'Full text:' Mine tailings at Cameco's operations are by-products of milling uranium ore having variable concentrations of uranium, metals, oxyanions and trace elements or elements of concern (EOC). Cameco has undertaken an Engineered Tailings (ET) program to optimize tailings performance and minimize environmental EOC impacts, regardless of the milled ore source. Applied geochemical and geotechnical tailings research is key within the ET program. In-situ drilling and experimental programs are used to understand long-term tailings behaviour and help validate source term predictions. Within this, the ET program proactively aids in the development of mill-based processes for production of tailings having improved long-term stability. (author)

Speedup predictions on large scientific parallel programs

International Nuclear Information System (INIS)

Williams, E.; Bobrowicz, F.

1985-01-01

How much speedup can we expect for large scientific parallel programs running on supercomputers. For insight into this problem we extend the parallel processing environment currently existing on the Cray X-MP (a shared memory multiprocessor with at most four processors) to a simulated N-processor environment, where N greater than or equal to 1. Several large scientific parallel programs from Los Alamos National Laboratory were run in this simulated environment, and speedups were predicted. A speedup of 14.4 on 16 processors was measured for one of the three most used codes at the Laboratory

Proactive Supply Chain Performance Management with Predictive Analytics

Directory of Open Access Journals (Sweden)

Nenad Stefanovic

2014-01-01

Full Text Available Today’s business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators. Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

Proactive supply chain performance management with predictive analytics.

Science.gov (United States)

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

Proactive Supply Chain Performance Management with Predictive Analytics

Science.gov (United States)

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

Performance prediction method for a multi-stage Knudsen pump

Science.gov (United States)

Kugimoto, K.; Hirota, Y.; Kizaki, Y.; Yamaguchi, H.; Niimi, T.

2017-12-01

In this study, the novel method to predict the performance of a multi-stage Knudsen pump is proposed. The performance prediction method is carried out in two steps numerically with the assistance of a simple experimental result. In the first step, the performance of a single-stage Knudsen pump was measured experimentally under various pressure conditions, and the relationship of the mass flow rate was obtained with respect to the average pressure between the inlet and outlet of the pump and the pressure difference between them. In the second step, the performance of a multi-stage pump was analyzed by a one-dimensional model derived from the mass conservation law. The performances predicted by the 1D-model of 1-stage, 2-stage, 3-stage, and 4-stage pumps were validated by the experimental results for the corresponding number of stages. It was concluded that the proposed prediction method works properly.

Calibration of PMIS pavement performance prediction models.

Science.gov (United States)

2012-02-01

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

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.

TRM performance prediction in Yucca Mountain welded tuff from linear cutter tests

International Nuclear Information System (INIS)

Gertsch, R.; Ozdemir, L.; Gertsch, L.

1992-01-01

Performance predictions were developed for tunnel boring machines operating in welded tuff for the construction of the experimental study facility and the potential nuclear waste repository at Yucca Mountain. The predictions were based on test data obtained from an extensive series of linear cutting tests performed on samples of Topopah Spring welded tuff from the Yucca Mountain Project site. Using the cutter force, spacing, and penetration data from the experimental program, the thrust, torque, power, and rate of penetration were estimated for a 25 ft diameter tunnel boring machine (TBM) operating in welded tuff. Guidelines were developed for the optimal design of the TBM cutterhead to achieve high production rates at the lowest possible excavation costs. The results show that the Topopah Spring welded tuff (TSw2) can be excavated at relatively high rates of advance with state-of-the-art TBMs. The results also show, however, that the TBM torque and power requirements will be higher than estimated based on rock physical properties and past tunneling experience in rock formations of similar strength

A Tool for Performance Modeling of Parallel Programs

Directory of Open Access Journals (Sweden)

J.A. González

2003-01-01

Full Text Available Current performance prediction analytical models try to characterize the performance behavior of actual machines through a small set of parameters. In practice, substantial deviations are observed. These differences are due to factors as memory hierarchies or network latency. A natural approach is to associate a different proportionality constant with each basic block, and analogously, to associate different latencies and bandwidths with each "communication block". Unfortunately, to use this approach implies that the evaluation of parameters must be done for each algorithm. This is a heavy task, implying experiment design, timing, statistics, pattern recognition and multi-parameter fitting algorithms. Software support is required. We present a compiler that takes as source a C program annotated with complexity formulas and produces as output an instrumented code. The trace files obtained from the execution of the resulting code are analyzed with an interactive interpreter, giving us, among other information, the values of those parameters.

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

Predicting Performance Ratings Using Motivational Antecedents

National Research Council Canada - National Science Library

Zazania, Michelle

1998-01-01

This research examined the role of motivation in predicting peer and trainer ratings of student performance and contrasted the relative importance of various antecedents for peer and trainer ratings...

Predicting the outcomes of performance error indicators on accreditation status in the nuclear power industry

International Nuclear Information System (INIS)

Wilson, P.A.

1986-01-01

The null hypothesis for this study suggested that there was no significant difference in the types of performance error indicators between accredited and non-accredited programs on the following types of indicators: (1) number of significant event reports per unit, (2) number of forced outages per unit, (3) number of unplanned automatic scrams per unit, and (4) amount of equivalent availability per unit. A sample of 90 nuclear power plants was selected for this study. Data were summarized from two data bases maintained by the Institute of Nuclear Power Operations. Results of this study did not support the research hypothesis. There was no significant difference between the accredited and non-accredited programs on any of the four performance error indicators. The primary conclusions of this include the following: (1) The four selected performance error indicators cannot be used individually or collectively to predict accreditation status in the nuclear power industry. (2) Annual performance error indicator ratings cannot be used to determine the effects of performance-based training on plant performance. (3) The four selected performance error indicators cannot be used to measure the effect of operator job performance on plant effectiveness

Prediction of Job Performance: Review of Military Studies

Science.gov (United States)

1982-03-01

an assessment center to predict filed leadership performance of Army officers and NCOs. Proceedings of the 19th Annual Military Testing Association...C. Behaviors, results, and organizational effectiveness: The problem of criteria. In Dunnette, M. D. (Ed.), Handbook of Industrial and organizatin ...than for the Navy enlisted group. 30. Dyer, F. N., & Hlilligoss, R. Z. Using an assessment center to predict field leadership performance of Army

Rotary engine performance limits predicted by a zero-dimensional model

Science.gov (United States)

Bartrand, Timothy A.; Willis, Edward A.

1992-01-01

A parametric study was performed to determine the performance limits of a rotary combustion engine. This study shows how well increasing the combustion rate, insulating, and turbocharging increase brake power and decrease fuel consumption. Several generalizations can be made from the findings. First, it was shown that the fastest combustion rate is not necessarily the best combustion rate. Second, several engine insulation schemes were employed for a turbocharged engine. Performance improved only for a highly insulated engine. Finally, the variability of turbocompounding and the influence of exhaust port shape were calculated. Rotary engines performance was predicted by an improved zero-dimensional computer model based on a model developed at the Massachusetts Institute of Technology in the 1980's. Independent variables in the study include turbocharging, manifold pressures, wall thermal properties, leakage area, and exhaust port geometry. Additions to the computer programs since its results were last published include turbocharging, manifold modeling, and improved friction power loss calculation. The baseline engine for this study is a single rotor 650 cc direct-injection stratified-charge engine with aluminum housings and a stainless steel rotor. Engine maps are provided for the baseline and turbocharged versions of the engine.

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

International Nuclear Information System (INIS)

Eden, H.F.; Mooers, C.N.K.

1990-06-01

The goal of COPS is to couple a program of regular observations to numerical models, through techniques of data assimilation, in order to provide a predictive capability for the US coastal ocean including the Great Lakes, estuaries, and the entire Exclusive Economic Zone (EEZ). The objectives of the program include: determining the predictability of the coastal ocean and the processes that govern the predictability; developing efficient prediction systems for the coastal ocean based on the assimilation of real-time observations into numerical models; and coupling the predictive systems for the physical behavior of the coastal ocean to predictive systems for biological, chemical, and geological processes to achieve an interdisciplinary capability. COPS will provide the basis for effective monitoring and prediction of coastal ocean conditions by optimizing the use of increased scientific understanding, improved observations, advanced computer models, and computer graphics to make the best possible estimates of sea level, currents, temperatures, salinities, and other properties of entire coastal regions

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.

Performance of local information-based link prediction: a sampling perspective

Science.gov (United States)

Zhao, Jichang; Feng, Xu; Dong, Li; Liang, Xiao; Xu, Ke

2012-08-01

Link prediction is pervasively employed to uncover the missing links in the snapshots of real-world networks, which are usually obtained through different kinds of sampling methods. In the previous literature, in order to evaluate the performance of the prediction, known edges in the sampled snapshot are divided into the training set and the probe set randomly, without considering the underlying sampling approaches. However, different sampling methods might lead to different missing links, especially for the biased ways. For this reason, random partition-based evaluation of performance is no longer convincing if we take the sampling method into account. In this paper, we try to re-evaluate the performance of local information-based link predictions through sampling method governed division of the training set and the probe set. It is interesting that we find that for different sampling methods, each prediction approach performs unevenly. Moreover, most of these predictions perform weakly when the sampling method is biased, which indicates that the performance of these methods might have been overestimated in the prior works.

Plant performance monitoring program at Krsko NPP

International Nuclear Information System (INIS)

Bach, B.; Kavsek, D.

2004-01-01

A high level of nuclear safety and plant reliability results from the complex interaction of a good design, operational safety and human performance. This is the reason for establishing a set of operational plant safety performance indicators, to enable monitoring of both plant performance and progress. Performance indicators are also used for setting challenging targets and goals for improvement, to gain additional perspective on performance relative to other plants and to provide an indication of a potential need to adjust priorities and resources to achieve improved overall plant performance. A specific indicator trend over a certain period can provide an early warning to plant management to evaluate the causes behind the observed changes. In addition to monitoring the changes and trends, it is also necessary to compare the indicators with identified targets and goals to evaluate performance strengths and weaknesses. Plant Performance Monitoring Program at Krsko NPP defines and ensures consistent collection, processing, analysis and use of predefined relevant plant operational data, providing a quantitative indication of nuclear power plant performance. When the program was developed, the conceptual framework described in IAEA TECDOC-1141 Operational Safety Performance Indicators for Nuclear Power Plants was used as its basis in order to secure that a reasonable set of quantitative indications of operational safety performance would be established. Safe, conservative, cautious and reliable operation of the Krsko NPP is a common goal for all plant personnel. It is provided by continuous assurance of both health and safety of the public and employees according to the plant policy stated in program MD-1 Notranje usmeritve in cilji NEK, which is the top plant program. Establishing a program of monitoring and assessing operational plant safety performance indicators represents effective safety culture of plant personnel.(author)

Do 'enabling technologies' affect customer performance in price-responsive load programs?

International Nuclear Information System (INIS)

Goldman, Charles A.; Kintner-Meyer, Michael; Heffner, Grayson

2002-01-01

Price-responsive load (PRL) programs vary significantly in overall design, the complexity of relationships between program administrators, load aggregators, and customers, and the availability of ''enabling technologies''. Enabling technologies include such features as web-based power system and price monitoring, control and dispatch of curtailable loads, communications and information systems links to program participants, availability of interval metering data to customers in near real time, and building/facility/end-use automation and management capabilities. Two state agencies - NYSERDA in New York and the CEC in California - have been conspicuous leaders in the demonstration of demand response (DR) programs utilizing enabling technologies. In partnership with key stakeholders in these two states (e.g., grid operator, state energy agencies, and program administrators), Lawrence Berkeley National Laboratory (LBNL) and Pacific Northwest National Laboratory (PNNL) surveyed 56 customers who worked with five contractors participating in CEC or NYSERDA-sponsored DR programs. We combined market research and actual load curtailment data when available (i.e., New York) or customer load reduction targets in order to explore the relative importance of contractor's program design features, sophistication of control strategies, and reliance on enabling technologies in predicting customer's ability to deliver load reductions in DR programs targeted to large commercial/industrial customers. We found preliminary evidence that DR enabling technology has a positive effect on load curtailment potential. Many customers indicated that web-based energy information tools were useful for facilitating demand response (e.g., assessing actual performance compared to load reduction contract commitments), that multiple notification channels facilitated timely response, and that support for and use of backup generation allowed customers to achieve significant and predictable load

Debugging a high performance computing program

Science.gov (United States)

Gooding, Thomas M.

2013-08-20

Methods, apparatus, and computer program products are disclosed for debugging a high performance computing program by gathering lists of addresses of calling instructions for a plurality of threads of execution of the program, assigning the threads to groups in dependence upon the addresses, and displaying the groups to identify defective threads.

Effects of Family Functioning and Parenting Style on Early Entrants' Academic Performance and Program Completion

Science.gov (United States)

Huey, Erron L.; Sayler, Michael F.; Rinn, Anne N.

2013-01-01

The purpose of the current study was to examine the predictive nature of parenting style and overall family environment on the academic performance and program completion of early college entrants. Furthermore, gender and family form were examined as possible moderators to these relationships. A total of 88 early college entrants participated in…

Evaluating Program about Performance of Circular Sodium Heat Pipe

International Nuclear Information System (INIS)

Kwak, Jae Sik; Kim, Hee Reyoung

2014-01-01

The superior heat transfer capability, structural simplicity, relatively inexpensive, insensitivity to the gravitational field, silence and reliability are some of its outstanding features. We study about heat transfer equation of heat pipe and program predicting performance which is considering geometrical shape of heat pipe by the related heat transfer equation of heat pipe. The operating temperature is 450 .deg. C - 950 .deg. C, working fluid is sodium, material for container is stainless steel, and type of wick is sintered metal. As a result of evaluating program about performance of circular sodium heat pipe based on MATLAB code, express correlation between radius and LHR, correlation between heat transfer length and LHR, correlation between wick and LHR, correlation between operating temperature and LHR. Generally radius values of heat pipe are proportional to LHR because of increase of mass flow which is main factor of heat flow. Heat transfer length values of heat pipe are inversely proportional to LHR and slightly inversely proportional to heat rate. Pore size is proportional to LHR. Although increase of pore size decrease capillary pressure, decrease more pressure drop in liquid phase. As a result, mass flow and heat rate are increase. But we have to do additional consideration about pore size and voidage in the aspect of safety and production technique

Evaluating Program about Performance of Circular Sodium Heat Pipe

Energy Technology Data Exchange (ETDEWEB)

Kwak, Jae Sik; Kim, Hee Reyoung [Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of)

2014-10-15

The superior heat transfer capability, structural simplicity, relatively inexpensive, insensitivity to the gravitational field, silence and reliability are some of its outstanding features. We study about heat transfer equation of heat pipe and program predicting performance which is considering geometrical shape of heat pipe by the related heat transfer equation of heat pipe. The operating temperature is 450 .deg. C - 950 .deg. C, working fluid is sodium, material for container is stainless steel, and type of wick is sintered metal. As a result of evaluating program about performance of circular sodium heat pipe based on MATLAB code, express correlation between radius and LHR, correlation between heat transfer length and LHR, correlation between wick and LHR, correlation between operating temperature and LHR. Generally radius values of heat pipe are proportional to LHR because of increase of mass flow which is main factor of heat flow. Heat transfer length values of heat pipe are inversely proportional to LHR and slightly inversely proportional to heat rate. Pore size is proportional to LHR. Although increase of pore size decrease capillary pressure, decrease more pressure drop in liquid phase. As a result, mass flow and heat rate are increase. But we have to do additional consideration about pore size and voidage in the aspect of safety and production technique.

Mean streamline analysis for performance prediction of cross-flow fans

International Nuclear Information System (INIS)

Kim, Jae Won; Oh, Hyoung Woo

2004-01-01

This paper presents the mean streamline analysis using the empirical loss correlations for performance prediction of cross-flow fans. Comparison of overall performance predictions with test data of a cross-flow fan system with a simplified vortex wall scroll casing and with the published experimental characteristics for a cross-flow fan has been carried out to demonstrate the accuracy of the proposed method. Predicted performance curves by the present mean streamline analysis agree well with experimental data for two different cross-flow fans over the normal operating conditions. The prediction method presented herein can be used efficiently as a tool for the preliminary design and performance analysis of general-purpose cross-flow fans

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.

Academic Performance of First-Year Students at a College of Pharmacy in East Tennessee: Models for Prediction

Science.gov (United States)

Clavier, Cheri Whitehead

2013-01-01

With the increase of students applying to pharmacy programs, it is imperative that admissions committees choose appropriate measures to analyze student readiness. The purpose of this research was to identify significant factors that predict the academic performance, defined as grade point average (GPA) at the end of the first professional year, of…

Predicting university performance in psychology: the role of previous performance and discipline-specific knowledge

OpenAIRE

Betts, LR; Elder, TJ; Hartley, J; Blurton, A

2008-01-01

Recent initiatives to enhance retention and widen participation ensure it is crucial to understand the factors that predict students' performance during their undergraduate degree. The present research used Structural Equation Modeling (SEM) to test three separate models that examined the extent to which British Psychology students' A-level entry qualifications predicted: (1) their performance in years 1-3 of their Psychology degree, and (2) their overall degree performance. Students' overall...

Program Performance Inventory: Six Juvenile Offender Programs.

Science.gov (United States)

Thomalla, Terri Groff; Dougherty, Victoria J.

This report describes the performance of 6 Connecticut juvenile justice alternative sanction programs in 14 qualitative areas: community reintegration; outcomes and evaluation; assessment methods; risk factors; escalation of criminal activity; family involvement; community involvement; work ethic and vocational training; education and life skills;…

Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

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Chien-Ho Ko

2013-01-01

Full Text Available Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs, Fuzzy Logic (FL, and Neural Networks (NNs. FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

Science.gov (United States)

Ko, Chien-Ho

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

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

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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.

A core competency-based objective structured clinical examination (OSCE) can predict future resident performance.

Science.gov (United States)

Wallenstein, Joshua; Heron, Sheryl; Santen, Sally; Shayne, Philip; Ander, Douglas

2010-10-01

This study evaluated the ability of an objective structured clinical examination (OSCE) administered in the first month of residency to predict future resident performance in the Accreditation Council for Graduate Medical Education (ACGME) core competencies. Eighteen Postgraduate Year 1 (PGY-1) residents completed a five-station OSCE in the first month of postgraduate training. Performance was graded in each of the ACGME core competencies. At the end of 18 months of training, faculty evaluations of resident performance in the emergency department (ED) were used to calculate a cumulative clinical evaluation score for each core competency. The correlations between OSCE scores and clinical evaluation scores at 18 months were assessed on an overall level and in each core competency. There was a statistically significant correlation between overall OSCE scores and overall clinical evaluation scores (R = 0.48, p competencies of patient care (R = 0.49, p competencies. An early-residency OSCE has the ability to predict future postgraduate performance on a global level and in specific core competencies. Used appropriately, such information can be a valuable tool for program directors in monitoring residents' progress and providing more tailored guidance. © 2010 by the Society for Academic Emergency Medicine.

Modelling the predictive performance of credit scoring

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Shi-Wei Shen

2013-07-01

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

Determination of Safety Performance Grade of NPP Using Integrated Safety Performance Assessment (ISPA) Program

International Nuclear Information System (INIS)

Chung, Dae Wook

2011-01-01

Since the beginning of 2000, the safety regulation of nuclear power plant (NPP) has been challenged to be conducted more reasonable, effective and efficient way using risk and performance information. In the United States, USNRC established Reactor Oversight Process (ROP) in 2000 for improving the effectiveness of safety regulation of operating NPPs. The main idea of ROP is to classify the NPPs into 5 categories based on the results of safety performance assessment and to conduct graded regulatory programs according to categorization, which might be interpreted as 'Graded Regulation'. However, the classification of safety performance categories is highly comprehensive and sensitive process so that safety performance assessment program should be prepared in integrated, objective and quantitative manner. Furthermore, the results of assessment should characterize and categorize the actual level of safety performance of specific NPP, integrating all the substantial elements for assessing the safety performance. In consideration of particular regulatory environment in Korea, the integrated safety performance assessment (ISPA) program is being under development for the use in the determination of safety performance grade (SPG) of a NPP. The ISPA program consists of 6 individual assessment programs (4 quantitative and 2 qualitative) which cover the overall safety performance of NPP. Some of the assessment programs which are already implemented are used directly or modified for incorporating risk aspects. The others which are not existing regulatory programs are newly developed. Eventually, all the assessment results from individual assessment programs are produced and integrated to determine the safety performance grade of a specific NPP

Comparison of Simple Versus Performance-Based Fall Prediction Models

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Shekhar K. Gadkaree BS

2015-05-01

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

User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.

Science.gov (United States)

Ahn, Minkyu; Cho, Hohyun; Ahn, Sangtae; Jun, Sung C

2018-01-01

Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation ( r = 0.64, p performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

The steady performance prediction of propeller-rudder-bulb system based on potential iterative method

International Nuclear Information System (INIS)

Liu, Y B; Su, Y M; Ju, L; Huang, S L

2012-01-01

A new numerical method was developed for predicting the steady hydrodynamic performance of propeller-rudder-bulb system. In the calculation, the rudder and bulb was taken into account as a whole, the potential based surface panel method was applied both to propeller and rudder-bulb system. The interaction between propeller and rudder-bulb was taken into account by velocity potential iteration in which the influence of propeller rotation was considered by the average influence coefficient. In the influence coefficient computation, the singular value should be found and deleted. Numerical results showed that the method presented is effective for predicting the steady hydrodynamic performance of propeller-rudder system and propeller-rudder-bulb system. Comparing with the induced velocity iterative method, the method presented can save programming and calculation time. Changing dimensions, the principal parameter—bulb size that affect energy-saving effect was studied, the results show that the bulb on rudder have a optimal size at the design advance coefficient.

When predictions take control: The effect of task predictions on task switching performance

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Wout eDuthoo

2012-08-01

Full Text Available In this paper, we aimed to investigate the role of self-generated predictions in the flexible control of behaviour. Therefore, we ran a task switching experiment in which participants were asked to try to predict the upcoming task in three conditions varying in switch rate (30%, 50% and 70%. Irrespective of their predictions, the colour of the target indicated which task participants had to perform. In line with previous studies (Mayr, 2006; Monsell & Mizon, 2006, the switch cost was attenuated as the switch rate increased. Importantly, a clear task repetition bias was found in all conditions, yet the task repetition prediction rate dropped from 78% over 66% to 49% with increasing switch probability in the three conditions. Irrespective of condition, the switch cost was strongly reduced in expectation of a task alternation compared to the cost of an unexpected task alternation following repetition predictions. Hence, our data suggest that the reduction in the switch cost with increasing switch probability is caused by a diminished expectancy for the task to repeat. Taken together, this paper highlights the importance of predictions in the flexible control of behaviour, and suggests a crucial role for task repetition expectancy in the context-sensitive adjusting of task switching performance.

Java Programs for Using Newmark's Method and Simplified Decoupled Analysis to Model Slope Performance During Earthquakes

Science.gov (United States)

Jibson, Randall W.; Jibson, Matthew W.

2003-01-01

Landslides typically cause a large proportion of earthquake damage, and the ability to predict slope performance during earthquakes is important for many types of seismic-hazard analysis and for the design of engineered slopes. Newmark's method for modeling a landslide as a rigid-plastic block sliding on an inclined plane provides a useful method for predicting approximate landslide displacements. Newmark's method estimates the displacement of a potential landslide block as it is subjected to earthquake shaking from a specific strong-motion record (earthquake acceleration-time history). A modification of Newmark's method, decoupled analysis, allows modeling landslides that are not assumed to be rigid blocks. This open-file report is available on CD-ROM and contains Java programs intended to facilitate performing both rigorous and simplified Newmark sliding-block analysis and a simplified model of decoupled analysis. For rigorous analysis, 2160 strong-motion records from 29 earthquakes are included along with a search interface for selecting records based on a wide variety of record properties. Utilities are available that allow users to add their own records to the program and use them for conducting Newmark analyses. Also included is a document containing detailed information about how to use Newmark's method to model dynamic slope performance. This program will run on any platform that supports the Java Runtime Environment (JRE) version 1.3, including Windows, Mac OSX, Linux, Solaris, etc. A minimum of 64 MB of available RAM is needed, and the fully installed program requires 400 MB of disk space.

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…

Plant corrosion: prediction of materials performance

International Nuclear Information System (INIS)

Strutt, J.E.; Nicholls, J.R.

1987-01-01

Seventeen papers have been compiled forming a book on computer-based approaches to corrosion prediction in a wide range of industrial sectors, including the chemical, petrochemical and power generation industries. Two papers have been selected and indexed separately. The first describes a system operating within BNFL's Reprocessing Division to predict materials performance in corrosive conditions to aid future plant design. The second describes the truncation of the distribution function of pit depths during high temperature oxidation of a 20Cr austenitic steel in the fuel cladding in AGR systems. (U.K.)

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

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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.

The electronic residency application service application can predict accreditation council for graduate medical education competency-based surgical resident performance.

Science.gov (United States)

Tolan, Amy M; Kaji, Amy H; Quach, Chi; Hines, O Joe; de Virgilio, Christian

2010-01-01

Program directors often struggle to determine which factors in the Electronic Residency Application Service (ERAS) application are important in the residency selection process. With the establishment of the Accreditation Council for Graduate Medical Education (ACGME) competencies, it would be important to know whether information available in the ERAS application can predict subsequent competency-based performance of general surgery residents. This study is a retrospective correlation of data points found in the ERAS application with core competency-based clinical rotation evaluations. ACGME competency-based evaluations as well as technical skills assessment from all rotations during residency were collected. The overall competency score was defined as an average of all 6 competencies and technical skills. A total of77 residents from two (one university and one community based university-affiliate) general surgery residency programs were included in the analysis. Receiving honors for many of the third year clerkships and AOA membership were associated with a number of the individual competencies. USMLE scores were predictive only of Medical Knowledge (p = 0.004). Factors associated with higher overall competency were female gender (p = 0.02), AOA (p = 0.06), overall number of honors received (p = 0.04), and honors in Ob/Gyn (p = 0.03) and Pediatrics (p = 0.05). Multivariable analysis showed honors in Ob/Gyn, female gender, older age, and total number of honors to be predictive of a number of individual core competencies. USMLE scores were only predictive of Medical Knowledge. The ERAS application is useful for predicting subsequent competency based performance in surgical residents. Receiving honors in the surgery clerkship, which has traditionally carried weight when evaluating a potential surgery resident, may not be as strong a predictor of future success. Copyright © 2010 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights

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.

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

Science.gov (United States)

Kanna, Balavenkatesh; Gu, Ying; Akhuetie, Jane; Dimitrov, Vihren

2009-01-01

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 ITE scores, reflecting exam-taking skills. Success in acquiring a fellowship was associated with consistent fellowship interest (p < 0.05) and research publications or presentations (p <0.05). None of the other characteristics including visa status were associated with the outcomes. 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. PMID:19594918

Predicting the Impacts of Intravehicular Displays on Driving Performance with Human Performance Modeling

Science.gov (United States)

Mitchell, Diane Kuhl; Wojciechowski, Josephine; Samms, Charneta

2012-01-01

A challenge facing the U.S. National Highway Traffic Safety Administration (NHTSA), as well as international safety experts, is the need to educate car drivers about the dangers associated with performing distraction tasks while driving. Researchers working for the U.S. Army Research Laboratory have developed a technique for predicting the increase in mental workload that results when distraction tasks are combined with driving. They implement this technique using human performance modeling. They have predicted workload associated with driving combined with cell phone use. In addition, they have predicted the workload associated with driving military vehicles combined with threat detection. Their technique can be used by safety personnel internationally to demonstrate the dangers of combining distracter tasks with driving and to mitigate the safety risks.

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.

Examining the Factors of Licensure Examination for Teachers Performance for Program Strategy Enhancement

Directory of Open Access Journals (Sweden)

Januard D. Dagdag

2017-11-01

Full Text Available Passing the Licensure Examination for Teachers (LET has been the ultimate focus of tertiary education institutions in the Philippines to meet the present demand of local and global parties and communities. Hence, various strategies are conducted to increase the likelihood that the prospect will happen. With this, the current study determines the predictors of LET performance of the 146 Bachelor of Secondary Education graduates that could serve as basis for enhancing program strategies for a better LET rating. A descriptive correlational method through the analysis of registration data on LET, grade weighted average, college admission test (CAT scores, and course audit scores was conducted. Descriptive statistics and correlational tests were employed in the analysis of these data. Results showed that low LET performance is influenced by low performances in academics and admission test, and limited course audit units taken. Admission test performance, however, does not predict LET performance in Major. On the other hand, course audit performance can only forecast licensure exam scores in Major. Hence, to increase LET performance, the program should consider benchmarking from LET performing institutions, choose the right faculty to teach a course, secure the validity and/or reliability of instructional materials and assessment tools with LET competencies, strictly implement the admission and retention policy, and assess regularly the efficacy of the course audit in all areas

A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

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Sergio Briguglio

2003-01-01

Full Text Available A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage and OpenMP (at the intra-node one. The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

NRC performance assessment program

International Nuclear Information System (INIS)

Coplan, S.M.

1986-01-01

The U.S. Nuclear Regulatory Commission's (NRC) performance assessment program includes the development of guidance to the U.S. Department of Energy (DOE) on preparation of a license application and on conducting the studies to support a license application. The nature of the licensing requirements of 10 CFR Part 60 create a need for performance assessments by the DOE. The NRC and DOE staffs each have specific roles in assuring the adequacy of those assessments. Performance allocation is an approach for determining what testing and analysis will be needed during site characterization to assure that an adequate data base is available to support the necessary performance assessments. From the standpoint of establishing is implementable methodology, the most challenging performance assessment needed for licensing is the one that will be used to determine compliance with the U.S. Environmental Protection Agency's (EPA) containment requirement

Using Machine Learning to Predict Student Performance

OpenAIRE

Pojon, Murat

2017-01-01

This thesis examines the application of machine learning algorithms to predict whether a student will be successful or not. The specific focus of the thesis is the comparison of machine learning methods and feature engineering techniques in terms of how much they improve the prediction performance. Three different machine learning methods were used in this thesis. They are linear regression, decision trees, and naïve Bayes classification. Feature engineering, the process of modification ...

Predicting performance and injury resilience from movement quality and fitness scores in a basketball team over 2 years.

Science.gov (United States)

McGill, Stuart M; Andersen, Jordan T; Horne, Arthur D

2012-07-01

The purpose of this study was to see if specific tests of fitness and movement quality could predict injury resilience and performance in a team of basketball players over 2 years (2 playing seasons). It was hypothesized that, in a basketball population, movement and fitness scores would predict performance scores and that movement and fitness scores would predict injury resilience. A basketball team from a major American university (N = 14) served as the test population in this longitudinal trial. Variables linked to fitness, movement ability, speed, strength, and agility were measured together with some National Basketball Association (NBA) combine tests. Dependent variables of performance indicators (such as games and minutes played, points scored, assists, rebounds, steal, and blocks) and injury reports were tracked for the subsequent 2 years. Results showed that better performance was linked with having a stiffer torso, more mobile hips, weaker left grip strength, and a longer standing long jump, to name a few. Of the 3 NBA combine tests administered here, only a faster lane agility time had significant links with performance. Some movement qualities and torso endurance were not linked. No patterns with injury emerged. These observations have implications for preseason testing and subsequent training programs in an attempt to reduce future injury and enhance playing performance.

Predictors of academic performance for applicants to an international dental studies program in the United States.

Science.gov (United States)

Pitigoi-Aron, Gabriela; King, Patricia A; Chambers, David W

2011-12-01

The number of U.S. and Canadian dental schools offering programs for dentists with degrees from other countries leading to the D.D.S. or D.M.D. degree has increased recently. This fact, along with the diversity of educational systems represented by candidates for these programs, increases the importance of identifying valid admissions predictors of success in international dental student programs. Data from 148 students accepted into the international dental studies program at the University of the Pacific from 1994 through 2004 were analyzed. Dependent variables were comprehensive cumulative GPA at the end of both the first and second years of the two-year program. The Test of English as a Foreign Language (TOEFL) and both Parts I and II of the National Board Dental Examination (NBDE) were significant positive predictors of success. Performance on laboratory tests of clinical skill in operative dentistry and in fixed prosthodontics and ratings from interviewers were not predictive of overall success in the program. Although this study confirms the predictive value of written tests such as the TOEFL and NBDE, it also contributes to the literature documenting inconsistent results regarding other types of predictors. It may be the case that characteristics of individual programs or features of the applicant pools for each may require use of admissions predictors that are unique to schools.

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.

Python high performance programming

CERN Document Server

Lanaro, Gabriele

2013-01-01

An exciting, easy-to-follow guide illustrating the techniques to boost the performance of Python code, and their applications with plenty of hands-on examples.If you are a programmer who likes the power and simplicity of Python and would like to use this language for performance-critical applications, this book is ideal for you. All that is required is a basic knowledge of the Python programming language. The book will cover basic and advanced topics so will be great for you whether you are a new or a seasoned Python developer.

Analytical predictions for the performance of a reinforced concrete containment model subject to overpressurization

International Nuclear Information System (INIS)

Weatherby, J.R.; Clauss, D.B.

1987-01-01

Under the sponsorship of the US Nuclear Regulatory Commission, Sandia National Laboratories is investigating methods for predicting the structural performance of nuclear reactor containment buildings under hypothesized severe accident conditions. As part of this program, a 1/6th-scale reinforced concrete containment model will be pressurized to failure in early 1987. Data generated by the test will be compared to analytical predictions of the structural response in order to assess the accuracy and reliability of the analytical techniques. As part of the pretest analysis effort, Sandia has conducted a number of analyses of the containment structure using the ABAQUS general purpose finite element code. This paper describes results from a nonlinear axisymmetric shell analysis as well as the material models and failure criteria used in conjunction with the analysis

School Breakfast Program and School Performance.

Science.gov (United States)

Meyers, Alan; And Others

Children who participate in the School Breakfast Program show significant improvement in academic performance and tardiness rates, and a trend toward improvement in absenteeism. The School Breakfast Program was created by Congress in 1966 to provide a breakfast on school days for low income children who would otherwise have none. Children…

Performance Prediction of Constrained Waveform Design for Adaptive Radar

Science.gov (United States)

2016-11-01

the famous Woodward quote, having a ubiquitous feeling for all radar waveform design (and performance prediction) researchers , that is found at the end...discuss research that develops performance prediction models to quantify the impact on SINR when an amplitude constraint is placed on a radar waveform...optimize the radar perfor- mance for the particular scenario and tasks. There have also been several survey papers on various topics in waveform design for

Predicting sample size required for classification performance

Directory of Open Access Journals (Sweden)

Figueroa Rosa L

2012-02-01

Full Text Available Abstract Background Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target. Methods We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method. Results A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p Conclusions This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.

High-Performance, Space-Storable, Bi-Propellant Program Status

Science.gov (United States)

Schneider, Steven J.

2002-01-01

Bipropellant propulsion systems currently represent the largest bus subsystem for many missions. These missions range from low Earth orbit satellite to geosynchronous communications and planetary exploration. The payoff of high performance bipropellant systems is illustrated by the fact that Aerojet Redmond has qualified a commercial NTO/MMH engine based on the high Isp technology recently delivered by this program. They are now qualifying a NTO/hydrazine version of this engine. The advanced rhenium thrust chambers recently provided by this program have raised the performance of earth storable propellants from 315 sec to 328 sec of specific impulse. The recently introduced rhenium technology is the first new technology introduced to satellite propulsion in 30 years. Typically, the lead time required to develop and qualify new chemical thruster technology is not compatible with program development schedules. These technology development programs must be supported by a long term, Base R&T Program, if the technology s to be matured. This technology program then addresses the need for high performance, storable, on-board chemical propulsion for planetary rendezvous and descent/ascent. The primary NASA customer for this technology is Space Science, which identifies this need for such programs as Mars Surface Return, Titan Explorer, Neptune Orbiter, and Europa Lander. High performance (390 sec) chemical propulsion is estimated to add 105% payload to the Mars Sample Return mission or alternatively reduce the launch mass by 33%. In many cases, the use of existing (flight heritage) propellant technology is accommodated by reducing mission objectives and/or increasing enroute travel times sacrificing the science value per unit cost of the program. Therefore, a high performance storable thruster utilizing fluorinated oxidizers with hydrazine is being developed.

Predicting Performance of a Face Recognition System Based on Image Quality

NARCIS (Netherlands)

Dutta, A.

2015-01-01

In this dissertation, we focus on several aspects of models that aim to predict performance of a face recognition system. Performance prediction models are commonly based on the following two types of performance predictor features: a) image quality features; and b) features derived solely from

Methodologies for predicting the part-load performance of aero-derivative gas turbines

DEFF Research Database (Denmark)

Haglind, Fredrik; Elmegaard, Brian

2009-01-01

Prediction of the part-load performance of gas turbines is advantageous in various applications. Sometimes reasonable part-load performance is sufficient, while in other cases complete agreement with the performance of an existing machine is desirable. This paper is aimed at providing some guidance...... on methodologies for predicting part-load performance of aero-derivative gas turbines. Two different design models – one simple and one more complex – are created. Subsequently, for each of these models, the part-load performance is predicted using component maps and turbine constants, respectively. Comparisons...... with manufacturer data are made. With respect to the design models, the simple model, featuring a compressor, combustor and turbines, results in equally good performance prediction in terms of thermal efficiency and exhaust temperature as does a more complex model. As for part-load predictions, the results suggest...

Alloy development for irradiation performance: program strategy

International Nuclear Information System (INIS)

Bloom, E.E.; Stiegler, J.O.; Wiffen, F.W.; Dalder, E.N.C.; Reuther, T.C.; Gold, R.E.; Holmes, J.J.; Kummer, D.L.; Nolfi, F.V.

1978-01-01

The objective of the Alloy Development for Irradiation Performance Program is the development of structural materials for use in the first wall and blanket region of fusion reactors. The goal of the program is a material that will survive an exposure of 40 MWyr/m 2 at a temperature which will allow use of a liquid-H 2 O heat transport system. Although the ultimate aim of the program is development of materials for commercial reactors by the end of this century, activities are organized to provide materials data for the relatively low performance interim machines that will precede commercial reactors

A human capital predictive model for agent performance in contact centres

Directory of Open Access Journals (Sweden)

Chris Jacobs

2011-10-01

Research purpose: The primary focus of this article was to develop a theoretically derived human capital predictive model for agent performance in contact centres and Business Process Outsourcing (BPO based on a review of current empirical research literature. Motivation for the study: The study was motivated by the need for a human capital predictive model that can predict agent and overall business performance. Research design: A nonempirical (theoretical research paradigm was adopted for this study and more specifically a theory or model-building approach was followed. A systematic review of published empirical research articles (for the period 2000–2009 in scholarly search portals was performed. Main findings: Eight building blocks of the human capital predictive model for agent performance in contact centres were identified. Forty-two of the human capital contact centre related articles are detailed in this study. Key empirical findings suggest that person– environment fit, job demands-resources, human resources management practices, engagement, agent well-being, agent competence; turnover intention; and agent performance are related to contact centre performance. Practical/managerial implications: The human capital predictive model serves as an operational management model that has performance implications for agents and ultimately influences the contact centre’s overall business performance. Contribution/value-add: This research can contribute to the fields of human resource management (HRM, human capital and performance management within the contact centre and BPO environment.

Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

Science.gov (United States)

Hung, Andrew J; Chen, Jian; Che, Zhengping; Nilanon, Tanachat; Jarc, Anthony; Titus, Micha; Oh, Paul J; Gill, Inderbir S; Liu, Yan

2018-05-01

Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP). We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting. The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p algorithm in predicting, were largely related to camera manipulation. To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.

Enhancing pavement performance prediction models for the Illinois Tollway System

OpenAIRE

Laxmikanth Premkumar; William R. Vavrik

2016-01-01

Accurate pavement performance prediction represents an important role in prioritizing future maintenance and rehabilitation needs, and predicting future pavement condition in a pavement management system. The Illinois State Toll Highway Authority (Tollway) with over 2000 lane miles of pavement utilizes the condition rating survey (CRS) methodology to rate pavement performance. Pavement performance models developed in the past for the Illinois Department of Transportation (IDOT) are used by th...

Goal Setting and Expectancy Theory Predictions of Effort and Performance.

Science.gov (United States)

Dossett, Dennis L.; Luce, Helen E.

Neither expectancy (VIE) theory nor goal setting alone are effective determinants of individual effort and task performance. To test the combined ability of VIE and goal setting to predict effort and performance, 44 real estate agents and their managers completed questionnaires. Quarterly income goals predicted managers' ratings of agents' effort,…

Measuring and Predicting Sleep and Performance During Military Operations

Science.gov (United States)

2012-08-23

strengths of this modeling approach is that accurate predictions of fatigue, performance, or alert- ness can be made from observed sleep timing...and, in which fatigue, performance, or alertness predictions are required prior to the task. Limitations of Current Models The strengths and...mean ± SD, 35.9 ± 1.2 hours), crews flew to Auckland , New Zealand, where another short layover was un- dertaken (23.6 ± 0.95 hours). A final flight

Enhancing pavement performance prediction models for the Illinois Tollway System

Directory of Open Access Journals (Sweden)

Laxmikanth Premkumar

2016-01-01

Full Text Available Accurate pavement performance prediction represents an important role in prioritizing future maintenance and rehabilitation needs, and predicting future pavement condition in a pavement management system. The Illinois State Toll Highway Authority (Tollway with over 2000 lane miles of pavement utilizes the condition rating survey (CRS methodology to rate pavement performance. Pavement performance models developed in the past for the Illinois Department of Transportation (IDOT are used by the Tollway to predict the future condition of its network. The model projects future CRS ratings based on pavement type, thickness, traffic, pavement age and current CRS rating. However, with time and inclusion of newer pavement types there was a need to calibrate the existing pavement performance models, as well as, develop models for newer pavement types.This study presents the results of calibrating the existing models, and developing new models for the various pavement types in the Illinois Tollway network. The predicted future condition of the pavements is used in estimating its remaining service life to failure, which is of immediate use in recommending future maintenance and rehabilitation requirements for the network. Keywords: Pavement performance models, Remaining life, Pavement management

Applying mathematical models to predict resident physician performance and alertness on traditional and novel work schedules.

Science.gov (United States)

Klerman, Elizabeth B; Beckett, Scott A; Landrigan, Christopher P

2016-09-13

In 2011 the U.S. Accreditation Council for Graduate Medical Education began limiting first year resident physicians (interns) to shifts of ≤16 consecutive hours. Controversy persists regarding the effectiveness of this policy for reducing errors and accidents while promoting education and patient care. Using a mathematical model of the effects of circadian rhythms and length of time awake on objective performance and subjective alertness, we quantitatively compared predictions for traditional intern schedules to those that limit work to ≤ 16 consecutive hours. We simulated two traditional schedules and three novel schedules using the mathematical model. The traditional schedules had extended duration work shifts (≥24 h) with overnight work shifts every second shift (including every third night, Q3) or every third shift (including every fourth night, Q4) night; the novel schedules had two different cross-cover (XC) night team schedules (XC-V1 and XC-V2) and a Rapid Cycle Rotation (RCR) schedule. Predicted objective performance and subjective alertness for each work shift were computed for each individual's schedule within a team and then combined for the team as a whole. Our primary outcome was the amount of time within a work shift during which a team's model-predicted objective performance and subjective alertness were lower than that expected after 16 or 24 h of continuous wake in an otherwise rested individual. The model predicted fewer hours with poor performance and alertness, especially during night-time work hours, for all three novel schedules than for either the traditional Q3 or Q4 schedules. Three proposed schedules that eliminate extended shifts may improve performance and alertness compared with traditional Q3 or Q4 schedules. Predicted times of worse performance and alertness were at night, which is also a time when supervision of trainees is lower. Mathematical modeling provides a quantitative comparison approach with potential to aid

Genomic Prediction of Testcross Performance in Canola (Brassica napus)

Science.gov (United States)

Jan, Habib U.; Abbadi, Amine; Lücke, Sophie; Nichols, Richard A.; Snowdon, Rod J.

2016-01-01

Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable

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

On the increase of predictive performance with high-level data fusion

International Nuclear Information System (INIS)

Doeswijk, T.G.; Smilde, A.K.; Hageman, J.A.; Westerhuis, J.A.; Eeuwijk, F.A. van

2011-01-01

The combination of the different data sources for classification purposes, also called data fusion, can be done at different levels: low-level, i.e. concatenating data matrices, medium-level, i.e. concatenating data matrices after feature selection and high-level, i.e. combining model outputs. In this paper the predictive performance of high-level data fusion is investigated. Partial least squares is used on each of the data sets and dummy variables representing the classes are used as response variables. Based on the estimated responses y-hat j for data set j and class k, a Gaussian distribution p(g k |y-hat j ) is fitted. A simulation study is performed that shows the theoretical performance of high-level data fusion for two classes and two data sets. Within group correlations of the predicted responses of the two models and differences between the predictive ability of each of the separate models and the fused models are studied. Results show that the error rate is always less than or equal to the best performing subset and can theoretically approach zero. Negative within group correlations always improve the predictive performance. However, if the data sets have a joint basis, as with metabolomics data, this is not likely to happen. For equally performing individual classifiers the best results are expected for small within group correlations. Fusion of a non-predictive classifier with a classifier that exhibits discriminative ability lead to increased predictive performance if the within group correlations are strong. An example with real life data shows the applicability of the simulation results.

A concept for performance management for Federal science programs

Science.gov (United States)

Whalen, Kevin G.

2017-11-06

The demonstration of clear linkages between planning, funding, outcomes, and performance management has created unique challenges for U.S. Federal science programs. An approach is presented here that characterizes science program strategic objectives by one of five “activity types”: (1) knowledge discovery, (2) knowledge development and delivery, (3) science support, (4) inventory and monitoring, and (5) knowledge synthesis and assessment. The activity types relate to performance measurement tools for tracking outcomes of research funded under the objective. The result is a multi-time scale, integrated performance measure that tracks individual performance metrics synthetically while also measuring progress toward long-term outcomes. Tracking performance on individual metrics provides explicit linkages to root causes of potentially suboptimal performance and captures both internal and external program drivers, such as customer relations and science support for managers. Functionally connecting strategic planning objectives with performance measurement tools is a practical approach for publicly funded science agencies that links planning, outcomes, and performance management—an enterprise that has created unique challenges for public-sector research and development programs.

Performance Assessment Strategy Plan for the Geologic Repository Program

International Nuclear Information System (INIS)

1990-01-01

Performance assessment is a major constituent of the program being conducted by the US Department of Energy (DOE) to develop a geologic repository. Performance assessment is the set of activities needed for quantitative evaluations to assess compliance with the performance requirements in the regulations for a geologic repository and to support the development of the repository. The strategy for these evaluations has been documented in the Performance Assessment Strategy Plan (DOE, 1989). The implementation of the performance assessment strategy is defined in this document. This paper discusses the scope and objectives of the implementation plan, the relationship of the plan to other program plans, summarizes the performance assessment areas and the integrated strategy of the performance assessment program. 1 fig., 3 tabs

Hanford grout: predicting long-term performance

International Nuclear Information System (INIS)

Sewart, G.H.; Mitchell, D.H.; Treat, R.L.; McMakin, A.H.

1987-01-01

Grouted disposal is being planned for the low-level portion of liquid radioactive wastes at the Hanford site in Washington state. The performance of the disposal system must be such that it will protect people and the environment for thousands of years after disposal. To predict whether a specific grout disposal system will comply with existing and foreseen regulations, a performance assessment (PA) is performed. Long-term PAs are conducted for a range of performance conditions. Performance assessment is an inexact science. Quantifying projected impacts is especially difficult when only scant data exist on the behavior of certain components of the disposal system over thousands of years. To develop defensible results, we are honing the models and obtaining experimental data. The combination of engineered features and PA refinements is being used to ensure that Hanford grout will meet its principal goal: to protect people and the environment in the future

Predicting space telerobotic operator training performance from human spatial ability assessment

Science.gov (United States)

Liu, Andrew M.; Oman, Charles M.; Galvan, Raquel; Natapoff, Alan

2013-11-01

Our goal was to determine whether existing tests of spatial ability can predict an astronaut's qualification test performance after robotic training. Because training astronauts to be qualified robotics operators is so long and expensive, NASA is interested in tools that can predict robotics performance before training begins. Currently, the Astronaut Office does not have a validated tool to predict robotics ability as part of its astronaut selection or training process. Commonly used tests of human spatial ability may provide such a tool to predict robotics ability. We tested the spatial ability of 50 active astronauts who had completed at least one robotics training course, then used logistic regression models to analyze the correlation between spatial ability test scores and the astronauts' performance in their evaluation test at the end of the training course. The fit of the logistic function to our data is statistically significant for several spatial tests. However, the prediction performance of the logistic model depends on the criterion threshold assumed. To clarify the critical selection issues, we show how the probability of correct classification vs. misclassification varies as a function of the mental rotation test criterion level. Since the costs of misclassification are low, the logistic models of spatial ability and robotic performance are reliable enough only to be used to customize regular and remedial training. We suggest several changes in tracking performance throughout robotics training that could improve the range and reliability of predictive models.

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.

Gender Differences in Performance Predictions: Evidence from the Cognitive Reflection Test.

Science.gov (United States)

Ring, Patrick; Neyse, Levent; David-Barett, Tamas; Schmidt, Ulrich

2016-01-01

This paper studies performance predictions in the 7-item Cognitive Reflection Test (CRT) and whether they differ by gender. After participants completed the CRT, they predicted their own (i), the other participants' (ii), men's (iii), and women's (iv) number of correct answers. In keeping with existing literature, men scored higher on the CRT than women and both men and women were too optimistic about their own performance. When we compare gender-specific predictions, we observe that men think they perform significantly better than other men and do so significantly more than women. The equality between women's predictions about their own performance and their female peers cannot be rejected. Our findings contribute to the growing literature on the underpinnings of behavior in economics and in psychology by uncovering gender differences in confidence about one's ability relative to same and opposite sex peers.

Burst muscle performance predicts the speed, acceleration, and turning performance of Anna's hummingbirds.

Science.gov (United States)

Segre, Paolo S; Dakin, Roslyn; Zordan, Victor B; Dickinson, Michael H; Straw, Andrew D; Altshuler, Douglas L

2015-11-19

Despite recent advances in the study of animal flight, the biomechanical determinants of maneuverability are poorly understood. It is thought that maneuverability may be influenced by intrinsic body mass and wing morphology, and by physiological muscle capacity, but this hypothesis has not yet been evaluated because it requires tracking a large number of free flight maneuvers from known individuals. We used an automated tracking system to record flight sequences from 20 Anna's hummingbirds flying solo and in competition in a large chamber. We found that burst muscle capacity predicted most performance metrics. Hummingbirds with higher burst capacity flew with faster velocities, accelerations, and rotations, and they used more demanding complex turns. In contrast, body mass did not predict variation in maneuvering performance, and wing morphology predicted only the use of arcing turns and high centripetal accelerations. Collectively, our results indicate that burst muscle capacity is a key predictor of maneuverability.

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

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

Computer-aided performance monitoring program at Diablo Canyon

International Nuclear Information System (INIS)

Nelson, T.; Glynn, R. III; Kessler, T.C.

1992-01-01

This paper describes the thermal performance monitoring program at Pacific Gas ampersand Electric Company's (PG ampersand E's) Diablo Canyon Nuclear Power Plant. The plant performance monitoring program at Diablo Canyon uses the THERMAC performance monitoring and analysis computer software provided by Expert-EASE Systems. THERMAC is used to collect performance data from the plant process computers, condition that data to adjust for measurement errors and missing data points, evaluate cycle and component-level performance, archive the data for trend analysis and generate performance reports. The current status of the program is that, after a fair amount of open-quotes tuningclose quotes of the basic open-quotes thermal kitclose quotes models provided with the initial THERMAC installation, we have successfully baselined both units to cycle isolation test data from previous reload cycles. Over the course of the past few months, we have accumulated enough data to generate meaningful performance trends and, as a result, have been able to use THERMAC to track a condenser fouling problem that was costing enough megawatts to attract corporate-level attention. Trends from THERMAC clearly related the megawatt loss to a steadily degrading condenser cleanliness factor and verified the subsequent gain in megawatts after the condenser was cleaned. In the future, we expect to rebaseline THERMAC to a beginning of cycle (BOC) data set and to use the program to help track feedwater nozzle fouling

Admissions Criteria as Predictors of Academic Performance in a Three-Year Pharmacy Program at a Historically Black Institution

Science.gov (United States)

Parmar, Jayesh R.; Purnell, Miriam; Lang, Lynn A.

2016-01-01

Objective. To determine the ability of University of Maryland Eastern Shore School of Pharmacy’s admissions criteria to predict students’ academic performance in a 3-year pharmacy program and to analyze transferability to African-American students. Methods. Statistical analyses were conducted on retrospective data for 174 students. Didactic and experiential scores were used as measures of academic performance. Results. Pharmacy College Admission Test (PCAT), grade point average (GPA), interview, and observational scores combined with previous pharmacy experience and biochemistry coursework predicted the students' academic performance except second-year (P2) experiential performance. For African-American students, didactic performance positively correlated with PCAT writing subtests, while the experiential performance positively correlated with previous pharmacy experience and observational score. For nonAfrican-American students, didactic performance positively correlated with PCAT multiple-choice subtests, and experiential performance with interview score. The prerequisite GPA positively correlated with both of the student subgroups’ didactic performance. Conclusion. Both PCAT and GPA were predictors of didactic performance, especially in nonAfrican-Americans. Pharmacy experience and observational scores were predictors of experiential performance, especially in African-Americans. PMID:26941432

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.

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

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.

Behavioral patterns of environmental performance evaluation programs.

Science.gov (United States)

Li, Wanxin; Mauerhofer, Volker

2016-11-01

During the past decades numerous environmental performance evaluation programs have been developed and implemented on different geographic scales. This paper develops a taxonomy of environmental management behavioral patterns in order to provide a practical comparison tool for environmental performance evaluation programs. Ten such programs purposively selected are mapped against the identified four behavioral patterns in the form of diagnosis, negotiation, learning, and socialization and learning. Overall, we found that schemes which serve to diagnose environmental abnormalities are mainly externally imposed and have been developed as a result of technical debates concerning data sources, methodology and ranking criteria. Learning oriented scheme is featured by processes through which free exchange of ideas, mutual and adaptive learning can occur. Scheme developed by higher authority for influencing behaviors of lower levels of government has been adopted by the evaluated to signal their excellent environmental performance. The socializing and learning classified evaluation schemes have incorporated dialogue, participation, and capacity building in program design. In conclusion we consider the 'fitness for purpose' of the various schemes, the merits of our analytical model and the future possibilities of fostering capacity building in the realm of wicked environmental challenges. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effectiveness of Human Research Protection Program Performance Measurements.

Science.gov (United States)

Tsan, Min-Fu; Nguyen, Yen

2017-10-01

We analyzed human research protection program performance metric data of all Department of Veterans Affairs research facilities obtained from 2010 to 2016. Among a total of 25 performance metrics, 21 (84%) showed improvement, four (16%) remained unchanged, and none deteriorated during the study period. The overall improvement from these 21 performance metrics was 81.1% ± 18.7% (mean ± SD), with a range of 30% to 100%. The four performance metrics that did not show improvement all had initial noncompliance/incidence rates of performance metrics that showed improvement ranged from 0.05% to 60%. However, of the 21 performance metrics that showed improvement, 10 had initial noncompliance/incidence rates of performance measurement is an effective tool in improving the performance of human research protection programs.

Gender Differences in Performance Predictions: Evidence from the Cognitive Reflection Test

Directory of Open Access Journals (Sweden)

Patrick Ring

2016-11-01

Full Text Available This paper studies performance predictions in the 7-item Cognitive Reflection Test (CRT and whether they differ by gender. After participants completed the CRT, they predicted their own (i, the other participants’ (ii, men’s (iii, and women’s (iv number of correct answers. In keeping with existing literature, men scored higher on the CRT than women and both men and women were too optimistic about their own performance. When we compare gender-specific predictions, we observe that men think they perform significantly better than other men and do so significantly more than women. The equality between women’s predictions about their own performance and their female peers cannot be rejected. Our findings contribute to the growing literature on the underpinnings of behavior in economics and in psychology by uncovering gender differences in confidence about one’s ability relative to same and opposite sex peers.

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/.

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...

Experience and benefits from using the EPRI MOV Performance Prediction Methodology in nuclear power plants

International Nuclear Information System (INIS)

Walker, T.; Damerell, P.S.

1999-01-01

The EPRI MOV Performance Prediction Methodology (PPM) is an effective tool for evaluating design basis thrust and torque requirements for MOVs. Use of the PPM has become more widespread in US nuclear power plants as they close out their Generic Letter (GL) 89-10 programs and address MOV periodic verification per GL 96-05. The PPM has also been used at plants outside the US, many of which are implementing programs similar to US plants' GL 89-10 programs. The USNRC Safety Evaluation of the PPM and the USNRC's discussion of the PPM in GL 96-05 make the PPM an attractive alternative to differential pressure (DP) testing, which can be costly and time-consuming. Significant experience and benefits, which are summarized in this paper, have been gained using the PPM. Although use of PPM requires a commitment of resources, the benefits of a solidly justified approach and a reduced need for DP testing provide a substantial safety and economic benefit. (author)

The trickle-down effect of predictability: Secondary task performance benefits from predictability in the primary task.

Directory of Open Access Journals (Sweden)

Magdalena Ewa Król

Full Text Available Predictions optimize processing by reducing attentional resources allocation to expected or predictable sensory data. Our study demonstrates that these saved processing resources can be then used on concurrent stimuli, and in consequence improve their processing and encoding. We illustrate this "trickle-down" effect with a dual task, where the primary task varied in terms of predictability. The primary task involved detection of a pre-specified symbol that appeared at some point of a short video of a dot moving along a random, semi-predictable or predictable trajectory. The concurrent secondary task involved memorization of photographs representing either emotionally neutral or non-neutral (social or threatening content. Performance in the secondary task was measured by a memory test. We found that participants allocated more attention to unpredictable (random and semi-predictable stimuli than to predictable stimuli. Additionally, when the stimuli in the primary task were more predictable, participants performed better in the secondary task, as evidenced by higher sensitivity in the memory test. Finally, social or threatening stimuli were allocated more "looking time" and a larger number of saccades than neutral stimuli. This effect was stronger for the threatening stimuli than social stimuli. Thus, predictability of environmental input is used in optimizing the allocation of attentional resources, which trickles-down and benefits the processing of concurrent stimuli.

The trickle-down effect of predictability: Secondary task performance benefits from predictability in the primary task.

Science.gov (United States)

Król, Magdalena Ewa; Król, Michał

2017-01-01

Predictions optimize processing by reducing attentional resources allocation to expected or predictable sensory data. Our study demonstrates that these saved processing resources can be then used on concurrent stimuli, and in consequence improve their processing and encoding. We illustrate this "trickle-down" effect with a dual task, where the primary task varied in terms of predictability. The primary task involved detection of a pre-specified symbol that appeared at some point of a short video of a dot moving along a random, semi-predictable or predictable trajectory. The concurrent secondary task involved memorization of photographs representing either emotionally neutral or non-neutral (social or threatening) content. Performance in the secondary task was measured by a memory test. We found that participants allocated more attention to unpredictable (random and semi-predictable) stimuli than to predictable stimuli. Additionally, when the stimuli in the primary task were more predictable, participants performed better in the secondary task, as evidenced by higher sensitivity in the memory test. Finally, social or threatening stimuli were allocated more "looking time" and a larger number of saccades than neutral stimuli. This effect was stronger for the threatening stimuli than social stimuli. Thus, predictability of environmental input is used in optimizing the allocation of attentional resources, which trickles-down and benefits the processing of concurrent stimuli.

Does Residency Selection Criteria Predict Performance in Orthopaedic Surgery Residency?

Science.gov (United States)

Raman, Tina; Alrabaa, Rami George; Sood, Amit; Maloof, Paul; Benevenia, Joseph; Berberian, Wayne

2016-04-01

More than 1000 candidates applied for orthopaedic residency positions in 2014, and the competition is intense; approximately one-third of the candidates failed to secure a position in the match. However, the criteria used in the selection process often are subjective and studies have differed in terms of which criteria predict either objective measures or subjective ratings of resident performance by faculty. Do preresidency selection factors serve as predictors of success in residency? Specifically, we asked which preresidency selection factors are associated or correlated with (1) objective measures of resident knowledge and performance; and (2) subjective ratings by faculty. Charts of 60 orthopaedic residents from our institution were reviewed. Preresidency selection criteria examined included United States Medical Licensing Examination (USMLE) Step 1 and Step 2 scores, Medical College Admission Test (MCAT) scores, number of clinical clerkship honors, number of letters of recommendation, number of away rotations, Alpha Omega Alpha (AOA) honor medical society membership, fourth-year subinternship at our institution, and number of publications. Resident performance was assessed using objective measures including American Board of Orthopaedic Surgery (ABOS) Part I scores and Orthopaedics In-Training Exam (OITE) scores and subjective ratings by faculty including global evaluation scores and faculty rankings of residents. We tested associations between preresidency criteria and the subsequent objective and subjective metrics using linear correlation analysis and Mann-Whitney tests when appropriate. Objective measures of resident performance namely, ABOS Part I scores, had a moderate linear correlation with the USMLE Step 2 scores (r = 0.55, p communication skills" subsection of the global evaluations. We found that USMLE Step 2, number of honors in medical school clerkships, and AOA membership demonstrated the strongest correlations with resident performance. Our

Still under the microscope: can a surgical aptitude test predict otolaryngology resident performance?

Science.gov (United States)

Moore, Eric J; Price, Daniel L; Van Abel, Kathryn M; Carlson, Matthew L

2015-02-01

Application to otolaryngology-head and neck surgery residency is highly competitive, and the interview process strives to select qualified applicants with a high aptitude for the specialty. Commonly employed criteria for applicant selection have failed to show correlation with proficiency during residency training. We evaluate the correlation between the results of a surgical aptitude test administered to otolaryngology resident applicants and their performance during residency. Retrospective study at an academic otolaryngology-head and neck surgery residency program. Between 2007 and 2013, 224 resident applicants participated in a previously described surgical aptitude test administered at a microvascular surgical station. The composite score and attitudinal scores for 24 consecutive residents who matched at our institution were recorded, and their residency performance was analyzed by faculty survey on a five-point scale. The composite and attitudinal scores were analyzed for correlation with residency performance score by regression analysis. Twenty-four residents were evaluated for overall quality as a clinician by eight faculty members who were blinded to the results of surgical aptitude testing. The results of these surveys showed good inter-rater reliability. Both the overall aptitude test scores and the subset attitudinal score showed reliability in predicting performance during residency training. The goal of the residency selection process is to evaluate the candidate's potential for success in residency and beyond. The results of this study suggest that a simple-to-administer clinical skills test may have predictive value for success in residency and clinician quality. 4. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Comparative values of medical school assessments in the prediction of internship performance.

Science.gov (United States)

Lee, Ming; Vermillion, Michelle

2018-02-01

Multiple undergraduate achievements have been used for graduate admission consideration. Their relative values in the prediction of residency performance are not clear. This study compared the contributions of major undergraduate assessments to the prediction of internship performance. Internship performance ratings of the graduates of a medical school were collected from 2012 to 2015. Hierarchical multiple regression analyses were used to examine the predictive values of undergraduate measures assessing basic and clinical sciences knowledge and clinical performances, after controlling for differences in the Medical College Admission Test (MCAT). Four hundred eighty (75%) graduates' archived data were used in the study. Analyses revealed that clinical competencies, assessed by the USMLE Step 2 CK, NBME medicine exam, and an eight-station objective structured clinical examination (OSCE), were strong predictors of internship performance. Neither the USMLE Step 1 nor the inpatient internal medicine clerkship evaluation predicted internship performance. The undergraduate assessments as a whole showed a significant collective relationship with internship performance (ΔR 2  = 0.12, p < 0.001). The study supports the use of clinical competency assessments, instead of pre-clinical measures, in graduate admission consideration. It also provides validity evidence for OSCE scores in the prediction of workplace performance.

Predicting the performance of fingerprint similarity searching.

Science.gov (United States)

Vogt, Martin; Bajorath, Jürgen

2011-01-01

Fingerprints are bit string representations of molecular structure that typically encode structural fragments, topological features, or pharmacophore patterns. Various fingerprint designs are utilized in virtual screening and their search performance essentially depends on three parameters: the nature of the fingerprint, the active compounds serving as reference molecules, and the composition of the screening database. It is of considerable interest and practical relevance to predict the performance of fingerprint similarity searching. A quantitative assessment of the potential that a fingerprint search might successfully retrieve active compounds, if available in the screening database, would substantially help to select the type of fingerprint most suitable for a given search problem. The method presented herein utilizes concepts from information theory to relate the fingerprint feature distributions of reference compounds to screening libraries. If these feature distributions do not sufficiently differ, active database compounds that are similar to reference molecules cannot be retrieved because they disappear in the "background." By quantifying the difference in feature distribution using the Kullback-Leibler divergence and relating the divergence to compound recovery rates obtained for different benchmark classes, fingerprint search performance can be quantitatively predicted.

Children's biological responsivity to acute stress predicts concurrent cognitive performance.

Science.gov (United States)

Roos, Leslie E; Beauchamp, Kathryn G; Giuliano, Ryan; Zalewski, Maureen; Kim, Hyoun K; Fisher, Philip A

2018-04-10

Although prior research has characterized stress system reactivity (i.e. hypothalamic-pituitary-adrenal axis, HPAA; autonomic nervous system, ANS) in children, it has yet to examine the extent to which biological reactivity predicts concurrent goal-directed behavior. Here, we employed a stressor paradigm that allowed concurrent assessment of both stress system reactivity and performance on a speeded-response task to investigate the links between biological reactivity and cognitive function under stress. We further investigated gender as a moderator given previous research suggesting that the ANS may be particularly predictive of behavior in males due to gender differences in socialization. In a sociodemographically diverse sample of young children (N = 58, M age = 5.38 yrs; 44% male), individual differences in sociodemographic covariates (age, household income), HPAA (i.e. cortisol), and ANS (i.e. respiratory sinus arrhythmia, RSA, indexing the parasympathetic branch; pre-ejection period, PEP, indexing the sympathetic branch) function were assessed as predictors of cognitive performance under stress. We hypothesized that higher income, older age, and greater cortisol reactivity would be associated with better performance overall, and flexible ANS responsivity (i.e. RSA withdrawal, PEP shortening) would be predictive of performance for males. Overall, females performed better than males. Two-group SEM analyses suggest that, for males, greater RSA withdrawal to the stressor was associated with better performance, while for females, older age, higher income, and greater cortisol reactivity were associated with better performance. Results highlight the relevance of stress system reactivity to cognitive performance under stress. Future research is needed to further elucidate for whom and in what situations biological reactivity predicts goal-directed behavior.

Human performance for the success of equipment reliability programs

International Nuclear Information System (INIS)

Woodcock, J.

2007-01-01

Human performance is a critical element of programs directed at equipment reliability. Reliable equipment performance requires broad support from all levels of plant management and throughout all plant departments. Experience at both nuclear power plants and fuel manufacturing plants shows that human performance must be addressed during all phases of program implementation from the beginning through the establishment of a living, on-going process. At the beginning, certain organizational and management actions during the initiation of the program set the stage for successful adoption by station personnel, leading to more rapid benefits. For the long term, equipment reliability is a living process needed throughout the lifetime of a station, a program which must be motivated and measured. Sustained acceptance and participation by the plant personnel is a requirement, and culture is a key ingredient. This paper will provide an overview of key human performance issues to be considered, using the application of the INPO AP-913 Equipment Reliability Guideline as a basis and gives some best practices for training, communicating and implementing programs. The very last part includes ways to tell if the program is effective

Hyperformance: predicting high-speed performance of a b-double

CSIR Research Space (South Africa)

Berman, Robert J

2016-11-01

Full Text Available of the vehicles. The prediction model bridges that gap in the form of a light-weight methodology to predict the PBS performance of a new vehicle design given a set of vehicle input data. Such a model was developed for typical South African 9-axle B-double PBS...

Performance Prediction Toolkit

Energy Technology Data Exchange (ETDEWEB)

2017-09-25

The Performance Prediction Toolkit (PPT), is a scalable co-design tool that contains the hardware and middle-ware models, which accept proxy applications as input in runtime prediction. PPT relies on Simian, a parallel discrete event simulation engine in Python or Lua, that uses the process concept, where each computing unit (host, node, core) is a Simian entity. Processes perform their task through message exchanges to remain active, sleep, wake-up, begin and end. The PPT hardware model of a compute core (such as a Haswell core) consists of a set of parameters, such as clock speed, memory hierarchy levels, their respective sizes, cache-lines, access times for different cache levels, average cycle counts of ALU operations, etc. These parameters are ideally read off a spec sheet or are learned using regression models learned from hardware counters (PAPI) data. The compute core model offers an API to the software model, a function called time_compute(), which takes as input a tasklist. A tasklist is an unordered set of ALU, and other CPU-type operations (in particular virtual memory loads and stores). The PPT application model mimics the loop structure of the application and replaces the computational kernels with a call to the hardware model's time_compute() function giving tasklists as input that model the compute kernel. A PPT application model thus consists of tasklists representing kernels and the high-er level loop structure that we like to think of as pseudo code. The key challenge for the hardware model's time_compute-function is to translate virtual memory accesses into actual cache hierarchy level hits and misses.PPT also contains another CPU core level hardware model, Analytical Memory Model (AMM). The AMM solves this challenge soundly, where our previous alternatives explicitly include the L1,L2,L3 hit-rates as inputs to the tasklists. Explicit hit-rates inevitably only reflect the application modeler's best guess, perhaps informed by a few

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

Performance improvement of developed program by using multi-thread technique

Directory of Open Access Journals (Sweden)

Surasak Jabal

2015-03-01

Full Text Available This research presented how to use a multi-thread programming technique to improve the performance of a program written by Windows Presentation Foundation (WPF. The Computer Assisted Instruction (CAI software, named GAME24, was selected to use as a case study. This study composed of two main parts. The first part was about design and modification of the program structure upon the Object Oriented Programing (OOP approach. The second part was about coding the program using the multi-thread technique which the number of threads were based on the calculated Catalan number. The result showed that the multi-thread programming technique increased the performance of the program 44%-88% compared to the single-thread technique. In addition, it has been found that the number of cores in the CPU also increase the performance of multithreaded program proportionally.

Standardizing the performance evaluation of short-term wind prediction models

DEFF Research Database (Denmark)

Madsen, Henrik; Pinson, Pierre; Kariniotakis, G.

2005-01-01

Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective...... evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term wind-poser preciction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated using results...... from both on-shore and off-shore wind forms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems....

Deep Recurrent Model for Server Load and Performance Prediction in Data Center

Directory of Open Access Journals (Sweden)

Zheng Huang

2017-01-01

Full Text Available Recurrent neural network (RNN has been widely applied to many sequential tagging tasks such as natural language process (NLP and time series analysis, and it has been proved that RNN works well in those areas. In this paper, we propose using RNN with long short-term memory (LSTM units for server load and performance prediction. Classical methods for performance prediction focus on building relation between performance and time domain, which makes a lot of unrealistic hypotheses. Our model is built based on events (user requests, which is the root cause of server performance. We predict the performance of the servers using RNN-LSTM by analyzing the log of servers in data center which contains user’s access sequence. Previous work for workload prediction could not generate detailed simulated workload, which is useful in testing the working condition of servers. Our method provides a new way to reproduce user request sequence to solve this problem by using RNN-LSTM. Experiment result shows that our models get a good performance in generating load and predicting performance on the data set which has been logged in online service. We did experiments with nginx web server and mysql database server, and our methods can been easily applied to other servers in data center.

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.

Predictive Validity of National Basketball Association Draft Combine on Future Performance.

Science.gov (United States)

Teramoto, Masaru; Cross, Chad L; Rieger, Randall H; Maak, Travis G; Willick, Stuart E

2018-02-01

Teramoto, M, Cross, CL, Rieger, RH, Maak, TG, and Willick, SE. Predictive validity of national basketball association draft combine on future performance. J Strength Cond Res 32(2): 396-408, 2018-The National Basketball Association (NBA) Draft Combine is an annual event where prospective players are evaluated in terms of their athletic abilities and basketball skills. Data collected at the Combine should help NBA teams select right the players for the upcoming NBA draft; however, its value for predicting future performance of players has not been examined. This study investigated predictive validity of the NBA Draft Combine on future performance of basketball players. We performed a principal component analysis (PCA) on the 2010-2015 Combine data to reduce correlated variables (N = 234), a correlation analysis on the Combine data and future on-court performance to examine relationships (maximum pairwise N = 217), and a robust principal component regression (PCR) analysis to predict first-year and 3-year on-court performance from the Combine measures (N = 148 and 127, respectively). Three components were identified within the Combine data through PCA (= Combine subscales): length-size, power-quickness, and upper-body strength. As per the correlation analysis, the individual Combine items for anthropometrics, including height without shoes, standing reach, weight, wingspan, and hand length, as well as the Combine subscale of length-size, had positive, medium-to-large-sized correlations (r = 0.313-0.545) with defensive performance quantified by Defensive Box Plus/Minus. The robust PCR analysis showed that the Combine subscale of length-size was a predictor most significantly associated with future on-court performance (p ≤ 0.05), including Win Shares, Box Plus/Minus, and Value Over Replacement Player, followed by upper-body strength. In conclusion, the NBA Draft Combine has value for predicting future performance of players.

A Performance Management Initiative for Local Health Department Vector Control Programs.

Science.gov (United States)

Gerding, Justin; Kirshy, Micaela; Moran, John W; Bialek, Ron; Lamers, Vanessa; Sarisky, John

2016-01-01

Local health department (LHD) vector control programs have experienced reductions in funding and capacity. Acknowledging this situation and its potential effect on the ability to respond to vector-borne diseases, the U.S. Centers for Disease Control and Prevention and the Public Health Foundation partnered on a performance management initiative for LHD vector control programs. The initiative involved 14 programs that conducted a performance assessment using the Environmental Public Health Performance Standards. The programs, assisted by quality improvement (QI) experts, used the assessment results to prioritize improvement areas that were addressed with QI projects intended to increase effectiveness and efficiency in the delivery of services such as responding to mosquito complaints and educating the public about vector-borne disease prevention. This article describes the initiative as a process LHD vector control programs may adapt to meet their performance management needs. This study also reviews aggregate performance assessment results and QI projects, which may reveal common aspects of LHD vector control program performance and priority improvement areas. LHD vector control programs interested in performance assessment and improvement may benefit from engaging in an approach similar to this performance management initiative.

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

The influence of critical thinking skills on performance and progression in a pre-registration nursing program.

Science.gov (United States)

Pitt, Victoria; Powis, David; Levett-Jones, Tracy; Hunter, Sharyn

2015-01-01

The importance of developing critical thinking skills in preregistration nursing students is recognized worldwide. Yet, there has been limited exploration of how students' critical thinking skill scores on entry to pre-registration nursing education influence their academic and clinical performance and progression. The aim of this study was to: i) describe entry and exit critical thinking scores of nursing students enrolled in a three year bachelor of nursing program in Australia in comparison to norm scores; ii) explore entry critical thinking scores in relation to demographic characteristics, students' performance and progression. This longitudinal correlational study used the Health Sciences Reasoning Test (HSRT) to measure critical thinking skills in a sample (n=134) of students, at entry and exit (three years later). A one sample t-test was used to determine if differences existed between matched student critical thinking scores between entry and exit points. Academic performance, clinical performance and progression data were collected and correlations with entry critical thinking scores were examined. There was a significant relationship between critical thinking scores, academic performance and students' risk of failing, especially in the first semester of study. Critical thinking scores were predictive of program completion within three years. The increase in critical thinking scores from entry to exit was significant for the 28 students measured. In comparison to norm scores, entry level critical thinking scores were significantly lower, but exit scores were comparable. Critical thinking scores had no significant relationship to clinical performance. Entry critical thinking scores significantly correlate to academic performance and predict students risk of course failure and ability to complete a nursing degree in three years. Students' critical thinking scores are an important determinant of their success and as such can inform curriculum development and

Predictive testing of performance of metals in HTR service environments

International Nuclear Information System (INIS)

Kondo, T.; Shindo, M.; Tamura, M.; Tsuji, H.; Kurata, Y.; Tsukada, T.

1982-01-01

Status of the material testing in simulated HTGR environment is reviewed with special attention focused on the methodology of the prediction of performance in long time. Importance of controlling effective chemical potentials relations in the material-environmental interface is stressed in regard of the complex inter-dependent kinetic relation between oxidation and carbon transport. Based on the recent experimental observations, proposals are made to establish some procedures for conservative prediction of the metal performance

Designing PV Incentive Programs to Promote Performance: A Reviewof Current Practice

Energy Technology Data Exchange (ETDEWEB)

Barbose, Galen; Wiser, Ryan; Bolinger, Mark

2007-06-01

Increasing levels of financial support for customer-sited photovoltaic (PV) systems, provided through publicly-funded incentive programs, has heightened concerns about the long-term performance of these systems. Given the barriers that customers face to ensuring that their PV systems perform well, and the responsibility that PV incentive programs bear to ensure that public funds are prudently spent, these programs should, and often do, play a critical role in ensuring that PV systems receiving incentives perform well. To provide a point of reference for assessing the current state of the art, and to inform program design efforts going forward, we examine the approaches to encouraging PV system performance used by 32 prominent PV incentive programs in the U.S. We identify eight general strategies or groups of related strategies that these programs have used to address performance issues, and highlight important differences in the implementation of these strategies among programs.

Provider Customer Service Program - Performance Data

Data.gov (United States)

U.S. Department of Health & Human Services — CMS is continuously analyzing performance and quality of the Provider Customer Service Programs (PCSPs) of the contractors and will be identifying trends and making...

Overview of the Hanford Site Performance Assurance Program

International Nuclear Information System (INIS)

Duncan, M.R.; Billings, M.P.; Delvin, W.L.; Scott, D.D.; Weatherby, J.W.

1991-01-01

This paper reports on a safeguards and security performance assurance program which encompasses the routine and special activities carried out to assure that safeguards and security subsystems and components are operating in a effective and reliable manner. At the Hanford Site, performance assurance involves widely varied activities, e.g., force-on-force exercises, functional testing of security components, and limited scope performance testing of material control and accountability subsystems. These activities belong to one of four categories: performance testing, functional testing, inspection, and preventive maintenance. Using categories has aided in identifying and assessing the relevant contribution each activity makes to the performance assurance program. Efforts have progressed toward incorporating performance assurance activities into the assessment of protection effectiveness required for Master Safeguards and Security Agreement development and its associated verification and validation process

Predicting Performance with Contextualized Inventories, No Frame-of-reference Effect?

NARCIS (Netherlands)

Holtrop, D.J.; Born, M.P.; de Vries, R.E.

2014-01-01

A recent meta-analysis showed that contextualized personality inventories have incremental predictive validity over generic personality inventories when predicting job performance. This study aimed to investigate the differences between two types of contextualization of items: Adding an 'at work'

14 CFR 152.319 - Monitoring and reporting of program performance.

Science.gov (United States)

2010-01-01

... performance. 152.319 Section 152.319 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION, DEPARTMENT OF TRANSPORTATION (CONTINUED) AIRPORTS AIRPORT AID PROGRAM Accounting and Reporting Requirements § 152.319 Monitoring and reporting of program performance. (a) The sponsor or planning agency shall monitor performance...

Sexual victimization history predicts academic performance in college women.

Science.gov (United States)

Baker, Majel R; Frazier, Patricia A; Greer, Christiaan; Paulsen, Jacob A; Howard, Kelli; Meredith, Liza N; Anders, Samantha L; Shallcross, Sandra L

2016-11-01

College women frequently report having experienced sexual victimization (SV) in their lifetime, including child sexual abuse and adolescent/adult sexual assault. Although the harmful mental health sequelae of SV have been extensively studied, recent research suggests that SV is also a risk factor for poorer college academic performance. The current studies examined whether exposure to SV uniquely predicted poorer college academic performance, even beyond contributions from three well-established predictors of academic performance: high school rank, composite standardized test scores (i.e., American College Testing [ACT]), and conscientiousness. Study 1 analyzed longitudinal data from a sample of female college students (N = 192) who were assessed at the beginning and end of one semester. SV predicted poorer cumulative end-of-semester grade point average (GPA) while controlling for well-established predictors of academic performance. Study 2 replicated these findings in a second longitudinal study of female college students (N = 390) and extended the analyses to include follow-up data on the freshmen and sophomore students (n = 206) 4 years later. SV predicted students' GPA in their final term at the university above the contributions of well-established academic predictors, and it was the only factor related to leaving college. These findings highlight the importance of expanding the scope of outcomes of SV to include academic performance, and they underscore the need to assess SV and other adverse experiences on college campuses to target students who may be at risk of poor performance or leaving college. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Comparison and Prediction of Preclinical Students' Performance in ...

African Journals Online (AJOL)

olayemitoyin

The data support the hypothesis that students who performed well in one discipline were likely to .... predict success in the clinical curriculum (Baciewicz,. 1990). Similarly ... the International Association of Medical Science. Educators. 17-20.

Comparing theories' performance in predicting violence.

Science.gov (United States)

Haas, Henriette; Cusson, Maurice

2015-01-01

The stakes of choosing the best theory as a basis for violence prevention and offender rehabilitation are high. However, no single theory of violence has ever been universally accepted by a majority of established researchers. Psychiatry, psychology and sociology are each subdivided into different schools relying upon different premises. All theories can produce empirical evidence for their validity, some of them stating the opposite of each other. Calculating different models with multivariate logistic regression on a dataset of N = 21,312 observations and ninety-two influences allowed a direct comparison of the performance of operationalizations of some of the most important schools. The psychopathology model ranked as the best model in terms of predicting violence right after the comprehensive interdisciplinary model. Next came the rational choice and lifestyle model and third the differential association and learning theory model. Other models namely the control theory model, the childhood-trauma model and the social conflict and reaction model turned out to have low sensitivities for predicting violence. Nevertheless, all models produced acceptable results in predictions of a non-violent outcome. Copyright © 2015. Published by Elsevier Ltd.

Materials balance area Custodian Performance Evaluation Program at PNL

International Nuclear Information System (INIS)

Dickman, D.A.

1991-07-01

The material balance area (MBA) custodian has primary responsibility for control and accountability of nuclear material within an MBA. In this role, the custodian operates as an extension of the facility material control and accountability (MC ampersand A) organization. To effectively meet administrative requirements and protection needs, the custodian must be fully trained in all aspects of MC ampersand A related to the MBA, and custodian performance must be periodically evaluated. DOE Policy requires that each facility provide for a program which assures that each facility provide for a program which assures that personnel performing MC ampersand A functions are (1) trained and/or qualified to perform their duties and responsibilities and (2) knowledgeable of requirements and procedures related to their functions. The MBA Custodian Performance Evaluation Program at PNL uses a variety of assessment techniques to meet this goal, including internal and independent MBA audits, periodic custodian testing, conduct of limited scope performance tests, daily monitoring of MC ampersand A documentation, and reviewing custodian performance during physical inventories. The data collected from these sources is analyzed and incorporated into an annual custodian performance evaluation document, given to each custodian and line management. Development of this program has resulted in significantly improved custodian performance and a marked decrease in finding and observations identified during MBA audits

Entity versus incremental theories predict older adults' memory performance.

Science.gov (United States)

Plaks, Jason E; Chasteen, Alison L

2013-12-01

The authors examined whether older adults' implicit theories regarding the modifiability of memory in particular (Studies 1 and 3) and abilities in general (Study 2) would predict memory performance. In Study 1, individual differences in older adults' endorsement of the "entity theory" (a belief that one's ability is fixed) or "incremental theory" (a belief that one's ability is malleable) of memory were measured using a version of the Implicit Theories Measure (Dweck, 1999). Memory performance was assessed with a free-recall task. Results indicated that the higher the endorsement of the incremental theory, the better the free recall. In Study 2, older and younger adults' theories were measured using a more general version of the Implicit Theories Measure that focused on the modifiability of abilities in general. Again, for older adults, the higher the incremental endorsement, the better the free recall. Moreover, as predicted, implicit theories did not predict younger adults' memory performance. In Study 3, participants read mock news articles reporting evidence in favor of either the entity or incremental theory. Those in the incremental condition outperformed those in the entity condition on reading span and free-recall tasks. These effects were mediated by pretask worry such that, for those in the entity condition, higher worry was associated with lower performance. Taken together, these studies suggest that variation in entity versus incremental endorsement represents a key predictor of older adults' memory performance. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Hybrid ANN–PLS approach to scroll compressor thermodynamic performance prediction

International Nuclear Information System (INIS)

Tian, Z.; Gu, B.; Yang, L.; Lu, Y.

2015-01-01

In this paper, a scroll compressor thermodynamic performance prediction was carried out by applying a hybrid ANN–PLS model. Firstly, an experimental platform with second-refrigeration calorimeter was set up and steady-state scroll compressor data sets were collected from experiments. Then totally 148 data sets were introduced to train and verify the validity of the ANN–PLS model for predicting the scroll compressor parameters such as volumetric efficiency, refrigerant mass flow rate, discharge temperature and power consumption. The ANN–PLS model was determined with 5 hidden neurons and 7 latent variables through the training process. Ultimately, the ANN–PLS model showed better performance than the ANN model and the PLS model working separately. ANN–PLS predictions agree well with the experimental values with mean relative errors (MREs) in the range of 0.34–1.96%, correlation coefficients (R 2 ) in the range of 0.9703–0.9999 and very low root mean square errors (RMSEs). - Highlights: • Hybrid ANN–PLS is utilized to predict the thermodynamic performance of scroll compressor. • ANN–PLS model is determined with 5 hidden neurons and 7 latent variables. • ANN–PLS model demonstrates better performance than ANN and PLS working separately. • The values of MRE and RMSE are in the range of 0.34–1.96% and 0.9703–0.9999, respectively

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.

Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

Science.gov (United States)

Boone, Spencer

2017-01-01

This presentation will discuss the updated Inclination Adjust Maneuver (IAM) performance prediction model that was developed for Aqua and Aura following the 2017 IAM series. This updated model uses statistical regression methods to identify potential long-term trends in maneuver parameters, yielding improved predictions when re-planning past maneuvers. The presentation has been reviewed and approved by Eric Moyer, ESMO Deputy Project Manager.

Prediction of Tennis Performance in Junior Elite Tennis Players

Directory of Open Access Journals (Sweden)

Tamara Kramer, Barbara C.H. Huijgen, Marije T. Elferink-Gemser, Chris Visscher

2017-03-01

Full Text Available Predicting current and future tennis performance can lead to improving the development of junior tennis players. The aim of this study is to investigate whether age, maturation, or physical fitness in junior elite tennis players in U13 can explain current and future tennis performance. The value of current tennis performance for future tennis performance is also investigated. A total of 86 junior elite tennis players (boys, n = 44; girls, n = 42 U13 (aged: 12.5 ± 0.3 years, and followed to U16, took part in this study. All players were top-30 ranked on the Dutch national ranking list at U13, and top-50 at U16. Age, maturation, and physical fitness, were measured at U13. A principal component analysis was used to extract four physical components from eight tests (medicine ball throwing overhead and reverse, ball throwing, SJ, CMJas, Sprint 5 and 10 meter, and the spider test. The possible relationship of age, maturation, and the physical components; “upper body power”, “lower body power”, “speed”, and “agility” with tennis performance at U13 and U16 was analyzed. Tennis performance was measured by using the ranking position on the Dutch national ranking list at U13 and U16. Regression analyses were conducted based on correlations between variables and tennis performance for boys and girls, separately. In boys U13, positive correlations were found between upper body power and tennis performance (R2 is 25%. In girls, positive correlations between maturation and lower body power with tennis performance were found at U13. Early maturing players were associated with a better tennis performance (R2 is 15%. In girls U16, only maturation correlated with tennis performance (R2 is 13%; later-maturing girls at U13 had better tennis performances at U16. Measuring junior elite tennis players at U13 is important for monitoring their development. These measurements did not predict future tennis performance of junior elite tennis players three

Burst muscle performance predicts the speed, acceleration, and turning performance of Anna’s hummingbirds

Science.gov (United States)

Segre, Paolo S; Dakin, Roslyn; Zordan, Victor B; Dickinson, Michael H; Straw, Andrew D; Altshuler, Douglas L

2015-01-01

Despite recent advances in the study of animal flight, the biomechanical determinants of maneuverability are poorly understood. It is thought that maneuverability may be influenced by intrinsic body mass and wing morphology, and by physiological muscle capacity, but this hypothesis has not yet been evaluated because it requires tracking a large number of free flight maneuvers from known individuals. We used an automated tracking system to record flight sequences from 20 Anna's hummingbirds flying solo and in competition in a large chamber. We found that burst muscle capacity predicted most performance metrics. Hummingbirds with higher burst capacity flew with faster velocities, accelerations, and rotations, and they used more demanding complex turns. In contrast, body mass did not predict variation in maneuvering performance, and wing morphology predicted only the use of arcing turns and high centripetal accelerations. Collectively, our results indicate that burst muscle capacity is a key predictor of maneuverability. DOI: http://dx.doi.org/10.7554/eLife.11159.001 PMID:26583753

Determining Mean Predicted Performance for Army Job Families

National Research Council Canada - National Science Library

Zeidner, Joseph

2003-01-01

The present study is designed to obtain mean predicted performance (MPPs) for the 9- and 17-job families, using composites based on 7 ASVAB tests, using a triple cross validation design permitting completely unbiased estimates of MPP...

Predicting law enforcement officer job performance with the Personality Assessment Inventory.

Science.gov (United States)

Lowmaster, Sara E; Morey, Leslie C

2012-01-01

This study examined the descriptive and predictive characteristics of the Personality Assessment Inventory (PAI; Morey, 1991) in a sample of 85 law enforcement officer candidates. Descriptive results indicate that mean PAI full-scale and subscale scores are consistently lower than normative community sample scores, with some exceptions noted typically associated with defensive responding. Predictive validity was examined by relating PAI full-scale and subscale scores to supervisor ratings in the areas of job performance, integrity problems, and abuse of disability status. Modest correlations were observed for all domains; however, predictive validity was moderated by defensive response style, with greater predictive validity observed among less defensive responders. These results suggest that the PAI's full scales and subscales are able to predict law enforcement officers' performance, but their utility is appreciably improved when taken in the context of indicators of defensive responding.

Predicting Document Retrieval System Performance: An Expected Precision Measure.

Science.gov (United States)

Losee, Robert M., Jr.

1987-01-01

Describes an expected precision (EP) measure designed to predict document retrieval performance. Highlights include decision theoretic models; precision and recall as measures of system performance; EP graphs; relevance feedback; and computing the retrieval status value of a document for two models, the Binary Independent Model and the Two Poisson…

High Performance Computing Modernization Program Kerberos Throughput Test Report

Science.gov (United States)

2017-10-26

Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/5524--17-9751 High Performance Computing Modernization Program Kerberos Throughput Test ...NUMBER 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 2. REPORT TYPE1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 6. AUTHOR(S) 8. PERFORMING...PAGE 18. NUMBER OF PAGES 17. LIMITATION OF ABSTRACT High Performance Computing Modernization Program Kerberos Throughput Test Report Daniel G. Gdula* and

The Waste Isolation Pilot Plant Performance Assessment Program

International Nuclear Information System (INIS)

Myers, J.; Coons, W.E.; Eastmond, R.; Morse, J.; Chakrabarti, S.; Zurkoff, J.; Colton, I.D.; Banz, I.

1986-01-01

The Waste Isolation Pilot Plant (WIPP) Performance Assessment Program involves a comprehensive analysis of the WIPP project with respect to the recently finalized Environmental Protection Agency regulations regarding the long-term geologic isolation of radioactive wastes. The performance assessment brings together the results of site characterization, underground experimental, and environmental studies into a rigorous determination of the performance of WIPP as a disposal system for transuranic radioactive waste. The Program consists of scenario development, geochemical, hydrologic, and thermomechanical support analyses and will address the specific containment and individual protection requirements specified in 40 CFR 191 sub-part B. Calculated releases from these interrelated analyses will be reported as an overall probability distribution of cumulative release resulting from all processes and events occurring over the 10,000 year post-closure period. In addition, results will include any doses to the public resulting from natural processes occurring over the 1,000 year post-closure period. The overall plan for the WIPP Performance Assessment Program is presented along with approaches to issues specific to the WIPP project

Intel Xeon Phi coprocessor high performance programming

CERN Document Server

Jeffers, James

2013-01-01

Authors Jim Jeffers and James Reinders spent two years helping educate customers about the prototype and pre-production hardware before Intel introduced the first Intel Xeon Phi coprocessor. They have distilled their own experiences coupled with insights from many expert customers, Intel Field Engineers, Application Engineers and Technical Consulting Engineers, to create this authoritative first book on the essentials of programming for this new architecture and these new products. This book is useful even before you ever touch a system with an Intel Xeon Phi coprocessor. To ensure that your applications run at maximum efficiency, the authors emphasize key techniques for programming any modern parallel computing system whether based on Intel Xeon processors, Intel Xeon Phi coprocessors, or other high performance microprocessors. Applying these techniques will generally increase your program performance on any system, and better prepare you for Intel Xeon Phi coprocessors and the Intel MIC architecture. It off...

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.

Performance Assessment National Review Group

International Nuclear Information System (INIS)

Lieberman, J.A.; Davis, S.N.; Harleman, D.R.F.

1985-02-01

Performance assessment involves predicting the potential radiological impact of a nuclear waste disposal system, taking into account all of the natural and engineered components of the system. It includes the analysis and evaluation of predicted system and component performance to determine compliance with regulatory performance criteria. In the context of the nuclear waste management program, performance assessment has five major purposes: to assist in the evaluation and selection of repository sites; to guide the research, development, and testing programs; to assist in the evaluation of repository designs; to assist in the evaluation of the design and performance of engineered barriers; and to show regulatory compliance and support repository licensing. Current performance assessment methodologies are still in the developmental stage. Only the simplest of bounding calculations have produced quantitative predictions of radionuclide releases. The methodologies require considerable extension and validation before they can provide answers suitable for project decisions and licensing. 135 refs., 9 figs., 1 tab

Evaluation of Design & Analysis Code, CACTUS, for Predicting Crossflow Hydrokinetic Turbine Performance

Energy Technology Data Exchange (ETDEWEB)

Wosnik, Martin [Univ. of New Hampshire, Durham, NH (United States). Center for Ocean Renewable Energy; Bachant, Pete [Univ. of New Hampshire, Durham, NH (United States). Center for Ocean Renewable Energy; Neary, Vincent Sinclair [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Murphy, Andrew W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-09-01

CACTUS, developed by Sandia National Laboratories, is an open-source code for the design and analysis of wind and hydrokinetic turbines. While it has undergone extensive validation for both vertical axis and horizontal axis wind turbines, and it has been demonstrated to accurately predict the performance of horizontal (axial-flow) hydrokinetic turbines, its ability to predict the performance of crossflow hydrokinetic turbines has yet to be tested. The present study addresses this problem by comparing the predicted performance curves derived from CACTUS simulations of the U.S. Department of Energy’s 1:6 scale reference model crossflow turbine to those derived by experimental measurements in a tow tank using the same model turbine at the University of New Hampshire. It shows that CACTUS cannot accurately predict the performance of this crossflow turbine, raising concerns on its application to crossflow hydrokinetic turbines generally. The lack of quality data on NACA 0021 foil aerodynamic (hydrodynamic) characteristics over the wide range of angles of attack (AoA) and Reynolds numbers is identified as the main cause for poor model prediction. A comparison of several different NACA 0021 foil data sources, derived using both physical and numerical modeling experiments, indicates significant discrepancies at the high AoA experienced by foils on crossflow turbines. Users of CACTUS for crossflow hydrokinetic turbines are, therefore, advised to limit its application to higher tip speed ratios (lower AoA), and to carefully verify the reliability and accuracy of their foil data. Accurate empirical data on the aerodynamic characteristics of the foil is the greatest limitation to predicting performance for crossflow turbines with semi-empirical models like CACTUS. Future improvements of CACTUS for crossflow turbine performance prediction will require the development of accurate foil aerodynamic characteristic data sets within the appropriate ranges of Reynolds numbers and AoA.

Effects of Training and Feedback on Accuracy of Predicting Rectosigmoid Neoplastic Lesions and Selection of Surveillance Intervals by Endoscopists Performing Optical Diagnosis of Diminutive Polyps.

Science.gov (United States)

Vleugels, Jasper L A; Dijkgraaf, Marcel G W; Hazewinkel, Yark; Wanders, Linda K; Fockens, Paul; Dekker, Evelien

2018-05-01

Real-time differentiation of diminutive polyps (1-5 mm) during endoscopy could replace histopathology analysis. According to guidelines, implementation of optical diagnosis into routine practice would require it to identify rectosigmoid neoplastic lesions with a negative predictive value (NPV) of more than 90%, using histologic findings as a reference, and agreement with histology-based surveillance intervals for more than 90% of cases. We performed a prospective study with 39 endoscopists accredited to perform colonoscopies on participants with positive results from fecal immunochemical tests in the Bowel Cancer Screening Program at 13 centers in the Netherlands. Endoscopists were trained in optical diagnosis using a validated module (Workgroup serrAted polypS and Polyposis). After meeting predefined performance thresholds in the training program, the endoscopists started a 1-year program (continuation phase) in which they performed narrow band imaging analyses during colonoscopies of participants in the screening program and predicted histological findings with confidence levels. The endoscopists were randomly assigned to groups that received feedback or no feedback on the accuracy of their predictions. Primary outcome measures were endoscopists' abilities to identify rectosigmoid neoplastic lesions (using histology as a reference) with NPVs of 90% or more, and selecting surveillance intervals that agreed with those determined by histology for at least 90% of cases. Of 39 endoscopists initially trained, 27 (69%) completed the training program. During the continuation phase, these 27 endoscopists performed 3144 colonoscopies in which 4504 diminutive polyps were removed. The endoscopists identified neoplastic lesions with a pooled NPV of 90.8% (95% confidence interval 88.6-92.6); their proposed surveillance intervals agreed with those determined by histologic analysis for 95.4% of cases (95% confidence interval 94.0-96.6). Findings did not differ between the group

Performance reliability prediction for thermal aging based on kalman filtering

International Nuclear Information System (INIS)

Ren Shuhong; Wen Zhenhua; Xue Fei; Zhao Wensheng

2015-01-01

The performance reliability of the nuclear power plant main pipeline that failed due to thermal aging was studied by the performance degradation theory. Firstly, through the data obtained from the accelerated thermal aging experiments, the degradation process of the impact strength and fracture toughness of austenitic stainless steel material of the main pipeline was analyzed. The time-varying performance degradation model based on the state space method was built, and the performance trends were predicted by using Kalman filtering. Then, the multi-parameter and real-time performance reliability prediction model for the main pipeline thermal aging was developed by considering the correlation between the impact properties and fracture toughness, and by using the stochastic process theory. Thus, the thermal aging performance reliability and reliability life of the main pipeline with multi-parameter were obtained, which provides the scientific basis for the optimization management of the aging maintenance decision making for nuclear power plant main pipelines. (authors)

The prediction of swimming performance in competition from behavioral information.

Science.gov (United States)

Rushall, B S; Leet, D

1979-06-01

The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.

Numerical modeling capabilities to predict repository performance

International Nuclear Information System (INIS)

1979-09-01

This report presents a summary of current numerical modeling capabilities that are applicable to the design and performance evaluation of underground repositories for the storage of nuclear waste. The report includes codes that are available in-house, within Golder Associates and Lawrence Livermore Laboratories; as well as those that are generally available within the industry and universities. The first listing of programs are in-house codes in the subject areas of hydrology, solute transport, thermal and mechanical stress analysis, and structural geology. The second listing of programs are divided by subject into the following categories: site selection, structural geology, mine structural design, mine ventilation, hydrology, and mine design/construction/operation. These programs are not specifically designed for use in the design and evaluation of an underground repository for nuclear waste; but several or most of them may be so used

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.

Development of the Performance Confirmation Program at Yucca Mountain, Nevada

International Nuclear Information System (INIS)

G.D. LeCain; D. Barr; D. Weaver; R. Snell; S.W. Goodin; F.D. Hansen

2006-01-01

The Yucca Mountain Performance Confirmation program consists of tests, monitoring activities, experiments, and analyses to evaluate the adequacy of assumptions, data, and analyses that form the basis of the conceptual and numerical models of flow and transport associated with a proposed radioactive waste repository at Yucca Mountain, Nevada. The Performance Confirmation program uses an eight-stage risk-informed, performance-based approach. Selection of the Performance Confirmation activities (a parameter and a test method) for inclusion in the Performance Confirmation program was done using a risk-informed performance-based decision analysis. The result of this analysis and review was a Performance Confirmation base portfolio that consists of 20 activities. The 20 Performance Confirmation activities include geologic, hydrologic, and construction/engineering testing. Several of the activities were initiated during site characterization and are ongoing. Others activities will commence during construction and/or post emplacement and will continue until repository closure

Computational Fluid Dynamics and Building Energy Performance Simulation

DEFF Research Database (Denmark)

Nielsen, Peter Vilhelm; Tryggvason, T.

1998-01-01

An interconnection between a building energy performance simulation program and a Computational Fluid Dynamics program (CFD) for room air distribution will be introduced for improvement of the predictions of both the energy consumption and the indoor environment. The building energy performance...... simulation program requires a detailed description of the energy flow in the air movement which can be obtained by a CFD program. The paper describes an energy consumption calculation in a large building, where the building energy simulation program is modified by CFD predictions of the flow between three...... zones connected by open areas with pressure and buoyancy driven air flow. The two programs are interconnected in an iterative procedure. The paper shows also an evaluation of the air quality in the main area of the buildings based on CFD predictions. It is shown that an interconnection between a CFD...

Essays on predictability of emerging markets growth and financial performance

OpenAIRE

Banegas, Maria Ayelen

2011-01-01

This dissertation seeks to better understand the underlying factors driving financial performance and economic activity in international markets. The first chapter "Predictability of Growth in Emerging Markets: Information in Financial Aggregates" tests for predictability of output growth in a panel of twenty-two emerging market economies. I use pooled panel data methods that control for endogeneity and persistence in the predictor variables to test the predictive power of a large set of fina...

The joint effects of personality and workplace social exchange relationships in predicting task performance and citizenship performance.

Science.gov (United States)

Kamdar, Dishan; Van Dyne, Linn

2007-09-01

This field study examines the joint effects of social exchange relationships at work (leader-member exchange and team-member exchange) and employee personality (conscientiousness and agreeableness) in predicting task performance and citizenship performance. Consistent with trait activation theory, matched data on 230 employees, their coworkers, and their supervisors demonstrated interactions in which high quality social exchange relationships weakened the positive relationships between personality and performance. Results demonstrate the benefits of consonant predictions in which predictors and outcomes are matched on the basis of specific targets. We discuss theoretical and practical implications. (c) 2007 APA.

STUDENT ACADEMIC PERFORMANCE PREDICTION USING SUPPORT VECTOR MACHINE

OpenAIRE

S.A. Oloruntoba1 ,J.L.Akinode2

2017-01-01

This paper investigates the relationship between students' preadmission academic profile and final academic performance. Data Sample of students in one of the Federal Polytechnic in south West part of Nigeria was used. The preadmission academic profile used for this study is the 'O' level grades(terminal high school results).The academic performance is defined using student's Grade Point Average(GPA). This research focused on using data mining technique to develop a model for predicting stude...

Prediction of Rowing Ergometer Performance from Functional Anaerobic Power, Strength and Anthropometric Components

Directory of Open Access Journals (Sweden)

Akça Firat

2014-07-01

Full Text Available The aim of this research was to develop different regression models to predict 2000 m rowing ergometer performance with the use of anthropometric, anaerobic and strength variables and to determine how precisely the prediction models constituted by different variables predict performance, when conducted together in the same equation or individually. 38 male collegiate rowers (20.17 ± 1.22 years participated in this study. Anthropometric, strength, 2000 m maximal rowing ergometer and rowing anaerobic power tests were applied. Multiple linear regression procedures were employed in SPSS 16 to constitute five different regression formulas using a different group of variables. The reliability of the regression models was expressed by R2 and the standard error of estimate (SEE. Relationships of all parameters with performance were investigated through Pearson correlation coefficients. The prediction model using a combination of anaerobic, strength and anthropometric variables was found to be the most reliable equation to predict 2000 m rowing ergometer performance (R2 = 0.92, SEE= 3.11 s. Besides, the equation that used rowing anaerobic and strength test results also provided a reliable prediction (R2 = 0.85, SEE= 4.27 s. As a conclusion, it seems clear that physiological determinants which are affected by anaerobic energy pathways should also get involved in the processes and models used for performance prediction and talent identification in rowing.

Enabling Technology for Monitoring & Predicting Gas Turbine Health & Performance in IGCC Powerplants

Energy Technology Data Exchange (ETDEWEB)

Kenneth A. Yackly

2005-12-01

The ''Enabling & Information Technology To Increase RAM for Advanced Powerplants'' program, by DOE request, was re-directed, de-scoped to two tasks, shortened to a 2-year period of performance, and refocused to develop, validate and accelerate the commercial use of enabling materials technologies and sensors for coal/IGCC powerplants. The new program was re-titled ''Enabling Technology for Monitoring & Predicting Gas Turbine Health & Performance in IGCC Powerplants''. This final report summarizes the work accomplished from March 1, 2003 to March 31, 2004 on the four original tasks, and the work accomplished from April 1, 2004 to July 30, 2005 on the two re-directed tasks. The program Tasks are summarized below: Task 1--IGCC Environmental Impact on high Temperature Materials: The first task was refocused to address IGCC environmental impacts on high temperature materials used in gas turbines. This task screened material performance and quantified the effects of high temperature erosion and corrosion of hot gas path materials in coal/IGCC applications. The materials of interest included those in current service as well as advanced, high-performance alloys and coatings. Task 2--Material In-Service Health Monitoring: The second task was reduced in scope to demonstrate new technologies to determine the inservice health of advanced technology coal/IGCC powerplants. The task focused on two critical sensing needs for advanced coal/IGCC gas turbines: (1) Fuel Quality Sensor to rapidly determine the fuel heating value for more precise control of the gas turbine, and detection of fuel impurities that could lead to rapid component degradation. (2) Infra-Red Pyrometer to continuously measure the temperature of gas turbine buckets, nozzles, and combustor hardware. Task 3--Advanced Methods for Combustion Monitoring and Control: The third task was originally to develop and validate advanced monitoring and control methods for coal/IGCC gas

Mastery and Performance Goals Predict Epistemic and Relational Conflict Regulation

Science.gov (United States)

Darnon, Celine; Muller, Dominique; Schrager, Sheree M.; Pannuzzo, Nelly; Butera, Fabrizio

2006-01-01

The present research examines whether mastery and performance goals predict different ways of reacting to a sociocognitive conflict with another person over materials to be learned, an issue not yet addressed by the achievement goal literature. Results from 2 studies showed that mastery goals predicted epistemic conflict regulation (a conflict…

Performance Prediction of Centrifugal Compressor for Drop-In Testing Using Low Global Warming Potential Alternative Refrigerants and Performance Test Codes

Directory of Open Access Journals (Sweden)

Joo Hoon Park

2017-12-01

Full Text Available As environmental regulations to stall global warming are strengthened around the world, studies using newly developed low global warming potential (GWP alternative refrigerants are increasing. In this study, substitute refrigerants, R-1234ze (E and R-1233zd (E, were used in the centrifugal compressor of an R-134a 2-stage centrifugal chiller with a fixed rotational speed. Performance predictions and thermodynamic analyses of the centrifugal compressor for drop-in testing were performed. A performance prediction method based on the existing ASME PTC-10 performance test code was proposed. The proposed method yielded the expected operating area and operating point of the centrifugal compressor with alternative refrigerants. The thermodynamic performance of the first and second stages of the centrifugal compressor was calculated as the polytropic state. To verify the suitability of the proposed method, the drop-in test results of the two alternative refrigerants were compared. The predicted operating range based on the permissible deviation of ASME PTC-10 confirmed that the temperature difference was very small at the same efficiency. Because the drop-in test of R-1234ze (E was performed within the expected operating range, the centrifugal compressor using R-1234ze (E is considered well predicted. However, the predictions of the operating point and operating range of R-1233zd (E were lower than those of the drop-in test. The proposed performance prediction method will assist in understanding thermodynamic performance at the expected operating point and operating area of a centrifugal compressor using alternative gases based on limited design and structure information.

Real-time Tsunami Inundation Prediction Using High Performance Computers

Science.gov (United States)

Oishi, Y.; Imamura, F.; Sugawara, D.

2014-12-01

Recently off-shore tsunami observation stations based on cabled ocean bottom pressure gauges are actively being deployed especially in Japan. These cabled systems are designed to provide real-time tsunami data before tsunamis reach coastlines for disaster mitigation purposes. To receive real benefits of these observations, real-time analysis techniques to make an effective use of these data are necessary. A representative study was made by Tsushima et al. (2009) that proposed a method to provide instant tsunami source prediction based on achieving tsunami waveform data. As time passes, the prediction is improved by using updated waveform data. After a tsunami source is predicted, tsunami waveforms are synthesized from pre-computed tsunami Green functions of linear long wave equations. Tsushima et al. (2014) updated the method by combining the tsunami waveform inversion with an instant inversion of coseismic crustal deformation and improved the prediction accuracy and speed in the early stages. For disaster mitigation purposes, real-time predictions of tsunami inundation are also important. In this study, we discuss the possibility of real-time tsunami inundation predictions, which require faster-than-real-time tsunami inundation simulation in addition to instant tsunami source analysis. Although the computational amount is large to solve non-linear shallow water equations for inundation predictions, it has become executable through the recent developments of high performance computing technologies. We conducted parallel computations of tsunami inundation and achieved 6.0 TFLOPS by using 19,000 CPU cores. We employed a leap-frog finite difference method with nested staggered grids of which resolution range from 405 m to 5 m. The resolution ratio of each nested domain was 1/3. Total number of grid points were 13 million, and the time step was 0.1 seconds. Tsunami sources of 2011 Tohoku-oki earthquake were tested. The inundation prediction up to 2 hours after the

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

IAMBUS, a computer code for the design and performance prediction of fast breeder fuel rods

International Nuclear Information System (INIS)

Toebbe, H.

1990-05-01

IAMBUS is a computer code for the thermal and mechanical design, in-pile performance prediction and post-irradiation analysis of fast breeder fuel rods. The code deals with steady, non-steady and transient operating conditions and enables to predict in-pile behavior of fuel rods in power reactors as well as in experimental rigs. Great effort went into the development of a realistic account of non-steady fuel rod operating conditions. The main emphasis is placed on characterizing the mechanical interaction taking place between the cladding tube and the fuel as a result of contact pressure and friction forces, with due consideration of axial and radial crack configuration within the fuel as well as the gradual transition at the elastic/plastic interface in respect to fuel behavior. IAMBUS can be readily adapted to various fuel and cladding materials. The specific models and material correlations of the reference version deal with the actual in-pile behavior and physical properties of the KNK II and SNR 300 related fuel rod design, confirmed by comparison of the fuel performance model with post-irradiation data. The comparison comprises steady, non-steady and transient irradiation experiments within the German/Belgian fuel rod irradiation program. The code is further validated by comparison of model predictions with post-irradiation data of standard fuel and breeder rods of Phenix and PFR as well as selected LWR fuel rods in non-steady operating conditions

Axisymmetric thrust-vectoring nozzle performance prediction

International Nuclear Information System (INIS)

Wilson, E. A.; Adler, D.; Bar-Yoseph, P.Z

1998-01-01

Throat-hinged geometrically variable converging-diverging thrust-vectoring nozzles directly affect the jet flow geometry and rotation angle at the nozzle exit as a function of the nozzle geometry, the nozzle pressure ratio and flight velocity. The consideration of nozzle divergence in the effective-geometric nozzle relation is theoretically considered here for the first time. In this study, an explicit calculation procedure is presented as a function of nozzle geometry at constant nozzle pressure ratio, zero velocity and altitude, and compared with experimental results in a civil thrust-vectoring scenario. This procedure may be used in dynamic thrust-vectoring nozzle design performance predictions or analysis for civil and military nozzles as well as in the definition of initial jet flow conditions in future numerical VSTOL/TV jet performance studies

Predicting BCI subject performance using probabilistic spatio-temporal filters.

Directory of Open Access Journals (Sweden)

Heung-Il Suk

Full Text Available Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms-a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects' performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject 'prototypes' (like μ - or β -rhythm type subjects or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

Stepwise-refinement for performance: a methodology for many-core programming

NARCIS (Netherlands)

Hijma, P.; van Nieuwpoort, R.V.; Jacobs, C.J.H.; Bal, H.E.

2015-01-01

Many-core hardware is targeted specifically at obtaining high performance, but reaching high performance is often challenging because hardware-specific details have to be taken into account. Although there are many programming systems that try to alleviate many-core programming, some providing a

Genome-Wide Polygenic Scores Predict Reading Performance Throughout the School Years.

Science.gov (United States)

Selzam, Saskia; Dale, Philip S; Wagner, Richard K; DeFries, John C; Cederlöf, Martin; O'Reilly, Paul F; Krapohl, Eva; Plomin, Robert

2017-07-04

It is now possible to create individual-specific genetic scores, called genome-wide polygenic scores (GPS). We used a GPS for years of education ( EduYears ) to predict reading performance assessed at UK National Curriculum Key Stages 1 (age 7), 2 (age 12) and 3 (age 14) and on reading tests administered at ages 7 and 12 in a UK sample of 5,825 unrelated individuals. EduYears GPS accounts for up to 5% of the variance in reading performance at age 14. GPS predictions remained significant after accounting for general cognitive ability and family socioeconomic status. Reading performance of children in the lowest and highest 12.5% of the EduYears GPS distribution differed by a mean growth in reading ability of approximately two school years. It seems certain that polygenic scores will be used to predict strengths and weaknesses in education.

Chemistry technician performance evaluation program Palo Verde Nuclear Generating Station

International Nuclear Information System (INIS)

Shawver, J.M.

1992-01-01

The Arizona Nuclear Power Project (ANPP), a three-reactor site located 50 miles west of Phoenix, Arizona, has developed and implemented a program for evaluating individual chemistry technician analytical performance on a routine basis. About 45 chemistry technicians are employed at the site, 15 at each operating unit. The technicians routinely perform trace level analyses for impurities of concern to PWRs. Each month a set of blind samples is provided by an outside vendor. The blind samples contain 16 parameters which are matrixed to approximate the PWR's primary and secondary cycles. Nine technicians receive the samples, three from each operating unit, and perform the required analyses. Acceptance criteria for successful performance on the blind parameters is based on the values found in the Institute of Nuclear Power Operations (INPO) Document 83-016, Revision 2, August 1989, Chemistry Quality Control Program. The goal of the program is to have each technician demonstrate acceptable performance on each of 16 analytical parameters. On completion of each monthly set, a summary report of all of the analytical results for the sample set is prepared. From the summary report, analytical bias can be detected, technician performance is documented, and overall laboratory performance can be evaluated. The program has been very successful at satisfying the INPO requirement that the analytical performance of each individual technician should be checked on at least a six-month frequency for all important parameters measured. This paper describes the program as implemented at the Palo Verde Nuclear Generating Station and provides a summary report and trend and bias graphs for illustrative purposes

Predicting Academic Performance Based on Students' Blog and Microblog Posts

NARCIS (Netherlands)

Dascalu, Mihai; Popescu, Elvira; Becheru, Alexandru; Crossley, Scott; Trausan-Matu, Stefan

2016-01-01

This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in

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.

Occupational-Specific Strength Predicts Astronaut-Related Task Performance in a Weighted Suit.

Science.gov (United States)

Taylor, Andrew; Kotarsky, Christopher J; Bond, Colin W; Hackney, Kyle J

2018-01-01

Future space missions beyond low Earth orbit will require deconditioned astronauts to perform occupationally relevant tasks within a planetary spacesuit. The prediction of time-to-completion (TTC) of astronaut tasks will be critical for crew safety, autonomous operations, and mission success. This exploratory study determined if the addition of task-specific strength testing to current standard lower body testing would enhance the prediction of TTC in a 1-G test battery. Eight healthy participants completed NASA lower body strength tests, occupationally specific strength tests, and performed six task simulations (hand drilling, construction wrenching, incline walking, collecting weighted samples, and dragging an unresponsive crewmember to safety) in a 48-kg weighted suit. The TTC for each task was recorded and summed to obtain a total TTC for the test battery. Linear regression was used to predict total TTC with two models: 1) NASA lower body strength tests; and 2) NASA lower body strength tests + occupationally specific strength tests. Total TTC of the test battery ranged from 20.2-44.5 min. The lower body strength test alone accounted for 61% of the variability in total TTC. The addition of hand drilling and wrenching strength tests accounted for 99% of the variability in total TTC. Adding occupationally specific strength tests (hand drilling and wrenching) to standard lower body strength tests successfully predicted total TTC in a performance test battery within a weighted suit. Future research should couple these strength tests with higher fidelity task simulations to determine the utility and efficacy of task performance prediction.Taylor A, Kotarsky CJ, Bond CW, Hackney KJ. Occupational-specific strength predicts astronaut-related task performance in a weighted suit. Aerosp Med Hum Perform. 2018; 89(1):58-62.

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine

Science.gov (United States)

Ramakrishnan, Sridhar; Wesensten, Nancy J.; Kamimori, Gary H.; Moon, James E.; Balkin, Thomas J.; Reifman, Jaques

2016-01-01

Study Objectives: Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. Methods: We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). Results: The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. Conclusions: The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. Citation: Ramakrishnan S, Wesensten NJ, Kamimori GH, Moon JE, Balkin TJ, Reifman J. A unified model of performance for predicting the effects of sleep and caffeine. SLEEP 2016;39(10):1827–1841. PMID:27397562

Calibration between Undergraduate Students' Prediction of and Actual Performance: The Role of Gender and Performance Attributions

Science.gov (United States)

Gutierrez, Antonio P.; Price, Addison F.

2017-01-01

This study investigated changes in male and female students' prediction and postdiction calibration accuracy and bias scores, and the predictive effects of explanatory styles on these variables beyond gender. Seventy undergraduate students rated their confidence in performance before and after a 40-item exam. There was an improvement in students'…

On predicting student performance using low-rank matrix factorization techniques

DEFF Research Database (Denmark)

Lorenzen, Stephan Sloth; Pham, Dang Ninh; Alstrup, Stephen

2017-01-01

Predicting the score of a student is one of the important problems in educational data mining. The scores given by an individual student reflect how a student understands and applies the knowledge conveyed in class. A reliable performance prediction enables teachers to identify weak students...... that require remedial support, generate adaptive hints, and improve the learning of students. This work focuses on predicting the score of students in the quiz system of the Clio Online learning platform, the largest Danish supplier of online learning materials, covering 90% of Danish elementary schools...... and the current version of the data set is very sparse, the very low-rank approximation can capture enough information. This means that the simple baseline approach achieves similar performance compared to other advanced methods. In future work, we will restrict the quiz data set, e.g. only including quizzes...

Predicting the seismic performance of typical R/C healthcare facilities: emphasis on hospitals

Science.gov (United States)

Bilgin, Huseyin; Frangu, Idlir

2017-09-01

Reinforced concrete (RC) type of buildings constitutes an important part of the current building stock in earthquake prone countries such as Albania. Seismic response of structures during a severe earthquake plays a vital role in the extent of structural damage and resulting injuries and losses. In this context, this study evaluates the expected performance of a five-story RC healthcare facility, representative of common practice in Albania, designed according to older codes. The design was based on the code requirements used in this region during the mid-1980s. Non-linear static and dynamic time history analyses were conducted on the structural model using the Zeus NL computer program. The dynamic time history analysis was conducted with a set of ground motions from real earthquakes. The building responses were estimated in global levels. FEMA 356 criteria were used to predict the seismic performance of the building. The structural response measures such as capacity curve and inter-story drift under the set of ground motions and pushover analyses results were compared and detailed seismic performance assessment was done. The main aim of this study is considering the application and methodology for the earthquake performance assessment of existing buildings. The seismic performance of the structural model varied significantly under different ground motions. Results indicate that case study building exhibit inadequate seismic performance under different seismic excitations. In addition, reasons for the poor performance of the building is discussed.

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.

Multi-Language Programming Environments for High Performance Java Computing

Directory of Open Access Journals (Sweden)

Vladimir Getov

1999-01-01

Full Text Available Recent developments in processor capabilities, software tools, programming languages and programming paradigms have brought about new approaches to high performance computing. A steadfast component of this dynamic evolution has been the scientific community’s reliance on established scientific packages. As a consequence, programmers of high‐performance applications are reluctant to embrace evolving languages such as Java. This paper describes the Java‐to‐C Interface (JCI tool which provides application programmers wishing to use Java with immediate accessibility to existing scientific packages. The JCI tool also facilitates rapid development and reuse of existing code. These benefits are provided at minimal cost to the programmer. While beneficial to the programmer, the additional advantages of mixed‐language programming in terms of application performance and portability are addressed in detail within the context of this paper. In addition, we discuss how the JCI tool is complementing other ongoing projects such as IBM’s High‐Performance Compiler for Java (HPCJ and IceT’s metacomputing environment.

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

Science.gov (United States)

Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J.; Dong, Bing; Huang, Yufei

2018-01-01

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures. PMID:29690601

Predicting energy performance of a net-zero energy building: A statistical approach

International Nuclear Information System (INIS)

Kneifel, Joshua; Webb, David

2016-01-01

Highlights: • A regression model is applied to actual energy data from a net-zero energy building. • The model is validated through a rigorous statistical analysis. • Comparisons are made between model predictions and those of a physics-based model. • The model is a viable baseline for evaluating future models from the energy data. - Abstract: Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid Climate Zone, and compares these

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

Computational Model-Based Prediction of Human Episodic Memory Performance Based on Eye Movements

Science.gov (United States)

Sato, Naoyuki; Yamaguchi, Yoko

Subjects' episodic memory performance is not simply reflected by eye movements. We use a ‘theta phase coding’ model of the hippocampus to predict subjects' memory performance from their eye movements. Results demonstrate the ability of the model to predict subjects' memory performance. These studies provide a novel approach to computational modeling in the human-machine interface.

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…

Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan

Directory of Open Access Journals (Sweden)

Senol Celik

Full Text Available ABSTRACT The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth and testicular (testicular length, scrotal length, and scrotal circumference measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1 and with interaction (MARS_2 terms. The superiority order in the predictive accuracy of the algorithms was found as CART > CHAID ≈ Exhaustive CHAID > MARS_2 > MARS_1 > RBF > MLP. Moreover, all tested algorithms provided a strong predictive accuracy for estimating body weight. However, MARS is the only algorithm that generated a prediction equation for body weight. Therefore, it is hoped that the available results might present a valuable contribution in terms of predicting body weight and describing the relationship between the body weight and body and testicular measurements in revealing breed standards and the conservation of indigenous gene sources for Mengali sheep breeding. Therefore, it will be possible to perform more profitable and productive sheep production. Use of data mining algorithms is useful for revealing the relationship between body weight and testicular traits in describing breed standards of Mengali sheep.

Multi-Language Programming Environments for High Performance Java Computing

OpenAIRE

Vladimir Getov; Paul Gray; Sava Mintchev; Vaidy Sunderam

1999-01-01

Recent developments in processor capabilities, software tools, programming languages and programming paradigms have brought about new approaches to high performance computing. A steadfast component of this dynamic evolution has been the scientific community’s reliance on established scientific packages. As a consequence, programmers of high‐performance applications are reluctant to embrace evolving languages such as Java. This paper describes the Java‐to‐C Interface (JCI) tool which provides ...

Do physiological measures predict selected CrossFit(®) benchmark performance?

Science.gov (United States)

Butcher, Scotty J; Neyedly, Tyler J; Horvey, Karla J; Benko, Chad R

2015-01-01

CrossFit(®) is a new but extremely popular method of exercise training and competition that involves constantly varied functional movements performed at high intensity. Despite the popularity of this training method, the physiological determinants of CrossFit performance have not yet been reported. The purpose of this study was to determine whether physiological and/or muscle strength measures could predict performance on three common CrossFit "Workouts of the Day" (WODs). Fourteen CrossFit Open or Regional athletes completed, on separate days, the WODs "Grace" (30 clean and jerks for time), "Fran" (three rounds of thrusters and pull-ups for 21, 15, and nine repetitions), and "Cindy" (20 minutes of rounds of five pull-ups, ten push-ups, and 15 bodyweight squats), as well as the "CrossFit Total" (1 repetition max [1RM] back squat, overhead press, and deadlift), maximal oxygen consumption (VO2max), and Wingate anaerobic power/capacity testing. Performance of Grace and Fran was related to whole-body strength (CrossFit Total) (r=-0.88 and -0.65, respectively) and anaerobic threshold (r=-0.61 and -0.53, respectively); however, whole-body strength was the only variable to survive the prediction regression for both of these WODs (R (2)=0.77 and 0.42, respectively). There were no significant associations or predictors for Cindy. CrossFit benchmark WOD performance cannot be predicted by VO2max, Wingate power/capacity, or either respiratory compensation or anaerobic thresholds. Of the data measured, only whole-body strength can partially explain performance on Grace and Fran, although anaerobic threshold also exhibited association with performance. Along with their typical training, CrossFit athletes should likely ensure an adequate level of strength and aerobic endurance to optimize performance on at least some benchmark WODs.

Do physiological measures predict selected CrossFit® benchmark performance?

Science.gov (United States)

Butcher, Scotty J; Neyedly, Tyler J; Horvey, Karla J; Benko, Chad R

2015-01-01

Purpose CrossFit® is a new but extremely popular method of exercise training and competition that involves constantly varied functional movements performed at high intensity. Despite the popularity of this training method, the physiological determinants of CrossFit performance have not yet been reported. The purpose of this study was to determine whether physiological and/or muscle strength measures could predict performance on three common CrossFit “Workouts of the Day” (WODs). Materials and methods Fourteen CrossFit Open or Regional athletes completed, on separate days, the WODs “Grace” (30 clean and jerks for time), “Fran” (three rounds of thrusters and pull-ups for 21, 15, and nine repetitions), and “Cindy” (20 minutes of rounds of five pull-ups, ten push-ups, and 15 bodyweight squats), as well as the “CrossFit Total” (1 repetition max [1RM] back squat, overhead press, and deadlift), maximal oxygen consumption (VO2max), and Wingate anaerobic power/capacity testing. Results Performance of Grace and Fran was related to whole-body strength (CrossFit Total) (r=−0.88 and −0.65, respectively) and anaerobic threshold (r=−0.61 and −0.53, respectively); however, whole-body strength was the only variable to survive the prediction regression for both of these WODs (R2=0.77 and 0.42, respectively). There were no significant associations or predictors for Cindy. Conclusion CrossFit benchmark WOD performance cannot be predicted by VO2max, Wingate power/capacity, or either respiratory compensation or anaerobic thresholds. Of the data measured, only whole-body strength can partially explain performance on Grace and Fran, although anaerobic threshold also exhibited association with performance. Along with their typical training, CrossFit athletes should likely ensure an adequate level of strength and aerobic endurance to optimize performance on at least some benchmark WODs. PMID:26261428

Programs to improve plant performance

International Nuclear Information System (INIS)

Felmus, N.L.

1987-01-01

Looking toward the 1990's, we see a period in which our industry will face the challenge of improving the performance of the nuclear plants which are built and operating. The skills and technology are at hand to make good plant performance a reality and we believe the time has come to use them to achieve that end. As reserve margins decline, utilities and their regulators will increasingly seek to tap the unexploited capacity tied up in plants operating below their optimum availability. This paper describes a number of the programs, plant improvements and operations improvements which can yield a significant increase in nuclear plant availability and capacity factor now and into the 1990's. (author)

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…

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

Science.gov (United States)

Ramakrishnan, Sridhar; Wesensten, Nancy J; Kamimori, Gary H; Moon, James E; Balkin, Thomas J; Reifman, Jaques

2016-10-01

Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. © 2016 Associated Professional Sleep Societies, LLC.

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.

Identification of New Tools to Predict Surgical Performance of Novices using a Plastic Surgery Simulator.

Science.gov (United States)

Kazan, Roy; Viezel-Mathieu, Alex; Cyr, Shantale; Hemmerling, Thomas M; Lin, Samuel J; Gilardino, Mirko S

2018-04-09

To identify new tools capable of predicting surgical performance of novices on an augmentation mammoplasty simulator. The pace of technical skills acquisition varies between residents and may necessitate more time than that allotted by residency training before reaching competence. Identifying applicants with superior innate technical abilities might shorten learning curves and the time to reach competence. The objective of this study is to identify new tools that could predict surgical performance of novices on a mammoplasty simulator. We recruited 14 medical students and recorded their performance in 2 skill-games: Mikado and Perplexus Epic, and in 2 video games: Star War Racer (Sony Playstation 3) and Super Monkey Ball 2 (Nintendo Wii). Then, each participant performed an augmentation mammoplasty procedure on a Mammoplasty Part-task Trainer, which allows the simulation of the essential steps of the procedure. The average age of participants was 25.4 years. Correlation studies showed significant association between Perplexus Epic, Star Wars Racer, Super Monkey Ball scores and the modified OSATS score with r s = 0.8491 (p 41 (p = 0.005), and r s = 0.7309 (p < 0.003), but not with the Mikado score r s = -0.0255 (p = 0.9). Linear regressions were strongest for Perplexus Epic and Super Monkey Ball scores with coefficients of determination of 0.59 and 0.55, respectively. A combined score (Perplexus/Super-Monkey-Ball) was computed and showed a significant correlation with the modified OSATS score having an r s = 0.8107 (p < 0.001) and R 2 = 0.75, respectively. This study identified a combination of skill games that correlated to better performance of novices on a surgical simulator. With refinement, such tools could serve to help screen plastic surgery applicants and identify those with higher surgical performance predictors. Copyright © 2018 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions

DEFF Research Database (Denmark)

Kim, Yohan; Sidney, John; Buus, Søren

2014-01-01

Background: It is important to accurately determine the performance of peptide: MHC binding predictions, as this enables users to compare and choose between different prediction methods and provides estimates of the expected error rate. Two common approaches to determine prediction performance...... are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last...

Goal orientation and work role performance: predicting adaptive and proactive work role performance through self-leadership strategies.

Science.gov (United States)

Marques-Quinteiro, Pedro; Curral, Luís Alberto

2012-01-01

This article explores the relationship between goal orientation, self-leadership dimensions, and adaptive and proactive work role performances. The authors hypothesize that learning orientation, in contrast to performance orientation, positively predicts proactive and adaptive work role performances and that this relationship is mediated by self-leadership behavior-focused strategies. It is posited that self-leadership natural reward strategies and thought pattern strategies are expected to moderate this relationship. Workers (N = 108) from a software company participated in this study. As expected, learning orientation did predict adaptive and proactive work role performance. Moreover, in the relationship between learning orientation and proactive work role performance through self-leadership behavior-focused strategies, a moderated mediation effect was found for self-leadership natural reward and thought pattern strategies. In the end, the authors discuss the results and implications are discussed and future research directions are proposed.

RTOD- RADIAL TURBINE OFF-DESIGN PERFORMANCE ANALYSIS

Science.gov (United States)

Glassman, A. J.

1994-01-01

The RTOD program was developed to accurately predict radial turbine off-design performance. The radial turbine has been used extensively in automotive turbochargers and aircraft auxiliary power units. It is now being given serious consideration for primary powerplant applications. In applications where the turbine will operate over a wide range of power settings, accurate off-design performance prediction is essential for a successful design. RTOD predictions have already illustrated a potential improvement in off-design performance offered by rotor back-sweep for high-work-factor radial turbines. RTOD can be used to analyze other potential performance enhancing design features. RTOD predicts the performance of a radial turbine (with or without rotor blade sweep) as a function of pressure ratio, speed, and stator setting. The program models the flow with the following: 1) stator viscous and trailing edge losses; 2) a vaneless space loss between the stator and the rotor; and 3) rotor incidence, viscous, trailing-edge, clearance, and disk friction losses. The stator and rotor viscous losses each represent the combined effects of profile, endwall, and secondary flow losses. The stator inlet and exit and the rotor inlet flows are modeled by a mean-line analysis, but a sector analysis is used at the rotor exit. The leakage flow through the clearance gap in a pivoting stator is also considered. User input includes gas properties, turbine geometry, and the stator and rotor viscous losses at a reference performance point. RTOD output includes predicted turbine performance over a specified operating range and any user selected flow parameters. The RTOD program is written in FORTRAN IV for batch execution and has been implemented on an IBM 370 series computer with a central memory requirement of approximately 100K of 8 bit bytes. The RTOD program was developed in 1983.

Predicting work Performance through selection interview ratings and Psychological assessment

Directory of Open Access Journals (Sweden)

Liziwe Nzama

2008-11-01

Full Text Available The aim of the study was to establish whether selection interviews used in conjunction with psychological assessments of personality traits and cognitive functioning contribute to predicting work performance. The sample consisted of 102 managers who were appointed recently in a retail organisation. The independent variables were selection interview ratings obtained on the basis of structured competency-based interview schedules by interviewing panels, fve broad dimensions of personality defned by the Five Factor Model as measured by the 15 Factor Questionnaire (15FQ+, and cognitive processing variables (current level of work, potential level of work, and 12 processing competencies measured by the Cognitive Process Profle (CPP. Work performance was measured through annual performance ratings that focused on measurable outputs of performance objectives. Only two predictor variables correlated statistically signifcantly with the criterion variable, namely interview ratings (r = 0.31 and CPP Verbal Abstraction (r = 0.34. Following multiple regression, only these variables contributed signifcantly to predicting work performance, but only 17.8% of the variance of the criterion was accounted for.

Predictive Performance Tuning of OpenACC Accelerated Applications

KAUST Repository

Siddiqui, Shahzeb; Feki, Saber

2014-01-01

, with the introduction of high level programming models such as OpenACC [1] and OpenMP 4.0 [2], these devices are becoming more accessible and practical to use by a larger scientific community. However, performance optimization of OpenACC accelerated applications usually

A Free Wake Numerical Simulation for Darrieus Vertical Axis Wind Turbine Performance Prediction

Science.gov (United States)

Belu, Radian

2010-11-01

In the last four decades, several aerodynamic prediction models have been formulated for the Darrieus wind turbine performances and characteristics. We can identified two families: stream-tube and vortex. The paper presents a simplified numerical techniques for simulating vertical axis wind turbine flow, based on the lifting line theory and a free vortex wake model, including dynamic stall effects for predicting the performances of a 3-D vertical axis wind turbine. A vortex model is used in which the wake is composed of trailing stream-wise and shedding span-wise vortices, whose strengths are equal to the change in the bound vortex strength as required by the Helmholz and Kelvin theorems. Performance parameters are computed by application of the Biot-Savart law along with the Kutta-Jukowski theorem and a semi-empirical stall model. We tested the developed model with an adaptation of the earlier multiple stream-tube performance prediction model for the Darrieus turbines. Predictions by using our method are shown to compare favorably with existing experimental data and the outputs of other numerical models. The method can predict accurately the local and global performances of a vertical axis wind turbine, and can be used in the design and optimization of wind turbines for built environment applications.

Camp Verde Adult Reading Program. Final Performance Report.

Science.gov (United States)

Maynard, David A.

This document begins with a four-page performance report describing how the Camp Verde Adult Reading Program site was relocated to the Community Center Complex, and the Town Council contracted directly with the Friends of the Camp Verde Library to provide for the requirements of the program. The U.S. Department of Education grant allowed the…

Predicting Arithmetical Achievement from Neuro-Psychological Performance: A Longitudinal Study.

Science.gov (United States)

Fayol, Michel; Barrouillet, Pierre; Marinthe, Catherine

1998-01-01

Assessed whether performances of 5- and 6-year olds in arithmetic tests can be predicted from their performances in neuropsychological tests. Participants completed neuropsychological, drawing, and arithmetic tests at 5 and 6 years of age. Findings at older age were correctly assumed by conclusions of first evaluation. (LBT)

Performance and wake predictions of HAWTs in wind farms

Energy Technology Data Exchange (ETDEWEB)

Leclerc, C.; Masson, C.; Paraschivoiu, I. [Ecole Polytechnique, Montreal (Canada)

1997-12-31

The present contribution proposes and describes a promising way towards performance prediction of an arbitrary array of turbines. It is based on the solution of the time-averaged, steady-state, incompressible Navier-Stokes equations with an appropriate turbulence closure model. The turbines are represented by distributions of momentum sources in the Navier-Stokes equations. In this paper, the applicability and viability of the proposed methodology is demonstrated using an axisymmetric implementation. The k-{epsilon} model has been chosen for the closure of the time-averaged, turbulent flow equations and the properties of the incident flow correspond to those of a neutral atmospheric boundary layer. The proposed mathematical model is solved using a Control-Volume Finite Element Method (CVFEM). Detailed results have been obtained using the proposed method for an isolated wind turbine and for two turbines one behind another. In the case of an isolated turbine, accurate wake velocity deficit predictions are obtained and an increase in power due to atmospheric turbulence is found in agreement with measurements. In the case of two turbines, the proposed methodology provides an appropriate modelling of the wind-turbine wake and a realistic prediction of the performance degradation of the downstream turbine.

What predicts performance during clinical psychology training?

OpenAIRE

Scior, Katrina; Bradley, Caroline E; Potts, Henry W W; Woolf, Katherine; de C Williams, Amanda C

2013-01-01

Objectives While the question of who is likely to be selected for clinical psychology training has been studied, evidence on performance during training is scant. This study explored data from seven consecutive intakes of the UK's largest clinical psychology training course, aiming to identify what factors predict better or poorer outcomes. Design Longitudinal cross-sectional study using prospective and retrospective data. Method Characteristics at application were analysed in relation to a r...

Predicting Performance on MOOC Assessments using Multi-Regression Models

OpenAIRE

Ren, Zhiyun; Rangwala, Huzefa; Johri, Aditya

2016-01-01

The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a stu- dent may achieve on a given grade-related assessment based on information, considered as prior performance or prior ac- tivity in the course. We develop a personalized linear mul- tiple regression (PLMR) model to predict the grade for a student, prior to attempt...

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.

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

International Nuclear Information System (INIS)

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

1983-01-01

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

Ski jump takeoff performance predictions for a mixed-flow, remote-lift STOVL aircraft

Science.gov (United States)

Birckelbaw, Lourdes G.

1992-01-01

A ski jump model was developed to predict ski jump takeoff performance for a short takeoff and vertical landing (STOVL) aircraft. The objective was to verify the model with results from a piloted simulation of a mixed flow, remote lift STOVL aircraft. The prediction model is discussed. The predicted results are compared with the piloted simulation results. The ski jump model can be utilized for basic research of other thrust vectoring STOVL aircraft performing a ski jump takeoff.

The Search Performance Evaluation and Prediction in Exploratory Search

OpenAIRE

LIU, FEI

2016-01-01

The exploratory search for complex search tasks requires an effective search behavior model to evaluate and predict user search performance. Few studies have investigated the relationship between user search behavior and search performance in exploratory search. This research adopts a mixed approach combining search system development, user search experiment, search query log analysis, and multivariate regression analysis to resolve the knowledge gap. Through this study, it is shown that expl...

Predictive Bias and Sensitivity in NRC Fuel Performance Codes

Energy Technology Data Exchange (ETDEWEB)

Geelhood, Kenneth J.; Luscher, Walter G.; Senor, David J.; Cunningham, Mitchel E.; Lanning, Donald D.; Adkins, Harold E.

2009-10-01

The latest versions of the fuel performance codes, FRAPCON-3 and FRAPTRAN were examined to determine if the codes are intrinsically conservative. Each individual model and type of code prediction was examined and compared to the data that was used to develop the model. In addition, a brief literature search was performed to determine if more recent data have become available since the original model development for model comparison.

Further developments in performance prediction techniques of adiabatic diesel engines

Energy Technology Data Exchange (ETDEWEB)

Rasihhan, Y

1990-01-01

The engine cycle simulation program 'SPICE', developed at Bath University, has been used extensively for insulated diesel engine research. The present study introduces more comprehensive engine heat transfer models thus enabling us to study the insulated engine heat transfer and performance characteristics in more detail. The new version of 'SPICE' separates the gas to wall heat transfer into two parts, convective and radiative. For this purpose, a detailed radiative heat transfer model which considers both the flame (gas and soot) and wall to wall radiative heat transfer is written. The previous engine resistance model is refined and replaced by a more detailed resistance model which considers piston-liner conduction heat transfer and 2-D heat flow in the liner. The wall surface temperature swing is also included in the engine heat transfer calculations which is quite significant in low conductivity ceramic insulated engines. A 1-D finite difference model is written for the transient heat transfer region of the wall and linked to the engine resistance model. This new version of 'SPICE' is used to predict the insulated engine heat transfer and performance for the experimental Petter PH1W engine for various insulation levels and schemes. An answer to the controversy of increase in engine heat loss with insulation is looked for. The effect of wall deposits on engine heat transfer and its significance for the insulated engine is highlighted. (Author).

Predicting Story Goodness Performance from Cognitive Measures Following Traumatic Brain Injury

Science.gov (United States)

Le, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-01-01

Purpose: This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Le, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. Method: One hundred…

Tuberculosis control program in the municipal context: performance evaluation

Directory of Open Access Journals (Sweden)

Tiemi Arakawa

Full Text Available ABSTRACT OBJECTIVE The objective of this study is to evaluate the performance of the Tuberculosis Control Program in municipalities of the State of São Paulo. METHODS This is a program evaluation research, with ecological design, which uses three non-hierarchical groups of the municipalities of the State of São Paulo according to their performance in relation to operational indicators. We have selected 195 municipalities with at least five new cases of tuberculosis notified in the Notification System of the State of São Paulo and with 20,000 inhabitants or more in 2010. The multiple correspondence analysis was used to identify the association between the groups of different performances, the epidemiological and demographic characteristics, and the characteristics of the health systems of the municipalities. RESULTS The group with the worst performance showed the highest rates of abandonment (average [avg] = 10.4, standard deviation [sd] = 9.4 and the lowest rates of supervision of Directly Observed Treatment (avg = 6.1, sd = 12.9, and it was associated with low incidence of tuberculosis, high tuberculosis and HIV, small population, high coverage of the Family Health Strategy/Program of Community Health Agents, and being located on the countryside. The group with the best performance presented the highest cure rate (avg = 83.7, sd = 10.5 and the highest rate of cases in Directly Observed Treatment (avg = 83.0, sd = 12.7; the group of regular performance showed regular results for outcome (avg cure = 79.8, sd = 13.2; abandonment avg = 9.5, sd = 8.3 and supervision of the Directly Observed Treatment (avg = 42.8, sd = 18.8. Large population, low coverage of the Family Health Strategy/Program of Community Health Agents, high incidence of tuberculosis and AIDS, and being located on the coast and in metropolitan areas were associated with these groups. CONCLUSIONS The findings highlight the importance of the Directly Observed Treatment in relation

The Performance Enhancement Group Program: Integrating Sport Psychology and Rehabilitation

Science.gov (United States)

Granito, Vincent J.; Hogan, Jeffery B.; Varnum, Lisa K.

1995-01-01

In an effort to improve the psychological health of the athlete who has sustained an injury, the Performance Enhancement Group program for injured athletes was created. This paper will offer a model for the Performance Enhancement Group program as a way to: 1) support the athlete, both mentally and physically; 2) deal with the demands of rehabilitation; and 3) facilitate the adjustments the athlete has to make while being out of the competitive arena. The program consists of responsibilities for professionals in sport psychology (ie, assessment/orientation, support, education, individual counseling, and evaluation) and athletic training (ie, organization/administration, recruitment and screening, support, application of techniques, and program compliance). The paper will emphasize that the success of the program is dependent on collaboration between professionals at all levels. PMID:16558357

Cognitive load predicts point-of-care ultrasound simulator performance.

Science.gov (United States)

Aldekhyl, Sara; Cavalcanti, Rodrigo B; Naismith, Laura M

2018-02-01

The ability to maintain good performance with low cognitive load is an important marker of expertise. Incorporating cognitive load measurements in the context of simulation training may help to inform judgements of competence. This exploratory study investigated relationships between demographic markers of expertise, cognitive load measures, and simulator performance in the context of point-of-care ultrasonography. Twenty-nine medical trainees and clinicians at the University of Toronto with a range of clinical ultrasound experience were recruited. Participants answered a demographic questionnaire then used an ultrasound simulator to perform targeted scanning tasks based on clinical vignettes. Participants were scored on their ability to both acquire and interpret ultrasound images. Cognitive load measures included participant self-report, eye-based physiological indices, and behavioural measures. Data were analyzed using a multilevel linear modelling approach, wherein observations were clustered by participants. Experienced participants outperformed novice participants on ultrasound image acquisition. Ultrasound image interpretation was comparable between the two groups. Ultrasound image acquisition performance was predicted by level of training, prior ultrasound training, and cognitive load. There was significant convergence between cognitive load measurement techniques. A marginal model of ultrasound image acquisition performance including prior ultrasound training and cognitive load as fixed effects provided the best overall fit for the observed data. In this proof-of-principle study, the combination of demographic and cognitive load measures provided more sensitive metrics to predict ultrasound simulator performance. Performance assessments which include cognitive load can help differentiate between levels of expertise in simulation environments, and may serve as better predictors of skill transfer to clinical practice.

Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method.

Science.gov (United States)

Valls, Joan; Castellà, Gerard; Dyba, Tadeusz; Clèries, Ramon

2015-06-01

Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal (GoF-optimal), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951-2006 in Spain and 1975-2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Computational Fluid Dynamics and Building Energy Performance Simulation

DEFF Research Database (Denmark)

Nielsen, Peter V.; Tryggvason, Tryggvi

An interconnection between a building energy performance simulation program and a Computational Fluid Dynamics program (CFD) for room air distribution will be introduced for improvement of the predictions of both the energy consumption and the indoor environment. The building energy performance...

When bad stress goes good: increased threat reactivity predicts improved category learning performance.

Science.gov (United States)

Ell, Shawn W; Cosley, Brandon; McCoy, Shannon K

2011-02-01

The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a procedural-based memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

Predictive factors for masticatory performance in Duchenne muscular dystrophy

NARCIS (Netherlands)

Bruggen, H.W. van; Engel-Hoek, L. van den; Steenks, M.H.; Bronkhorst, E.M.; Creugers, N.H.; Groot, I.J.M. de; Kalaykova, S.

2014-01-01

Patients with Duchenne muscular dystrophy (DMD) report masticatory and swallowing problems. Such problems may cause complications such as choking, and feeling of food sticking in the throat. We investigated whether masticatory performance in DMD is objectively impaired, and explored predictive

Harvested Energy Prediction Schemes for Wireless Sensor Networks: Performance Evaluation and Enhancements

Directory of Open Access Journals (Sweden)

Muhammad

2017-01-01

Full Text Available We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy model, the so-called Improved Profile-Energy (IPro-Energy, and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM, Pro-Energy, and Weather Conditioned Moving Average (WCMA. The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.

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.

Hanford performance evaluation program for Hanford site analytical services

International Nuclear Information System (INIS)

Markel, L.P.

1995-09-01

The U.S. Department of Energy (DOE) Order 5700.6C, Quality Assurance, and Title 10 of the Code of Federal Regulations, Part 830.120, Quality Assurance Requirements, states that it is the responsibility of DOE contractors to ensure that ''quality is achieved and maintained by those who have been assigned the responsibility for performing the work.'' Hanford Analytical Services Quality Assurance Plan (HASQAP) is designed to meet the needs of the Richland Operations Office (RL) for maintaining a consistent level of quality for the analytical chemistry services provided by contractor and commmercial analytical laboratory operations. Therefore, services supporting Hanford environmental monitoring, environmental restoration, and waste management analytical services shall meet appropriate quality standards. This performance evaluation program will monitor the quality standards of all analytical laboratories supporting the Hanforad Site including on-site and off-site laboratories. The monitoring and evaluation of laboratory performance can be completed by the use of several tools. This program will discuss the tools that will be utilized for laboratory performance evaluations. Revision 0 will primarily focus on presently available programs using readily available performance evaluation materials provided by DOE, EPA or commercial sources. Discussion of project specific PE materials and evaluations will be described in section 9.0 and Appendix A

Do Maximal Roller Skiing Speed and Double Poling Performance Predict Youth Cross-Country Skiing Performance?

Directory of Open Access Journals (Sweden)

Roland Stöggl, Erich Müller, Thomas Stöggl

2017-09-01

Full Text Available The aims of the current study were to analyze whether specific roller skiing tests and cycle length are determinants of youth cross-country (XC skiing performance, and to evaluate sex specific differences by applying non-invasive diagnostics. Forty-nine young XC skiers (33 boys; 13.8 ± 0.6 yrs and 16 girls; 13.4 ± 0.9 yrs performed roller skiing tests consisting of both shorter (50 m and longer durations (575 m. Test results were correlated with on snow XC skiing performance (PXC based on 3 skating and 3 classical distance competitions (3 to 6 km. The main findings of the current study were: 1 Anthropometrics and maturity status were related to boys’, but not to girls’ PXC; 2 Significant moderate to acceptable correlations between girls’ and boys’ short duration maximal roller skiing speed (double poling, V2 skating, leg skating and PXC were found; 3 Boys’ PXC was best predicted by double poling test performance on flat and uphill, while girls’ performance was mainly predicted by uphill double poling test performance; 4 When controlling for maturity offset, boys’ PXC was still highly associated with the roller skiing tests. The use of simple non-invasive roller skiing tests for determination of PXC represents practicable support for ski clubs, schools or skiing federations in the guidance and evaluation of young talent.

75 FR 9544 - Inmate Work and Performance Pay Program

Science.gov (United States)

2010-03-03

... inmate may receive performance pay only for that portion of the month that the inmate was working... Inmate Work and Performance Pay Program AGENCY: Bureau of Prisons, Justice. ACTION: Proposed rule... work and performance pay by removing redundant language and provisions that relate solely to staff...

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 .

River Protection Project waste feed delivery program technical performance measurement assessment plan

International Nuclear Information System (INIS)

O'TOOLE, S.M.

1999-01-01

This plan establishes a formal technical performance-monitoring program. Technical performance is assessed by establishing requirements based performance goals at the beginning of a program and routinely evaluating progress in meeting these goals at predetermined milestones throughout the project life cycle

Designing PV Incentive Programs to Promote System Performance: AReview of Current Practice

Energy Technology Data Exchange (ETDEWEB)

Barbose, Galen; Wiser, Ryan; Bolinger, Mark

2006-11-12

Some stakeholders continue to voice concerns about the performance of customer-sited photovoltaic (PV) systems, particularly because these systems typically receive financial support through ratepayer- or publicly-funded programs. Although much remains to be understood about the extent and specific causes of poor PV system performance, several studies of the larger programs and markets have shed some light on the issue. An evaluation of the California Energy Commission (CEC)'s Emerging Renewables Program, for example, found that 7% of systems, in a sample of 95, had lower-than-expected power output due to shading or soiling (KEMA 2005). About 3% of a larger sample of 140 systems were not operating at all or were operating well below expected output, due to failed equipment, faulty installation workmanship, and/or a lack of basic maintenance. In a recent evaluation of the other statewide PV incentive program in California, the Self-Generation Incentive Program, 9 of 52 projects sampled were found to have annual capacity factors less than 14.5%, although reasons for these low capacity factors generally were not identified (Itron 2005). Studies of PV systems in Germany and Japan, the two largest PV markets worldwide, have also revealed some performance problems associated with issues such as shading, equipment and installation defects, inverter failure, and deviations from module manufacturers' specifications (Otani et al. 2004, Jahn & Nasse 2004). Although owners of PV systems have an inherent incentive to ensure that their systems perform well, many homeowners and building operators may lack the necessary information and expertise to carry out this task effectively. Given this barrier, and the responsibility of PV incentive programs to ensure that public funds are prudently spent, these programs should (and often do) play a critical role in promoting PV system performance. Performance-based incentives (PBIs), which are based on actual energy production

IPEP: Laboratory performance evaluation reports for management of DOE EM programs

International Nuclear Information System (INIS)

Hensley, J.E.; Lindahl, P.C.; Streets, W.E.

1995-01-01

Environmental restoration program/project managers at DOE's Office of Environmental Management (EM) are making important decisions based on analytical data generated by contracted laboratories. The Analytical Services Division, EM-263, is developing the Integrated Performance Evaluation Program (IPEP) to assess the performance of those laboratories, based on results from Performance Evaluation (PE) programs. The IPEP reports will be used by the laboratories to foster self-assessment and improvement. In addition, IPEP will produce PE reports for three levels of EM management (Operations/Project Offices, Area Program Offices, and Deputy Assistant Secretary Office). These reports will be used to assess whether contracted analytical laboratories have the capability to produce environmental data of the quality necessary for making environmental restoration and waste management decisions

Pay for performance programs in Australia: a need for guiding principles.

Science.gov (United States)

Scott, Ian A

2008-11-01

Pay-for-performance (P4P) programs which reward clinical providers with incentive payments based on one or more measures of quality of care are now common in the United States and the United Kingdom and it is likely they will attract increasing interest in Australia. However, empirical evidence demonstrating effectiveness of such programs is limited and many existing programs have not had rigorous outcome evaluation. To maximise success, future P4P programs should incorporate the lessons and insights obtained from previous experience. Based on a review of published trials, program evaluations and position statements, the following principles that may guide future program design and implementation were synthesised: 1) formulate a rationale and a business case for P4P; 2) use established evidence-based performance measures; 3) use rigorous and verifiable methods of data collection and analysis; 4) define performance targets using absolute and relative thresholds; 5) use rewards that are sufficient, equitable and transparent; 6) address appropriateness of provider responses and avoid perverse incentives; 7) implement communication and feedback strategies; 8) use existing organisational structures to implement P4P programs; 9) attribute credit for performance to participants in ways that foster population-based perspectives; and 10) invest in outcomes and health service research. Recommendations flowing from these principles relevant to Australian settings are provided.

10 CFR 600.341 - Monitoring and reporting program and financial performance.

Science.gov (United States)

2010-01-01

... 10 Energy 4 2010-01-01 2010-01-01 false Monitoring and reporting program and financial performance. 600.341 Section 600.341 Energy DEPARTMENT OF ENERGY (CONTINUED) ASSISTANCE REGULATIONS FINANCIAL... Organizations Post-Award Requirements § 600.341 Monitoring and reporting program and financial performance. (a...

Advanced Certification Program for Computer Graphic Specialists. Final Performance Report.

Science.gov (United States)

Parkland Coll., Champaign, IL.

A pioneer program in computer graphics was implemented at Parkland College (Illinois) to meet the demand for specialized technicians to visualize data generated on high performance computers. In summer 1989, 23 students were accepted into the pilot program. Courses included C programming, calculus and analytic geometry, computer graphics, and…

Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

Science.gov (United States)

Ickes, Jacob C.

Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form

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...

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.

Performance prediction of a proton exchange membrane fuel cell using the ANFIS model

Energy Technology Data Exchange (ETDEWEB)

Vural, Yasemin; Ingham, Derek B.; Pourkashanian, Mohamed [Centre for Computational Fluid Dynamics, University of Leeds, Houldsworth Building, LS2 9JT Leeds (United Kingdom)

2009-11-15

In this study, the performance (current-voltage curve) prediction of a Proton Exchange Membrane Fuel Cell (PEMFC) is performed for different operational conditions using an Adaptive Neuro-Fuzzy Inference System (ANFIS). First, ANFIS is trained with a set of input and output data. The trained model is then tested with an independent set of experimental data. The trained and tested model is then used to predict the performance curve of the PEMFC under various operational conditions. The model shows very good agreement with the experimental data and this indicates that ANFIS is capable of predicting fuel cell performance (in terms of cell voltage) with a high accuracy in an easy, rapid and cost effective way for the case presented. Finally, the capabilities and the limitations of the model for the application in fuel cells have been discussed. (author)

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 ...

Decline curve based models for predicting natural gas well performance

Directory of Open Access Journals (Sweden)

Arash Kamari

2017-06-01

Full Text Available The productivity of a gas well declines over its production life as cannot cover economic policies. To overcome such problems, the production performance of gas wells should be predicted by applying reliable methods to analyse the decline trend. Therefore, reliable models are developed in this study on the basis of powerful artificial intelligence techniques viz. the artificial neural network (ANN modelling strategy, least square support vector machine (LSSVM approach, adaptive neuro-fuzzy inference system (ANFIS, and decision tree (DT method for the prediction of cumulative gas production as well as initial decline rate multiplied by time as a function of the Arps' decline curve exponent and ratio of initial gas flow rate over total gas flow rate. It was concluded that the results obtained based on the models developed in current study are in satisfactory agreement with the actual gas well production data. Furthermore, the results of comparative study performed demonstrates that the LSSVM strategy is superior to the other models investigated for the prediction of both cumulative gas production, and initial decline rate multiplied by time.

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.

Analytical prediction of thermal performance of hypervapotron and its application to ITER

International Nuclear Information System (INIS)

Baxi, C.B.; Falter, H.

1992-09-01

A hypervapotron (HV) is a water cooled device made of high thermal conductivity material such as copper. A surface heat flux of up to 30 MW/m 2 has been achieved in copper hypervapotrans cooled by water at a velocity of 10 m/s and at a pressure of six bar. Hypervapotrons have been used in the past as beam dumps at the Joint European Torus (JET). It is planned to use them for diverter cooling during Mark II upgrade of the JET. Although a large amount of experimental data has been collected on these devices, an analytical performance prediction has not been done before due to the complexity of the heat transfer mechanisms. A method to analytically predict the thermal performance of the hypervapotron is described. The method uses a combination of a number of thermal hydraulic correlations and a finite element analysis. The analytical prediction shows an excellent agreement with experimental results over a wide range of velocities, pressures, subcooling, and geometries. The method was used to predict the performance of hypervapotron made of beryllium. Merits for the use of hypervapotrons for International Thermonuclear Experimental Reactor (ITER) and Tokamak Physics Experiment (TPX) are discussed

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.

Prediction of small spark ignited engine performance using producer gas as fuel

Directory of Open Access Journals (Sweden)

N. Homdoung

2015-03-01

Full Text Available Producer gas from biomass gasification is expected to contribute to greater energy mix in the future. Therefore, effect of producer gas on engine performance is of great interest. Evaluation of engine performances can be hard and costly. Ideally, they may be predicted mathematically. This work was to apply mathematical models in evaluating performance of a small producer gas engine. The engine was a spark ignition, single cylinder unit with a CR of 14:1. Simulation was carried out on full load and varying engine speeds. From simulated results, it was found that the simple mathematical model can predict the performance of the gas engine and gave good agreement with experimental results. The differences were within ±7%.

Immediate Effects of Different Trunk Exercise Programs on Jump Performance.

Science.gov (United States)

Imai, A; Kaneoka, K; Okubo, Y; Shiraki, H

2016-03-01

The aim of this study was to investigate the immediate effects of trunk stabilization exercise (SE) and conventional trunk exercise (CE) programs on jump performance. 13 adolescent male soccer players performed 2 kinds of jump testing before and immediate after 3 experimental conditions: SE, CE, and non-exercise (NE). The SE program consisted of the elbow-toe, hand-knee, and back bridge, and the CE program consisted of the sit-up, sit-up with trunk rotation and back extension. Testing of a countermovement jump (CMJ) and rebound jump (RJ) were performed to assess jump performance. Jump height of the CMJ and RJ-index, contact time, and jump height of the RJ were analyzed. The RJ index was improved significantly only after SE (p=0.017). However, contact time and jump height did not improve significantly in the SE condition. Moreover, no significant interaction or main effects of time or group were observed in the CMJ. Consequently, this study showed the different immediate effect on the RJ between the SE and CE, and suggested the possibility that the SE used in this study is useful as a warm-up program to improve the explosive movements. © Georg Thieme Verlag KG Stuttgart · New York.

A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques

Science.gov (United States)

Uswatun Khasanah, Annisa; Harwati

2017-06-01

Student’s performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student’s performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student’s attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.

Cobra Strikes! High-Performance Car Inspires Students, Markets Program

Science.gov (United States)

Jenkins, Bonita

2008-01-01

Nestled in the Lower Piedmont region of upstate South Carolina, Piedmont Technical College (PTC) is one of 16 technical colleges in the state. Automotive technology is one of its most popular programs. The program features an instructive, motivating activity that the author describes in this article: building a high-performance car. The Cobra…

The U.S. Department of Energy Laboratory Accreditation Program for testing the performance of extremity dosimetry systems: a summary of the program status

International Nuclear Information System (INIS)

Cummings, F.M.; Carlson, R.D.; Gesell, T.F.; Loesch, R.M.

1992-01-01

In 1986, The U.S. Department of Energy (DOE) implemented a program to test the performance of its personnel whole-body dosimetry systems. This program was the DOE Laboratory Accreditation Program (DOELAP). The program parallels the performance testing program specified in the American National Standard for Dosimetry - Personnel Dosimetry Performance -Criteria for Testing (ANSI N13.11-1983), but also addresses the additional dosimetry needs of DOE facilities. As an extension of the whole-body performance testing program, the DOE is now developing a program to test the performance of personnel extremity dosimetry systems. The draft DOE standard for testing extremity dosimetry systems is much less complex than the whole-body dosimetry standard and reflects the limitations imposed on extremity dosimetry by dosimeter design and irradiation geometry. A pilot performance test session has been conducted to evaluate the proposed performance-testing standard. (author)

Material balance area custodian performance evaluation program at PNL

International Nuclear Information System (INIS)

Dickman, D.A.

1991-01-01

This paper reports that the material balance area (MBA) custodian has primary responsibility for control and accountability of nuclear material within an MBA. In this role, the custodian operates as an extension of the facility material control and accountability (MC and A) organization. To effectively meet administrative requirements and protection needs, the custodian must be fully trained in all aspects of MC and A related to the MBA, and custodian performance must be periodically evaluated. U.S. Department of Energy (DOE) Policy requires that each facility provide for a program which ensures that personnel performing MC and A functions are trained and/or qualified to perform their duties and responsibilities and knowledgeable of requirements and procedures related to their functions. the MBA Custodian Performance Evaluation Program at Pacific Northwest Laboratory (PNL) uses a variety of assessment techniques to meet this goal, including internal and independent MBA audits, periodic custodian testing, limited scope performance tests, daily monitoring of MC and A documentation, and reviewing custodian performance during physical inventories

Incremental Validity of Personality Measures in Predicting Underwater Performance and Adaptation.

Science.gov (United States)

Colodro, Joaquín; Garcés-de-Los-Fayos, Enrique J; López-García, Juan J; Colodro-Conde, Lucía

2015-03-17

Intelligence and personality traits are currently considered effective predictors of human behavior and job performance. However, there are few studies about their relevance in the underwater environment. Data from a sample of military personnel performing scuba diving courses were analyzed with regression techniques, testing the contribution of individual differences and ascertaining the incremental validity of the personality in an environment with extreme psychophysical demands. The results confirmed the incremental validity of personality traits (ΔR 2 = .20, f 2 = .25) over the predictive contribution of general mental ability (ΔR 2 = .07, f 2 = .08) in divers' performance. Moreover, personality (R(L)2 = .34) also showed a higher validity to predict underwater adaptation than general mental ability ( R(L)2 = .09). The ROC curve indicated 86% of the maximum possible discrimination power for the prediction of underwater adaptation, AUC = .86, p personality traits as predictors of an effective response to the changing circumstances of military scuba diving. They also may improve the understanding of the behavioral effects and psychophysiological complications of diving and can also provide guidance for psychological intervention and prevention of risk in this extreme environment.

A comparison of SAR ATR performance with information theoretic predictions

Science.gov (United States)

Blacknell, David

2003-09-01

Performance assessment of automatic target detection and recognition algorithms for SAR systems (or indeed any other sensors) is essential if the military utility of the system / algorithm mix is to be quantified. This is a relatively straightforward task if extensive trials data from an existing system is used. However, a crucial requirement is to assess the potential performance of novel systems as a guide to procurement decisions. This task is no longer straightforward since a hypothetical system cannot provide experimental trials data. QinetiQ has previously developed a theoretical technique for classification algorithm performance assessment based on information theory. The purpose of the study presented here has been to validate this approach. To this end, experimental SAR imagery of targets has been collected using the QinetiQ Enhanced Surveillance Radar to allow algorithm performance assessments as a number of parameters are varied. In particular, performance comparisons can be made for (i) resolutions up to 0.1m, (ii) single channel versus polarimetric (iii) targets in the open versus targets in scrubland and (iv) use versus non-use of camouflage. The change in performance as these parameters are varied has been quantified from the experimental imagery whilst the information theoretic approach has been used to predict the expected variation of performance with parameter value. A comparison of these measured and predicted assessments has revealed the strengths and weaknesses of the theoretical technique as will be discussed in the paper.

Trait impulsivity predicts D-KEFS tower test performance in university students.

Science.gov (United States)

Lyvers, Michael; Basch, Vanessa; Duff, Helen; Edwards, Mark S

2015-01-01

The present study examined a widely used self-report index of trait impulsiveness in relation to performance on a well-known neuropsychological executive function test in 70 university undergraduate students (50 women, 20 men) aged 18 to 24 years old. Participants completed the Barratt Impulsiveness Scale (BIS-11) and the Frontal Systems Behavior Scale (FrSBe), after which they performed the Tower Test of the Delis-Kaplan Executive Function System. Hierarchical linear regression showed that after controlling for gender, current alcohol consumption, age at onset of weekly alcohol use, and FrSBe scores, BIS-11 significantly predicted Tower Test Achievement scores, β = -.44, p impulsiveness is associated with poorer executive cognitive performance even in a sample likely to be characterized by relatively high general cognitive functioning (i.e., university students). The results also support the role of inhibition as a key aspect of executive task performance. Elevated scores on the BIS-11 and FrSBe are known to be linked to risky drinking in young adults as confirmed in this sample; however, only BIS-11 predicted Tower Test performance.

Do physiological measures predict selected CrossFit® benchmark performance?

Directory of Open Access Journals (Sweden)

Butcher SJ

2015-07-01

Full Text Available Scotty J Butcher,1,2 Tyler J Neyedly,3 Karla J Horvey,1 Chad R Benko2,41Physical Therapy, University of Saskatchewan, 2BOSS Strength Institute, 3Physiology, University of Saskatchewan, 4Synergy Strength and Conditioning, Saskatoon, SK, CanadaPurpose: CrossFit® is a new but extremely popular method of exercise training and competition that involves constantly varied functional movements performed at high intensity. Despite the popularity of this training method, the physiological determinants of CrossFit performance have not yet been reported. The purpose of this study was to determine whether physiological and/or muscle strength measures could predict performance on three common CrossFit "Workouts of the Day" (WODs.Materials and methods: Fourteen CrossFit Open or Regional athletes completed, on separate days, the WODs "Grace" (30 clean and jerks for time, "Fran" (three rounds of thrusters and pull-ups for 21, 15, and nine repetitions, and "Cindy" (20 minutes of rounds of five pull-ups, ten push-ups, and 15 bodyweight squats, as well as the "CrossFit Total" (1 repetition max [1RM] back squat, overhead press, and deadlift, maximal oxygen consumption (VO2max, and Wingate anaerobic power/capacity testing.Results: Performance of Grace and Fran was related to whole-body strength (CrossFit Total (r=-0.88 and -0.65, respectively and anaerobic threshold (r=-0.61 and -0.53, respectively; however, whole-body strength was the only variable to survive the prediction regression for both of these WODs (R2=0.77 and 0.42, respectively. There were no significant associations or predictors for Cindy.Conclusion: CrossFit benchmark WOD performance cannot be predicted by VO2max, Wingate power/capacity, or either respiratory compensation or anaerobic thresholds. Of the data measured, only whole-body strength can partially explain performance on Grace and Fran, although anaerobic threshold also exhibited association with performance. Along with their typical training

Retrospective lifetime dietary patterns predict cognitive performance in community-dwelling older Australians.

Science.gov (United States)

Hosking, Diane E; Nettelbeck, Ted; Wilson, Carlene; Danthiir, Vanessa

2014-07-28

Dietary intake is a modifiable exposure that may have an impact on cognitive outcomes in older age. The long-term aetiology of cognitive decline and dementia, however, suggests that the relevance of dietary intake extends across the lifetime. In the present study, we tested whether retrospective dietary patterns from the life periods of childhood, early adulthood, adulthood and middle age predicted cognitive performance in a cognitively healthy sample of 352 older Australian adults >65 years. Participants completed the Lifetime Diet Questionnaire and a battery of cognitive tests designed to comprehensively assess multiple cognitive domains. In separate regression models, lifetime dietary patterns were the predictors of cognitive factor scores representing ten constructs derived by confirmatory factor analysis of the cognitive test battery. All regression models were progressively adjusted for the potential confounders of current diet, age, sex, years of education, English as native language, smoking history, income level, apoE ɛ4 status, physical activity, other past dietary patterns and health-related variables. In the adjusted models, lifetime dietary patterns predicted cognitive performance in this sample of older adults. In models additionally adjusted for intake from the other life periods and mechanistic health-related variables, dietary patterns from the childhood period alone reached significance. Higher consumption of the 'coffee and high-sugar, high-fat extras' pattern predicted poorer performance on simple/choice reaction time, working memory, retrieval fluency, short-term memory and reasoning. The 'vegetable and non-processed' pattern negatively predicted simple/choice reaction time, and the 'traditional Australian' pattern positively predicted perceptual speed and retrieval fluency. Identifying early-life dietary antecedents of older-age cognitive performance contributes to formulating strategies for delaying or preventing cognitive decline.

Predicting memory performance in normal ageing using different measures of hippocampal size

International Nuclear Information System (INIS)

Lye, T.C.; Creasey, H.; Kril, J.J.; Grayson, D.A.; Piguet, O.; Bennett, H.P.; Ridley, L.J.; Broe, G.A.

2006-01-01

A number of different methods have been employed to correct hippocampal volumes for individual variation in head size. Researchers have previously used qualitative visual inspection to gauge hippocampal atrophy. The purpose of this study was to determine the best measure(s) of hippocampal size for predicting memory functioning in 102 community-dwelling individuals over 80 years of age. Hippocampal size was estimated using magnetic resonance imaging (MRI) volumetry and qualitative visual assessment. Right and left hippocampal volumes were adjusted by three different estimates of head size: total intracranial volume (TICV), whole-brain volume including ventricles (WB+V) and a more refined measure of whole-brain volume with ventricles extracted (WB). We compared the relative efficacy of these three volumetric adjustment methods and visual ratings of hippocampal size in predicting memory performance using linear regression. All four measures of hippocampal size were significant predictors of memory performance. TICV-adjusted volumes performed most poorly in accounting for variance in memory scores. Hippocampal volumes adjusted by either measure of whole-brain volume performed equally well, although qualitative visual ratings of the hippocampus were at least as effective as the volumetric measures in predicting memory performance in community-dwelling individuals in the ninth or tenth decade of life. (orig.)

On line performance monitoring for predictive maintenance [Paper No.: VIA - 2

International Nuclear Information System (INIS)

Gupta, R.K.; Chandra, Rajesh

1981-01-01

There will always be progressive deterioration in the performance of dynamic equipment due to normal inevitable wear, malfunctions, failures and other reasons. In most cases it is possible to monitor some parameters of a system which would get progressively affected with the deterioration in the health of the system. By on-line monitoring of such predetermined parameters, compared with preset base data generated for a healthy system earlier, would prove very helpful in avoiding breakdowns and in proper planning of preventive and predictive maintenance. With increasing use of on-line computerised controls the generation of design base data and also the in-built self checking feature of monitoring the equipment health can be achieved by incorporating suitable software. This type of system will be helpful in: (a) predicting the life of component, (b) prewarning the operator about impending malfunctions, (c) establishing a maintenance schedule and spare inventory, and (d) analysing the failures. This type of centralised predictive maintenance is increasingly becoming important where: (a) the number of equipments are large, (b) the operation of equipment is critical from safety criteria, and (c) the minimum safety margin in the performance of the component is to be maintained. Keeping this in mind, the Fuel Handling System of Narora Atomic Power Project and the future power plants having computerised controls will have facility for on-line performance monitoring for predictive maintenance. The paper also describes methodology of the technique in detail, with a few representative cases. (author)

Comparison of measured and predicted long term performance of grid a connected photovoltaic system

International Nuclear Information System (INIS)

Mondol, Jayanta Deb; Yohanis, Yigzaw G.; Norton, Brian

2007-01-01

Predicted performance of a grid connected photovoltaic (PV) system using TRNSYS was compared with measured data. A site specific global-diffuse correlation model was developed and used to calculate the beam and diffuse components of global horizontal insolation. A PV module temperature equation and a correlation relating input and output power of an inverter were developed using measured data and used in TRNSYS to perform PV array and inverter outputs simulation. Different combinations of the tilted surface radiation model, global-diffuse correlation model and PV module temperature equation were used in the simulations. Statistical error analysis was performed to compare the results for each combination. The simulation accuracy was improved by using the new global-diffuse correlation and module temperature equation in the TRNSYS simulation. For an isotropic sky tilted surface radiation model, the average monthly difference between measured and predicted PV output before and after modification of the TRNSYS component were 10.2% and 3.3%, respectively, and, for an anisotropic sky model, 15.4% and 10.7%, respectively. For inverter output, the corresponding errors were 10.4% and 3.3% and 15.8% and 8.6%, respectively. Measured PV efficiency, overall system efficiency, inverter efficiency and performance ratio of the system were compared with the predicted results. The predicted PV performance parameters agreed more closely with the measured parameters in summer than in winter. The difference between predicted performances using an isotropic and an anisotropic sky tilted surface models is between 1% and 2%

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...

76 FR 52548 - National Veterinary Accreditation Program; Currently Accredited Veterinarians Performing...

Science.gov (United States)

2011-08-23

.... APHIS-2006-0093] RIN 0579-AC04 National Veterinary Accreditation Program; Currently Accredited... accredited in the National Veterinary Accreditation Program (NVAP) may continue to perform accredited duties..., 2011. FOR FURTHER INFORMATION CONTACT: Dr. Todd Behre, National Veterinary Accreditation Program, VS...

Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems

Energy Technology Data Exchange (ETDEWEB)

Becker, Steffen [University of Tasmania, Hobart 7001, Tasmania (Australia); Karri, Vishy [Australian College of Kuwait (Kuwait)

2010-09-15

Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of {+-}3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications. (author)

Technical player profiles related to the physical fitness of young female volleyball players predict team performance.

Science.gov (United States)

Dávila-Romero, C; Hernández-Mocholí, M A; García-Hermoso, A

2015-03-01

This study is divided into three sequential stages: identification of fitness and game performance profiles (individual player performance), an assessment of the relationship between these profiles, and an assessment of the relationship between individual player profiles and team performance during play (in championship performance). The overall study sample comprised 525 (19 teams) female volleyball players aged 12-16 years and a subsample (N.=43) used to examine study aims one and two was selected from overall sample. Anthropometric, fitness and individual player performance (actual game) data were collected in the subsample. These data were analyzed through clustering methods, ANOVA and independence chi-square test. Then, we investigated whether the proportion of players with the highest individual player performance profile might predict a team's results in the championship. Cluster analysis identified three volleyball fitness profiles (high, medium, and low) and two individual player performance profiles (high and low). The results showed a relationship between both types of profile (fitness and individual player performance). Then, linear regression revealed a moderate relationship between the number of players with a high volleyball fitness profile and a team's results in the championship (R2=0.23). The current study findings may enable coaches and trainers to manage training programs more efficiently in order to obtain tailor-made training, identify volleyball-specific physical fitness training requirements and reach better results during competitions.

Neighborhood Integration and Connectivity Predict Cognitive Performance and Decline

Directory of Open Access Journals (Sweden)

Amber Watts PhD

2015-08-01

Full Text Available Objective: Neighborhood characteristics may be important for promoting walking, but little research has focused on older adults, especially those with cognitive impairment. We evaluated the role of neighborhood characteristics on cognitive function and decline over a 2-year period adjusting for measures of walking. Method: In a study of 64 older adults with and without mild Alzheimer’s disease (AD, we evaluated neighborhood integration and connectivity using geographical information systems data and space syntax analysis. In multiple regression analyses, we used these characteristics to predict 2-year declines in factor analytically derived cognitive scores (attention, verbal memory, mental status adjusting for age, sex, education, and self-reported walking. Results : Neighborhood integration and connectivity predicted cognitive performance at baseline, and changes in cognitive performance over 2 years. The relationships between neighborhood characteristics and cognitive performance were not fully explained by self-reported walking. Discussion : Clearer definitions of specific neighborhood characteristics associated with walkability are needed to better understand the mechanisms by which neighborhoods may impact cognitive outcomes. These results have implications for measuring neighborhood characteristics, design and maintenance of living spaces, and interventions to increase walking among older adults. We offer suggestions for future research measuring neighborhood characteristics and cognitive function.

Performance indicator program for U.S. Department of Energy reactors and facilities

International Nuclear Information System (INIS)

Sastry, R.; Fielding, J.R.; Snyder, B.J.; Usher, J.; Boccio, J.

1990-01-01

The U.S. Department of Energy (DOE) is developing a Performance Indicator (PI) Program for all facilities. The objective is to periodically collect, statistically analyze and present performance-related information in a concise and consistent format for DOE and safety of facility operations. A set of 14 DOE-Hq. defined PI's has been established after review of programs used by other organizations. Since July 1989, these PI's have been used in a trial program for eight diverse DOE facilities. Electronic reporting is directly to the DOE Safety Performance Measurement System computer. This paper reports on results demonstrated for the feasibility and usefulness of a DOE-wide PI Program and steps being taken to include all DOE facilities

Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students

Directory of Open Access Journals (Sweden)

Osman Yildiz

2013-12-01

Full Text Available It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the development of a mathematical model intended to predict distance education students’ year-end academic performance using the first eight-week data on the learning management system. First, two fuzzy models were constructed, namely the classical fuzzy model and the expert fuzzy model, the latter being based on expert opinion. Afterwards, a gene-fuzzy model was developed optimizing membership functions through genetic algorithm. The data on distance education were collected through Moodle, an open source learning management system. The data were on a total of 218 students who enrolled in Basic Computer Sciences in 2012. The input data consisted of the following variables: When a student logged on to the system for the last time after the content of a lesson was uploaded, how often he/she logged on to the system, how long he/she stayed online in the last login, what score he/she got in the quiz taken in Week 4, and what score he/she got in the midterm exam taken in Week 8. A comparison was made among the predictions of the three models concerning the students’ year-end academic performance.

Safety analysis report for packaging (onsite) transuranic performance demonstration program sample packaging

International Nuclear Information System (INIS)

Mccoy, J.C.

1997-01-01

The Transuranic Performance Demonstration Program (TPDP) sample packaging is used to transport highway route controlled quantities of weapons grade (WG) plutonium samples from the Plutonium Finishing Plant (PFP) to the Waste Receiving and Processing (WRAP) facility and back. The purpose of these shipments is to test the nondestructive assay equipment in the WRAP facility as part of the Nondestructive Waste Assay PDP. The PDP is part of the U. S. Department of Energy (DOE) National TRU Program managed by the U. S. Department of Energy, Carlsbad Area Office, Carlsbad, New Mexico. Details of this program are found in CAO-94-1045, Performance Demonstration Program Plan for Nondestructive Assay for the TRU Waste Characterization Program (CAO 1994); INEL-96/0129, Design of Benign Matrix Drums for the Non-Destructive Assay Performance Demonstration Program for the National TRU Program (INEL 1996a); and INEL-96/0245, Design of Phase 1 Radioactive Working Reference Materials for the Nondestructive Assay Performance Demonstration Program for the National TRU Program (INEL 1996b). Other program documentation is maintained by the national TRU program and each DOE site participating in the program. This safety analysis report for packaging (SARP) provides the analyses and evaluations necessary to demonstrate that the TRU PDP sample packaging meets the onsite transportation safety requirements of WHC-CM-2-14, Hazardous Material Packaging and Shipping, for an onsite Transportation Hazard Indicator (THI) 2 packaging. This SARP, however, does not include evaluation of any operations within the PFP or WRAP facilities, including handling, maintenance, storage, or operating requirements, except as they apply directly to transportation between the gate of PFP and the gate of the WRAP facility. All other activities are subject to the requirements of the facility safety analysis reports (FSAR) of the PFP or WRAP facility and requirements of the PDP

Driving and Low Vision: Validity of Assessments for Predicting Performance of Drivers

Science.gov (United States)

Strong, J. Graham; Jutai, Jeffrey W.; Russell-Minda, Elizabeth; Evans, Mal

2008-01-01

The authors conducted a systematic review to examine whether vision-related assessments can predict the driving performance of individuals who have low vision. The results indicate that measures of visual field, contrast sensitivity, cognitive and attention-based tests, and driver screening tools have variable utility for predicting real-world…

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...

Performance Demonstration Program Plan for Nondestructive Assay for the TRU Waste Characterization Program. Revision 1

International Nuclear Information System (INIS)

1997-01-01

The Performance Demonstration Program (PDP) for Nondestructive Assay (NDA) consists of a series of tests conducted on a regular frequency to evaluate the capability for nondestructive assay of transuranic (TRU) waste throughout the Department of Energy (DOE) complex. Each test is termed a PDP cycle. These evaluation cycles provide an objective measure of the reliability of measurements performed with TRU waste characterization systems. Measurement facility performance will be demonstrated by the successful analysis of blind audit samples according to the criteria set by this Program Plan. Intercomparison between measurement groups of the DOE complex will be achieved by comparing the results of measurements on similar or identical blind samples reported by the different measurement facilities. Blind audit samples (hereinafter referred to as PDP samples) will be used as an independent means to assess the performance of measurement groups regarding compliance with established Quality Assurance Objectives (QAOs). As defined for this program, a PDP sample consists of a 55-gallon matrix drum emplaced with radioactive standards and fabricated matrix inserts. These PDP sample components, once manufactured, will be secured and stored at each participating measurement facility designated and authorized by Carlsbad Area Office (CAO) under secure conditions to protect them from loss, tampering, or accidental damage

Individualized performance prediction during total sleep deprivation: accounting for trait vulnerability to sleep loss.

Science.gov (United States)

Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Thorsley, David; Wesensten, Nancy J; Balkin, Thomas J; Reifman, Jaques

2012-01-01

Individual differences in vulnerability to sleep loss can be considerable, and thus, recent efforts have focused on developing individualized models for predicting the effects of sleep loss on performance. Individualized models constructed using a Bayesian formulation, which combines an individual's available performance data with a priori performance predictions from a group-average model, typically need at least 40 h of individual data before showing significant improvement over the group-average model predictions. Here, we improve upon the basic Bayesian formulation for developing individualized models by observing that individuals may be classified into three sleep-loss phenotypes: resilient, average, and vulnerable. For each phenotype, we developed a phenotype-specific group-average model and used these models to identify each individual's phenotype. We then used the phenotype-specific models within the Bayesian formulation to make individualized predictions. Results on psychomotor vigilance test data from 48 individuals indicated that, on average, ∼85% of individual phenotypes were accurately identified within 30 h of wakefulness. The percentage improvement of the proposed approach in 10-h-ahead predictions was 16% for resilient subjects and 6% for vulnerable subjects. The trade-off for these improvements was a slight decrease in prediction accuracy for average subjects.

Numerical simulation of turbulence flow in a Kaplan turbine -Evaluation on turbine performance prediction accuracy-

International Nuclear Information System (INIS)

Ko, P; Kurosawa, S

2014-01-01

The understanding and accurate prediction of the flow behaviour related to cavitation and pressure fluctuation in a Kaplan turbine are important to the design work enhancing the turbine performance including the elongation of the operation life span and the improvement of turbine efficiency. In this paper, high accuracy turbine and cavitation performance prediction method based on entire flow passage for a Kaplan turbine is presented and evaluated. Two-phase flow field is predicted by solving Reynolds-Averaged Navier-Stokes equations expressed by volume of fluid method tracking the free surface and combined with Reynolds Stress model. The growth and collapse of cavitation bubbles are modelled by the modified Rayleigh-Plesset equation. The prediction accuracy is evaluated by comparing with the model test results of Ns 400 Kaplan model turbine. As a result that the experimentally measured data including turbine efficiency, cavitation performance, and pressure fluctuation are accurately predicted. Furthermore, the cavitation occurrence on the runner blade surface and the influence to the hydraulic loss of the flow passage are discussed. Evaluated prediction method for the turbine flow and performance is introduced to facilitate the future design and research works on Kaplan type turbine

Numerical simulation of turbulence flow in a Kaplan turbine -Evaluation on turbine performance prediction accuracy-

Science.gov (United States)

Ko, P.; Kurosawa, S.

2014-03-01

The understanding and accurate prediction of the flow behaviour related to cavitation and pressure fluctuation in a Kaplan turbine are important to the design work enhancing the turbine performance including the elongation of the operation life span and the improvement of turbine efficiency. In this paper, high accuracy turbine and cavitation performance prediction method based on entire flow passage for a Kaplan turbine is presented and evaluated. Two-phase flow field is predicted by solving Reynolds-Averaged Navier-Stokes equations expressed by volume of fluid method tracking the free surface and combined with Reynolds Stress model. The growth and collapse of cavitation bubbles are modelled by the modified Rayleigh-Plesset equation. The prediction accuracy is evaluated by comparing with the model test results of Ns 400 Kaplan model turbine. As a result that the experimentally measured data including turbine efficiency, cavitation performance, and pressure fluctuation are accurately predicted. Furthermore, the cavitation occurrence on the runner blade surface and the influence to the hydraulic loss of the flow passage are discussed. Evaluated prediction method for the turbine flow and performance is introduced to facilitate the future design and research works on Kaplan type turbine.

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).

75 FR 59605 - National Veterinary Accreditation Program; Currently Accredited Veterinarians Performing...

Science.gov (United States)

2010-09-28

.... APHIS-2006-0093] RIN 0579-AC04 National Veterinary Accreditation Program; Currently Accredited... Veterinary Accreditation Program (NVAP) may continue to perform accredited duties and to elect to continue to..., National Veterinary Accreditation Program, VS, APHIS, 4700 River Road Unit 200, Riverdale, MD 20737; (301...

Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

Science.gov (United States)

Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

2014-01-01

This paper presents recent thermal model results of the Advanced Stirling Radioisotope Generator (ASRG). The three-dimensional (3D) ASRG thermal power model was built using the Thermal Desktop(trademark) thermal analyzer. The model was correlated with ASRG engineering unit test data and ASRG flight unit predictions from Lockheed Martin's (LM's) I-deas(trademark) TMG thermal model. The auxiliary cooling system (ACS) of the ASRG is also included in the ASRG thermal model. The ACS is designed to remove waste heat from the ASRG so that it can be used to heat spacecraft components. The performance of the ACS is reported under nominal conditions and during a Venus flyby scenario. The results for the nominal case are validated with data from Lockheed Martin. Transient thermal analysis results of ASRG for a Venus flyby with a representative trajectory are also presented. In addition, model results of an ASRG mounted on a Cassini-like spacecraft with a sunshade are presented to show a way to mitigate the high temperatures of a Venus flyby. It was predicted that the sunshade can lower the temperature of the ASRG alternator by 20 C for the representative Venus flyby trajectory. The 3D model also was modified to predict generator performance after a single Advanced Stirling Convertor failure. The geometry of the Microtherm HT insulation block on the outboard side was modified to match deformation and shrinkage observed during testing of a prototypic ASRG test fixture by LM. Test conditions and test data were used to correlate the model by adjusting the thermal conductivity of the deformed insulation to match the post-heat-dump steady state temperatures. Results for these conditions showed that the performance of the still-functioning inboard ACS was unaffected.

Model Performance Evaluation and Scenario Analysis (MPESA)

Science.gov (United States)

Model Performance Evaluation and Scenario Analysis (MPESA) assesses the performance with which models predict time series data. The tool was developed Hydrological Simulation Program-Fortran (HSPF) and the Stormwater Management Model (SWMM)

From mission to measures: performance measure development for a Teen Pregnancy Prevention Program.

Science.gov (United States)

Farb, Amy Feldman; Burrus, Barri; Wallace, Ina F; Wilson, Ellen K; Peele, John E

2014-03-01

The Office of Adolescent Health (OAH) sought to create a comprehensive set of performance measures to capture the performance of the Teen Pregnancy Prevention (TPP) program. This performance measurement system needed to provide measures that could be used internally (by both OAH and the TPP grantees) for management and program improvement as well as externally to communicate the program's progress to other interested stakeholders and Congress. This article describes the selected measures and outlines the considerations behind the TPP measurement development process. Issues faced, challenges encountered, and lessons learned have broad applicability for other federal agencies and, specifically, for TPP programs interested in assessing their own performance and progress. Published by Elsevier Inc.

Prediction of intrinsic motivation and sports performance using 2 x 2 achievement goal framework.

Science.gov (United States)

Li, Chiung-Huang; Chi, Likang; Yeh, Suh-Ruu; Guo, Kwei-Bin; Ou, Cheng-Tsung; Kao, Chun-Chieh

2011-04-01

The purpose of this study was to examine the influence of 2 x 2 achievement goals on intrinsic motivation and performance in handball. Participants were 164 high school athletes. All completed the 2 x 2 Achievement Goals Questionnaire for Sport and the Intrinsic Motivation subscale of the Sport Motivation Scale; the coach for each team rated his athletes' overall sports performance. Using simultaneous-regression analyses, mastery-approach goals positively predicted both intrinsic motivation and performance in sports, whereas performance-avoidance goals negatively predicted sports performance. These results suggest that athletes who pursue task mastery and improvement of their competence perform well and enjoy their participation. In contrast, those who focus on avoiding normative incompetence perform poorly.

Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

DEFF Research Database (Denmark)

Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

2008-01-01

This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...

A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.

Science.gov (United States)

Lai, Fu-Jou; Chang, Hong-Tsun; Huang, Yueh-Min; Wu, Wei-Sheng

2014-01-01

Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation. We collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast. In this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new

Test Program for the Performance Analysis of DNS64 Servers

Directory of Open Access Journals (Sweden)

Gábor Lencse

2015-09-01

Full Text Available In our earlier research papers, bash shell scripts using the host Linux command were applied for testing the performance and stability of different DNS64 server imple­mentations. Because of their inefficiency, a small multi-threaded C/C++ program (named dns64perf was written which can directly send DNS AAAA record queries. After the introduction to the essential theoretical background about the structure of DNS messages and TCP/IP socket interface programming, the design decisions and implementation details of our DNS64 performance test program are disclosed. The efficiency of dns64perf is compared to that of the old method using bash shell scripts. The result is convincing: dns64perf can send at least 95 times more DNS AAAA record queries per second. The source code of dns64perf is published under the GNU GPLv3 license to support the work of other researchers in the field of testing the performance of DNS64 servers.

Predicting story goodness performance from cognitive measures following traumatic brain injury.

Science.gov (United States)

Lê, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-05-01

This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Lê, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. One hundred sixty-seven individuals with TBI participated in the study. Story retellings were analyzed using the SGI protocol. Three cognitive measures--Delis-Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) Sorting Test, Wechsler Memory Scale--Third Edition (WMS-III; Wechsler, 1997) Working Memory Primary Index (WMI), and WMS-III Immediate Memory Primary Index (IMI)--were entered into a multiple linear regression model for each discourse measure. Two sets of regression analyses were performed, the first with the Sorting Test as the first predictor and the second with it as the last. The first set of regression analyses identified the Sorting Test and IMI as the only significant predictors of performance on measures of the SGI. The second set identified all measures as significant predictors when evaluating each step of the regression function. The cognitive variables predicted performance on the SGI measures, although there were differences in the amount of explained variance. The results (a) suggest that storytelling ability draws on a number of underlying skills and (b) underscore the importance of using discrete cognitive tasks rather than broad cognitive indices to investigate the cognitive substrates of discourse.

Performance behavior of prediction filters for respiratory motion compensation in radiotherapy

Directory of Open Access Journals (Sweden)

Jöhl Alexander

2017-09-01

Full Text Available Introduction: In radiotherapy, tumors may move due to the patient’s respiration, which decreases treatment accuracy. Some motion mitigation methods require measuring the tumor position during treatment. Current available sensors often suffer from time delays, which degrade the motion mitigation performance. However, the tumor motion is often periodic and continuous, which allows predicting the motion ahead. Method and Materials: A couch tracking system was simulated in MATLAB and five prediction filters selected from literature were implemented and tested on 51 respiration signals (median length: 103 s. The five filters were the linear filter (LF, the local regression (LOESS, the neural network (NN, the support vector regression (SVR, and the wavelet least mean squares (wLMS. The time delay to compensate was 320 ms. The normalized root mean square error (nRMSE was calculated for all prediction filters and respiration signals. The correlation coefficients between the nRMSE of the prediction filters were computed. Results: The prediction filters were grouped into a low and a high nRMSE group. The low nRMSE group consisted of the LF, the NN, and the wLMS with a median nRMSE of 0.14, 0.15, and 0.14, respectively. The high nRMSE group consisted of the LOESS and the SVR with both a median nRMSE of 0.34. The correlations between the low nRMSE filters were above 0.87 and between the high nRMSE filters it was 0.64. Conclusion: The low nRMSE prediction filters not only have similar median nRMSEs but also similar nRMSEs for the same respiration signals as the high correlation shows. Therefore, good prediction filters perform similarly for identical respiration patterns, which might indicate a minimally achievable nRMSE for a given respiration pattern.

Analysis of predicted and measured performance of an integrated compound parabolic concentrator (ICPC)

Energy Technology Data Exchange (ETDEWEB)

Winston, R.; O' Gallagher, J.J.; Muschaweck, J.; Mahoney, A.R.; Dudley, V.

1999-07-01

A variety of configurations of evacuated Integrated Compound Parabolic Concentrator (ICPC) tubes have been under development for many years. A particularly favorable optical design corresponds to the unit concentration limit for a fin CPC solution which is then coupled to a practical, thin, wedge-shaped absorber. Prototype collector modules using tubes with two different fin orientations (horizontal and vertical) have been fabricated and tested. Comprehensive measurements of the optical characteristics of the reflector and absorber have been used together with a detailed ray trace analysis to predict the optical performance characteristics of these designs. The observed performance agrees well with the predicted performance.

Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

Science.gov (United States)

Moura, Antonio Divino; Hastenrath, Stefan

2004-07-01

Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

Towards cycle-accurate performance predictions for real-time embedded systems

NARCIS (Netherlands)

Triantafyllidis, K.; Bondarev, E.; With, de P.H.N.; Arabnia, H.R.; Deligiannidis, L.; Jandieri, G.

2013-01-01

In this paper we present a model-based performance analysis method for component-based real-time systems, featuring cycle-accurate predictions of latencies and enhanced system robustness. The method incorporates the following phases: (a) instruction-level profiling of SW components, (b) modeling the

Numerical prediction of a bulb turbine performance hill chart through RANS simulations

International Nuclear Information System (INIS)

Guénette, V; Houde, S; Ciocan, G D; Deschênes, C; Dumas, G; Huang, J

2012-01-01

Within the framework of an international research consortium on low-head hydraulic turbine flow dynamics, the predictive behavior of Reynolds Averaged Navier-Stokes (RANS) simulations of the efficiency (η) hill chart of a bulb turbine is investigated. The paper presents the impacts of the blade tip gap and the hub gaps on performance predictions.

Performance Feedback Processing Is Positively Biased As Predicted by Attribution Theory.

Directory of Open Access Journals (Sweden)

Christoph W Korn

Full Text Available A considerable literature on attribution theory has shown that healthy individuals exhibit a positivity bias when inferring the causes of evaluative feedback on their performance. They tend to attribute positive feedback internally (e.g., to their own abilities but negative feedback externally (e.g., to environmental factors. However, all empirical demonstrations of this bias suffer from at least one of the three following drawbacks: First, participants directly judge explicit causes for their performance. Second, participants have to imagine events instead of experiencing them. Third, participants assess their performance only after receiving feedback and thus differences in baseline assessments cannot be excluded. It is therefore unclear whether the classically reported positivity bias generalizes to setups without these drawbacks. Here, we aimed at establishing the relevance of attributions for decision-making by showing an attribution-related positivity bias in a decision-making task. We developed a novel task, which allowed us to test how participants changed their evaluations in response to positive and negative feedback about performance. Specifically, we used videos of actors expressing different facial emotional expressions. Participants were first asked to evaluate the actors' credibility in expressing a particular emotion. After this initial rating, participants performed an emotion recognition task and did--or did not--receive feedback on their veridical performance. Finally, participants re-rated the actors' credibility, which provided a measure of how they changed their evaluations after feedback. Attribution theory predicts that participants change their evaluations of the actors' credibility toward the positive after receiving positive performance feedback and toward the negative after negative performance feedback. Our results were in line with this prediction. A control condition without feedback showed that correct or

Performance Feedback Processing Is Positively Biased As Predicted by Attribution Theory.

Science.gov (United States)

Korn, Christoph W; Rosenblau, Gabriela; Rodriguez Buritica, Julia M; Heekeren, Hauke R

2016-01-01

A considerable literature on attribution theory has shown that healthy individuals exhibit a positivity bias when inferring the causes of evaluative feedback on their performance. They tend to attribute positive feedback internally (e.g., to their own abilities) but negative feedback externally (e.g., to environmental factors). However, all empirical demonstrations of this bias suffer from at least one of the three following drawbacks: First, participants directly judge explicit causes for their performance. Second, participants have to imagine events instead of experiencing them. Third, participants assess their performance only after receiving feedback and thus differences in baseline assessments cannot be excluded. It is therefore unclear whether the classically reported positivity bias generalizes to setups without these drawbacks. Here, we aimed at establishing the relevance of attributions for decision-making by showing an attribution-related positivity bias in a decision-making task. We developed a novel task, which allowed us to test how participants changed their evaluations in response to positive and negative feedback about performance. Specifically, we used videos of actors expressing different facial emotional expressions. Participants were first asked to evaluate the actors' credibility in expressing a particular emotion. After this initial rating, participants performed an emotion recognition task and did--or did not--receive feedback on their veridical performance. Finally, participants re-rated the actors' credibility, which provided a measure of how they changed their evaluations after feedback. Attribution theory predicts that participants change their evaluations of the actors' credibility toward the positive after receiving positive performance feedback and toward the negative after negative performance feedback. Our results were in line with this prediction. A control condition without feedback showed that correct or incorrect performance

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

Performance Confirmation Plan

International Nuclear Information System (INIS)

Lindner, E.N.

2000-01-01

As described, the purpose of the Performance Confirmation Plan is to specify monitoring, testing, and analysis activities for evaluating the accuracy and adequacy of the information used to determine that performance objectives for postclosure will be met. This plan defines a number of specific performance confirmation activities and associated test concepts in support of the MGR that will be implemented to fulfill this purpose. In doing so, the plan defines an approach to identify key factors and processes, predict performance, establish tolerances and test criteria, collect data (through monitoring, testing, and experiments), analyze these data, and recommend appropriate action. The process of defining which factors to address under performance confirmation incorporates input from several areas. In all cases, key performance confirmation factors are those factors which are: (1) important to safety, (2) measurable and predictable, and (3) relevant to the program (i.e., a factor that is affected by construction, emplacement, or is a time-dependent variable). For the present version of the plan, performance confirmation factors important to safety are identified using the principal factors from the RSS (CRWMS M and O 2000a) (which is derived from TSPA analyses) together with other available performance assessment analyses. With this basis, key performance confirmation factors have been identified, and test concepts and test descriptions have been developed in the plan. Other activities are also incorporated into the performance confirmation program outside of these key factors. Additional activities and tests have been incorporated when they are prescribed by requirements and regulations or are necessary to address data needs and model validation requirements relevant to postclosure safety. These other activities have been included with identified factors to construct the overall performance confirmation program

Performance Confirmation Plan

International Nuclear Information System (INIS)

Lindner, E.N.

2000-01-01

As described, the purpose of the Performance Confirmation Plan is to specify monitoring, testing, and analysis activities for evaluating the accuracy and adequacy of the information used to determine that performance objectives for postclosure will be met. This plan defines a number of specific performance confirmation activities and associated test concepts in support of the MGR that will be implemented to fulfill this purpose. In doing so, the plan defines an approach to identify key factors and processes, predict performance, establish tolerances and test criteria, collect data (through monitoring, testing, and experiments), analyze these data, and recommend appropriate action. The process of defining which factors to address under performance confirmation incorporates input from several areas. In all cases, key performance confirmation factors are those factors which are: (1) important to safety, (2) measurable and predictable, and (3) relevant to the program (i.e., a factor that i s affected by construction, emplacement, or is a time-dependent variable). For the present version of the plan, performance confirmation factors important to safety are identified using the principal factors from the RSS (CRWMS M and O 2000a) (which is derived from TSPA analyses) together with other available performance assessment analyses. With this basis, key performance confirmation factors have been identified, and test concepts and test descriptions have been developed in the plan. Other activities are also incorporated into the performance confirmation program outside of these key factors. Additional activities and tests have been incorporated when they are prescribed by requirements and regulations or are necessary to address data needs and model validation requirements relevant to postclosure safety. These other activities have been included with identified factors to construct the overall performance confirmation program

Predicting performance and performance satisfaction: mindfulness and beliefs about the ability to deal with social barriers in sport.

Science.gov (United States)

Blecharz, Jan; Luszczynska, Aleksandra; Scholz, Urte; Schwarzer, Ralf; Siekanska, Malgorzata; Cieslak, Roman

2014-05-01

This research investigates the role of beliefs about the ability to deal with specific social barriers and its relationships to mindfulness, football performance, and satisfaction with one's own and team performance. Study 1 aimed at eliciting these social barriers. Study 2 tested (i) whether self-efficacy referring to social barriers would predict performance over and above task-related self-efficacy and collective efficacy and (ii) the mediating role of self-efficacy to overcome social barriers in the relationship between mindfulness and performance. Participants were football (soccer) players aged 16-21 years (Study 1: N=30; Study 2: N=101, longitudinal sample: n=88). Study 1 resulted in eliciting 82 social barriers referring to team, peer leadership, and coaches. Study 2 showed that task-related self-efficacy and collective efficacy explained performance satisfaction at seven-month follow-up, whereas self-efficacy referring to social barriers explained shooting performance at seven-month follow-up. Indirect associations between mindfulness and performance were found with three types of self-efficacy referring to social barriers, operating as parallel mediators. Results provide evidence for the role of beliefs about the ability to cope with social barriers and show a complex interplay between different types of self-efficacy and collective efficacy in predicting team sport performance.

Performance in the WIPP nondestructive assay performance demonstration program

Energy Technology Data Exchange (ETDEWEB)

Marcinkiewicz, C.J. [Consolidated Technical Services, Inc., Frederick, MD (United States); Connolly, M.J.; Becker, G.K. [Lockheed Martin Idaho Technologies Company, Idaho Falls, ID (United States)

1997-11-01

Measurement facilities performing nondestructive assay (NDA) of wastes intended for disposal at the United States Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) are required to demonstrate their ability to meet specific Quality Assurance Objectives (QAOs). This demonstration is performed, in part, by participation in the NDA Performance Demonstration Program (PDP). The PDP is funded and managed by the Carlsbad Area Office (CAO) of DOE and is conducted by the Idaho National Engineering Laboratory. It tests the characteristics of precision, system bias and/or total uncertainty through the measurement of variable, blind combinations of simulated waste drums and certified radioactive standards. Each facility must successfully participate in the PDP using each different type of measurement system planned for use in waste characterization. The first cycle of the PDP using each different type of measurement system planned for use in waste characterization. The first cycle of the PDP was completed in July 1996 and the second is scheduled for completion by December 1996. Seven sites reported data in cycle 1 for 11 different measurement systems. This paper describes the design and operation of the PDP and provides the performance data from cycle 1. It also describes the preliminary results from cycle 2 and updates the status and future plans for the NDA PDP. 4 refs., 9 figs., 11 tabs.

Designing PV Incentive Programs to Promote Performance: A Reviewof Current Practice in the U.S.

Energy Technology Data Exchange (ETDEWEB)

Barbose, Galen; Wiser, Ryan; Bolinger, Mark

2006-10-06

In the U.S., the increasing financial support for customer-sited photovoltaic (PV) systems provided through publicly-funded incentive programs has heightened concerns about the long-term performance of these systems. Given the barriers that customers face to ensuring that their PV systems perform well, and the responsibility that PV incentive programs bear to ensure that public funds are prudently spent, these programs should, and often do, play a critical role in addressing PV system performance. To provide a point of reference for assessing the current state of the art, and to inform program design efforts going forward, we examine the approaches to encouraging PV system performance used by 32 prominent PV incentive programs in the U.S. We identify eight general strategies or groups of related strategies that these programs have used to address factors that affect performance, and describe key implementation details. Based on this review, we then offer recommendations for how PV incentive programs can be effectively designed to mitigate potential performance issues.

The DECADE performance assessment program

International Nuclear Information System (INIS)

Weber, B.V.; Ottinger, P.F.; Commisso, R.J.; Thompson, J.; Rowley, J.E.; Filios, P.; Babineau, M.A.

1996-01-01

Previous analyses of DECADE Module 1 experiments indicated significant current loss between the plasma opening switch (POS) and an electron-beam load. A program was initiated to diagnose and improve the power flow to assess the performance of a multi-module DECADE system. Power flow measurements between the POS and load indicate high vacuum flow, distributed current loss and azimuthal asymmetries. A decreased load impedance reduces the fraction of the load current flowing in vacuum. Improved plasma source symmetry reduces losses near the load for long conduction times. Increased POS impedance is required to significantly improve the power coupling to the load. (author). 6 figs., 9 refs

The DECADE performance assessment program

Energy Technology Data Exchange (ETDEWEB)

Weber, B V; Ottinger, P F; Commisso, R J [Naval Research Lab., Washington, DC (United States). Plasma Physics Div.; Goyer, J R; Kortbawi, D [Physics International Co., Berkeley, CA (United States); Thompson, J [Maxwell Labs., San Diego, CA (United States); Rowley, J E; Filios, P [Defense Nuclear Agency, Alexandria, VA (United States); Babineau, M A [Sverdlup Technology, Tullahoma, TN (United States)

1997-12-31

Previous analyses of DECADE Module 1 experiments indicated significant current loss between the plasma opening switch (POS) and an electron-beam load. A program was initiated to diagnose and improve the power flow to assess the performance of a multi-module DECADE system. Power flow measurements between the POS and load indicate high vacuum flow, distributed current loss and azimuthal asymmetries. A decreased load impedance reduces the fraction of the load current flowing in vacuum. Improved plasma source symmetry reduces losses near the load for long conduction times. Increased POS impedance is required to significantly improve the power coupling to the load. (author). 6 figs., 9 refs.

Thinking processes used by high-performing students in a computer programming task

Directory of Open Access Journals (Sweden)

Marietjie Havenga

2011-07-01

Full Text Available Computer programmers must be able to understand programming source code and write programs that execute complex tasks to solve real-world problems. This article is a trans- disciplinary study at the intersection of computer programming, education and psychology. It outlines the role of mental processes in the process of programming and indicates how successful thinking processes can support computer science students in writing correct and well-defined programs. A mixed methods approach was used to better understand the thinking activities and programming processes of participating students. Data collection involved both computer programs and students’ reflective thinking processes recorded in their journals. This enabled analysis of psychological dimensions of participants’ thinking processes and their problem-solving activities as they considered a programming problem. Findings indicate that the cognitive, reflective and psychological processes used by high-performing programmers contributed to their success in solving a complex programming problem. Based on the thinking processes of high performers, we propose a model of integrated thinking processes, which can support computer programming students. Keywords: Computer programming, education, mixed methods research, thinking processes.  Disciplines: Computer programming, education, psychology

Image processing system performance prediction and product quality evaluation

Science.gov (United States)

Stein, E. K.; Hammill, H. B. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A new technique for image processing system performance prediction and product quality evaluation was developed. It was entirely objective, quantitative, and general, and should prove useful in system design and quality control. The technique and its application to determination of quality control procedures for the Earth Resources Technology Satellite NASA Data Processing Facility are described.

Enhancing performance of next generation FSO communication systems using soft computing-based predictions.

Science.gov (United States)

Kazaura, Kamugisha; Omae, Kazunori; Suzuki, Toshiji; Matsumoto, Mitsuji; Mutafungwa, Edward; Korhonen, Timo O; Murakami, Tadaaki; Takahashi, Koichi; Matsumoto, Hideki; Wakamori, Kazuhiko; Arimoto, Yoshinori

2006-06-12

The deterioration and deformation of a free-space optical beam wave-front as it propagates through the atmosphere can reduce the link availability and may introduce burst errors thus degrading the performance of the system. We investigate the suitability of utilizing soft-computing (SC) based tools for improving performance of free-space optical (FSO) communications systems. The SC based tools are used for the prediction of key parameters of a FSO communications system. Measured data collected from an experimental FSO communication system is used as training and testing data for a proposed multi-layer neural network predictor (MNNP) used to predict future parameter values. The predicted parameters are essential for reducing transmission errors by improving the antenna's accuracy of tracking data beams. This is particularly essential for periods considered to be of strong atmospheric turbulence. The parameter values predicted using the proposed tool show acceptable conformity with original measurements.

Rotary engine performance computer program (RCEMAP and RCEMAPPC): User's guide

Science.gov (United States)

Bartrand, Timothy A.; Willis, Edward A.

1993-01-01

This report is a user's guide for a computer code that simulates the performance of several rotary combustion engine configurations. It is intended to assist prospective users in getting started with RCEMAP and/or RCEMAPPC. RCEMAP (Rotary Combustion Engine performance MAP generating code) is the mainframe version, while RCEMAPPC is a simplified subset designed for the personal computer, or PC, environment. Both versions are based on an open, zero-dimensional combustion system model for the prediction of instantaneous pressures, temperature, chemical composition and other in-chamber thermodynamic properties. Both versions predict overall engine performance and thermal characteristics, including bmep, bsfc, exhaust gas temperature, average material temperatures, and turbocharger operating conditions. Required inputs include engine geometry, materials, constants for use in the combustion heat release model, and turbomachinery maps. Illustrative examples and sample input files for both versions are included.

In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models.

Science.gov (United States)

Sjögren, Erik; Thörn, Helena; Tannergren, Christer

2016-06-06

Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and

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

45 CFR 1183.40 - Monitoring and reporting program performance.

Science.gov (United States)

2010-10-01

... FOUNDATION ON THE ARTS AND THE HUMANITIES INSTITUTE OF MUSEUM AND LIBRARY SERVICES UNIFORM ADMINISTRATIVE... must cover each program, function or activity. (b) Nonconstruction performance reports. The Federal...

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

US Department of Energy Mixed Waste Integrated Program performance systems analysis

International Nuclear Information System (INIS)

Ferrada, J.J.; Berry, J.B.

1994-01-01

The primary goal of this project is to support decision making for the U.S. Department of Energy (DOE)/EM-50 Mixed Waste Integrated Program (MWIP) and the Mixed Low-Level Waste Program. A systems approach to the assessment of enhanced waste form(s) production will be employed including, coordination and configuration management of activities in specific technology development tasks. The purpose of this paper is to describe the development and application of a methodology for implementing a performance systems analysis on mixed waste treatment process technologies. The second section describes a conventional approach to process systems analysis followed by a methodology to estimate uncertainties when analyzing innovative technologies. Principles from these methodologies have been used to develop a performance systems analysis for MWIP. The third section describes the systems analysis tools. The fourth section explains how the performance systems analysis will be used to analyze MWIP process alternatives. The fifth and sixth sections summarize this paper and describe future work for this project. Baseline treatment process technologies (i.e., commercially available technologies) and waste management strategies are evaluated systematically using the ASPEN PLUS program applications developed by the DOE Mixed Waste Treatment Project (MWTP). Alternatives to the baseline (i.e., technologies developed by DOE's Office of Technology Development) are analyzed using FLOW, a user-friendly program developed at Oak Ridge National Laboratory (ORNL). Currently, this program is capable of calculating rough order-of-magnitude mass and energy balances to assess the performance of the alternative technologies as compared to the baseline process. In the future, FLOW will be capable of communicating information to the ASPEN PLUS program

Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

DEFF Research Database (Denmark)

Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

2008-01-01

This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...

Neither here, nor there: impression management does not predict expatriate adjustment and job performance

OpenAIRE

HANNAH JACKSON FOLDES; DENIZ S. ONES; HANDAN KEPIR SINANGIL

2006-01-01

Social desirability scale scores reflect substantive individual differences related to personality. The objective of the current study was to examine whether social desirability, and impression management specifically (a component of social desirability), is predictive of adjustment and job performance for expatriates. Based on theoretical considerations, it was proposed that impression management might be linked to expatriate job performance in a predictive and mediated relationship through ...

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

Modeling and prediction of flotation performance using support vector regression

Directory of Open Access Journals (Sweden)

Despotović Vladimir

2017-01-01

Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

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.

The role of sleep in predicting college academic performance: is it a unique predictor?

Science.gov (United States)

Taylor, Daniel J; Vatthauer, Karlyn E; Bramoweth, Adam D; Ruggero, Camilo; Roane, Brandy

2013-01-01

Few studies have looked at the predictability of academic performance (i.e., cumulative grade point average [GPA]) using sleep when common nonsleep predictors of academic performance are included. This project studied psychological, demographic, educational, and sleep risk factors of decreased academic performance in college undergraduates. Participants (N = 867) completed a questionnaire packet and sleep diary. It was hypothesized that low total sleep time (TST), increased sleep onset latency, later bedtimes, later wake times, and TST inconsistency would predict decreased academic performance. The most significant predictors of academic performance were high school GPA, standardized test scores (i.e., SAT/ACT), TST, time awake before arising (TWAK), TST inconsistency, and the quadratic terms of perceived stress (PSS) and TST.