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Sample records for performance predictions based

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

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

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

  2. 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.

  3. 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)

  4. 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.

  5. 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.

  6. 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

  7. Decline Curve Based Models for Predicting Natural Gas Well Performance

    OpenAIRE

    Kamari, Arash; Mohammadi, Amir H.; Lee, Moonyong; Mahmood, Tariq; Bahadori, Alireza

    2016-01-01

    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 ...

  8. 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

  9. 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.

  10. Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    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.

  11. 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.

  12. 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.

  13. Does teacher evaluation based on student performance predict motivation, well-being, and ill-being?

    Science.gov (United States)

    Cuevas, Ricardo; Ntoumanis, Nikos; Fernandez-Bustos, Juan G; Bartholomew, Kimberley

    2018-06-01

    This study tests an explanatory model based on self-determination theory, which posits that pressure experienced by teachers when they are evaluated based on their students' academic performance will differentially predict teacher adaptive and maladaptive motivation, well-being, and ill-being. A total of 360 Spanish physical education teachers completed a multi-scale inventory. We found support for a structural equation model that showed that perceived pressure predicted teacher autonomous motivation negatively, predicted amotivation positively, and was unrelated to controlled motivation. In addition, autonomous motivation predicted vitality positively and exhaustion negatively, whereas controlled motivation and amotivation predicted vitality negatively and exhaustion positively. Amotivation significantly mediated the relation between pressure and vitality and between pressure and exhaustion. The results underline the potential negative impact of pressure felt by teachers due to this type of evaluation on teacher motivation and psychological health. Copyright © 2018 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

  14. 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.

  15. An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.

    Directory of Open Access Journals (Sweden)

    Qian Wan

    Full Text Available We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

  16. 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

  17. Simulation-Based Performance Evaluation of Predictive-Hashing Based Multicast Authentication Protocol

    Directory of Open Access Journals (Sweden)

    Seonho Choi

    2012-12-01

    Full Text Available A predictive-hashing based Denial-of-Service (DoS resistant multicast authentication protocol was proposed based upon predictive-hashing, one-way key chain, erasure codes, and distillation codes techniques [4, 5]. It was claimed that this new scheme should be more resistant to various types of DoS attacks, and its worst-case resource requirements were derived in terms of coarse-level system parameters including CPU times for signature verification and erasure/distillation decoding operations, attack levels, etc. To show the effectiveness of our approach and to analyze exact resource requirements in various attack scenarios with different parameter settings, we designed and implemented an attack simulator which is platformindependent. Various attack scenarios may be created with different attack types and parameters against a receiver equipped with the predictive-hashing based protocol. The design of the simulator is explained, and the simulation results are presented with detailed resource usage statistics. In addition, resistance level to various types of DoS attacks is formulated with a newly defined resistance metric. By comparing these results to those from another approach, PRABS [8], we show that the resistance level of our protocol is greatly enhanced even in the presence of many attack streams.

  18. Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.

    Directory of Open Access Journals (Sweden)

    Camille Jeunet

    Full Text Available Mental-Imagery based Brain-Computer Interfaces (MI-BCIs allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG, which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants' BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants' performance with a mean error of less than 3 points. This study determined how users' profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.

  19. Assess and Predict Automatic Generation Control Performances for Thermal Power Generation Units Based on Modeling Techniques

    Science.gov (United States)

    Zhao, Yan; Yang, Zijiang; Gao, Song; Liu, Jinbiao

    2018-02-01

    Automatic generation control(AGC) is a key technology to maintain real time power generation and load balance, and to ensure the quality of power supply. Power grids require each power generation unit to have a satisfactory AGC performance, being specified in two detailed rules. The two rules provide a set of indices to measure the AGC performance of power generation unit. However, the commonly-used method to calculate these indices is based on particular data samples from AGC responses and will lead to incorrect results in practice. This paper proposes a new method to estimate the AGC performance indices via system identification techniques. In addition, a nonlinear regression model between performance indices and load command is built in order to predict the AGC performance indices. The effectiveness of the proposed method is validated through industrial case studies.

  20. Predicting the Performance of Chain Saw Machines Based on Shore Scleroscope Hardness

    Science.gov (United States)

    Tumac, Deniz

    2014-03-01

    Shore hardness has been used to estimate several physical and mechanical properties of rocks over the last few decades. However, the number of researches correlating Shore hardness with rock cutting performance is quite limited. Also, rather limited researches have been carried out on predicting the performance of chain saw machines. This study differs from the previous investigations in the way that Shore hardness values (SH1, SH2, and deformation coefficient) are used to determine the field performance of chain saw machines. The measured Shore hardness values are correlated with the physical and mechanical properties of natural stone samples, cutting parameters (normal force, cutting force, and specific energy) obtained from linear cutting tests in unrelieved cutting mode, and areal net cutting rate of chain saw machines. Two empirical models developed previously are improved for the prediction of the areal net cutting rate of chain saw machines. The first model is based on a revised chain saw penetration index, which uses SH1, machine weight, and useful arm cutting depth as predictors. The second model is based on the power consumed for only cutting the stone, arm thickness, and specific energy as a function of the deformation coefficient. While cutting force has a strong relationship with Shore hardness values, the normal force has a weak or moderate correlation. Uniaxial compressive strength, Cerchar abrasivity index, and density can also be predicted by Shore hardness values.

  1. Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains

    Science.gov (United States)

    Kang, Yong-Shin; Park, Il-Ha; Youm, Sekyoung

    2016-01-01

    In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase. PMID:27983654

  2. Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains.

    Science.gov (United States)

    Kang, Yong-Shin; Park, Il-Ha; Youm, Sekyoung

    2016-12-14

    In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase.

  3. Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains

    Directory of Open Access Journals (Sweden)

    Yong-Shin Kang

    2016-12-01

    Full Text Available In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase.

  4. The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression

    Directory of Open Access Journals (Sweden)

    Chunxiao Zhang

    2012-01-01

    Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

  5. Reliability residual-life prediction method for thermal aging based on performance degradation

    International Nuclear Information System (INIS)

    Ren Shuhong; Xue Fei; Yu Weiwei; Ti Wenxin; Liu Xiaotian

    2013-01-01

    The paper makes the study of the nuclear power plant main pipeline. The residual-life of the main pipeline that failed due to thermal aging has been studied by the use of performance degradation theory and Bayesian updating methods. Firstly, the thermal aging impact property degradation process of the main pipeline austenitic stainless steel has been analyzed by the accelerated thermal aging test data. Then, the thermal aging residual-life prediction model based on the impact property degradation data is built by Bayesian updating methods. Finally, these models are applied in practical situations. It is shown that the proposed methods are feasible and the prediction accuracy meets the needs of the project. Also, it provides a foundation for the scientific management of aging management of the main pipeline. (authors)

  6. 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.

  7. 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.

  8. PV (photovoltaics) performance evaluation and simulation-based energy yield prediction for tropical buildings

    International Nuclear Information System (INIS)

    Saber, Esmail M.; Lee, Siew Eang; Manthapuri, Sumanth; Yi, Wang; Deb, Chirag

    2014-01-01

    Air pollution and climate change increased the importance of renewable energy resources like solar energy in the last decades. Rack-mounted PhotoVoltaics (PV) and Building Integrated PhotoVoltaics (BIPV) are the most common photovoltaic systems which convert incident solar radiation on façade or surrounding area to electricity. In this paper the performance of different solar cell types is evaluated for the tropical weather of Singapore. As a case study, on-site measured data of PV systems implemented in a zero energy building in Singapore, is analyzed. Different types of PV systems (silicon wafer and thin film) have been installed on rooftop, façade, car park shelter, railing and etc. The impact of different solar cell generations, arrays environmental conditions (no shading, dappled shading, full shading), orientation (South, North, East or West facing) and inclination (between PV module and horizontal direction) is investigated on performance of modules. In the second stage of research, the whole PV systems in the case study are simulated in EnergyPlus energy simulation software with several PV performance models including Simple, Equivalent one-diode and Sandia. The predicted results by different models are compared with measured data and the validated model is used to provide simulation-based energy yield predictions for wide ranges of scenarios. It has been concluded that orientation of low-slope rooftop PV has negligible impact on annual energy yield but in case of PV external sunshade, east façade and panel slope of 30–40° are the most suitable location and inclination. - Highlights: • Characteristics of PV systems in tropics are analyzed in depth. • The ambiguity toward amorphous panel energy yield in tropics is discussed. • Equivalent-one diode and Sandia models can fairly predict the energy yield. • A general guideline is provided to estimate the energy yield of PV systems in tropics

  9. Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

    2018-02-01

    Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

  10. Robust and predictive fuzzy key performance indicators for condition-based treatment of squats in railway infrastructures

    NARCIS (Netherlands)

    Jamshidi, A.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Li, Z.

    2017-01-01

    This paper presents a condition-based treatment methodology for a type of rail surface defect called squat. The proposed methodology is based on a set of robust and predictive fuzzy key performance indicators. A fuzzy Takagi-Sugeno interval model is used to predict squat evolution for different

  11. Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

    Science.gov (United States)

    Wang, Shan; Jiang, Zhi-Qiang; Li, Sai-Ping; Zhou, Wei-Xing

    2015-12-01

    Technical trading rules have a long history of being used by practitioners in financial markets. The profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousand traditional technical trading rules on the Shanghai Securities Composite Index (SSCI) from May 21, 1992 through June 30, 2013 and China Securities Index 300 (CSI 300) from April 8, 2005 through June 30, 2013 to check whether an effective trading strategy could be found by using the performance measurements based on the return and Sharpe ratio. To correct for the influence of the data-snooping effect, we adopt the Superior Predictive Ability test to evaluate if there exists a trading rule that can significantly outperform the benchmark. The result shows that for SSCI, technical trading rules offer significant profitability, while for CSI 300, this ability is lost. We further partition the SSCI into two sub-series and find that the efficiency of technical trading in sub-series, which have exactly the same spanning period as that of CSI 300, is severely weakened. By testing the trading rules on both indexes with a five-year moving window, we find that during the financial bubble from 2005 to 2007, the effectiveness of technical trading rules is greatly improved. This is consistent with the predictive ability of technical trading rules which appears when the market is less efficient.

  12. 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.

  13. Determination of Constructs and Dimensions of Employability Skills Based Work Performance Prediction: A Triangular Approach

    OpenAIRE

    Rahmat, Normala; Buntat, Yahya; Ayub, Abdul Rahman

    2015-01-01

    The level of the employability skills of the graduates as determined by job role and mapped to the employability skills, which correspond to the requirement of employers, will have significant impact on the graduates’ job performance. The main objective of this study was to identify the constructs and dimensions of employability skills, which can predict the work performance of electronic polytechnic graduate in electrical and electronics industry. A triangular qualitative approach was used i...

  14. Comparison of ITER performance predicted by semi-empirical and theory-based transport models

    International Nuclear Information System (INIS)

    Mukhovatov, V.; Shimomura, Y.; Polevoi, A.

    2003-01-01

    The values of Q=(fusion power)/(auxiliary heating power) predicted for ITER by three different methods, i.e., transport model based on empirical confinement scaling, dimensionless scaling technique, and theory-based transport models are compared. The energy confinement time given by the ITERH-98(y,2) scaling for an inductive scenario with plasma current of 15 MA and plasma density 15% below the Greenwald value is 3.6 s with one technical standard deviation of ±14%. These data are translated into a Q interval of [7-13] at the auxiliary heating power P aux = 40 MW and [7-28] at the minimum heating power satisfying a good confinement ELMy H-mode. Predictions of dimensionless scalings and theory-based transport models such as Weiland, MMM and IFS/PPPL overlap with the empirical scaling predictions within the margins of uncertainty. (author)

  15. Gate valve performance prediction

    International Nuclear Information System (INIS)

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

    1994-01-01

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

  16. Prediction of Lunar- and Martian-Based Intra- and Site-to-Site Task Performance.

    Science.gov (United States)

    Ade, Carl J; Broxterman, Ryan M; Craig, Jesse C; Schlup, Susanna J; Wilcox, Samuel L; Warren, Steve; Kuehl, Phillip; Gude, Dana; Jia, Chen; Barstow, Thomas J

    2016-04-01

    This study aimed to investigate the feasibility of determining the physiological parameters associated with the ability to complete simulated exploration type tasks at metabolic rates which might be expected for lunar and Martian ambulation. Running V̇O2max and gas exchange threshold (GET) were measured in 21 volunteers. Two simulated extravehicular activity field tests were completed in 1 G in regular athletic apparel at two intensities designed to elicit metabolic rates of ∼20.0 and ∼30.0 ml · kg(-1) · min(-1), which are similar to those previously reported for ambulation in simulated lunar- and Martian-based environments, respectively. All subjects were able to complete the field test at the lunar intensity, but 28% were unable to complete the field test at the Martian intensity (non-Finishers). During the Martian field test there were no differences in V̇O2 between Finishers and non-Finishers, but the non-Finishers achieved a greater %V̇O2max compared to Finishers (78.4 ± 4.6% vs. 64.9 ± 9.6%). Logistic regression analysis revealed fitness thresholds for a predicted probability of 0.5, at which Finishing and non-Finishing are equally likely, and 0.75, at which an individual has a 75% chance of Finishing, to be a V̇O2max of 38.4 ml · kg(-1) · min(-1) and 40.0 ml · kg(-1) · min(-1) or a GET of 20.1 ml · kg(-1) · min(-1) and 25.1 ml · kg(-1) · min(-1), respectively (χ(2) = 10.2). Logistic regression analysis also revealed that the expected %V̇O2max required to complete a field test could be used to successfully predict performance (χ(2) = 19.3). The results of the present investigation highlight the potential utility of V̇O2max, particularly as it relates to the metabolic demands of a surface ambulation, in defining successful completion of planetary-based exploration field tests.

  17. Are performance-based measures predictive of work participation in patients with musculoskeletal disorders? A systematic review.

    Science.gov (United States)

    Kuijer, P P F M; Gouttebarge, V; Brouwer, S; Reneman, M F; Frings-Dresen, M H W

    2012-02-01

    Assessments of whether patients with musculoskeletal disorders (MSDs) can participate in work mainly consist of case history, physical examinations, and self-reports. Performance-based measures might add value in these assessments. This study answers the question: how well do performance-based measures predict work participation in patients with MSDs? A systematic literature search was performed to obtain longitudinal studies that used reliable performance-based measures to predict work participation in patients with MSDs. The following five sources of information were used to retrieve relevant studies: PubMed, Embase, AMA Guide to the Evaluation of Functional Ability, references of the included papers, and the expertise and personal file of the authors. A quality assessment specific for prognostic studies and an evidence synthesis were performed. Of the 1,230 retrieved studies, eighteen fulfilled the inclusion criteria. The studies included 4,113 patients, and the median follow-up period was 12 months. Twelve studies took possible confounders into account. Five studies were of good quality and thirteen of moderate quality. Two good-quality and all thirteen moderate-quality studies (83%) reported that performance-based measures were predictive of work participation. Two good-quality studies (11%) reported both an association and no association between performance-based measures and work participation. One good-quality study (6%) found no effect. A performance-based lifting test was used in fourteen studies and appeared to be predictive of work participation in thirteen studies. Strong evidence exists that a number of performance-based measures are predictive of work participation in patients with MSDs, especially lifting tests. Overall, the explained variance was modest.

  18. Performance predictions for solar-chemical converters based on photoelectrochemical I-V curves

    Energy Technology Data Exchange (ETDEWEB)

    Luttmer, J.D.; Trachtenberg, I.

    1985-06-01

    Texas Instruments' solar energy system contains a solar-chemical converter (SCC) which converts solar energy into chemical energy via the electrolysis of hydrobromic acid (HBr) into hydrogen (H/sub 2/) and bromine (Br/sub 2/). Previous predictions of SCC performance have employed electrical dry-probe data and a computer simulation model to predict the H/sub 2/ generation rates. The method of prediction described here makes use of the photoelectrochemical Icurves to determine the ''wet'' probe parameters of V /SUB oc/ J /SUB sc/ FF, and efficiency for anodes and cathodes. The advantages of this technique over the dry-probe/computer simulation method are discussed. A comparison of predicted and measured H/sub 2/ generation rates is presented. Solar to chemical efficiencies of 8.6% have been both predicted and measured for the electrolysis of 48% HBr to hydrogen and bromine by a full anode/cathode array. Individual cathode solar to hydrogen efficiencies of 9.5% have been obtained.

  19. RANS Based Methodology for Predicting the Influence of Leading Edge Erosion on Airfoil Performance

    Energy Technology Data Exchange (ETDEWEB)

    Langel, Christopher M. [Univ. of California, Davis, CA (United States). Dept. of Mechanical and Aerospace Engineering; Chow, Raymond C. [Univ. of California, Davis, CA (United States). Dept. of Mechanical and Aerospace Engineering; van Dam, C. P. [Univ. of California, Davis, CA (United States). Dept. of Mechanical and Aerospace Engineering; Maniaci, David Charles [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Wind Energy Technologies Dept.

    2017-10-01

    The impact of surface roughness on flows over aerodynamically designed surfaces is of interested in a number of different fields. It has long been known the surface roughness will likely accelerate the laminar- turbulent transition process by creating additional disturbances in the boundary layer. However, there are very few tools available to predict the effects surface roughness will have on boundary layer flow. There are numerous implications of the premature appearance of a turbulent boundary layer. Increases in local skin friction, boundary layer thickness, and turbulent mixing can impact global flow properties compounding the effects of surface roughness. With this motivation, an investigation into the effects of surface roughness on boundary layer transition has been conducted. The effort involved both an extensive experimental campaign, and the development of a high fidelity roughness model implemented in a R ANS solver. Vast a mounts of experimental data was generated at the Texas A&M Oran W. Nicks Low Speed Wind Tunnel for the calibration and validation of the roughness model described in this work, as well as future efforts. The present work focuses on the development of the computational model including a description of the calibration process. The primary methodology presented introduces a scalar field variable and associated transport equation that interacts with a correlation based transition model. The additional equation allows for non-local effects of surface roughness to be accounted for downstream of rough wall sections while maintaining a "local" formulation. The scalar field is determined through a boundary condition function that has been calibrated to flat plate cases with sand grain roughness. The model was initially tested on a NACA 0012 airfoil with roughness strips applied to the leading edge. Further calibration of the roughness model was performed using results from the companion experimental study on a NACA 633 -418 airfoil

  20. 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.

  1. Chromosomal regions involved in hybrid performance and heterosis : their AFLP-based identification and practical use in prediction models

    NARCIS (Netherlands)

    Vuylsteke, M.; Kuiper, M.; Stam, P.

    2000-01-01

    In this paper, a novel approach towards the prediction of hybrid performance and heterosis is presented. Here, we describe an approach based on: (i) the assessment of associations between AFLPÒ22 AFLPÒ is a registered trademark of Keygene N.V. ,33 The methylation AFLPÒ method is subject to a patent

  2. In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment

    Directory of Open Access Journals (Sweden)

    Chitale Meghana

    2013-02-01

    Full Text Available Abstract Background Many Automatic Function Prediction (AFP methods were developed to cope with an increasing growth of the number of gene sequences that are available from high throughput sequencing experiments. To support the development of AFP methods, it is essential to have community wide experiments for evaluating performance of existing AFP methods. Critical Assessment of Function Annotation (CAFA is one such community experiment. The meeting of CAFA was held as a Special Interest Group (SIG meeting at the Intelligent Systems in Molecular Biology (ISMB conference in 2011. Here, we perform a detailed analysis of two sequence-based function prediction methods, PFP and ESG, which were developed in our lab, using the predictions submitted to CAFA. Results We evaluate PFP and ESG using four different measures in comparison with BLAST, Prior, and GOtcha. In addition to the predictions submitted to CAFA, we further investigate performance of a different scoring function to rank order predictions by PFP as well as PFP/ESG predictions enriched with Priors that simply adds frequently occurring Gene Ontology terms as a part of predictions. Prediction accuracies of each method were also evaluated separately for different functional categories. Successful and unsuccessful predictions by PFP and ESG are also discussed in comparison with BLAST. Conclusion The in-depth analysis discussed here will complement the overall assessment by the CAFA organizers. Since PFP and ESG are based on sequence database search results, our analyses are not only useful for PFP and ESG users but will also shed light on the relationship of the sequence similarity space and functions that can be inferred from the sequences.

  3. Ground-based adaptive optics coronagraphic performance under closed-loop predictive control

    Science.gov (United States)

    Males, Jared R.; Guyon, Olivier

    2018-01-01

    The discovery of the exoplanet Proxima b highlights the potential for the coming generation of giant segmented mirror telescopes (GSMTs) to characterize terrestrial-potentially habitable-planets orbiting nearby stars with direct imaging. This will require continued development and implementation of optimized adaptive optics systems feeding coronagraphs on the GSMTs. Such development should proceed with an understanding of the fundamental limits imposed by atmospheric turbulence. Here, we seek to address this question with a semianalytic framework for calculating the postcoronagraph contrast in a closed-loop adaptive optics system. We do this starting with the temporal power spectra of the Fourier basis calculated assuming frozen flow turbulence, and then apply closed-loop transfer functions. We include the benefits of a simple predictive controller, which we show could provide over a factor of 1400 gain in raw point spread function contrast at 1 λ/D on bright stars, and more than a factor of 30 gain on an I=7.5 mag star such as Proxima. More sophisticated predictive control can be expected to improve this even further. Assuming a photon-noise limited observing technique such as high-dispersion coronagraphy, these gains in raw contrast will decrease integration times by the same large factors. Predictive control of atmospheric turbulence should therefore be seen as one of the key technologies that will enable ground-based telescopes to characterize terrestrial planets.

  4. 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

  5. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

  6. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

    Science.gov (United States)

    Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

    2017-01-01

    Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  7. Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

    Directory of Open Access Journals (Sweden)

    Rebeca Cerezo

    2017-08-01

    Full Text Available Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs. Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques.Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples.Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance.Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

  8. Performance of a process-based hydrodynamic model in predicting shoreline change

    Science.gov (United States)

    Safak, I.; Warner, J. C.; List, J. H.

    2012-12-01

    Shoreline change is controlled by a complex combination of processes that include waves, currents, sediment characteristics and availability, geologic framework, human interventions, and sea level rise. A comprehensive data set of shoreline position (14 shorelines between 1978-2002) along the continuous and relatively non-interrupted North Carolina Coast from Oregon Inlet to Cape Hatteras (65 km) reveals a spatial pattern of alternating erosion and accretion, with an erosional average shoreline change rate of -1.6 m/yr and up to -8 m/yr in some locations. This data set gives a unique opportunity to study long-term shoreline change in an area hit by frequent storm events while relatively uninfluenced by human interventions and the effects of tidal inlets. Accurate predictions of long-term shoreline change may require a model that accurately resolves surf zone processes and sediment transport patterns. Conventional methods for predicting shoreline change such as one-line models and regression of shoreline positions have been designed for computational efficiency. These methods, however, not only have several underlying restrictions (validity for small angle of wave approach, assuming bottom contours and shoreline to be parallel, depth of closure, etc.) but also their empirical estimates of sediment transport rates in the surf zone have been shown to vary greatly from the calculations of process-based hydrodynamic models. We focus on hind-casting long-term shoreline change using components of the process-based, three-dimensional coupled-ocean-atmosphere-wave-sediment transport modeling system (COAWST). COAWST is forced with historical predictions of atmospheric and oceanographic data from public-domain global models. Through a method of coupled concurrent grid-refinement approach in COAWST, the finest grid with resolution of O(10 m) that covers the surf zone along the section of interest is forced at its spatial boundaries with waves and currents computed on the grids

  9. 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

  10. Predicting the performance of amorphous and crystalline silicon based photovoltaic solar thermal collectors

    International Nuclear Information System (INIS)

    Daghigh, Ronak; Ibrahim, Adnan; Jin, Goh Li; Ruslan, Mohd Hafidz; Sopian, Kamaruzzaman

    2011-01-01

    BIPVT is an application where solar PV/T modules are integrated into the building structure. System design parameters such as thermal conductivity and fin efficiency, type of cells, type of coolant and operating conditions are factors which influence the performance of BIPVT. Attempts have been made to improve the efficiency of building-integrated photovoltaic thermal (BIPVT). A new design concept of water-based PVT collector for building-integrated applications has been designed and evaluated. The results of simulation study of amorphous silicon (a-Si) PV/T and crystalline silicon (c-Si) module types are based on the metrological condition of Malaysia for a typical day in March. At a flow rate of 0.02 kg/s, solar radiation level between 700 and 900 W/m 2 and ambient temperature between 22 and 32 o C, the electrical, thermal and combined photovoltaic thermal efficiencies for the PV/T (a-Si) were 4.9%, 72% and 77%, respectively. Moreover, the electrical, thermal and combined photovoltaic thermal efficiencies of the PV/T (c-Si) were 11.6%, 51% and 63%.

  11. Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

    Science.gov (United States)

    Mainsah, B. O.; Reeves, G.; Collins, L. M.; Throckmorton, C. S.

    2017-08-01

    Objective. The role of a brain-computer interface (BCI) is to discern a user’s intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. Approach. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. Main results. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional

  12. Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.

    Science.gov (United States)

    Mainsah, B O; Reeves, G; Collins, L M; Throckmorton, C S

    2017-08-01

    The role of a brain-computer interface (BCI) is to discern a user's intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. By accounting for

  13. Artificial neural network analysis based on genetic algorithm to predict the performance characteristics of a cross flow cooling tower

    Science.gov (United States)

    Wu, Jiasheng; Cao, Lin; Zhang, Guoqiang

    2018-02-01

    Cooling tower of air conditioning has been widely used as cooling equipment, and there will be broad application prospect if it can be reversibly used as heat source under heat pump heating operation condition. In view of the complex non-linear relationship of each parameter in the process of heat and mass transfer inside tower, In this paper, the BP neural network model based on genetic algorithm optimization (GABP neural network model) is established for the reverse use of cross flow cooling tower. The model adopts the structure of 6 inputs, 13 hidden nodes and 8 outputs. With this model, the outlet air dry bulb temperature, wet bulb temperature, water temperature, heat, sensible heat ratio and heat absorbing efficiency, Lewis number, a total of 8 the proportion of main performance parameters were predicted. Furthermore, the established network model is used to predict the water temperature and heat absorption of the tower at different inlet temperatures. The mean relative error MRE between BP predicted value and experimental value are 4.47%, 3.63%, 2.38%, 3.71%, 6.35%,3.14%, 13.95% and 6.80% respectively; the mean relative error MRE between GABP predicted value and experimental value are 2.66%, 3.04%, 2.27%, 3.02%, 6.89%, 3.17%, 11.50% and 6.57% respectively. The results show that the prediction results of GABP network model are better than that of BP network model; the simulation results are basically consistent with the actual situation. The GABP network model can well predict the heat and mass transfer performance of the cross flow cooling tower.

  14. Empirical component model to predict the overall performance of heating coils: Calibrations and tests based on manufacturer catalogue data

    International Nuclear Information System (INIS)

    Ruivo, Celestino R.; Angrisani, Giovanni

    2015-01-01

    Highlights: • An empirical model for predicting the performance of heating coils is presented. • Low and high heating capacity cases are used for calibration. • Versions based on several effectiveness correlations are tested. • Catalogue data are considered in approach testing. • The approach is a suitable component model to be used in dynamic simulation tools. - Abstract: A simplified methodology for predicting the overall behaviour of heating coils is presented in this paper. The coil performance is predicted by the ε-NTU method. Usually manufacturers do not provide information about the overall thermal resistance or the geometric details that are required either for the device selection or to apply known empirical correlations for the estimation of the involved thermal resistances. In the present work, heating capacity tables from the manufacturer catalogue are used to calibrate simplified approaches based on the classical theory of heat exchangers, namely the effectiveness method. Only two reference operating cases are required to calibrate each approach. The validity of the simplified approaches is investigated for a relatively high number of operating cases, listed in the technical catalogue of a manufacturer. Four types of coils of three sizes of air handling units are considered. A comparison is conducted between the heating coil capacities provided by the methodology and the values given by the manufacturer catalogue. The results show that several of the proposed approaches are suitable component models to be integrated in dynamic simulation tools of air conditioning systems such as TRNSYS or EnergyPlus

  15. Preoperative prediction of inpatient recovery of function after total hip arthroplasty using performance-based tests: a prospective cohort study.

    Science.gov (United States)

    Oosting, Ellen; Hoogeboom, Thomas J; Appelman-de Vries, Suzan A; Swets, Adam; Dronkers, Jaap J; van Meeteren, Nico L U

    2016-01-01

    The aim of this study was to evaluate the value of conventional factors, the Risk Assessment and Predictor Tool (RAPT) and performance-based functional tests as predictors of delayed recovery after total hip arthroplasty (THA). A prospective cohort study in a regional hospital in the Netherlands with 315 patients was attending for THA in 2012. The dependent variable recovery of function was assessed with the Modified Iowa Levels of Assistance scale. Delayed recovery was defined as taking more than 3 days to walk independently. Independent variables were age, sex, BMI, Charnley score, RAPT score and scores for four performance-based tests [2-minute walk test, timed up and go test (TUG), 10-meter walking test (10 mW) and hand grip strength]. Regression analysis with all variables identified older age (>70 years), Charnley score C, slow walking speed (10 mW >10.0 s) and poor functional mobility (TUG >10.5 s) as the best predictors of delayed recovery of function. This model (AUC 0.85, 95% CI 0.79-0.91) performed better than a model with conventional factors and RAPT scores, and significantly better (p = 0.04) than a model with only conventional factors (AUC 0.81, 95% CI 0.74-0.87). The combination of performance-based tests and conventional factors predicted inpatient functional recovery after THA. Two simple functional performance-based tests have a significant added value to a more conventional screening with age and comorbidities to predict recovery of functioning immediately after total hip surgery. Patients over 70 years old, with comorbidities, with a TUG score >10.5 s and a walking speed >1.0 m/s are at risk for delayed recovery of functioning. Those high risk patients need an accurate discharge plan and could benefit from targeted pre- and postoperative therapeutic exercise programs.

  16. Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-12-15

    The goal of this work is to predict the daily performance (COP) of a ground-source heat pump (GSHP) system with the minimum data set based on an adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy weighted pre-processing (FWP) method. To evaluate the effectiveness of our proposal (FWP-ANFIS), a computer simulation is developed on MATLAB environment. The comparison of the proposed hybridized system's results with the standard ANFIS results is carried out and the results are given in the tables. The efficiency of the proposed method was demonstrated by using the 3-fold cross-validation test. The statistical methods, such as the root-mean squared (RMS), the coefficient of multiple determinations (R{sup 2}) and the coefficient of variation (cov), are given to compare the predicted and actual values for model validation. The average R{sup 2} values is 0.9998, the average RMS value is 0.0272 and the average cov value is 0.7733, which can be considered as very promising. The data set for the COP of GSHP system available included 38 data patterns. The simulation results show that the FWP-based ANFIS can be used in an alternative way in these systems. The prediction results of the proposed structure were much better than the standard ANFIS results. Therefore, instead of limited experimental data found in the literature, faster and simpler solutions are obtained using hybridized structures such as FWP-based ANFIS. (author)

  17. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    Science.gov (United States)

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. 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

  19. Predicting Students' Academic Performance Based on School and Socio-Demographic Characteristics

    Science.gov (United States)

    Thiele, Tamara; Singleton, Alexander; Pope, Daniel; Stanistreet, Debbi

    2016-01-01

    Students' trajectories into university are often uniquely dependent on school qualifications though these alone are limited as predictors of academic potential. This study endorses this, examining associations between school grades, school type, school performance, socio-economic deprivation, neighbourhood participation, sex and academic…

  20. Outcome measures based on classification performance fail to predict the intelligibility of binary-masked speech

    DEFF Research Database (Denmark)

    Kressner, Abigail Anne; May, Tobias; Rozell, Christopher J.

    2016-01-01

    To date, the most commonly used outcome measure for assessing ideal binary mask estimation algorithms is based on the difference between the hit rate and the false alarm rate (H-FA). Recently, the error distribution has been shown to substantially affect intelligibility. However, H-FA treats each...... evaluations should not be made solely on the basis of these metrics....

  1. EPRI MOV performance prediction program

    International Nuclear Information System (INIS)

    Hosler, J.F.; Damerell, P.S.; Eidson, M.G.; Estep, N.E.

    1994-01-01

    An overview of the EPRI Motor-Operated Valve (MOV) Performance Prediction Program is presented. The objectives of this Program are to better understand the factors affecting the performance of MOVs and to develop and validate methodologies to predict MOV performance. The Program involves valve analytical modeling, separate-effects testing to refine the models, and flow-loop and in-plant MOV testing to provide a basis for model validation. The ultimate product of the Program is an MOV Performance Prediction Methodology applicable to common gate, globe, and butterfly valves. The methodology predicts thrust and torque requirements at design-basis flow and differential pressure conditions, assesses the potential for gate valve internal damage, and provides test methods to quantify potential for gate valve internal damage, and provides test methods to quantify potential variations in actuator output thrust with loading condition. Key findings and their potential impact on MOV design and engineering application are summarized

  2. 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.

  3. Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Jixiang Fan

    2015-09-01

    Full Text Available In this paper, a map-based optimal energy management strategy is proposed to improve the consumption economy of a plug-in parallel hybrid electric vehicle. In the design of the maps, which provide both the torque split between engine and motor and the gear shift, not only the current vehicle speed and power demand, but also the optimality based on the predicted trajectory of vehicle dynamics are considered. To seek the optimality, the equivalent consumption, which trades off the fuel and electricity usages, is chosen as the cost function. Moreover, in order to decrease the model errors in the process of optimization conducted in the discrete time domain, the variational integrator is employed to calculate the evolution of the vehicle dynamics. To evaluate the proposed energy management strategy, the simulation results performed on a professional GT-Suit simulator are demonstrated and the comparison to a real-time optimization method is also given to show the advantage of the proposed off-line optimization approach.

  4. State-Space Modeling and Performance Analysis of Variable-Speed Wind Turbine Based on a Model Predictive Control Approach

    Directory of Open Access Journals (Sweden)

    H. Bassi

    2017-04-01

    Full Text Available Advancements in wind energy technologies have led wind turbines from fixed speed to variable speed operation. This paper introduces an innovative version of a variable-speed wind turbine based on a model predictive control (MPC approach. The proposed approach provides maximum power point tracking (MPPT, whose main objective is to capture the maximum wind energy in spite of the variable nature of the wind’s speed. The proposed MPC approach also reduces the constraints of the two main functional parts of the wind turbine: the full load and partial load segments. The pitch angle for full load and the rotating force for the partial load have been fixed concurrently in order to balance power generation as well as to reduce the operations of the pitch angle. A mathematical analysis of the proposed system using state-space approach is introduced. The simulation results using MATLAB/SIMULINK show that the performance of the wind turbine with the MPC approach is improved compared to the traditional PID controller in both low and high wind speeds.

  5. Are performance-based measures predictive of work participation in patients with musculoskeletal disorders? A systematic review

    NARCIS (Netherlands)

    Kuijer, P. P. F. M.; Gouttebarge, V.; Brouwer, S.; Reneman, M. F.; Frings-Dresen, M. H. W.

    Assessments of whether patients with musculoskeletal disorders (MSDs) can participate in work mainly consist of case history, physical examinations, and self-reports. Performance-based measures might add value in these assessments. This study answers the question: how well do performance-based

  6. Are the Performance Based Logistics Prophets Using Science or Alchemy to Create Life-Cycle Affordability? Using Theory to Predict the Efficacy of Performance Based Logistics

    Science.gov (United States)

    2013-10-01

    certain PBL ecosystems , their suppliers, customers, and integrator, can efficiently adapt to environmental changes, and thus predict competitive...capability = budget – cost. An effective entrepreneurial program leader will increase capability (i.e., lethality, maintainability, and/or

  7. [Development and Application of a Performance Prediction Model for Home Care Nursing Based on a Balanced Scorecard using the Bayesian Belief Network].

    Science.gov (United States)

    Noh, Wonjung; Seomun, Gyeongae

    2015-06-01

    This study was conducted to develop key performance indicators (KPIs) for home care nursing (HCN) based on a balanced scorecard, and to construct a performance prediction model of strategic objectives using the Bayesian Belief Network (BBN). This methodological study included four steps: establishment of KPIs, performance prediction modeling, development of a performance prediction model using BBN, and simulation of a suggested nursing management strategy. An HCN expert group and a staff group participated. The content validity index was analyzed using STATA 13.0, and BBN was analyzed using HUGIN 8.0. We generated a list of KPIs composed of 4 perspectives, 10 strategic objectives, and 31 KPIs. In the validity test of the performance prediction model, the factor with the greatest variance for increasing profit was maximum cost reduction of HCN services. The factor with the smallest variance for increasing profit was a minimum image improvement for HCN. During sensitivity analysis, the probability of the expert group did not affect the sensitivity. Furthermore, simulation of a 10% image improvement predicted the most effective way to increase profit. KPIs of HCN can estimate financial and non-financial performance. The performance prediction model for HCN will be useful to improve performance.

  8. 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)

  9. Genomic Prediction of Barley Hybrid Performance

    Directory of Open Access Journals (Sweden)

    Norman Philipp

    2016-07-01

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

  10. 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

  11. Prediction based on mean subset

    DEFF Research Database (Denmark)

    Øjelund, Henrik; Brown, P. J.; Madsen, Henrik

    2002-01-01

    , it is found that the proposed mean subset method has superior prediction performance than prediction based on the best subset method, and in some settings also better than the ridge regression and lasso methods. The conclusions drawn from the Monte Carlo study is corroborated in an example in which prediction......Shrinkage methods have traditionally been applied in prediction problems. In this article we develop a shrinkage method (mean subset) that forms an average of regression coefficients from individual subsets of the explanatory variables. A Bayesian approach is taken to derive an expression of how...... the coefficient vectors from each subset should be weighted. It is not computationally feasible to calculate the mean subset coefficient vector for larger problems, and thus we suggest an algorithm to find an approximation to the mean subset coefficient vector. In a comprehensive Monte Carlo simulation study...

  12. 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.)

  13. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

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

  14. 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...

  15. 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…

  16. Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

    Directory of Open Access Journals (Sweden)

    Xiaochun Sun

    Full Text Available Genomic selection (GS procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA and reproducing kernel Hilbert spaces (RKHS regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

  17. Nonparametric method for genomics-based prediction of performance of quantitative traits involving epistasis in plant breeding.

    Science.gov (United States)

    Sun, Xiaochun; Ma, Ping; Mumm, Rita H

    2012-01-01

    Genomic selection (GS) procedures have proven useful in estimating breeding value and predicting phenotype with genome-wide molecular marker information. However, issues of high dimensionality, multicollinearity, and the inability to deal effectively with epistasis can jeopardize accuracy and predictive ability. We, therefore, propose a new nonparametric method, pRKHS, which combines the features of supervised principal component analysis (SPCA) and reproducing kernel Hilbert spaces (RKHS) regression, with versions for traits with no/low epistasis, pRKHS-NE, to high epistasis, pRKHS-E. Instead of assigning a specific relationship to represent the underlying epistasis, the method maps genotype to phenotype in a nonparametric way, thus requiring fewer genetic assumptions. SPCA decreases the number of markers needed for prediction by filtering out low-signal markers with the optimal marker set determined by cross-validation. Principal components are computed from reduced marker matrix (called supervised principal components, SPC) and included in the smoothing spline ANOVA model as independent variables to fit the data. The new method was evaluated in comparison with current popular methods for practicing GS, specifically RR-BLUP, BayesA, BayesB, as well as a newer method by Crossa et al., RKHS-M, using both simulated and real data. Results demonstrate that pRKHS generally delivers greater predictive ability, particularly when epistasis impacts trait expression. Beyond prediction, the new method also facilitates inferences about the extent to which epistasis influences trait expression.

  18. 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...

  19. Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

    Directory of Open Access Journals (Sweden)

    Hans-Stefan Bauer

    2015-04-01

    Full Text Available Quantitative precipitation estimation and forecasting (QPE and QPF are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

  20. Falling in the elderly: Do statistical models matter for performance criteria of fall prediction? Results from two large population-based studies.

    Science.gov (United States)

    Kabeshova, Anastasiia; Launay, Cyrille P; Gromov, Vasilii A; Fantino, Bruno; Levinoff, Elise J; Allali, Gilles; Beauchet, Olivier

    2016-01-01

    To compare performance criteria (i.e., sensitivity, specificity, positive predictive value, negative predictive value, area under receiver operating characteristic curve and accuracy) of linear and non-linear statistical models for fall risk in older community-dwellers. Participants were recruited in two large population-based studies, "Prévention des Chutes, Réseau 4" (PCR4, n=1760, cross-sectional design, retrospective collection of falls) and "Prévention des Chutes Personnes Agées" (PCPA, n=1765, cohort design, prospective collection of falls). Six linear statistical models (i.e., logistic regression, discriminant analysis, Bayes network algorithm, decision tree, random forest, boosted trees), three non-linear statistical models corresponding to artificial neural networks (multilayer perceptron, genetic algorithm and neuroevolution of augmenting topologies [NEAT]) and the adaptive neuro fuzzy interference system (ANFIS) were used. Falls ≥1 characterizing fallers and falls ≥2 characterizing recurrent fallers were used as outcomes. Data of studies were analyzed separately and together. NEAT and ANFIS had better performance criteria compared to other models. The highest performance criteria were reported with NEAT when using PCR4 database and falls ≥1, and with both NEAT and ANFIS when pooling data together and using falls ≥2. However, sensitivity and specificity were unbalanced. Sensitivity was higher than specificity when identifying fallers, whereas the converse was found when predicting recurrent fallers. Our results showed that NEAT and ANFIS were non-linear statistical models with the best performance criteria for the prediction of falls but their sensitivity and specificity were unbalanced, underscoring that models should be used respectively for the screening of fallers and the diagnosis of recurrent fallers. Copyright © 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

  1. Evaluation of the performance and limitations of empirical partition-relations and process based multisurface models to predict trace element solubility in soils

    Energy Technology Data Exchange (ETDEWEB)

    Groenenberg, J.E.; Bonten, L.T.C. [Alterra, Wageningen UR, P.O. Box 47, 6700 AA Wageningen (Netherlands); Dijkstra, J.J. [Energy research Centre of the Netherlands ECN, P.O. Box 1, 1755 ZG Petten (Netherlands); De Vries, W. [Department of Environmental Systems Analysis, Wageningen University, Wageningen UR, P.O. Box 47, 6700 AA Wageningen (Netherlands); Comans, R.N.J. [Department of Soil Quality, Wageningen University, Wageningen UR, P.O. Box 47, 6700 AA Wageningen (Netherlands)

    2012-07-15

    Here we evaluate the performance and limitations of two frequently used model-types to predict trace element solubility in soils: regression based 'partition-relations' and thermodynamically based 'multisurface models', for a large set of elements. For this purpose partition-relations were derived for As, Ba, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sb, Se, V, Zn. The multi-surface model included aqueous speciation, mineral equilibria, sorption to organic matter, Fe/Al-(hydr)oxides and clay. Both approaches were evaluated by their application to independent data for a wide variety of conditions. We conclude that Freundlich-based partition-relations are robust predictors for most cations and can be used for independent soils, but within the environmental conditions of the data used for their derivation. The multisurface model is shown to be able to successfully predict solution concentrations over a wide range of conditions. Predicted trends for oxy-anions agree well for both approaches but with larger (random) deviations than for cations.

  2. On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices

    Directory of Open Access Journals (Sweden)

    Babita Majhi

    2014-09-01

    Full Text Available This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer and the output mean square error (MSE of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500 and Dow Jones Industrial Average (DJIA stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF-based forecasting model in terms of the mean average percentage error (MAPE, directional accuracy (DA, Thelis’ U and average relative variance (ARV values.

  3. Performance of JEF2.2 based continuous energy cross sections in predicting the multiplication factor of critical systems

    International Nuclear Information System (INIS)

    John, T.M.; de Leege, P.F.A.; Hoogenboom, J.E.

    1996-01-01

    The continuous energy representation of cross sections for neutronics calculations avoids the requirement of resonance self shielding and the assumptions about the neutron spectrum used for weighing cross sections, required in the preparation of a multigroup cross sections library. The cross sections library prepared for a particular temperature of the nuclide is valid irrespective of the environment of the nuclide and can be used in calculations for many types of reactors. It is comparatively easier to incorporate them in Monte Carlo simulation of neutron transport. The Monte Carlo code MCNP is capable of using a continuous energy representation of nuclear cross sections in simulation of neutron or photon transport. The ACER module of NJOY is able to generate the continuous energy cross section of any nuclide in a format that can be used by MCNP, from any evaluated data file in ENDF/B format. Continuous energy cross sections prepared from the evaluated data file JEF2.2 was used to analyse some standard critical benchmarks and also the critical configuration of the HOR, a 2 MW research reactor at Delft, the Netherlands. Results show that continuous energy cross sections prepared from JEF2.2 evaluated file predicts the multiplication factor of critical systems very close to unity. (author). 6 refs., 2 tabs., 1 fig

  4. Spectral analysis-based risk score enables early prediction of mortality and cerebral performance in patients undergoing therapeutic hypothermia for ventricular fibrillation and comatose status

    Science.gov (United States)

    Filgueiras-Rama, David; Calvo, Conrado J.; Salvador-Montañés, Óscar; Cádenas, Rosalía; Ruiz-Cantador, Jose; Armada, Eduardo; Rey, Juan Ramón; Merino, J.L.; Peinado, Rafael; Pérez-Castellano, Nicasio; Pérez-Villacastín, Julián; Quintanilla, Jorge G.; Jiménez, Santiago; Castells, Francisco; Chorro, Francisco J.; López-Sendón, J.L.; Berenfeld, Omer; Jalife, José; López de Sá, Esteban; Millet, José

    2017-01-01

    Background Early prognosis in comatose survivors after cardiac arrest due to ventricular fibrillation (VF) is unreliable, especially in patients undergoing mild hypothermia. We aimed at developing a reliable risk-score to enable early prediction of cerebral performance and survival. Methods Sixty-one out of 239 consecutive patients undergoing mild hypothermia after cardiac arrest, with eventual return of spontaneous circulation (ROSC), and comatose status on admission fulfilled the inclusion criteria. Background clinical variables, VF time and frequency domain fundamental variables were considered. The primary and secondary outcomes were a favorable neurological performance (FNP) during hospitalization and survival to hospital discharge, respectively. The predictive model was developed in a retrospective cohort (n=32; September 2006–September 2011, 48.5 ± 10.5 months of follow-up) and further validated in a prospective cohort (n = 29; October 2011–July 2013, 5 ± 1.8 months of follow-up). Results FNP was present in 16 (50.0%) and 21 patients (72.4%) in the retrospective and prospective cohorts, respectively. Seventeen (53.1%) and 21 patients (72.4%), respectively, survived to hospital discharge. Both outcomes were significantly associated (p < 0.001). Retrospective multivariate analysis provided a prediction model (sensitivity= 0.94, specificity = 1) that included spectral dominant frequency, derived power density and peak ratios between high and low frequency bands, and the number of shocks delivered before ROSC. Validation on the prospective cohort showed sensitivity = 0.88 and specificity = 0.91. A model-derived risk-score properly predicted 93% of FNP. Testing the model on follow-up showed a c-statistic ≥ 0.89. Conclusions A spectral analysis-based model reliably correlates time-dependent VF spectral changes with acute cerebral injury in comatose survivors undergoing mild hypothermia after cardiac arrest. PMID:25828128

  5. SIMPLIFIED PREDICTIVE MODELS FOR CO₂ SEQUESTRATION PERFORMANCE ASSESSMENT RESEARCH TOPICAL REPORT ON TASK #3 STATISTICAL LEARNING BASED MODELS

    Energy Technology Data Exchange (ETDEWEB)

    Mishra, Srikanta; Schuetter, Jared

    2014-11-01

    We compare two approaches for building a statistical proxy model (metamodel) for CO₂ geologic sequestration from the results of full-physics compositional simulations. The first approach involves a classical Box-Behnken or Augmented Pairs experimental design with a quadratic polynomial response surface. The second approach used a space-filling maxmin Latin Hypercube sampling or maximum entropy design with the choice of five different meta-modeling techniques: quadratic polynomial, kriging with constant and quadratic trend terms, multivariate adaptive regression spline (MARS) and additivity and variance stabilization (AVAS). Simulations results for CO₂ injection into a reservoir-caprock system with 9 design variables (and 97 samples) were used to generate the data for developing the proxy models. The fitted models were validated with using an independent data set and a cross-validation approach for three different performance metrics: total storage efficiency, CO₂ plume radius and average reservoir pressure. The Box-Behnken–quadratic polynomial metamodel performed the best, followed closely by the maximin LHS–kriging metamodel.

  6. 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...

  7. 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...

  8. 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

  9. Pavement Performance : Approaches Using Predictive Analytics

    Science.gov (United States)

    2018-03-23

    Acceptable pavement condition is paramount to road safety. Using predictive analytics techniques, this project attempted to develop models that provide an assessment of pavement condition based on an array of indictors that include pavement distress,...

  10. The relative importance of managerial competencies for predicting the perceived job performance of Broad-Based Black Economic Empowerment verification practitioners

    Directory of Open Access Journals (Sweden)

    Barbara M. Seate

    2016-04-01

    Full Text Available Orientation: There is a need for the growing Broad-Based Black Economic Empowerment (B-BBEE verification industry to assess competencies and determine skills gaps for the management of the verification practitioners’ perceived job performance. Knowing which managerial competencies are important for different managerial functions is vital for developing and improving training and development programmes. Research purpose: The purpose of this study was to determine the managerial capabilities that are required of the B-BBEE verification practitioners, in order to improve their perceived job performance. Motivation for the study: The growing number of the B-BBEE verification practitioners calls for more focused training and development. Generating such a training and development programme demands empirical research into the relative importance of managerial competencies. Research approach, design and method: A quantitative design using the survey approach was adopted. A questionnaire was administered to a stratified sample of 87 B-BBEE verification practitioners. Data were analysed using the Statistical Package for Social Sciences (version 22.0 and Smart Partial Least Squares software. Main findings: The results of the correlation analysis revealed that there were strong and positive associations between technical skills, interpersonal skills, compliance to standards and ethics, managerial skills and perceived job performance. Results of the regression analysis showed that managerial skills, compliance to standards and ethics and interpersonal skills were statistically significant in predicting perceived job performance. However, technical skills were insignificant in predicting perceived job performance. Practical/managerial implications: The study has shown that the B-BBEE verification industry, insofar as the technical skills of the practitioners are concerned, does have suitably qualified staff with the requisite educational qualifications. At

  11. 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 ...

  12. 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...

  13. Speech Intelligibility Prediction Based on Mutual Information

    DEFF Research Database (Denmark)

    Jensen, Jesper; Taal, Cees H.

    2014-01-01

    This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related...... to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using...... a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in predicting...

  14. 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

  15. Base Station Performance Model

    OpenAIRE

    Walsh, Barbara; Farrell, Ronan

    2005-01-01

    At present the testing of power amplifiers within base station transmitters is limited to testing at component level as opposed to testing at the system level. While the detection of catastrophic failure is possible, that of performance degradation is not. This paper proposes a base station model with respect to transmitter output power with the aim of introducing system level monitoring of the power amplifier behaviour within the base station. Our model reflects the expe...

  16. 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

  17. 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.

  18. 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...

  19. Performance samples on academic tasks : improving prediction of academic performance

    NARCIS (Netherlands)

    Tanilon, Jenny

    2011-01-01

    This thesis is about the development and validation of a performance-based test, labeled as Performance Samples on academic tasks in Education and Child Studies (PSEd). PSEd is designed to identify students who are most able to perform the academic tasks involved in an Education and Child Studies

  20. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2002-01-01

    Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

  1. SYRUS: Understanding and Predicting Multitasking Performance

    National Research Council Canada - National Science Library

    Oswald, Frederick L; Hambrick, D. Z; Jones, L. A; Ghumman, Sonia S

    2007-01-01

    .... Fourth, related to the previous point, a summarization of our initial empirical work on multitasking, based on college-student participants who engaged in a computerized multitasking performance task...

  2. 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.

  3. The robust model predictive control based on mixed H2/H∞ approach with separated performance formulations and its ISpS analysis

    Science.gov (United States)

    Li, Dewei; Li, Jiwei; Xi, Yugeng; Gao, Furong

    2017-12-01

    In practical applications, systems are always influenced by parameter uncertainties and external disturbance. Both the H2 performance and the H∞ performance are important for the real applications. For a constrained system, the previous designs of mixed H2/H∞ robust model predictive control (RMPC) optimise one performance with the other performance requirement as a constraint. But the two performances cannot be optimised at the same time. In this paper, an improved design of mixed H2/H∞ RMPC for polytopic uncertain systems with external disturbances is proposed to optimise them simultaneously. In the proposed design, the original uncertain system is decomposed into two subsystems by the additive character of linear systems. Two different Lyapunov functions are used to separately formulate the two performance indices for the two subsystems. Then, the proposed RMPC is designed to optimise both the two performances by the weighting method with the satisfaction of the H∞ performance requirement. Meanwhile, to make the design more practical, a simplified design is also developed. The recursive feasible conditions of the proposed RMPC are discussed and the closed-loop input state practical stable is proven. The numerical examples reflect the enlarged feasible region and the improved performance of the proposed design.

  4. 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.

  5. Experimental and artificial neural network based prediction of performance and emission characteristics of DI diesel engine using Calophyllum inophyllum methyl ester at different nozzle opening pressure

    Science.gov (United States)

    Vairamuthu, G.; Thangagiri, B.; Sundarapandian, S.

    2018-01-01

    The present work investigates the effect of varying Nozzle Opening Pressures (NOP) from 220 bar to 250 bar on performance, emissions and combustion characteristics of Calophyllum inophyllum Methyl Ester (CIME) in a constant speed, Direct Injection (DI) diesel engine using Artificial Neural Network (ANN) approach. An ANN model has been developed to predict a correlation between specific fuel consumption (SFC), brake thermal efficiency (BTE), exhaust gas temperature (EGT), Unburnt hydrocarbon (UBHC), CO, CO2, NOx and smoke density using load, blend (B0 and B100) and NOP as input data. A standard Back-Propagation Algorithm (BPA) for the engine is used in this model. A Multi Layer Perceptron network (MLP) is used for nonlinear mapping between the input and the output parameters. An ANN model can predict the performance of diesel engine and the exhaust emissions with correlation coefficient (R2) in the range of 0.98-1. Mean Relative Errors (MRE) values are in the range of 0.46-5.8%, while the Mean Square Errors (MSE) are found to be very low. It is evident that the ANN models are reliable tools for the prediction of DI diesel engine performance and emissions. The test results show that the optimum NOP is 250 bar with B100.

  6. 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.

  7. 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

  8. Predicting Performance in Higher Education Using Proximal Predictors

    NARCIS (Netherlands)

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

    2016-01-01

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

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. Predicting the educational performance of Isfahan University students of medical sciences based on their behaviour profile, mental health and demographic characteristic.

    Science.gov (United States)

    Samouei, Rahele; Fooladvand, Maryam; Janghorban, Shahla; Khorvash, Fariba

    2015-01-01

    The issue of students' academic failure is one of the most important educational, economic, and social issues. Cognizance of the factors related to academic downfall is so efficient in its prevention and control and leads to protecting governmental assets and labor force. In order to achieve this goal, this study intends to determine the predictive factors of the students' academic performance in Isfahan University of Medical Sciences in terms of their personality profile, mental health, and their demographic characteristics. This study was a descriptive-correlation study on 771 students who entered Isfahan University of Medical Sciences between 2005 and 2007. The information was gathered through using the students' educational and clinical files (for measuring personality characteristics and mental health) and SAMA Software (To get the mean scores). Minnesota Multiphasic Personality Inventory short form and General Health Questionnaire were used for collecting clinical data. The data were analyzed using SPSS 15 (stepwise regression coefficient, variance analysis, Student's t-test, and Spearman correlation coefficient). The results showed that the aforementioned students obtained a normal average for their personality profile and mental health indicators. Of all the reviewed variables, education, age, gender, depression, and hypochondria were the predictive factors of the students' educational performance. It could be concluded that some of the personality features, mental health indicators, and personality profile play such a significant role in the students' educational life that the disorder in any of them affects the students' educational performance and academic failure.

  14. Predicting Performance in Higher Education Using Proximal Predictors

    Science.gov (United States)

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

    2016-01-01

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

  15. 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.

  16. Performance analysis and prediction in triathlon.

    Science.gov (United States)

    Ofoghi, Bahadorreza; Zeleznikow, John; Macmahon, Clare; Rehula, Jan; Dwyer, Dan B

    2016-01-01

    Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008-2012). The analysis reveals patterns of performance in five components of triathlon (three race "legs" and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.

  17. Predicting the Performance of Organic Corrosion Inhibitors

    Directory of Open Access Journals (Sweden)

    David A. Winkler

    2017-12-01

    Full Text Available The withdrawal of effective but toxic corrosion inhibitors has provided an impetus for the discovery of new, benign organic compounds to fill that role. Concurrently, developments in the high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, and the increased capability of machine learning methods have made discovery of new corrosion inhibitors much faster and cheaper than it used to be. We summarize these technical developments in the corrosion inhibition field and describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. We briefly summarize the literature on quantitative structure–property relationships models of small organic molecule corrosion inhibitors. The success of these models provides a paradigm for rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys in diverse environments.

  18. Data-Based Predictive Control with Multirate Prediction Step

    Science.gov (United States)

    Barlow, Jonathan S.

    2010-01-01

    Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.

  19. Wavelet-based prediction of oil prices

    International Nuclear Information System (INIS)

    Yousefi, Shahriar; Weinreich, Ilona; Reinarz, Dominik

    2005-01-01

    This paper illustrates an application of wavelets as a possible vehicle for investigating the issue of market efficiency in futures markets for oil. The paper provides a short introduction to the wavelets and a few interesting wavelet-based contributions in economics and finance are briefly reviewed. A wavelet-based prediction procedure is introduced and market data on crude oil is used to provide forecasts over different forecasting horizons. The results are compared with data from futures markets for oil and the relative performance of this procedure is used to investigate whether futures markets are efficiently priced

  20. 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

  1. 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...

  2. The Microbiology of Subsurface, Salt-Based Nuclear Waste Repositories: Using Microbial Ecology, Bioenergetics, and Projected Conditions to Help Predict Microbial Effects on Repository Performance

    International Nuclear Information System (INIS)

    Swanson, Juliet S.; Cherkouk, Andrea; Arnold, Thuro; Meleshyn, Artur; Reed, Donald T.

    2016-01-01

    This report summarizes the potential role of microorganisms in salt-based nuclear waste repositories using available information on the microbial ecology of hypersaline environments, the bioenergetics of survival under high ionic strength conditions, and ''repository microbiology'' related studies. In areas where microbial activity is in question, there may be a need to shift the research focus toward feasibility studies rather than studies that generate actual input for performance assessments. In areas where activity is not necessary to affect performance (e.g., biocolloid transport), repository-relevant data should be generated. Both approaches will lend a realistic perspective to a safety case/performance scenario that will most likely underscore the conservative value of that case.

  3. The Microbiology of Subsurface, Salt-Based Nuclear Waste Repositories: Using Microbial Ecology, Bioenergetics, and Projected Conditions to Help Predict Microbial Effects on Repository Performance

    Energy Technology Data Exchange (ETDEWEB)

    Swanson, Juliet S. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Cherkouk, Andrea [Helmholtz-Zentrum Dresden-Rossendorf, Rossendorf (Germany); Arnold, Thuro [Helmholtz-Zentrum Dresden-Rossendorf, Rossendorf (Germany); Meleshyn, Artur [Gesellschaft fur Anlagen und Reaktorsicherheit, Braunschweig (Germany); Reed, Donald T. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-11-17

    This report summarizes the potential role of microorganisms in salt-based nuclear waste repositories using available information on the microbial ecology of hypersaline environments, the bioenergetics of survival under high ionic strength conditions, and “repository microbiology” related studies. In areas where microbial activity is in question, there may be a need to shift the research focus toward feasibility studies rather than studies that generate actual input for performance assessments. In areas where activity is not necessary to affect performance (e.g., biocolloid transport), repository-relevant data should be generated. Both approaches will lend a realistic perspective to a safety case/performance scenario that will most likely underscore the conservative value of that case.

  4. 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.

  5. TWT transmitter fault prediction based on ANFIS

    Science.gov (United States)

    Li, Mengyan; Li, Junshan; Li, Shuangshuang; Wang, Wenqing; Li, Fen

    2017-11-01

    Fault prediction is an important component of health management, and plays an important role in the reliability guarantee of complex electronic equipments. Transmitter is a unit with high failure rate. The cathode performance of TWT is a common fault of transmitter. In this dissertation, a model based on a set of key parameters of TWT is proposed. By choosing proper parameters and applying adaptive neural network training model, this method, combined with analytic hierarchy process (AHP), has a certain reference value for the overall health judgment of TWT transmitters.

  6. Link prediction based on nonequilibrium cooperation effect

    Science.gov (United States)

    Li, Lanxi; Zhu, Xuzhen; Tian, Hui

    2018-04-01

    Link prediction in complex networks has become a common focus of many researchers. But most existing methods concentrate on neighbors, and rarely consider degree heterogeneity of two endpoints. Node degree represents the importance or status of endpoints. We describe the large-degree heterogeneity as the nonequilibrium between nodes. This nonequilibrium facilitates a stable cooperation between endpoints, so that two endpoints with large-degree heterogeneity tend to connect stably. We name such a phenomenon as the nonequilibrium cooperation effect. Therefore, this paper proposes a link prediction method based on the nonequilibrium cooperation effect to improve accuracy. Theoretical analysis will be processed in advance, and at the end, experiments will be performed in 12 real-world networks to compare the mainstream methods with our indices in the network through numerical analysis.

  7. Dst Prediction Based on Solar Wind Parameters

    Directory of Open Access Journals (Sweden)

    Yoon-Kyung Park

    2009-12-01

    Full Text Available We reevaluate the Burton equation (Burton et al. 1975 of predicting Dst index using high quality hourly solar wind data supplied by the ACE satellite for the period from 1998 to 2006. Sixty magnetic storms with monotonously decreasing main phase are selected. In order to determine the injection term (Q and the decay time (tau of the equation, we examine the relationships between Dst* and VB_s, Delta Dst* and VB_s, and Delta Dst* and Dst* during the magnetic storms. For this analysis, we take into account one hour of the propagation time from the ACE satellite to the magnetopause, and a half hour of the response time of the magnetosphere/ring current to the solar wind forcing. The injection term is found to be Q({nT}/h=-3.56VB_s for VB_s>0.5mV/m and Q({nT}/h=0 for VB_s leq0.5mV/m. The tau (hour is estimated as 0.060 Dst* + 16.65 for Dst*>-175nT and 6.15 hours for Dst* leq -175nT. Based on these empirical relationships, we predict the 60 magnetic storms and find that the correlation coefficient between the observed and predicted Dst* is 0.88. To evaluate the performance of our prediction scheme, the 60 magnetic storms are predicted again using the models by Burton et al. (1975 and O'Brien & McPherron (2000a. The correlation coefficients thus obtained are 0.85, the same value for both of the two models. In this respect, our model is slightly improved over the other two models as far as the correlation coefficients is concerned. Particularly our model does a better job than the other two models in predicting intense magnetic storms (Dst* lesssim -200nT.

  8. 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

  9. Performance-Based Funding Brief

    Science.gov (United States)

    Washington Higher Education Coordinating Board, 2011

    2011-01-01

    A number of states have made progress in implementing performance-based funding (PFB) and accountability. This policy brief summarizes main features of performance-based funding systems in three states: Tennessee, Ohio, and Indiana. The brief also identifies key issues that states considering performance-based funding must address, as well as…

  10. 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.

  11. Predicting Liaison: an Example-Based Approach

    NARCIS (Netherlands)

    Greefhorst, A.P.M.; Bosch, A.P.J. van den

    2016-01-01

    Predicting liaison in French is a non-trivial problem to model. We compare a memory-based machine-learning algorithm with a rule-based baseline. The memory-based learner is trained to predict whether liaison occurs between two words on the basis of lexical, orthographic, morphosyntactic, and

  12. 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.

  13. 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

  14. Stochastic Prediction of Ventilation System Performance

    DEFF Research Database (Denmark)

    Haghighat, F.; Brohus, Henrik; Frier, Christian

    The paper briefly reviews the existing techniques for predicting the airflow rate due to the random nature of forcing functions, e.g. wind speed. The effort is to establish the relationship between the statistics of the output of a system and the statistics of the random input variables and param......The paper briefly reviews the existing techniques for predicting the airflow rate due to the random nature of forcing functions, e.g. wind speed. The effort is to establish the relationship between the statistics of the output of a system and the statistics of the random input variables...

  15. 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

  16. 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.

  17. System Predicts Critical Runway Performance Parameters

    Science.gov (United States)

    Millen, Ernest W.; Person, Lee H., Jr.

    1990-01-01

    Runway-navigation-monitor (RNM) and critical-distances-process electronic equipment designed to provide pilot with timely and reliable predictive navigation information relating to takeoff, landing and runway-turnoff operations. Enables pilot to make critical decisions about runway maneuvers with high confidence during emergencies. Utilizes ground-referenced position data only to drive purely navigational monitor system independent of statuses of systems in aircraft.

  18. 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.

  19. 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.

  20. High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology.

    Science.gov (United States)

    Spoerk, Jakob; Gendrin, Christelle; Weber, Christoph; Figl, Michael; Pawiro, Supriyanto Ardjo; Furtado, Hugo; Fabri, Daniella; Bloch, Christoph; Bergmann, Helmar; Gröller, Eduard; Birkfellner, Wolfgang

    2012-02-01

    A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D Registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512×512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. Copyright © 2011. Published by Elsevier GmbH.

  1. High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology

    Energy Technology Data Exchange (ETDEWEB)

    Spoerk, Jakob; Gendrin, Christelle; Weber, Christoph [Medical University of Vienna (Austria). Center of Medical Physics and Biomedical Engineering] [and others

    2012-07-01

    A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D Registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference X-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512 x 512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. (orig.)

  2. Aerodynamic performance prediction of Darrieus-type wind turbines

    Directory of Open Access Journals (Sweden)

    Ion NILĂ

    2010-06-01

    Full Text Available The prediction of Darrieus wind turbine aerodynamic performances provides the necessarydesign and operational data base related to the wind potential. In this sense it provides the type ofturbine suitable to the area where it is to be installed. Two calculation methods are analyzed for arotor with straight blades. The first one is a global method that allows an assessment of the turbinenominal power by a brief calculation. This method leads to an overestimation of performances. Thesecond is the calculation method of the gust factor and momentum which deals with the pale as beingcomposed of different elements that don’t influence each other. This method, developed based on thetheory of the turbine blades, leads to values close to the statistical data obtained experimentally. Thevalues obtained by the calculation method of gust factor - momentum led to the concept of a Darrieusturbine, which will be tested for different wind values in the INCAS subsonic wind tunnel.

  3. Predicting Students' Performance in the Senior Secondary ...

    African Journals Online (AJOL)

    cce

    correlation design. ... the JSC examinations were a good predictor of performance at SSC ..... Table 12: Effects of the Independent Variables (JSCE 2000) on the .... JAMB Brochure, Abuja: Joint Admissions and Matriculation Examinations, 2-3.

  4. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  5. The wind power prediction research based on mind evolutionary algorithm

    Science.gov (United States)

    Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

    2018-04-01

    When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

  6. Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

    NARCIS (Netherlands)

    Parmentier, I.; Harrigan, R.; Buermann, W.; Mitchard, E.T.A.; Saatchi, S.; Malhi, Y.; Bongers, F.; Hawthorne, W.D.; Leal, M.E.; Lewis, S.; Nusbaumer, L.; Sheil, D.; Sosef, M.S.M.; Bakayoko, A.; Chuyong, G.; Chatelain, C.; Comiskey, J.; Dauby, G.; Doucet, J.L.; Hardy, O.

    2011-01-01

    Aim Our aim was to evaluate the extent to which we can predict and map tree alpha diversity across broad spatial scales either by using climate and remote sensing data or by exploiting spatial autocorrelation patterns. Location Tropical rain forest, West Africa and Atlantic Central Africa. Methods

  7. 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.

  8. Dichotic listening performance predicts language comprehension.

    Science.gov (United States)

    Asbjørnsen, Arve E; Helland, Turid

    2006-05-01

    Dichotic listening performance is considered a reliable and valid procedure for the assessment of language lateralisation in the brain. However, the documentation of a relationship between language functions and dichotic listening performance is sparse, although it is accepted that dichotic listening measures language perception. In particular, language comprehension should show close correspondence to perception of language stimuli. In the present study, we tested samples of reading-impaired and normally achieving children between 10 and 13 years of age with tests of reading skills, language comprehension, and dichotic listening to consonant-vowel (CV) syllables. A high correlation between the language scores and the dichotic listening performance was expected. However, since the left ear score is believed to be an error when assessing language laterality, covariation was expected for the right ear scores only. In addition, directing attention to one ear input was believed to reduce the influence of random factors, and thus show a more concise estimate of left hemisphere language capacity. Thus, a stronger correlation between language comprehension skills and the dichotic listening performance when attending to the right ear was expected. The analyses yielded a positive correlation between the right ear score in DL and language comprehension, an effect that was stronger when attending to the right ear. The present results confirm the assumption that dichotic listening with CV syllables measures an aspect of language perception and language skills that is related to general language comprehension.

  9. Challenges of student selection: Predicting academic performance ...

    African Journals Online (AJOL)

    Finding accurate predictors of tertiary academic performance, specifically for disadvantaged students, is essential because of budget constraints and the need of the labour market to address employment equity. Increased retention, throughput and decreased dropout rates are vital. When making admission decisions, the

  10. Relationships between self-downing beliefs and math performance in Greek adolescent students: A predictive study based on Rational-Emotive Behavior Education (REBE) as theoretical perspective

    OpenAIRE

    Katsiki, Alexandra; Minnaert, Alexander; Katsikis, Demitris

    2017-01-01

    There is ample evidence from both educational practice and research that math performance is often associated with increased levels of test anxiety, stress and discomfort and that students’ cognitions account for this performance. Rational-Emotive Behavior Education (REBE), derived from Ellis’s Rational-EmotiveBehavior Theory (REBT), supports that it is mainly students’ cognitions in the form of core (ir) rational beliefs that determine their performance and overallschool achievement. However...

  11. Prediction of performance and evaluation of flexible pavement rehabilitation strategies

    Directory of Open Access Journals (Sweden)

    Kang-Won Wayne Lee

    2017-04-01

    Full Text Available Five test sections with different additives and strategies were established to rehabilitate a State-maintained highway more effectively in Rhode Island (RI: control, calcium chloride, asphalt emulsion, Portland cement and geogrid. Resilient moduli of subgrade soils and subbase materials before and after full depth rehabilitation were employed as input parameters to predict the performance of pavement structures using AASHTOWare Pavement ME Design (Pavement ME software in terms of rutting, cracking and roughness. It was attempted to use Level 1 input (which includes traffic full spectrum data, climate data and structural layer properties for Pavement ME. Traffic data was obtained from a Weigh-in-Motion (WIM instrument and Providence station was used for collecting climatic data. Volumetric properties, dynamic modulus and creep compliance were used as input parameters for 19 mm (0.75 in. warm mix asphalt (WMA base and 12.5 mm (0.5 in. WMA surface layer. The results indicated that all test sections observed AC top-down (longitudinal cracking except Portland cement section which passed for all criteria. The order in terms of performance (best to worst for all test sections by Pavement ME was Portland cement, calcium chloride, control, geogrid, and asphalt emulsion. It was also observed that all test sections passed for both bottom up and top down fatigue cracking by increasing thickness of either of the two top asphalt layers. Test sections with five different base/subbase materials were evaluated in last two years through visual condition survey and measurements of deflection and roughness to confirm the prediction, but there was no serious distress and roughness. Thus these experiments allowed selecting the best rehabilitation/reconstruction techniques for the particular and/or similar highway, and a framework was formulated to select an optimal technique and/or strategy for future rehabilitation/reconstruction projects. Finally, guidelines for

  12. Trust-based collective view prediction

    CERN Document Server

    Luo, Tiejian; Xu, Guandong; Zhou, Jia

    2013-01-01

    Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users' past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View

  13. 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,…

  14. Statistical and Machine Learning Models to Predict Programming Performance

    OpenAIRE

    Bergin, Susan

    2006-01-01

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

  15. Relationships between self-downing beliefs and math performance in Greek adolescent students : A predictive study based on Rational-Emotive Behavior Education (REBE) as theoretical perspective

    NARCIS (Netherlands)

    Katsiki, Alexandra; Minnaert, Alexander; Katsikis, Demitris

    2017-01-01

    There is ample evidence from both educational practice and research that math performance is often associated with increased levels of test anxiety, stress and discomfort and that students’ cognitions account for this performance. Rational-Emotive Behavior Education (REBE), derived from Ellis’s

  16. Predicting sales performance: Strengthening the personality – job performance linkage

    NARCIS (Netherlands)

    T.B. Sitser (Thomas)

    2014-01-01

    markdownabstract__Abstract__ Many organizations worldwide use personality measures to select applicants for sales jobs or to assess incumbent sales employees. In the present dissertation, consisting of four independent studies, five approaches to strengthen the personality-sales performance

  17. Simplified Predictive Models for CO2 Sequestration Performance Assessment: Research Topical Report on Task #4 - Reduced-Order Method (ROM) Based Models

    Energy Technology Data Exchange (ETDEWEB)

    Mishra, Srikanta; Jin, Larry; He, Jincong; Durlofsky, Louis

    2015-06-30

    Reduced-order models provide a means for greatly accelerating the detailed simulations that will be required to manage CO2 storage operations. In this work, we investigate the use of one such method, POD-TPWL, which has previously been shown to be effective in oil reservoir simulation problems. This method combines trajectory piecewise linearization (TPWL), in which the solution to a new (test) problem is represented through a linearization around the solution to a previously-simulated (training) problem, with proper orthogonal decomposition (POD), which enables solution states to be expressed in terms of a relatively small number of parameters. We describe the application of POD-TPWL for CO2-water systems simulated using a compositional procedure. Stanford’s Automatic Differentiation-based General Purpose Research Simulator (AD-GPRS) performs the full-order training simulations and provides the output (derivative matrices and system states) required by the POD-TPWL method. A new POD-TPWL capability introduced in this work is the use of horizontal injection wells that operate under rate (rather than bottom-hole pressure) control. Simulation results are presented for CO2 injection into a synthetic aquifer and into a simplified model of the Mount Simon formation. Test cases involve the use of time-varying well controls that differ from those used in training runs. Results of reasonable accuracy are consistently achieved for relevant well quantities. Runtime speedups of around a factor of 370 relative to full- order AD-GPRS simulations are achieved, though the preprocessing needed for POD-TPWL model construction corresponds to the computational requirements for about 2.3 full-order simulation runs. A preliminary treatment for POD-TPWL modeling in which test cases differ from training runs in terms of geological parameters (rather than well controls) is also presented. Results in this case involve only small differences between

  18. Cognitive performance modeling based on general systems performance theory.

    Science.gov (United States)

    Kondraske, George V

    2010-01-01

    General Systems Performance Theory (GSPT) was initially motivated by problems associated with quantifying different aspects of human performance. It has proved to be invaluable for measurement development and understanding quantitative relationships between human subsystem capacities and performance in complex tasks. It is now desired to bring focus to the application of GSPT to modeling of cognitive system performance. Previous studies involving two complex tasks (i.e., driving and performing laparoscopic surgery) and incorporating measures that are clearly related to cognitive performance (information processing speed and short-term memory capacity) were revisited. A GSPT-derived method of task analysis and performance prediction termed Nonlinear Causal Resource Analysis (NCRA) was employed to determine the demand on basic cognitive performance resources required to support different levels of complex task performance. This approach is presented as a means to determine a cognitive workload profile and the subsequent computation of a single number measure of cognitive workload (CW). Computation of CW may be a viable alternative to measuring it. Various possible "more basic" performance resources that contribute to cognitive system performance are discussed. It is concluded from this preliminary exploration that a GSPT-based approach can contribute to defining cognitive performance models that are useful for both individual subjects and specific groups (e.g., military pilots).

  19. Tracking Neuronal Connectivity from Electric Brain Signals to Predict Performance.

    Science.gov (United States)

    Vecchio, Fabrizio; Miraglia, Francesca; Rossini, Paolo Maria

    2018-05-01

    The human brain is a complex container of interconnected networks. Network neuroscience is a recent venture aiming to explore the connection matrix built from the human brain or human "Connectome." Network-based algorithms provide parameters that define global organization of the brain; when they are applied to electroencephalographic (EEG) signals network, configuration and excitability can be monitored in millisecond time frames, providing remarkable information on their instantaneous efficacy also for a given task's performance via online evaluation of the underlying instantaneous networks before, during, and after the task. Here we provide an updated summary on the connectome analysis for the prediction of performance via the study of task-related dynamics of brain network organization from EEG signals.

  20. Does IQ Really Predict Job Performance?

    Science.gov (United States)

    Richardson, Ken; Norgate, Sarah H.

    2015-01-01

    IQ has played a prominent part in developmental and adult psychology for decades. In the absence of a clear theoretical model of internal cognitive functions, however, construct validity for IQ tests has always been difficult to establish. Test validity, therefore, has always been indirect, by correlating individual differences in test scores with what are assumed to be other criteria of intelligence. Job performance has, for several reasons, been one such criterion. Correlations of around 0.5 have been regularly cited as evidence of test validity, and as justification for the use of the tests in developmental studies, in educational and occupational selection and in research programs on sources of individual differences. Here, those correlations are examined together with the quality of the original data and the many corrections needed to arrive at them. It is concluded that considerable caution needs to be exercised in citing such correlations for test validation purposes. PMID:26405429

  1. 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

  2. Predicting students' intention to use stimulants for academic performance enhancement.

    Science.gov (United States)

    Ponnet, Koen; Wouters, Edwin; Walrave, Michel; Heirman, Wannes; Van Hal, Guido

    2015-02-01

    The non-medical use of stimulants for academic performance enhancement is becoming a more common practice among college and university students. The objective of this study is to gain a better understanding of students' intention to use stimulant medication for the purpose of enhancing their academic performance. Based on an extended model of Ajzen's theory of planned behavior, we examined the predictive value of attitude, subjective norm, perceived behavioral control, psychological distress, procrastination, substance use, and alcohol use on students' intention to use stimulants to improve their academic performance. The sample consisted of 3,589 Flemish university and college students (mean age: 21.59, SD: 4.09), who participated anonymously in an online survey conducted in March and April 2013. Structural equation modeling was used to investigate the relationships among the study variables. Our results indicate that subjective norm is the strongest predictor of students' intention to use stimulant medication, followed by attitude and perceived behavioral control. To a lesser extent, procrastinating tendencies, psychological distress, and substance abuse contribute to students' intention. Conclusions/ Importance: Based on these findings, we provide several recommendations on how to curtail students' intention to use stimulant medication for the purpose of improving their academic performance. In addition, we urge researchers to identify other psychological variables that might be related to students' intention.

  3. 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...

  4. Long-term deficits in episodic memory after ischemic stroke: evaluation and prediction of verbal and visual memory performance based on lesion characteristics

    NARCIS (Netherlands)

    Schouten, Eveline A.; Schiemanck, Sven K.; Brand, Nico; Post, Marcel W. M.

    2009-01-01

    We investigated the relationship between ischemic lesion characteristics (hemispheric side, cortical and subcortical level, volume) and memory performance, 1 year after stroke. Verbal and visual memory of 86 patients with stroke were assessed with Rey Auditory-Verbal Learning Test and the Doors

  5. Variability, Predictability, and Race Factors Affecting Performance in Elite Biathlon.

    Science.gov (United States)

    Skattebo, Øyvind; Losnegard, Thomas

    2018-03-01

    To investigate variability, predictability, and smallest worthwhile performance enhancement in elite biathlon sprint events. In addition, the effects of race factors on performance were assessed. Data from 2005 to 2015 including >10,000 and >1000 observations for each sex for all athletes and annual top-10 athletes, respectively, were included. Generalized linear mixed models were constructed based on total race time, skiing time, shooting time, and proportions of targets hit. Within-athlete race-to-race variability was expressed as coefficient of variation of performance times and standard deviation (SD) in proportion units (%) of targets hit. The models were adjusted for random and fixed effects of subject identity, season, event identity, and race factors. The within-athlete variability was independent of sex and performance standard of athletes: 2.5-3.2% for total race time, 1.5-1.8% for skiing time, and 11-15% for shooting times. The SD of the proportion of hits was ∼10% in both shootings combined (meaning ±1 hit in 10 shots). The predictability in total race time was very high to extremely high for all athletes (ICC .78-.84) but trivial for top-10 athletes (ICC .05). Race times during World Championships and Olympics were ∼2-3% faster than in World Cups. Moreover, race time increased by ∼2% per 1000 m of altitude, by ∼5% per 1% of gradient, by 1-2% per 1 m/s of wind speed, and by ∼2-4% on soft vs hard tracks. Researchers and practitioners should focus on strategies that improve biathletes' performance by at least 0.8-0.9%, corresponding to the smallest worthwhile enhancement (0.3 × within-athlete variability).

  6. Performance Management or Performance Based Management?

    OpenAIRE

    Cristina PROTOPOPESCU

    2013-01-01

    In this paper we present some considerations about performance and performance management. Starting with the challenge of defining the performance concept, we intend to establish if „performance management” can be a new management system or it is just a sophisticated term for a HR strategy in order to improve the performance of teams and individuals. We also try to discuss the conection between performance management and management by objectives. Whether or not it is exageratted to talk about...

  7. 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.

  8. In Vitro Lipolysis Data Does Not Adequately Predict the In Vivo Performance of Lipid-Based Drug Delivery Systems Containing Fenofibrate

    DEFF Research Database (Denmark)

    Thomas, Nicky; Richter, Katharina; Pedersen, Thomas B

    2014-01-01

    on identical self-nanoemulsifying drug delivery systems (SNEDDS) containing 200 mg of fenofibrate, either dissolved or suspended, were subjected to combined gastric (pH 2) and intestinal (pH 6.5) in vitro lipolysis. Based on the solubilization profiles and SRM the rank-order SNEDDS (75% drug load) > super...

  9. High performance liquid chromatography of substituted aromatics with the metal-organic framework MIL-100(Fe): Mechanism analysis and model-based prediction.

    Science.gov (United States)

    Qin, Weiwei; Silvestre, Martin Eduardo; Li, Yongli; Franzreb, Matthias

    2016-02-05

    Metal-organic framework (MOF) MIL-100(Fe) with well-defined thickness was homogenously coated onto the outer surface of magnetic microparticles via a liquid-phase epitaxy method. The as-synthesized MIL-100(Fe) was used as stationary phase for high-performance liquid chromatography (HPLC) and separations of two groups of mixed aromatic hydrocarbons (toluene, styrene and p-xylene; acetanilide, 2-nirtoaniline and 1-naphthylamine) using methanol/water as mobile phase were performed to evaluate its performance. Increasing water content of the mobile phase composition can greatly improve the separations on the expense of a longer elution time. Stepwise elution significantly shortens the elution time of acetanilide, 2-nirtoaniline and 1-naphthylamine mixtures, while still achieving a baseline separation. Combining the experimental results and in-depth modeling using a recently developed chromatographic software (ChromX), adsorption equilibrium parameters, including the affinities and maximum capacities, for each analyte toward the MIL-100(Fe) are obtained. In addition, the pore diffusivity of aromatic hydrocarbons within MIL-100(Fe) was determined to be 5×10(-12)m(2)s(-1). While the affinities of MIL-100(Fe) toward the analyte molecules differs much, the maximum capacities of the analytes are in a narrow range with q*MOFmax,toluene=3.55molL(-1), q*MOFmax,styrene or p-xylene=3.53molL(-1), and q*MOFmax,anilines=3.12molL(-1) corresponding to approximately 842 toluene and 838 styrene or p-xylene, and 740 aniline molecules per MIL-100(Fe) unit cell, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. 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...

  11. 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.

  12. 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…

  13. Calorimeter prediction based on multiple exponentials

    International Nuclear Information System (INIS)

    Smith, M.K.; Bracken, D.S.

    2002-01-01

    Calorimetry allows very precise measurements of nuclear material to be carried out, but it also requires relatively long measurement times to do so. The ability to accurately predict the equilibrium response of a calorimeter would significantly reduce the amount of time required for calorimetric assays. An algorithm has been developed that is effective at predicting the equilibrium response. This multi-exponential prediction algorithm is based on an iterative technique using commercial fitting routines that fit a constant plus a variable number of exponential terms to calorimeter data. Details of the implementation and the results of trials on a large number of calorimeter data sets will be presented

  14. 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.

  15. 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

  16. 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

  17. 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

  18. 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

  19. Quantifying human disturbance in watersheds: Variable selection and performance of a GIS-based disturbance index for predicting the biological condition of perennial streams

    Science.gov (United States)

    Falcone, James A.; Carlisle, Daren M.; Weber, Lisa C.

    2010-01-01

    Characterizing the relative severity of human disturbance in watersheds is often part of stream assessments and is frequently done with the aid of Geographic Information System (GIS)-derived data. However, the choice of variables and how they are used to quantify disturbance are often subjective. In this study, we developed a number of disturbance indices by testing sets of variables, scoring methods, and weightings of 33 potential disturbance factors derived from readily available GIS data. The indices were calibrated using 770 watersheds located in the western United States for which the severity of disturbance had previously been classified from detailed local data by the United States Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). The indices were calibrated by determining which variable or variable combinations and aggregation method best differentiated between least- and most-disturbed sites. Indices composed of several variables performed better than any individual variable, and best results came from a threshold method of scoring using six uncorrelated variables: housing unit density, road density, pesticide application, dam storage, land cover along a mainstem buffer, and distance to nearest canal/pipeline. The final index was validated with 192 withheld watersheds and correctly classified about two-thirds (68%) of least- and most-disturbed sites. These results provide information about the potential for using a disturbance index as a screening tool for a priori ranking of watersheds at a regional/national scale, and which landscape variables and methods of combination may be most helpful in doing so.

  20. Does developmental timing of exposure to child maltreatment predict memory performance in adulthood? Results from a large, population-based sample.

    Science.gov (United States)

    Dunn, Erin C; Busso, Daniel S; Raffeld, Miriam R; Smoller, Jordan W; Nelson, Charles A; Doyle, Alysa E; Luk, Gigi

    2016-01-01

    Although maltreatment is a known risk factor for multiple adverse outcomes across the lifespan, its effects on cognitive development, especially memory, are poorly understood. Using data from a large, nationally representative sample of young adults (Add Health), we examined the effects of physical and sexual abuse on working and short-term memory in adulthood. We examined the association between exposure to maltreatment as well as its timing of first onset after adjusting for covariates. Of our sample, 16.50% of respondents were exposed to physical abuse and 4.36% to sexual abuse by age 17. An analysis comparing unexposed respondents to those exposed to physical or sexual abuse did not yield any significant differences in adult memory performance. However, two developmental time periods emerged as important for shaping memory following exposure to sexual abuse, but in opposite ways. Relative to non-exposed respondents, those exposed to sexual abuse during early childhood (ages 3-5), had better number recall and those first exposed during adolescence (ages 14-17) had worse number recall. However, other variables, including socioeconomic status, played a larger role (than maltreatment) on working and short-term memory. We conclude that a simple examination of "exposed" versus "unexposed" respondents may obscure potentially important within-group differences that are revealed by examining the effects of age at onset to maltreatment. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  2. 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

  3. Microarray-based cancer prediction using soft computing approach.

    Science.gov (United States)

    Wang, Xiaosheng; Gotoh, Osamu

    2009-05-26

    One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.

  4. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

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

  5. 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.

  6. Knowledge-based Fragment Binding Prediction

    Science.gov (United States)

    Tang, Grace W.; Altman, Russ B.

    2014-01-01

    Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

  7. Application of Machine Learning Algorithms for the Query Performance Prediction

    Directory of Open Access Journals (Sweden)

    MILICEVIC, M.

    2015-08-01

    Full Text Available This paper analyzes the relationship between the system load/throughput and the query response time in a real Online transaction processing (OLTP system environment. Although OLTP systems are characterized by short transactions, which normally entail high availability and consistent short response times, the need for operational reporting may jeopardize these objectives. We suggest a new approach to performance prediction for concurrent database workloads, based on the system state vector which consists of 36 attributes. There is no bias to the importance of certain attributes, but the machine learning methods are used to determine which attributes better describe the behavior of the particular database server and how to model that system. During the learning phase, the system's profile is created using multiple reference queries, which are selected to represent frequent business processes. The possibility of the accurate response time prediction may be a foundation for automated decision-making for database (DB query scheduling. Possible applications of the proposed method include adaptive resource allocation, quality of service (QoS management or real-time dynamic query scheduling (e.g. estimation of the optimal moment for a complex query execution.

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

    Science.gov (United States)

    Porter, J; Galfano, V J

    1987-01-01

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

  9. Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

    Science.gov (United States)

    Zhao, Yingwen; Fu, Guangyuan; Wang, Jun; Guo, Maozu; Yu, Guoxian

    2018-02-23

    Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash. Copyright © 2018 Elsevier Inc. All rights reserved.

  10. 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…

  11. Ensemble-based prediction of RNA secondary structures.

    Science.gov (United States)

    Aghaeepour, Nima; Hoos, Holger H

    2013-04-24

    Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between

  12. Risk-based performance indicators

    International Nuclear Information System (INIS)

    Azarm, M.A.; Boccio, J.L.; Vesely, W.E.; Lofgren, E.

    1987-01-01

    The purpose of risk-based indicators is to monitor plant safety. Safety is measured by monitoring the potential for core melt (core-melt frequency) and the public risk. Targets for these measures can be set consistent with NRC safety goals. In this process, the performance of safety systems, support systems, major components, and initiating events can be monitored using measures such as unavailability, failure or occurrence frequency. The changes in performance measures and their trends are determined from the time behavior of monitored measures by differentiation between stochastical and actual variations. Therefore, degradation, as well as improvement in the plant safety performance, can be determined. The development of risk-based performance indicators will also provide the means to trace a change in the safety measures to specific problem areas which are amenable to root cause analysis and inspection audits. In addition, systematic methods will be developed to identify specific improvement policies using the plant information system for the identified problem areas. The final product of the performance indicator project will be a methodology, and an integrated and validated set of software packages which, if properly interfaced with the logic model software of a plant, can monitor the plant performance as plant information is provided as input

  13. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  14. Predicting Learned Helplessness Based on Personality

    Science.gov (United States)

    Maadikhah, Elham; Erfani, Nasrollah

    2014-01-01

    Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

  15. 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.

  16. 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.

  17. 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…

  18. Prediction of Gas Injection Performance for Heterogeneous Reservoirs

    Energy Technology Data Exchange (ETDEWEB)

    Blunt, Martin J.; Orr, Franklin M.

    1999-05-17

    This report describes research carried out in the Department of Petroleum Engineering at Stanford University from September 1997 - September 1998 under the second year of a three-year grant from the Department of Energy on the "Prediction of Gas Injection Performance for Heterogeneous Reservoirs." The research effort is an integrated study of the factors affecting gas injection, from the pore scale to the field scale, and involves theoretical analysis, laboratory experiments, and numerical simulation. The original proposal described research in four areas: (1) Pore scale modeling of three phase flow in porous media; (2) Laboratory experiments and analysis of factors influencing gas injection performance at the core scale with an emphasis on the fundamentals of three phase flow; (3) Benchmark simulations of gas injection at the field scale; and (4) Development of streamline-based reservoir simulator. Each state of the research is planned to provide input and insight into the next stage, such that at the end we should have an integrated understanding of the key factors affecting field scale displacements.

  19. PREDICTING THERMAL PERFORMANCE OF ROOFING SYSTEMS IN SURABAYA

    Directory of Open Access Journals (Sweden)

    MINTOROGO Danny Santoso

    2015-07-01

    Full Text Available Traditional roofing systems in the developing country likes Indonesia are still be dominated by the 30o, 45o, and more pitched angle roofs; the roofing cover materials are widely used to traditional clay roof tiles, then modern concrete roof tiles, and ceramic roof tiles. In the 90’s decay, shop houses are prosperous built with flat concrete roofs dominant. Green roofs and roof ponds are almost rarely built to meet the sustainable environmental issues. Some tested various roof systems in Surabaya were carried out to observe the roof thermal performances. Mathematical equation model from three references are also performed in order to compare with the real project tested. Calculated with equation (Kabre et al., the 30o pitched concrete-roof-tile, 30o clay-roof-tile, 45o pitched concrete-roof-tile are the worst thermal heat flux coming to room respectively. In contrast, the bare soil concrete roof and roof pond system are the least heat flux streamed onto room. Based on predicted calculation without insulation and cross-ventilation attic space, the roof pond and bare soil concrete roof (greenery roof are the appropriate roof systems for the Surabaya’s climate; meanwhile the most un-recommended roof is pitched 30o or 45o angle with concrete-roof tiles roofing systems.

  20. Prediction on carbon dioxide emissions based on fuzzy rules

    Science.gov (United States)

    Pauzi, Herrini; Abdullah, Lazim

    2014-06-01

    There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

  1. Cage-based performance capture

    CERN Document Server

    Savoye, Yann

    2014-01-01

    Nowadays, highly-detailed animations of live-actor performances are increasingly easier to acquire and 3D Video has reached considerable attentions in visual media production. In this book, we address the problem of extracting or acquiring and then reusing non-rigid parametrization for video-based animations. At first sight, a crucial challenge is to reproduce plausible boneless deformations while preserving global and local captured properties of dynamic surfaces with a limited number of controllable, flexible and reusable parameters. To solve this challenge, we directly rely on a skin-detached dimension reduction thanks to the well-known cage-based paradigm. First, we achieve Scalable Inverse Cage-based Modeling by transposing the inverse kinematics paradigm on surfaces. Thus, we introduce a cage inversion process with user-specified screen-space constraints. Secondly, we convert non-rigid animated surfaces into a sequence of optimal cage parameters via Cage-based Animation Conversion. Building upon this re...

  2. Meta-path based heterogeneous combat network link prediction

    Science.gov (United States)

    Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

    2017-09-01

    The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

  3. Protein structure based prediction of catalytic residues.

    Science.gov (United States)

    Fajardo, J Eduardo; Fiser, Andras

    2013-02-22

    Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

  4. Drug-target interaction prediction from PSSM based evolutionary information.

    Science.gov (United States)

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Highway traffic noise prediction based on GIS

    Science.gov (United States)

    Zhao, Jianghua; Qin, Qiming

    2014-05-01

    Before building a new road, we need to predict the traffic noise generated by vehicles. Traditional traffic noise prediction methods are based on certain locations and they are not only time-consuming, high cost, but also cannot be visualized. Geographical Information System (GIS) can not only solve the problem of manual data processing, but also can get noise values at any point. The paper selected a road segment from Wenxi to Heyang. According to the geographical overview of the study area and the comparison between several models, we combine the JTG B03-2006 model and the HJ2.4-2009 model to predict the traffic noise depending on the circumstances. Finally, we interpolate the noise values at each prediction point and then generate contours of noise. By overlaying the village data on the noise contour layer, we can get the thematic maps. The use of GIS for road traffic noise prediction greatly facilitates the decision-makers because of GIS spatial analysis function and visualization capabilities. We can clearly see the districts where noise are excessive, and thus it becomes convenient to optimize the road line and take noise reduction measures such as installing sound barriers and relocating villages and so on.

  6. 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...

  7. 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.

  8. 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...

  9. 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.

  10. 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...

  11. 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.

  12. 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 ...

  13. Analysis of Factors that Predict Clinical Performance in Medical School

    Science.gov (United States)

    White, Casey B.; Dey, Eric L.; Fantone, Joseph C.

    2009-01-01

    Academic achievement indices including GPAs and MCAT scores are used to predict the spectrum of medical student academic performance types. However, use of these measures ignores two changes influencing medical school admissions: student diversity and affirmative action, and an increased focus on communication skills. To determine if GPA and MCAT…

  14. 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...

  15. 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.

  16. Covariance of charged amino acids at positions 322 and 440 of HIV-1 Env contributes to coreceptor specificity of subtype B viruses, and can be used to improve the performance of V3 sequence-based coreceptor usage prediction algorithms.

    Directory of Open Access Journals (Sweden)

    Kieran Cashin

    Full Text Available The ability to determine coreceptor usage of patient-derived human immunodeficiency virus type 1 (HIV-1 strains is clinically important, particularly for the administration of the CCR5 antagonist maraviroc. The envelope glycoprotein (Env determinants of coreceptor specificity lie primarily within the gp120 V3 loop region, although other Env determinants have been shown to influence gp120-coreceptor interactions. Here, we determined whether conserved amino acid alterations outside the V3 loop that contribute to coreceptor usage exist, and whether these alterations improve the performance of V3 sequence-based coreceptor usage prediction algorithms. We demonstrate a significant covariant association between charged amino acids at position 322 in V3 and position 440 in the C4 Env region that contributes to the specificity of HIV-1 subtype B strains for CCR5 or CXCR4. Specifically, positively charged Lys/Arg at position 322 and negatively charged Asp/Glu at position 440 occurred more frequently in CXCR4-using viruses, whereas negatively charged Asp/Glu at position 322 and positively charged Arg at position 440 occurred more frequently in R5 strains. In the context of CD4-bound gp120, structural models suggest that covariation of amino acids at Env positions 322 and 440 has the potential to alter electrostatic interactions that are formed between gp120 and charged amino acids in the CCR5 N-terminus. We further demonstrate that inclusion of a "440 rule" can improve the sensitivity of several V3 sequence-based genotypic algorithms for predicting coreceptor usage of subtype B HIV-1 strains, without compromising specificity, and significantly improves the AUROC of the geno2pheno algorithm when set to its recommended false positive rate of 5.75%. Together, our results provide further mechanistic insights into the intra-molecular interactions within Env that contribute to coreceptor specificity of subtype B HIV-1 strains, and demonstrate that incorporation

  17. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  18. 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.

  19. 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...

  20. 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'…

  1. Predictive Measures of Locomotor Performance on an Unstable Walking Surface

    Science.gov (United States)

    Bloomberg, J. J.; Peters, B. T.; Mulavara, A. P.; Caldwell, E. E.; Batson, C. D.; De Dios, Y. E.; Gadd, N. E.; Goel, R.; Wood, S. J.; Cohen, H. S.; hide

    2016-01-01

    Locomotion requires integration of visual, vestibular, and somatosensory information to produce the appropriate motor output to control movement. The degree to which these sensory inputs are weighted and reorganized in discordant sensory environments varies by individual and may be predictive of the ability to adapt to novel environments. The goals of this project are to: 1) develop a set of predictive measures capable of identifying individual differences in sensorimotor adaptability, and 2) use this information to inform the design of training countermeasures designed to enhance the ability of astronauts to adapt to gravitational transitions improving balance and locomotor performance after a Mars landing and enhancing egress capability after a landing on Earth.

  2. Driver's mental workload prediction model based on physiological indices.

    Science.gov (United States)

    Yan, Shengyuan; Tran, Cong Chi; Wei, Yingying; Habiyaremye, Jean Luc

    2017-09-15

    Developing an early warning model to predict the driver's mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers' MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver's MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R 2 value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver's work performance.

  3. Power system dynamic state estimation using prediction based evolutionary technique

    International Nuclear Information System (INIS)

    Basetti, Vedik; Chandel, Ashwani K.; Chandel, Rajeevan

    2016-01-01

    In this paper, a new robust LWS (least winsorized square) estimator is proposed for dynamic state estimation of a power system. One of the main advantages of this estimator is that it has an inbuilt bad data rejection property and is less sensitive to bad data measurements. In the proposed approach, Brown's double exponential smoothing technique has been utilised for its reliable performance at the prediction step. The state estimation problem is solved as an optimisation problem using a new jDE-self adaptive differential evolution with prediction based population re-initialisation technique at the filtering step. This new stochastic search technique has been embedded with different state scenarios using the predicted state. The effectiveness of the proposed LWS technique is validated under different conditions, namely normal operation, bad data, sudden load change, and loss of transmission line conditions on three different IEEE test bus systems. The performance of the proposed approach is compared with the conventional extended Kalman filter. On the basis of various performance indices, the results thus obtained show that the proposed technique increases the accuracy and robustness of power system dynamic state estimation performance. - Highlights: • To estimate the states of the power system under dynamic environment. • The performance of the EKF method is degraded during anomaly conditions. • The proposed method remains robust towards anomalies. • The proposed method provides precise state estimates even in the presence of anomalies. • The results show that prediction accuracy is enhanced by using the proposed model.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. Estimating Stochastic Volatility Models using Prediction-based Estimating Functions

    DEFF Research Database (Denmark)

    Lunde, Asger; Brix, Anne Floor

    to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from......In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared...... to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF...

  7. 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

  8. Locomotion With Loads: Practical Techniques for Predicting Performance Outcomes

    Science.gov (United States)

    2015-05-01

    Metabolic energy consumption as a function of speed and body size in birds and mammals. J Exp Biol. 97, 1-21. Weyand, P., Smith, B., Puyau, M. and...height, weight (including load), speed, and grade algorithms proposed will allow walking metabolic rates to be predicted to within 6.0 and 12.0% in...gait, metabolism , performance, load carriage 16. SECURITY CLASSIFICATION OF: Unclassified 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME

  9. 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).

  10. 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.

  11. 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

  12. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

  13. Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

    Directory of Open Access Journals (Sweden)

    Wenbiao Li

    2016-04-01

    Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

  14. New Temperature-based Models for Predicting Global Solar Radiation

    International Nuclear Information System (INIS)

    Hassan, Gasser E.; Youssef, M. Elsayed; Mohamed, Zahraa E.; Ali, Mohamed A.; Hanafy, Ahmed A.

    2016-01-01

    Highlights: • New temperature-based models for estimating solar radiation are investigated. • The models are validated against 20-years measured data of global solar radiation. • The new temperature-based model shows the best performance for coastal sites. • The new temperature-based model is more accurate than the sunshine-based models. • The new model is highly applicable with weather temperature forecast techniques. - Abstract: This study presents new ambient-temperature-based models for estimating global solar radiation as alternatives to the widely used sunshine-based models owing to the unavailability of sunshine data at all locations around the world. Seventeen new temperature-based models are established, validated and compared with other three models proposed in the literature (the Annandale, Allen and Goodin models) to estimate the monthly average daily global solar radiation on a horizontal surface. These models are developed using a 20-year measured dataset of global solar radiation for the case study location (Lat. 30°51′N and long. 29°34′E), and then, the general formulae of the newly suggested models are examined for ten different locations around Egypt. Moreover, the local formulae for the models are established and validated for two coastal locations where the general formulae give inaccurate predictions. Mostly common statistical errors are utilized to evaluate the performance of these models and identify the most accurate model. The obtained results show that the local formula for the most accurate new model provides good predictions for global solar radiation at different locations, especially at coastal sites. Moreover, the local and general formulas of the most accurate temperature-based model also perform better than the two most accurate sunshine-based models from the literature. The quick and accurate estimations of the global solar radiation using this approach can be employed in the design and evaluation of performance for

  15. Predicting introductory programming performance: A multi-institutional multivariate study

    Science.gov (United States)

    Bergin, Susan; Reilly, Ronan

    2006-12-01

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

  16. Blind Test of Physics-Based Prediction of Protein Structures

    Science.gov (United States)

    Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

    2009-01-01

    We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

  17. 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.

  18. Empirical comparison of web-based antimicrobial peptide prediction tools.

    Science.gov (United States)

    Gabere, Musa Nur; Noble, William Stafford

    2017-07-01

    Antimicrobial peptides (AMPs) are innate immune molecules that exhibit activities against a range of microbes, including bacteria, fungi, viruses and protozoa. Recent increases in microbial resistance against current drugs has led to a concomitant increase in the need for novel antimicrobial agents. Over the last decade, a number of AMP prediction tools have been designed and made freely available online. These AMP prediction tools show potential to discriminate AMPs from non-AMPs, but the relative quality of the predictions produced by the various tools is difficult to quantify. We compiled two sets of AMP and non-AMP peptides, separated into three categories-antimicrobial, antibacterial and bacteriocins. Using these benchmark data sets, we carried out a systematic evaluation of ten publicly available AMP prediction methods. Among the six general AMP prediction tools-ADAM, CAMPR3(RF), CAMPR3(SVM), MLAMP, DBAASP and MLAMP-we find that CAMPR3(RF) provides a statistically significant improvement in performance, as measured by the area under the receiver operating characteristic (ROC) curve, relative to the other five methods. Surprisingly, for antibacterial prediction, the original AntiBP method significantly outperforms its successor, AntiBP2 based on one benchmark dataset. The two bacteriocin prediction tools, BAGEL3 and BACTIBASE, both provide very good performance and BAGEL3 outperforms its predecessor, BACTIBASE, on the larger of the two benchmarks. gaberemu@ngha.med.sa or william-noble@uw.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  19. Numerical simulation of a twin screw expander for performance prediction

    Science.gov (United States)

    Papes, Iva; Degroote, Joris; Vierendeels, Jan

    2015-08-01

    With the increasing use of twin screw expanders in waste heat recovery applications, the performance prediction of these machines plays an important role. This paper presents a mathematical model for calculating the performance of a twin screw expander. From the mass and energy conservation laws, differential equations are derived which are then solved together with the appropriate Equation of State in the instantaneous control volumes. Different flow processes that occur inside the screw expander such as filling (accompanied by a substantial pressure loss) and leakage flows through the clearances are accounted for in the model. The mathematical model employs all geometrical parameters such as chamber volume, suction and leakage areas. With R245fa as working fluid, the Aungier Redlich-Kwong Equation of State has been used in order to include real gas effects. To calculate the mass flow rates through the leakage paths formed inside the screw expander, flow coefficients are considered as constant and they are derived from 3D Computational Fluid Dynamic calculations at given working conditions and applied to all other working conditions. The outcome of the mathematical model is the P-V indicator diagram which is compared to CFD results of the same twin screw expander. Since CFD calculations require significant computational time, developed mathematical model can be used for the faster performance prediction.

  20. 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.

  1. 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.

  2. Bayesian calibration of power plant models for accurate performance prediction

    International Nuclear Information System (INIS)

    Boksteen, Sowande Z.; Buijtenen, Jos P. van; Pecnik, Rene; Vecht, Dick van der

    2014-01-01

    Highlights: • Bayesian calibration is applied to power plant performance prediction. • Measurements from a plant in operation are used for model calibration. • A gas turbine performance model and steam cycle model are calibrated. • An integrated plant model is derived. • Part load efficiency is accurately predicted as a function of ambient conditions. - Abstract: Gas turbine combined cycles are expected to play an increasingly important role in the balancing of supply and demand in future energy markets. Thermodynamic modeling of these energy systems is frequently applied to assist in decision making processes related to the management of plant operation and maintenance. In most cases, model inputs, parameters and outputs are treated as deterministic quantities and plant operators make decisions with limited or no regard of uncertainties. As the steady integration of wind and solar energy into the energy market induces extra uncertainties, part load operation and reliability are becoming increasingly important. In the current study, methods are proposed to not only quantify various types of uncertainties in measurements and plant model parameters using measured data, but to also assess their effect on various aspects of performance prediction. The authors aim to account for model parameter and measurement uncertainty, and for systematic discrepancy of models with respect to reality. For this purpose, the Bayesian calibration framework of Kennedy and O’Hagan is used, which is especially suitable for high-dimensional industrial problems. The article derives a calibrated model of the plant efficiency as a function of ambient conditions and operational parameters, which is also accurate in part load. The article shows that complete statistical modeling of power plants not only enhances process models, but can also increases confidence in operational decisions

  3. Predictive neuromechanical simulations indicate why walking performance declines with ageing.

    Science.gov (United States)

    Song, Seungmoon; Geyer, Hartmut

    2018-04-01

    Although the natural decline in walking performance with ageing affects the quality of life of a growing elderly population, its physiological origins remain unknown. By using predictive neuromechanical simulations of human walking with age-related neuro-musculo-skeletal changes, we find evidence that the loss of muscle strength and muscle contraction speed dominantly contribute to the reduced walking economy and speed. The findings imply that focusing on recovering these muscular changes may be the only effective way to improve performance in elderly walking. More generally, the work is of interest for investigating the physiological causes of altered gait due to age, injury and disorders. Healthy elderly people walk slower and energetically less efficiently than young adults. This decline in walking performance lowers the quality of life for a growing ageing population, and understanding its physiological origin is critical for devising interventions that can delay or revert it. However, the origin of the decline in walking performance remains unknown, as ageing produces a range of physiological changes whose individual effects on gait are difficult to separate in experiments with human subjects. Here we use a predictive neuromechanical model to separately address the effects of common age-related changes to the skeletal, muscular and nervous systems. We find in computer simulations of this model that the combined changes produce gait consistent with elderly walking and that mainly the loss of muscle strength and mass reduces energy efficiency. In addition, we find that the slower preferred walking speed of elderly people emerges in the simulations when adapting to muscle fatigue, again mainly caused by muscle-related changes. The results suggest that a focus on recovering these muscular changes may be the only effective way to improve performance in elderly walking. © 2018 The Authors. The Journal of Physiology © 2018 The Physiological Society.

  4. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  5. Performance Prediction and Validation: Data, Frameworks, and Considerations

    Energy Technology Data Exchange (ETDEWEB)

    Tinnesand, Heidi

    2017-05-19

    Improving the predictability and reliability of wind power generation and operations will reduce costs and potentially establish a framework to attract new capital into the distributed wind sector, a key cost reduction requirement highlighted in results from the distributed wind future market assessment conducted with dWind. Quantifying and refining the accuracy of project performance estimates will also directly address several of the key challenges identified by industry stakeholders in 2015 as part of the distributed wind resource assessment workshop and be cross-cutting for several other facets of the distributed wind portfolio. This presentation covers the efforts undertaken in 2016 to address these topics.

  6. Numerical analysis of the performance prediction for a thermoelectric generator

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Chang Nyung [Kyung Hee University, Yongin (Korea, Republic of)

    2015-09-15

    The present study develops a two-dimensional numerical code that can predict the performance of a thermoelectric generator module including a p-leg/n-leg pair and top and bottom electrodes. The present code can simulate the detailed thermoelectric phenomena including the heat flow, electric current, Joule heating, Peltier heating, and Thomson heating, together with the efficiency of the modules whose properties depend on the temperature. The present numerical code can be used for the design optimization of a thermoelectric power generator.

  7. Predicting Students’ Performance using Modified ID3 Algorithm

    OpenAIRE

    Ramanathan L; Saksham Dhanda; Suresh Kumar D

    2013-01-01

    The ability to predict performance of students is very crucial in our present education system. We can use data mining concepts for this purpose. ID3 algorithm is one of the famous algorithms present today to generate decision trees. But this algorithm has a shortcoming that it is inclined to attributes with many values. So , this research aims to overcome this shortcoming of the algorithm by using gain ratio(instead of information gain) as well as by giving weights to each attribute at every...

  8. Performance prediction of a multi-basin solar still

    International Nuclear Information System (INIS)

    Mahdi, N.Al.

    1992-01-01

    A transient analysis for the prediction of the performance of a multi-basin solar still is presented. The energy-balance equations for the glass covers, the water masses and the absorber plate are manipulated to obtain a set of ordinary differential equations which are solved numerically. The analysis is applied to investigate the effect of the number of basins on the daily productivity of the still. Meteorological data corresponding to a June day in Bahrain have been used for the computation. The results indicate that the daily distillate output is increased by increasing the number of basins in the still. (author)

  9. 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

  10. EMD-Based Predictive Deep Belief Network for Time Series Prediction: An Application to Drought Forecasting

    Directory of Open Access Journals (Sweden)

    Norbert A. Agana

    2018-02-01

    Full Text Available Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction is essential because it can aid in the preparedness and impact-related management of its effects. This study considers the drought forecasting problem by developing a hybrid predictive model using a denoised empirical mode decomposition (EMD and a deep belief network (DBN. The proposed method first decomposes the data into several intrinsic mode functions (IMFs using EMD, and a reconstruction of the original data is obtained by considering only relevant IMFs. Detrended fluctuation analysis (DFA was applied to each IMF to determine the threshold for robust denoising performance. Based on their scaling exponents, irrelevant intrinsic mode functions are identified and suppressed. The proposed method was applied to predict different time scale drought indices across the Colorado River basin using a standardized streamflow index (SSI as the drought index. The results obtained using the proposed method was compared with standard methods such as multilayer perceptron (MLP and support vector regression (SVR. The proposed hybrid model showed improvement in prediction accuracy, especially for multi-step ahead predictions.

  11. 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)

  12. Cold-Blooded Attention: Finger Temperature Predicts Attentional Performance.

    Science.gov (United States)

    Vergara, Rodrigo C; Moënne-Loccoz, Cristóbal; Maldonado, Pedro E

    2017-01-01

    Thermal stress has been shown to increase the chances of unsafe behavior during industrial and driving performances due to reductions in mental and attentional resources. Nonetheless, establishing appropriate safety standards regarding environmental temperature has been a major problem, as modulations are also be affected by the task type, complexity, workload, duration, and previous experience with the task. To bypass this attentional and thermoregulatory problem, we focused on the body rather than environmental temperature. Specifically, we measured tympanic, forehead, finger and environmental temperatures accompanied by a battery of attentional tasks. We considered a 10 min baseline period wherein subjects were instructed to sit and relax, followed by three attentional tasks: a continuous performance task (CPT), a flanker task (FT) and a counting task (CT). Using multiple linear regression models, we evaluated which variable(s) were the best predictors of performance. The results showed a decrement in finger temperature due to instruction and task engagement that was absent when the subject was instructed to relax. No changes were observed in tympanic or forehead temperatures, while the environmental temperature remained almost constant for each subject. Specifically, the magnitude of the change in finger temperature was the best predictor of performance in all three attentional tasks. The results presented here suggest that finger temperature can be used as a predictor of alertness, as it predicted performance in attentional tasks better than environmental temperature. These findings strongly support that peripheral temperature can be used as a tool to prevent unsafe behaviors and accidents.

  13. Cold-Blooded Attention: Finger Temperature Predicts Attentional Performance

    Directory of Open Access Journals (Sweden)

    Rodrigo C. Vergara

    2017-09-01

    Full Text Available Thermal stress has been shown to increase the chances of unsafe behavior during industrial and driving performances due to reductions in mental and attentional resources. Nonetheless, establishing appropriate safety standards regarding environmental temperature has been a major problem, as modulations are also be affected by the task type, complexity, workload, duration, and previous experience with the task. To bypass this attentional and thermoregulatory problem, we focused on the body rather than environmental temperature. Specifically, we measured tympanic, forehead, finger and environmental temperatures accompanied by a battery of attentional tasks. We considered a 10 min baseline period wherein subjects were instructed to sit and relax, followed by three attentional tasks: a continuous performance task (CPT, a flanker task (FT and a counting task (CT. Using multiple linear regression models, we evaluated which variable(s were the best predictors of performance. The results showed a decrement in finger temperature due to instruction and task engagement that was absent when the subject was instructed to relax. No changes were observed in tympanic or forehead temperatures, while the environmental temperature remained almost constant for each subject. Specifically, the magnitude of the change in finger temperature was the best predictor of performance in all three attentional tasks. The results presented here suggest that finger temperature can be used as a predictor of alertness, as it predicted performance in attentional tasks better than environmental temperature. These findings strongly support that peripheral temperature can be used as a tool to prevent unsafe behaviors and accidents.

  14. Performance prediction of rotary compressor with hydrocarbon refrigerant mixtures

    Energy Technology Data Exchange (ETDEWEB)

    Park, M.W.; Chung, Y.G. [Hanyang University Graduate School, Seoul (Korea); Park, K.W. [LG Industrial System Corporation Limited (Korea); Park, H.Y. [Hanyang University, Seoul (Korea)

    1999-04-01

    This paper presents the modeling approach that can be predicted transient behavior of rotary compressor. Mass and energy conservation laws are applied to the control volume, and real gas state equation is used to obtain thermodynamic properties of refrigerant. The valve equation is solved to analyze discharge process also. Dynamic analysis of vane and roller is carried out to gain friction work. From above modeling, the performance of rotary compressor with radial clearance and friction loss is investigated numerically. The performance of each refrigerant and the possibility of using the hydrocarbon refrigerant mixtures in an existing rotary compressor are estimated by applying R12, R134a, R290/R600a mixture also. (author). 6 refs., 13 figs., 1 tab.

  15. Planetary Suit Hip Bearing Model for Predicting Design vs. Performance

    Science.gov (United States)

    Cowley, Matthew S.; Margerum, Sarah; Harvil, Lauren; Rajulu, Sudhakar

    2011-01-01

    , the suited performance trends were comparable between the model and the suited subjects. With the three off-nominal bearing configurations compared to the nominal bearing configurations, human subjects showed decreases in hip flexion of 64%, 6%, and 13% and in hip abduction of 59%, 2%, and 20%. Likewise the solid model showed decreases in hip flexion of 58%, 1%, and 25% and in hip abduction of 56%, 0%, and 30%, under the same condition changes from the nominal configuration. Differences seen between the model predictions and the human subject performance data could be attributed to the model lacking dynamic elements and performing kinematic analysis only, the level of fit of the subjects with the suit, the levels of the subject s suit experience.

  16. Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

    Directory of Open Access Journals (Sweden)

    Jin Xin

    2017-10-01

    Full Text Available This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

  17. Examining the Roles of Reasoning and Working Memory in Predicting Casual Game Performance across Extended Gameplay.

    Science.gov (United States)

    Kranz, Michael B; Baniqued, Pauline L; Voss, Michelle W; Lee, Hyunkyu; Kramer, Arthur F

    2017-01-01

    The variety and availability of casual video games presents an exciting opportunity for applications such as cognitive training. Casual games have been associated with fluid abilities such as working memory (WM) and reasoning, but the importance of these cognitive constructs in predicting performance may change across extended gameplay and vary with game structure. The current investigation examined the relationship between cognitive abilities and casual game performance over time by analyzing first and final session performance over 4-5 weeks of game play. We focused on two groups of subjects who played different types of casual games previously shown to relate to WM and reasoning when played for a single session: (1) puzzle-based games played adaptively across sessions and (2) speeded switching games played non-adaptively across sessions. Reasoning uniquely predicted first session casual game scores for both groups and accounted for much of the relationship with WM. Furthermore, over time, WM became uniquely important for predicting casual game performance for the puzzle-based adaptive games but not for the speeded switching non-adaptive games. These results extend the burgeoning literature on cognitive abilities involved in video games by showing differential relationships of fluid abilities across different game types and extended play. More broadly, the current study illustrates the usefulness of using multiple cognitive measures in predicting performance, and provides potential directions for game-based cognitive training research.

  18. Examining the Roles of Reasoning and Working Memory in Predicting Casual Game Performance across Extended Gameplay

    Science.gov (United States)

    Kranz, Michael B.; Baniqued, Pauline L.; Voss, Michelle W.; Lee, Hyunkyu; Kramer, Arthur F.

    2017-01-01

    The variety and availability of casual video games presents an exciting opportunity for applications such as cognitive training. Casual games have been associated with fluid abilities such as working memory (WM) and reasoning, but the importance of these cognitive constructs in predicting performance may change across extended gameplay and vary with game structure. The current investigation examined the relationship between cognitive abilities and casual game performance over time by analyzing first and final session performance over 4–5 weeks of game play. We focused on two groups of subjects who played different types of casual games previously shown to relate to WM and reasoning when played for a single session: (1) puzzle-based games played adaptively across sessions and (2) speeded switching games played non-adaptively across sessions. Reasoning uniquely predicted first session casual game scores for both groups and accounted for much of the relationship with WM. Furthermore, over time, WM became uniquely important for predicting casual game performance for the puzzle-based adaptive games but not for the speeded switching non-adaptive games. These results extend the burgeoning literature on cognitive abilities involved in video games by showing differential relationships of fluid abilities across different game types and extended play. More broadly, the current study illustrates the usefulness of using multiple cognitive measures in predicting performance, and provides potential directions for game-based cognitive training research. PMID:28326042

  19. A unified tool for performance modelling and prediction

    International Nuclear Information System (INIS)

    Gilmore, Stephen; Kloul, Leila

    2005-01-01

    We describe a novel performability modelling approach, which facilitates the efficient solution of performance models extracted from high-level descriptions of systems. The notation which we use for our high-level designs is the Unified Modelling Language (UML) graphical modelling language. The technology which provides the efficient representation capability for the underlying performance model is the multi-terminal binary decision diagram (MTBDD)-based PRISM probabilistic model checker. The UML models are compiled through an intermediate language, the stochastic process algebra PEPA, before translation into MTBDDs for solution. We illustrate our approach on a real-world analysis problem from the domain of mobile telephony

  20. Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments

    Directory of Open Access Journals (Sweden)

    Marjan Čeh

    2018-05-01

    Full Text Available The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1 the non-linear nature of the prediction assignment task; (2 input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3 the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE, coefficient of dispersion (COD revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.

  1. Bankruptcy Prediction Based on the Autonomy Ratio

    Directory of Open Access Journals (Sweden)

    Daniel Brîndescu Olariu

    2016-11-01

    Full Text Available The theory and practice of the financial ratio analysis suggest the existence of a negative correlation between the autonomy ratio and the bankruptcy risk. Previous studies conducted on a sample of companies from Timis County (largest county in Romania confirm this hypothesis and recommend the autonomy ratio as a useful tool for measuring the bankruptcy risk two years in advance. The objective of the current research was to develop a methodology for measuring the bankruptcy risk that would be applicable for the companies from the Timis County (specific methodologies are considered necessary for each region. The target population consisted of all the companies from Timis County with annual sales of over 10,000 lei (aprox. 2,200 Euros. The research was performed over all the target population. The study has thus included 53,252 yearly financial statements from the period 2007 – 2010. The results of the study allow for the setting of benchmarks, as well as the configuration of a methodology of analysis. The proposed methodology cannot predict with perfect accuracy the state of the company, but it allows for a valuation of the risk level to which the company is subjected.

  2. The prediction of the hydrodynamic performance of tidal current turbines

    International Nuclear Information System (INIS)

    Xiao, B Y; Zhou, L J; Xiao, Y X; Wang, Z W

    2013-01-01

    Nowadays tidal current energy is considered to be one of the most promising alternative green energy resources and tidal current turbines are used for power generation. Prediction of the open water performance around tidal turbines is important for the reason that it can give some advice on installation and array of tidal current turbines. This paper presents numerical computations of tidal current turbines by using a numerical model which is constructed to simulate an isolated turbine. This paper aims at studying the installation of marine current turbine of which the hydro-environmental impacts influence by means of numerical simulation. Such impacts include free-stream velocity magnitude, seabed and inflow direction of velocity. The results of the open water performance prediction show that the power output and efficiency of marine current turbine varies from different marine environments. The velocity distribution should be clearly and the suitable unit installation depth and direction be clearly chosen, which can ensure the most effective strategy for energy capture before installing the marine current turbine. The findings of this paper are expected to be beneficial in developing tidal current turbines and array in the future

  3. 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....

  4. 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.

  5. 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.

  6. 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...

  7. Deep-Learning-Based Drug-Target Interaction Prediction.

    Science.gov (United States)

    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  8. 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.

  9. 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

  10. Link Prediction in Evolving Networks Based on Popularity of Nodes.

    Science.gov (United States)

    Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

    2017-08-02

    Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

  11. Chaos Time Series Prediction Based on Membrane Optimization Algorithms

    Directory of Open Access Journals (Sweden)

    Meng Li

    2015-01-01

    Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.

  12. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  13. Basic considerations in predicting error probabilities in human task performance

    International Nuclear Information System (INIS)

    Fleishman, E.A.; Buffardi, L.C.; Allen, J.A.; Gaskins, R.C. III

    1990-04-01

    It is well established that human error plays a major role in the malfunctioning of complex systems. This report takes a broad look at the study of human error and addresses the conceptual, methodological, and measurement issues involved in defining and describing errors in complex systems. In addition, a review of existing sources of human reliability data and approaches to human performance data base development is presented. Alternative task taxonomies, which are promising for establishing the comparability on nuclear and non-nuclear tasks, are also identified. Based on such taxonomic schemes, various data base prototypes for generalizing human error rates across settings are proposed. 60 refs., 3 figs., 7 tabs

  14. Near peripheral motion contrast threshold predicts older drivers' simulator performance.

    Science.gov (United States)

    Henderson, Steven; Gagnon, Sylvain; Collin, Charles; Tabone, Ricardo; Stinchcombe, Arne

    2013-01-01

    Our group has previously demonstrated that peripheral motion contrast threshold (PMCT) is significantly associated with self-reported accident risk of older drivers (questionnaire assessment), and with Useful Field of View(®) subtest 2 (UFOV2). It has not been shown, however, that PMCT is significantly associated with driving performance. Using the method of descending limits (spatial two-alternative forced choice) we assessed motion contrast thresholds of 28 young participants (25-45), and 21 older drivers (63-86) for 0.4 cycle/degree drifting Gabor stimuli at 15° eccentricity and examined whether it was related to performance on a simulated on-road test and to a measure of visual attention (UFOV(®) subtests 2 and 3). Peripheral motion contrast thresholds (PMCT) of younger participants were significantly lower than older participants. PMCT and UFOV2 significantly predicted driving examiners' scores of older drivers' simulator performance, as well as number of crashes. Within the older group, PMCT correlated significantly with UFOV2, UFOV3, and age. Within the younger group, PMCT was not significantly related to either UFOV(®) scores or age. Partial correlations showed that: substantial association between PMCT and UFOV2 was not age-related (within the older driver group); PMCT and UFOV2 tapped a common visual function; and PMCT assessed a component not captured by UFOV2. PMCT is potentially a useful assessment tool for predicting accident risk of older drivers, and for informing efforts to develop effective countermeasures to remediate this functional deficit as much as possible. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Prediction of mortality based on facial characteristics

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

    2016-05-01

    Full Text Available Recent studies have shown that characteristics of the face contain a wealth of information about health, age and chronic clinical conditions. Such studies involve objective measurement of facial features correlated with historical health information. But some individuals also claim to be adept at gauging mortality based on a glance at a person’s photograph. To test this claim, we invited 12 such individuals to see if they could determine if a person was alive or dead based solely on a brief examination of facial photographs. All photos used in the experiment were transformed into a uniform gray scale and then counterbalanced across eight categories: gender, age, gaze direction, glasses, head position, smile, hair color, and image resolution. Participants examined 404 photographs displayed on a computer monitor, one photo at a time, each shown for a maximum of 8 seconds. Half of the individuals in the photos were deceased, and half were alive at the time the experiment was conducted. Participants were asked to press a button if they thought the person in a photo was living or deceased. Overall mean accuracy on this task was 53.8%, where 50% was expected by chance (p < 0.004, two-tail. Statistically significant accuracy was independently obtained in 5 of the 12 participants. We also collected 32-channel electrophysiological recordings and observed a robust difference between images of deceased individuals correctly vs. incorrectly classified in the early event related potential at 100 ms post-stimulus onset. Our results support claims of individuals who report that some as-yet unknown features of the face predict mortality. The results are also compatible with claims about clairvoyance and warrants further investigation.

  16. Transcription factor binding sites prediction based on modified nucleosomes.

    Directory of Open Access Journals (Sweden)

    Mohammad Talebzadeh

    Full Text Available In computational methods, position weight matrices (PWMs are commonly applied for transcription factor binding site (TFBS prediction. Although these matrices are more accurate than simple consensus sequences to predict actual binding sites, they usually produce a large number of false positive (FP predictions and so are impoverished sources of information. Several studies have employed additional sources of information such as sequence conservation or the vicinity to transcription start sites to distinguish true binding regions from random ones. Recently, the spatial distribution of modified nucleosomes has been shown to be associated with different promoter architectures. These aligned patterns can facilitate DNA accessibility for transcription factors. We hypothesize that using data from these aligned and periodic patterns can improve the performance of binding region prediction. In this study, we propose two effective features, "modified nucleosomes neighboring" and "modified nucleosomes occupancy", to decrease FP in binding site discovery. Based on these features, we designed a logistic regression classifier which estimates the probability of a region as a TFBS. Our model learned each feature based on Sp1 binding sites on Chromosome 1 and was tested on the other chromosomes in human CD4+T cells. In this work, we investigated 21 histone modifications and found that only 8 out of 21 marks are strongly correlated with transcription factor binding regions. To prove that these features are not specific to Sp1, we combined the logistic regression classifier with the PWM, and created a new model to search TFBSs on the genome. We tested the model using transcription factors MAZ, PU.1 and ELF1 and compared the results to those using only the PWM. The results show that our model can predict Transcription factor binding regions more successfully. The relative simplicity of the model and capability of integrating other features make it a superior method

  17. Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding.

    Science.gov (United States)

    Zhang, Xianguo; Huang, Tiejun; Tian, Yonghong; Gao, Wen

    2014-02-01

    The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.

  18. 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.

  19. Extending Theory-Based Quantitative Predictions to New Health Behaviors.

    Science.gov (United States)

    Brick, Leslie Ann D; Velicer, Wayne F; Redding, Colleen A; Rossi, Joseph S; Prochaska, James O

    2016-04-01

    Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing. A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory. This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel. Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions. Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

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

    Directory of Open Access Journals (Sweden)

    HANNAH JACKSON FOLDES

    2006-09-01

    Full Text Available 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 adjustment. Job performance ratings provided by host country national co-workers were obtained for 308 expatriates on assignment in Turkey. Expatriates responded to a measure of personality and cross cultural adjustment. It was found that impression management scale scores were not related to either adjustment or job performance. These results are discussed in the broader context of research on social desirability, expatriate job performance, and expatriate research in general.

  1. Performance analysis of tracked panel according to predicted global radiation

    International Nuclear Information System (INIS)

    Chang, T.P.

    2009-01-01

    In this paper, the performance of a south facing single-axis tracked panel was analyzed according to global radiation predicted by empirical model. Mathematic expressions appropriate for single-axis tracking system were derived to calculate the radiation on it. Instantaneous increments of solar energy collected by the tracked panel relative to fixed panel are illustrated. The validity of the empirical model to Taiwan area will also be examined with the actual irradiation data observed in Taipei. The results are summarized as follows: the gains made by the tracked panel relative to a fixed panel are between 20.0% and 33.9% for four specified days of year, between 20.9% and 33.2% for the four seasons and 27.6% over the entire year. For latitudes below 65 deg., the yearly optimal tilt angle of a fixed panel is close to 0.8 times latitude, the irradiation ratio of the tracked panel to the fixed panel is about 1.3, which are smaller than the corresponding values calculated from extraterrestrial radiation, suggesting us that the installation angle should be adjusted toward a flatter angle and that the gain of the tracked panel will reduce while it works in cloudy climate or in air pollution environment. Although the captured radiation increases with the maximal rotation angle of panel, but the benefit on the global radiation case is still not so good as that on extraterrestrial radiation case. The irradiation data observed is much less than the data predicted by the empirical model, however the trend of fitting curve to the observed data is somewhat in agreement with that to the predicted one; the yearly gain is 14.3% when a tracked panel is employed throughout the year.

  2. Music-related reward responses predict episodic memory performance.

    Science.gov (United States)

    Ferreri, Laura; Rodriguez-Fornells, Antoni

    2017-12-01

    Music represents a special type of reward involving the recruitment of the mesolimbic dopaminergic system. According to recent theories on episodic memory formation, as dopamine strengthens the synaptic potentiation produced by learning, stimuli triggering dopamine release could result in long-term memory improvements. Here, we behaviourally test whether music-related reward responses could modulate episodic memory performance. Thirty participants rated (in terms of arousal, familiarity, emotional valence, and reward) and encoded unfamiliar classical music excerpts. Twenty-four hours later, their episodic memory was tested (old/new recognition and remember/know paradigm). Results revealed an influence of music-related reward responses on memory: excerpts rated as more rewarding were significantly better recognized and remembered. Furthermore, inter-individual differences in the ability to experience musical reward, measured through the Barcelona Music Reward Questionnaire, positively predicted memory performance. Taken together, these findings shed new light on the relationship between music, reward and memory, showing for the first time that music-driven reward responses are directly implicated in higher cognitive functions and can account for individual differences in memory performance.

  3. Mental Strategies Predict Performance and Satisfaction with Performance among Soccer Players.

    Science.gov (United States)

    Kruk, Magdalena; Blecharz, Jan; Boberska, Monika; Zarychta, Karolina; Luszczynska, Aleksandra

    2017-10-01

    This study investigated the changes in mental strategies across the season and their effects on performance and satisfaction with individual performance. Data were collected three times: at the pre-season at Time 1 (T1; baseline), in the mid-season at Time 2 (T2; two-month follow-up), and at the end-of-season at Time 3 (T3; nine-month follow-up) among male soccer players (N = 97) aged 16-27. Athletes completed the questionnaires assessing the use of nine psychological strategies in competition and the level of satisfaction with individual performance. Endurance performance was measured objectively with a 300 m run. A high level of relaxation (T1) explained better 300 m run performance (T3) and a high level of self-talk explained a higher satisfaction with individual performance (T3). A rare use of distractibility and emotional control (T1) predicted a higher level of satisfaction with individual performance (T3). No predictive role of other psychological strategies was found. The use of emotional control, relaxation, and distractibility increased over the season, whereas the use of imagery and negative thinking declined. Besides the roles of self-talk, imagery, relaxation and goal-setting, the effects of distractibility and emotional control should be taken into account when considering athletes' mental training programs.

  4. Financial performance evaluation and bankruptcy prediction (failure1

    Directory of Open Access Journals (Sweden)

    Talal A. Al-Kassar, Dr.

    2014-10-01

    The research also demonstrates the need to include measures of both financial and non-financial performance in the evaluation as they complement each other. Without both financial and non-financial, the evaluation process is incomplete and does not provide desired results or the correct image of the process. The research suggests including comprehensive measures of performance evaluation of projects by using indicators of adopted criteria. Thus, the application of both models leads to better results and assists users in maintaining greater objectivity while obtaining more accurate results than from analysis based on personal evaluation alone.

  5. Operational Numerical Weather Prediction systems based on Linux cluster architectures

    International Nuclear Information System (INIS)

    Pasqui, M.; Baldi, M.; Gozzini, B.; Maracchi, G.; Giuliani, G.; Montagnani, S.

    2005-01-01

    The progress in weather forecast and atmospheric science has been always closely linked to the improvement of computing technology. In order to have more accurate weather forecasts and climate predictions, more powerful computing resources are needed, in addition to more complex and better-performing numerical models. To overcome such a large computing request, powerful workstations or massive parallel systems have been used. In the last few years, parallel architectures, based on the Linux operating system, have been introduced and became popular, representing real high performance-low cost systems. In this work the Linux cluster experience achieved at the Laboratory far Meteorology and Environmental Analysis (LaMMA-CNR-IBIMET) is described and tips and performances analysed

  6. Simplified Predictive Models for CO2 Sequestration Performance Assessment

    Science.gov (United States)

    Mishra, Srikanta; RaviGanesh, Priya; Schuetter, Jared; Mooney, Douglas; He, Jincong; Durlofsky, Louis

    2014-05-01

    We present results from an ongoing research project that seeks to develop and validate a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formation. The overall research goal is to provide tools for predicting: (a) injection well and formation pressure buildup, and (b) lateral and vertical CO2 plume migration. Simplified modeling approaches that are being developed in this research fall under three categories: (1) Simplified physics-based modeling (SPM), where only the most relevant physical processes are modeled, (2) Statistical-learning based modeling (SLM), where the simulator is replaced with a "response surface", and (3) Reduced-order method based modeling (RMM), where mathematical approximations reduce the computational burden. The system of interest is a single vertical well injecting supercritical CO2 into a 2-D layered reservoir-caprock system with variable layer permeabilities. In the first category (SPM), we use a set of well-designed full-physics compositional simulations to understand key processes and parameters affecting pressure propagation and buoyant plume migration. Based on these simulations, we have developed correlations for dimensionless injectivity as a function of the slope of fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. In the second category (SLM), we develop statistical "proxy models" using the simulation domain described previously with two different approaches: (a) classical Box-Behnken experimental design with a quadratic response surface fit, and (b) maximin Latin Hypercube sampling (LHS) based design with a Kriging metamodel fit using a quadratic trend and Gaussian correlation structure. For roughly the same number of

  7. 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.

  8. Module-based outcome prediction using breast cancer compendia.

    Directory of Open Access Journals (Sweden)

    Martin H van Vliet

    Full Text Available BACKGROUND: The availability of large collections of microarray datasets (compendia, or knowledge about grouping of genes into pathways (gene sets, is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space. This should be favorable from a machine learning perspective and result in more robust predictors. METHODOLOGY: We extracted modules of regulated genes from gene sets, and compendia. Through supervised analysis, we constructed predictors which employ modules predictive of breast cancer outcome. To validate these predictors we applied them to independent data, from the same institution (intra-dataset, and other institutions (inter-dataset. CONCLUSIONS: We show that modules derived from single breast cancer datasets achieve better performance on the validation data compared to gene-based predictors. We also show that there is a trend in compendium specificity and predictive performance: modules derived from a single breast cancer dataset, and a breast cancer specific compendium perform better compared to those derived from a human cancer compendium. Additionally, the module-based predictor provides a much richer insight into the underlying biology. Frequently selected gene sets are associated with processes such as cell cycle, E2F regulation, DNA damage response, proteasome and glycolysis. We analyzed two modules related to cell cycle, and the OCT1 transcription factor, respectively. On an individual basis, these modules provide a significant separation in survival subgroups on the training and independent validation data.

  9. 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

  10. Human Posture and Movement Prediction based on Musculoskeletal Modeling

    DEFF Research Database (Denmark)

    Farahani, Saeed Davoudabadi

    2014-01-01

    Abstract This thesis explores an optimization-based formulation, so-called inverse-inverse dynamics, for the prediction of human posture and motion dynamics performing various tasks. It is explained how this technique enables us to predict natural kinematic and kinetic patterns for human posture...... and motion using AnyBody Modeling System (AMS). AMS uses inverse dynamics to analyze musculoskeletal systems and is, therefore, limited by its dependency on input kinematics. We propose to alleviate this dependency by assuming that voluntary postures and movement strategies in humans are guided by a desire...... expenditure, joint forces and other physiological properties derived from the detailed musculoskeletal analysis. Several attempts have been made to uncover the principles underlying motion control strategies in the literature. In case of some movements, like human squat jumping, there is almost no doubt...

  11. Sensorimotor abilities predict on-field performance in professional baseball.

    Science.gov (United States)

    Burris, Kyle; Vittetoe, Kelly; Ramger, Benjamin; Suresh, Sunith; Tokdar, Surya T; Reiter, Jerome P; Appelbaum, L Gregory

    2018-01-08

    Baseball players must be able to see and react in an instant, yet it is hotly debated whether superior performance is associated with superior sensorimotor abilities. In this study, we compare sensorimotor abilities, measured through 8 psychomotor tasks comprising the Nike Sensory Station assessment battery, and game statistics in a sample of 252 professional baseball players to evaluate the links between sensorimotor skills and on-field performance. For this purpose, we develop a series of Bayesian hierarchical latent variable models enabling us to compare statistics across professional baseball leagues. Within this framework, we find that sensorimotor abilities are significant predictors of on-base percentage, walk rate and strikeout rate, accounting for age, position, and league. We find no such relationship for either slugging percentage or fielder-independent pitching. The pattern of results suggests performance contributions from both visual-sensory and visual-motor abilities and indicates that sensorimotor screenings may be useful for player scouting.

  12. FERAL : Network-based classifier with application to breast cancer outcome prediction

    NARCIS (Netherlands)

    Allahyar, A.; De Ridder, J.

    2015-01-01

    Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

  13. Performance prediction of hot mix asphalt from asphalt binders

    International Nuclear Information System (INIS)

    Hafeez, I.; Kamal, M.A.; Shahzad, Q.; Bashir, N.; Ahadi, M.R.

    2012-01-01

    Asphalt binder being a high weight hydrocarbon contains asphaltene and maltene and is widely used as cementing materials in the construction of flexible pavements. Its performance in hot mix asphalt also depends on combining with different proportions of aggregates. The main objective of this study was to characterize asphalt cement rheological behavior and to investigate the influence of asphalt on asphalt-aggregate mixtures prepared with virgin binders and using polymers. Binder rheology and mixtures stiffness were determined under a range of cyclic loadings and temperature conditions. Master curves were developed for the evaluation of relationship between parameters like complex modulus and phase angle at different frequencies. Horizontal shift factors were also computed to determine time and temperature response of binders and mixes. The results showed that the stiffness of both the binder and the mixes depends on temperature and frequency of load. Polymer modified binder is least susceptible to temperature variations as compared to other virgin asphalt cement. Performance of asphalt mixtures can be predicted from those of asphalt binders using the master curve technique. (author)

  14. Preparatory neural activity predicts performance on a conflict task.

    Science.gov (United States)

    Stern, Emily R; Wager, Tor D; Egner, Tobias; Hirsch, Joy; Mangels, Jennifer A

    2007-10-24

    Advance preparation has been shown to improve the efficiency of conflict resolution. Yet, with little empirical work directly linking preparatory neural activity to the performance benefits of advance cueing, it is not clear whether this relationship results from preparatory activation of task-specific networks, or from activity associated with general alerting processes. Here, fMRI data were acquired during a spatial Stroop task in which advance cues either informed subjects of the upcoming relevant feature of conflict stimuli (spatial or semantic) or were neutral. Informative cues decreased reaction time (RT) relative to neutral cues, and cues indicating that spatial information would be task-relevant elicited greater activity than neutral cues in multiple areas, including right anterior prefrontal and bilateral parietal cortex. Additionally, preparatory activation in bilateral parietal cortex and right dorsolateral prefrontal cortex predicted faster RT when subjects responded to spatial location. No regions were found to be specific to semantic cues at conventional thresholds, and lowering the threshold further revealed little overlap between activity associated with spatial and semantic cueing effects, thereby demonstrating a single dissociation between activations related to preparing a spatial versus semantic task-set. This relationship between preparatory activation of spatial processing networks and efficient conflict resolution suggests that advance information can benefit performance by leading to domain-specific biasing of task-relevant information.

  15. 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).

  16. 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.

  17. Maintenance personnel performance simulation (MAPPS): a model for predicting maintenance performance reliability in nuclear power plants

    International Nuclear Information System (INIS)

    Knee, H.E.; Krois, P.A.; Haas, P.M.; Siegel, A.I.; Ryan, T.G.

    1983-01-01

    The NRC has developed a structured, quantitative, predictive methodology in the form of a computerized simulation model for assessing maintainer task performance. Objective of the overall program is to develop, validate, and disseminate a practical, useful, and acceptable methodology for the quantitative assessment of NPP maintenance personnel reliability. The program was organized into four phases: (1) scoping study, (2) model development, (3) model evaluation, and (4) model dissemination. The program is currently nearing completion of Phase 2 - Model Development

  18. Prediction-based dynamic load-sharing heuristics

    Science.gov (United States)

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

    1993-01-01

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

  19. Exploration of Machine Learning Approaches to Predict Pavement Performance

    Science.gov (United States)

    2018-03-23

    Machine learning (ML) techniques were used to model and predict pavement condition index (PCI) for various pavement types using a variety of input variables. The primary objective of this research was to develop and assess PCI predictive models for t...

  20. Gaussian Process Regression for WDM System Performance Prediction

    DEFF Research Database (Denmark)

    Wass, Jesper; Thrane, Jakob; Piels, Molly

    2017-01-01

    Gaussian process regression is numerically and experimentally investigated to predict the bit error rate of a 24 x 28 CiBd QPSK WDM system. The proposed method produces accurate predictions from multi-dimensional and sparse measurement data.......Gaussian process regression is numerically and experimentally investigated to predict the bit error rate of a 24 x 28 CiBd QPSK WDM system. The proposed method produces accurate predictions from multi-dimensional and sparse measurement data....

  1. Predictability of depression severity based on posterior alpha oscillations.

    Science.gov (United States)

    Jiang, H; Popov, T; Jylänki, P; Bi, K; Yao, Z; Lu, Q; Jensen, O; van Gerven, M A J

    2016-04-01

    We aimed to integrate neural data and an advanced machine learning technique to predict individual major depressive disorder (MDD) patient severity. MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) resting awake with eyes closed. Individual power spectra were calculated by a Fourier transform. Sources were reconstructed via beamforming technique. Bayesian linear regression was applied to predict depression severity based on the spatial distribution of oscillatory power. In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was observed in fronto-central and posterior areas respectively, whereas increased beta (14-30 Hz) power was observed in fronto-central regions. In particular, posterior alpha power was negatively related to depression severity. The Bayesian linear regression model showed significant depression severity prediction performance based on the spatial distribution of both alpha (r=0.68, p=0.0005) and beta power (r=0.56, p=0.007) respectively. Our findings point to a specific alteration of oscillatory brain activity in MDD patients during rest as characterized from MEG data in terms of spectral and spatial distribution. The proposed model yielded a quantitative and objective estimation for the depression severity, which in turn has a potential for diagnosis and monitoring of the recovery process. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  2. 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

  3. Climbing fibers predict movement kinematics and performance errors.

    Science.gov (United States)

    Streng, Martha L; Popa, Laurentiu S; Ebner, Timothy J

    2017-09-01

    Requisite for understanding cerebellar function is a complete characterization of the signals provided by complex spike (CS) discharge of Purkinje cells, the output neurons of the cerebellar cortex. Numerous studies have provided insights into CS function, with the most predominant view being that they are evoked by error events. However, several reports suggest that CSs encode other aspects of movements and do not always respond to errors or unexpected perturbations. Here, we evaluated CS firing during a pseudo-random manual tracking task in the monkey ( Macaca mulatta ). This task provides extensive coverage of the work space and relative independence of movement parameters, delivering a robust data set to assess the signals that activate climbing fibers. Using reverse correlation, we determined feedforward and feedback CSs firing probability maps with position, velocity, and acceleration, as well as position error, a measure of tracking performance. The direction and magnitude of the CS modulation were quantified using linear regression analysis. The major findings are that CSs significantly encode all three kinematic parameters and position error, with acceleration modulation particularly common. The modulation is not related to "events," either for position error or kinematics. Instead, CSs are spatially tuned and provide a linear representation of each parameter evaluated. The CS modulation is largely predictive. Similar analyses show that the simple spike firing is modulated by the same parameters as the CSs. Therefore, CSs carry a broader array of signals than previously described and argue for climbing fiber input having a prominent role in online motor control. NEW & NOTEWORTHY This article demonstrates that complex spike (CS) discharge of cerebellar Purkinje cells encodes multiple parameters of movement, including motor errors and kinematics. The CS firing is not driven by error or kinematic events; instead it provides a linear representation of each

  4. 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

  5. Playing off the curve - testing quantitative predictions of skill acquisition theories in development of chess performance.

    Science.gov (United States)

    Gaschler, Robert; Progscha, Johanna; Smallbone, Kieran; Ram, Nilam; Bilalić, Merim

    2014-01-01

    Learning curves have been proposed as an adequate description of learning processes, no matter whether the processes manifest within minutes or across years. Different mechanisms underlying skill acquisition can lead to differences in the shape of learning curves. In the current study, we analyze the tournament performance data of 1383 chess players who begin competing at young age and play tournaments for at least 10 years. We analyze the performance development with the goal to test the adequacy of learning curves, and the skill acquisition theories they are based on, for describing and predicting expertise acquisition. On the one hand, we show that the skill acquisition theories implying a negative exponential learning curve do a better job in both describing early performance gains and predicting later trajectories of chess performance than those theories implying a power function learning curve. On the other hand, the learning curves of a large proportion of players show systematic qualitative deviations from the predictions of either type of skill acquisition theory. While skill acquisition theories predict larger performance gains in early years and smaller gains in later years, a substantial number of players begin to show substantial improvements with a delay of several years (and no improvement in the first years), deviations not fully accounted for by quantity of practice. The current work adds to the debate on how learning processes on a small time scale combine to large-scale changes.

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

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

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

  7. 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.

  8. Theoretical bases analysis of scientific prediction on marketing principles

    OpenAIRE

    A.S. Rosohata

    2012-01-01

    The article presents an overview categorical apparatus of scientific predictions and theoretical foundations results of scientific forecasting. They are integral part of effective management of economic activities. The approaches to the prediction of scientists in different fields of Social science and the categories modification of scientific prediction, based on principles of marketing are proposed.

  9. Speaker Prediction based on Head Orientations

    NARCIS (Netherlands)

    Rienks, R.J.; Poppe, Ronald Walter; van Otterlo, M.; Poel, Mannes; Poel, M.; Nijholt, A.; Nijholt, Antinus

    2005-01-01

    To gain insight into gaze behavior in meetings, this paper compares the results from a Naive Bayes classifier, Neural Networks and humans on speaker prediction in four-person meetings given solely the azimuth head angles. The Naive Bayes classifier scored 69.4% correctly, Neural Networks 62.3% and

  10. Evaluation and prediction of the performance of positive displacement motor

    Energy Technology Data Exchange (ETDEWEB)

    Tudor, R.; Ginzburg, L. [Canadian Fracmaster Ltd., Calgary, AB (Canada); Xu, H. [Japan National Oil Corp (Japan); Li, J.; Robello, G.; Grigor, C.

    1998-12-31

    Test results of positive displacement motors (PDMs) collected by using various PDMs from a number of different suppliers have been analyzed. Various correlations have been developed and motor performance pumped with incompressible drilling fluid was evaluated based on test data provided by suppliers in the form of pressure drop versus torque output. Conclusions drawn from the study suggest that when a motor is operated at less than full load, the correlation between mechanical power and hydraulic power across the PDM power section can be described with a simple linear equation (different for each PDM type). Assuming the availability of patented geometric information for each PDM type, the performance of PDMs can be described by both the geometric parameters of the motor and the rheological properties of the circulation fluid. 9 refs., 8 figs.

  11. Frontoparietal white matter integrity predicts haptic performance in chronic stroke

    Directory of Open Access Journals (Sweden)

    Alexandra L. Borstad

    2016-01-01

    . Age strongly correlated with the shared variance across tracts in the control, but not in the poststroke participants. A moderate to good relationship was found between ipsilesional T–M1 MD and affected hand HASTe score (r = −0.62, p = 0.006 and less affected hand HASTe score (r = −0.53, p = 0.022. Regression analysis revealed approximately 90% of the variance in affected hand HASTe score was predicted by the white matter integrity in the frontoparietal network (as indexed by MD in poststroke participants while 87% of the variance in HASTe score was predicted in control participants. This study demonstrates the importance of frontoparietal white matter in mediating haptic performance and specifically identifies that T–M1 and precuneus interhemispheric tracts may be appropriate targets for piloting rehabilitation interventions, such as noninvasive brain stimulation, when the goal is to improve poststroke haptic performance.

  12. Frontoparietal white matter integrity predicts haptic performance in chronic stroke.

    Science.gov (United States)

    Borstad, Alexandra L; Choi, Seongjin; Schmalbrock, Petra; Nichols-Larsen, Deborah S

    2016-01-01

    strongly correlated with the shared variance across tracts in the control, but not in the poststroke participants. A moderate to good relationship was found between ipsilesional T-M1 MD and affected hand HASTe score (r = - 0.62, p = 0.006) and less affected hand HASTe score (r = - 0.53, p = 0.022). Regression analysis revealed approximately 90% of the variance in affected hand HASTe score was predicted by the white matter integrity in the frontoparietal network (as indexed by MD) in poststroke participants while 87% of the variance in HASTe score was predicted in control participants. This study demonstrates the importance of frontoparietal white matter in mediating haptic performance and specifically identifies that T-M1 and precuneus interhemispheric tracts may be appropriate targets for piloting rehabilitation interventions, such as noninvasive brain stimulation, when the goal is to improve poststroke haptic performance.

  13. Predicting Drug-Target Interactions Based on Small Positive Samples.

    Science.gov (United States)

    Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

    2018-01-01

    A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

  14. A New Approach to Fatigue Life Prediction Based on Nucleation and Growth (Preprint)

    National Research Council Canada - National Science Library

    McClung, R. C; Francis, W. L; Hudak, S. J

    2006-01-01

    Prediction of total fatigue life in components is often performed by summing "initiation" and "propagation" life phases, where initiation life is based on stress-life or strain-life methods calibrated...

  15. Optimization of arterial age prediction models based in pulse wave

    Energy Technology Data Exchange (ETDEWEB)

    Scandurra, A G [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Meschino, G J [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Passoni, L I [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Dai Pra, A L [Engineering Aplied Artificial Intelligence Group, Mathematics Department, Mar del Plata University (Argentina); Introzzi, A R [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Clara, F M [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina)

    2007-11-15

    We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff.

  16. Optimization of arterial age prediction models based in pulse wave

    International Nuclear Information System (INIS)

    Scandurra, A G; Meschino, G J; Passoni, L I; Dai Pra, A L; Introzzi, A R; Clara, F M

    2007-01-01

    We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff

  17. 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)

  18. 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

  19. Development of a global, predictive and performance approach of reinforced concrete structure durability based on durability indicators. Overview and future prospects. Microstructure characterization of concretes, study of their hydric and transport properties, assessment of free deformations and prediction of buildings lifetime

    International Nuclear Information System (INIS)

    Baroghel-Bouny, Veronique

    2008-12-01

    This document synthesizes the objectives, the adopted approach, as well as the main scientific results and the products (test methods, for example), obtained during the researches carried out or supervised by the author within various frameworks, primarily over the period 1995-2005, at LCPC as Head of the Section 'Microstructure and Durability of Concretes'. This document presents in particular a performance-based, global and predictive approach of the durability of (reinforced) concrete structures, based on the concept of durability indicators, and combining lab tests and numerical simulations. This type of approach was developed for the protection against rebar corrosion of reinforced concrete and against the degradations generated by alkali-silica reaction, within the framework of the Working Group of the French Association of Civil Engineering (AFGC) 'Concrete design for a given service life of structures - Durability indicators'. The objectives of this new approach and the selected durability indicators - simple but particularly relevant parameters with respect to the implied physicochemical mechanisms in a given environment - as well as the bases of this selection, are first of all presented. Significant examples, resulting in particular from the Research Project 'Transfers in concretes and durability of structures', from the Research Project 'Durability of reinforced concrete and of its components: management and performance-based approach', as well as from studies carried out within the framework of the Topic 'Durability' of the French National Project BHP 2000, are then presented. These examples address the understanding of the mechanisms (moisture transport, carbonation, chloride penetration, freezing, non-restrained deformations,.) or the development of tools for the characterization of concrete microstructure and of parameters related to the durability with respect to reinforcement corrosion. Among the examples given, are reported the quantification of

  20. Rutting Prediction in Asphalt Pavement Based on Viscoelastic Theory

    Directory of Open Access Journals (Sweden)

    Nahi Mohammed Hadi

    2016-01-01

    Full Text Available Rutting is one of the most disturbing failures on the asphalt roads due to the interrupting it is caused to the drivers. Predicting of asphalt pavement rutting is essential tool leads to better asphalt mixture design. This work describes a method of predicting the behaviour of various asphalt pavement mixes and linking these to an accelerated performance testing. The objective of this study is to develop a finite element model based on viscoplastic theory for simulating the laboratory testing of asphalt mixes in Hamburg Wheel Rut Tester (HWRT for rutting. The creep parameters C1, C2 and C3 are developed from the triaxial repeated load creep test at 50°C and at a frequency of 1 Hz and the modulus of elasticity and Poisson’ s ratio determined at the same temperature. Viscoelastic model (creep model is adopted using a FE simulator (ANSYS in order to calculate the rutting for various mixes under a uniform loading pressure of 500 kPa. An eight-node with a three Degrees of Freedom (UX, UY, and UZ Element is used for the simulation. The creep model developed for HWRT tester was verified by comparing the predicted rut depths with the measured one and by comparing the rut depth with ABAQUS result from literature. Reasonable agreement can be obtained between the predicted rut depths and the measured one. Moreover, it is found that creep model parameter C1 and C3 have a strong relationship with rutting. It was clear that the parameter C1 strongly influences rutting than the parameter C3. Finally, it can be concluded that creep model based on finite element method can be used as an effective tool to analyse rutting of asphalt pavements.

  1. 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

  2. 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

  3. Prediction of mandibular rotation: an empirical test of clinician performance.

    Science.gov (United States)

    Baumrind, S; Korn, E L; West, E E

    1984-11-01

    An experiment was conducted in an attempt to determine empirically how effective a number of expert clinicians were at differentiating "backward rotators" from "forward rotators" on the basis of head-film information which might reasonably have been available to them prior to instituting treatment for the correction of Class II malocclusion. As a result of a previously reported ongoing study, pre- and posttreatment head films were available for 188 patients treated in the mixed dentition for the correction of Class II malocclusion and for 50 untreated Class II subjects. These subjects were divided into 14 groups (average size of group, 17; range, 6 to 23) solely on the basis of type of treatment and the clinician from whose clinic the records had originated. From within each group, we selected the two or three subjects who had exhibited the most extreme backward rotation and the two or three subjects who had exhibited the most extreme forward rotation of the mandible during the interval between films. The sole criterion for classification was magnitude of change in the mandibular plane angle of Downs between the pre- and posttreatment films of each patient. The resulting sample contained 32 backward-rotator subjects and 32 forward-rotator subjects. Five expert judges (mean clinical experience, 28 years) were asked to identify the backward-rotator subjects by examination of the pretreatment films. The findings may be summarized as follows: (1) No judge performed significantly better than chance. (2) There was strong evidence that the judges used a shared, though relatively ineffective, set of rules in making their discriminations between forward and backward rotators. (3) Statistical analysis of the predictive power of a set of standard cephalometric measurements which had previously been made for this set of subjects indicated that the numerical data also failed to identify potential backward rotators at a rate significantly better than chance. We infer from these

  4. Domain Adaptation for Pedestrian Detection Based on Prediction Consistency

    Directory of Open Access Journals (Sweden)

    Yu Li-ping

    2014-01-01

    Full Text Available Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain. Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene.

  5. Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding

    OpenAIRE

    Wu, Han-Zhou; Wang, Hong-Xia; Shi, Yun-Qing

    2016-01-01

    This paper presents a novel reversible data hiding (RDH) algorithm for gray-scaled images, in which the prediction-error of prediction error (PPE) of a pixel is used to carry the secret data. In the proposed method, the pixels to be embedded are firstly predicted with their neighboring pixels to obtain the corresponding prediction errors (PEs). Then, by exploiting the PEs of the neighboring pixels, the prediction of the PEs of the pixels can be determined. And, a sorting technique based on th...

  6. Revised MITG design, fabrication procedure, and performance predictions

    International Nuclear Information System (INIS)

    Schock, A.

    1983-01-01

    The design, analysis, and key features of the Modular Isotopic Thermoelectric Generator (MITG) were described in a 1981 IECEC paper; and the design, fabrication, testing, and post-test analysis of test assemblies simulating prototypical MITG modules were described in preceding papers in these proceedings. These analyses succeeded in identifying and explaining the principal causes of thermal-stress problems encountered in the tests, and in confirming the effectiveness of design changes for alleviating them. The present paper presents additional design improvements for solving these and other problems, and describes new thermoelectric material properties generated by independent laboratories over the past two years. Based on these changes and on a revised fabrication procedure, it presents a reoptimization of the MITG design and computes the power-to-weight ratio for the revised design. That ratio is appreciably lower than the 1981 prediction, primarily because of changes in material properties; but it is still much higher than the specific power of current-generation RTGs

  7. TAP 2: Performance-Based Training Manual

    International Nuclear Information System (INIS)

    1993-08-01

    Cornerstone of safe operation of DOE nuclear facilities is personnel performing day-to-day functions which accomplish the facility mission. Performance-based training is fundamental to the safe operation. This manual has been developed to support the Training Accreditation Program (TAP) and assist contractors in efforts to develop performance-based training programs. It provides contractors with narrative procedures on performance-based training that can be modified and incorporated for facility-specific application. It is divided into sections dealing with analysis, design, development, implementation, and evaluation

  8. Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

    Directory of Open Access Journals (Sweden)

    Peng Lu

    2018-01-01

    Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

  9. Cloud-based Predictive Modeling System and its Application to Asthma Readmission Prediction

    Science.gov (United States)

    Chen, Robert; Su, Hang; Khalilia, Mohammed; Lin, Sizhe; Peng, Yue; Davis, Tod; Hirsh, Daniel A; Searles, Elizabeth; Tejedor-Sojo, Javier; Thompson, Michael; Sun, Jimeng

    2015-01-01

    The predictive modeling process is time consuming and requires clinical researchers to handle complex electronic health record (EHR) data in restricted computational environments. To address this problem, we implemented a cloud-based predictive modeling system via a hybrid setup combining a secure private server with the Amazon Web Services (AWS) Elastic MapReduce platform. EHR data is preprocessed on a private server and the resulting de-identified event sequences are hosted on AWS. Based on user-specified modeling configurations, an on-demand web service launches a cluster of Elastic Compute 2 (EC2) instances on AWS to perform feature selection and classification algorithms in a distributed fashion. Afterwards, the secure private server aggregates results and displays them via interactive visualization. We tested the system on a pediatric asthma readmission task on a de-identified EHR dataset of 2,967 patients. We conduct a larger scale experiment on the CMS Linkable 2008–2010 Medicare Data Entrepreneurs’ Synthetic Public Use File dataset of 2 million patients, which achieves over 25-fold speedup compared to sequential execution. PMID:26958172

  10. Predictive tool of energy performance of cold storage in agrifood industries: The Portuguese case study

    International Nuclear Information System (INIS)

    Nunes, José; Neves, Diogo; Gaspar, Pedro D.; Silva, Pedro D.; Andrade, Luís P.

    2014-01-01

    Highlights: • A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed. • The correlations used by the predictive tool result from the greatest number of data sets collected to date in Portugal. • Strong relationships between raw material, energy consumption and volume of cold stores were established. • Case studies were analyzed that demonstrate the applicability of the tool. • The tool results are useful in the decision-making process of practice measures for the improvement of energy efficiency. - Abstract: Food processing and conservation represent decisive factors for the sustainability of the planet given the significant growth of the world population in the last decades. Therefore, the cooling process during the manufacture and/or storage of food products has been subject of study and improvement in order to ensure the food supply with good quality and safety. A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed in order to contribute to the improvement of the energy efficiency of this industry. The predictive tool is based on a set of characteristic correlated parameters: amount of raw material annually processed, annual energy consumption and volume of cold rooms. Case studies of application of the predictive tool consider industries in the meat sector, specifically slaughterhouses. The results obtained help on the decision-making of practice measures for improvement of the energy efficiency in this industry

  11. Performance prediction of electrohydrodynamic thrusters by the perturbation method

    International Nuclear Information System (INIS)

    Shibata, H.; Watanabe, Y.; Suzuki, K.

    2016-01-01

    In this paper, we present a novel method for analyzing electrohydrodynamic (EHD) thrusters. The method is based on a perturbation technique applied to a set of drift-diffusion equations, similar to the one introduced in our previous study on estimating breakdown voltage. The thrust-to-current ratio is generalized to represent the performance of EHD thrusters. We have compared the thrust-to-current ratio obtained theoretically with that obtained from the proposed method under atmospheric air conditions, and we have obtained good quantitative agreement. Also, we have conducted a numerical simulation in more complex thruster geometries, such as the dual-stage thruster developed by Masuyama and Barrett [Proc. R. Soc. A 469, 20120623 (2013)]. We quantitatively clarify the fact that if the magnitude of a third electrode voltage is low, the effective gap distance shortens, whereas if the magnitude of the third electrode voltage is sufficiently high, the effective gap distance lengthens.

  12. Performance prediction for a magnetostrictive actuator using a simplified model

    Science.gov (United States)

    Yoo, Jin-Hyeong; Jones, Nicholas J.

    2018-03-01

    Iron-Gallium alloys (Galfenol) are promising transducer materials that combine high magnetostriction, desirable mechanical properties, high permeability, and a wide operational temperature range. Most of all, the material is capable of operating under tensile stress, and is relatively resistant to shock. These materials are generally characterized using a solid, cylindrically-shaped specimen under controlled compressive stress and magnetization conditions. Because the magnetostriction strongly depends on both the applied stress and magnetization, the characterization of the material is usually conducted under controlled conditions so each parameter is varied independently of the other. However, in a real application the applied stress and magnetization will not be maintained constant during operation. Even though the controlled characterization measurement gives insight into standard material properties, usage of this data in an application, while possible, is not straight forward. This study presents an engineering modeling methodology for magnetostrictive materials based on a piezo-electric governing equation. This model suggests phenomenological, nonlinear, three-dimensional functions for strain and magnetic flux density responses as functions of applied stress and magnetic field. Load line performances as a function of maximum magnetic field input were simulated based on the model. To verify the modeling performance, a polycrystalline magnetostrictive rod (Fe-Ga alloy, Galfenol) was characterized under compressive loads using a dead-weight test setup, with strain gages on the rod and a magnetic field driving coil around the sample. The magnetic flux density through the Galfenol rod was measured with a sensing coil; the compressive loads were measured using a load cell on the bottom of the Galfenol rod. The experimental results are compared with the simulation results using the suggested model, showing good agreement.

  13. Intrinsic motivation and extrinsic incentives jointly predict performance: a 40-year meta-analysis.

    Science.gov (United States)

    Cerasoli, Christopher P; Nicklin, Jessica M; Ford, Michael T

    2014-07-01

    More than 4 decades of research and 9 meta-analyses have focused on the undermining effect: namely, the debate over whether the provision of extrinsic incentives erodes intrinsic motivation. This review and meta-analysis builds on such previous reviews by focusing on the interrelationship among intrinsic motivation, extrinsic incentives, and performance, with reference to 2 moderators: performance type (quality vs. quantity) and incentive contingency (directly performance-salient vs. indirectly performance-salient), which have not been systematically reviewed to date. Based on random-effects meta-analytic methods, findings from school, work, and physical domains (k = 183, N = 212,468) indicate that intrinsic motivation is a medium to strong predictor of performance (ρ = .21-45). The importance of intrinsic motivation to performance remained in place whether incentives were presented. In addition, incentive salience influenced the predictive validity of intrinsic motivation for performance: In a "crowding out" fashion, intrinsic motivation was less important to performance when incentives were directly tied to performance and was more important when incentives were indirectly tied to performance. Considered simultaneously through meta-analytic regression, intrinsic motivation predicted more unique variance in quality of performance, whereas incentives were a better predictor of quantity of performance. With respect to performance, incentives and intrinsic motivation are not necessarily antagonistic and are best considered simultaneously. Future research should consider using nonperformance criteria (e.g., well-being, job satisfaction) as well as applying the percent-of-maximum-possible (POMP) method in meta-analyses. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  14. Cognition and procedure representational requirements for predictive human performance models

    Science.gov (United States)

    Corker, K.

    1992-01-01

    Models and modeling environments for human performance are becoming significant contributors to early system design and analysis procedures. Issues of levels of automation, physical environment, informational environment, and manning requirements are being addressed by such man/machine analysis systems. The research reported here investigates the close interaction between models of human cognition and models that described procedural performance. We describe a methodology for the decomposition of aircrew procedures that supports interaction with models of cognition on the basis of procedures observed; that serves to identify cockpit/avionics information sources and crew information requirements; and that provides the structure to support methods for function allocation among crew and aiding systems. Our approach is to develop an object-oriented, modular, executable software representation of the aircrew, the aircraft, and the procedures necessary to satisfy flight-phase goals. We then encode in a time-based language, taxonomies of the conceptual, relational, and procedural constraints among the cockpit avionics and control system and the aircrew. We have designed and implemented a goals/procedures hierarchic representation sufficient to describe procedural flow in the cockpit. We then execute the procedural representation in simulation software and calculate the values of the flight instruments, aircraft state variables and crew resources using the constraints available from the relationship taxonomies. The system provides a flexible, extensible, manipulative and executable representation of aircrew and procedures that is generally applicable to crew/procedure task-analysis. The representation supports developed methods of intent inference, and is extensible to include issues of information requirements and functional allocation. We are attempting to link the procedural representation to models of cognitive functions to establish several intent inference methods

  15. Submillimetre wave imaging and security: imaging performance and prediction

    Science.gov (United States)

    Appleby, R.; Ferguson, S.

    2016-10-01

    Within the European Commission Seventh Framework Programme (FP7), CONSORTIS (Concealed Object Stand-Off Real-Time Imaging for Security) has designed and is fabricating a stand-off system operating at sub-millimetre wave frequencies for the detection of objects concealed on people. This system scans people as they walk by the sensor. This paper presents the top level system design which brings together both passive and active sensors to provide good performance. The passive system operates in two bands between 100 and 600GHz and is based on a cryogen free cooled focal plane array sensor whilst the active system is a solid-state 340GHz radar. A modified version of OpenFX was used for modelling the passive system. This model was recently modified to include realistic location-specific skin temperature and to accept animated characters wearing up to three layers of clothing that move dynamically, such as those typically found in cinematography. Targets under clothing have been modelled and the performance simulated. The strengths and weaknesses of this modelling approach are discussed.

  16. Star-sensor-based predictive Kalman filter for satelliteattitude estimation

    Institute of Scientific and Technical Information of China (English)

    林玉荣; 邓正隆

    2002-01-01

    A real-time attitude estimation algorithm, namely the predictive Kalman filter, is presented. This algorithm can accurately estimate the three-axis attitude of a satellite using only star sensor measurements. The implementation of the filter includes two steps: first, predicting the torque modeling error, and then estimating the attitude. Simulation results indicate that the predictive Kalman filter provides robust performance in the presence of both significant errors in the assumed model and in the initial conditions.

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

    Directory of Open Access Journals (Sweden)

    Alberto Micco

    2015-06-01

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

  18. An estimator-based distributed voltage-predictive control strategy for ac islanded microgrids

    DEFF Research Database (Denmark)

    Wang, Yanbo; Chen, Zhe; Wang, Xiongfei

    2015-01-01

    This paper presents an estimator-based voltage predictive control strategy for AC islanded microgrids, which is able to perform voltage control without any communication facilities. The proposed control strategy is composed of a network voltage estimator and a voltage predictive controller for each...... and has a good capability to reject uncertain perturbations of islanded microgrids....

  19. Online physician ratings fail to predict actual performance on measures of quality, value, and peer review.

    Science.gov (United States)

    Daskivich, Timothy J; Houman, Justin; Fuller, Garth; Black, Jeanne T; Kim, Hyung L; Spiegel, Brennan

    2018-04-01

    Patients use online consumer ratings to identify high-performing physicians, but it is unclear if ratings are valid measures of clinical performance. We sought to determine whether online ratings of specialist physicians from 5 platforms predict quality of care, value of care, and peer-assessed physician performance. We conducted an observational study of 78 physicians representing 8 medical and surgical specialties. We assessed the association of consumer ratings with specialty-specific performance scores (metrics including adherence to Choosing Wisely measures, 30-day readmissions, length of stay, and adjusted cost of care), primary care physician peer-review scores, and administrator peer-review scores. Across ratings platforms, multivariable models showed no significant association between mean consumer ratings and specialty-specific performance scores (β-coefficient range, -0.04, 0.04), primary care physician scores (β-coefficient range, -0.01, 0.3), and administrator scores (β-coefficient range, -0.2, 0.1). There was no association between ratings and score subdomains addressing quality or value-based care. Among physicians in the lowest quartile of specialty-specific performance scores, only 5%-32% had consumer ratings in the lowest quartile across platforms. Ratings were consistent across platforms; a physician's score on one platform significantly predicted his/her score on another in 5 of 10 comparisons. Online ratings of specialist physicians do not predict objective measures of quality of care or peer assessment of clinical performance. Scores are consistent across platforms, suggesting that they jointly measure a latent construct that is unrelated to performance. Online consumer ratings should not be used in isolation to select physicians, given their poor association with clinical performance.

  20. Simulation based comparison of predictive maintenance policies

    NARCIS (Netherlands)

    Tinga, Tiedo; Janssen, R.H.P.; Steenbergen, R.; van Gelder, P.

    2013-01-01

    When an asset is operated in variable conditions, its operational efficiency can be improved significantly when the maintenance is performed in a dynamic manner. This means that variations in usage and operating environment are taken into account when deciding on the length of the maintenance

  1. Size-based predictions of food web patterns

    DEFF Research Database (Denmark)

    Zhang, Lai; Hartvig, Martin; Knudsen, Kim

    2014-01-01

    We employ size-based theoretical arguments to derive simple analytic predictions of ecological patterns and properties of natural communities: size-spectrum exponent, maximum trophic level, and susceptibility to invasive species. The predictions are brought about by assuming that an infinite number...... of species are continuously distributed on a size-trait axis. It is, however, an open question whether such predictions are valid for a food web with a finite number of species embedded in a network structure. We address this question by comparing the size-based predictions to results from dynamic food web...... simulations with varying species richness. To this end, we develop a new size- and trait-based food web model that can be simplified into an analytically solvable size-based model. We confirm existing solutions for the size distribution and derive novel predictions for maximum trophic level and invasion...

  2. Base Oils Biodegradability Prediction with Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Malika Trabelsi

    2010-02-01

    Full Text Available In this paper, we apply various data mining techniques including continuous numeric and discrete classification prediction models of base oils biodegradability, with emphasis on improving prediction accuracy. The results show that highly biodegradable oils can be better predicted through numeric models. In contrast, classification models did not uncover a similar dichotomy. With the exception of Memory Based Reasoning and Decision Trees, tested classification techniques achieved high classification prediction. However, the technique of Decision Trees helped uncover the most significant predictors. A simple classification rule derived based on this predictor resulted in good classification accuracy. The application of this rule enables efficient classification of base oils into either low or high biodegradability classes with high accuracy. For the latter, a higher precision biodegradability prediction can be obtained using continuous modeling techniques.

  3. Assessing Prediction Performance of Neoadjuvant Chemotherapy Response in Bladder Cancer

    OpenAIRE

    Cremer, Chris

    2016-01-01

    Neoadjuvant chemotherapy is a treatment routinely prescribed to patients diagnosed with muscle-invasive bladder cancer. Unfortunately, not all patients are responsive to this treatment and would greatly benefit from an accurate prediction of their expected response to chemotherapy. In this project, I attempt to develop a model that will predict response using tumour microarray data. I show that using my dataset, every method is insufficient at accurately classifying responders and non-respond...

  4. Performance-based regulation. Panel Discussion

    International Nuclear Information System (INIS)

    Youngblood, Robert; Bier, Vicki M.; Bukowski, Richard W.; Prasad Kadambi, N.; Koonce, James F.

    2001-01-01

    Full text of publication follows: Performance-based regulation is a part of the NRC's Strategic Plan and is realizing steady progress in conceptual development for actual applications. For example, high-level, conceptual guidelines have been proposed that would apply to reactors, materials, and waste areas. Performance-based approaches are also being applied in other regulated industries such as FAA and OSHA. The discussion will include comments from speakers from different parts of the nuclear industry and other industries regarding benefits and weaknesses of performance-based regulation. (authors)

  5. Phenobarbital in intensive care unit pediatric population: predictive performances of population pharmacokinetic model.

    Science.gov (United States)

    Marsot, Amélie; Michel, Fabrice; Chasseloup, Estelle; Paut, Olivier; Guilhaumou, Romain; Blin, Olivier

    2017-10-01

    An external evaluation of phenobarbital population pharmacokinetic model described by Marsot et al. was performed in pediatric intensive care unit. Model evaluation is an important issue for dose adjustment. This external evaluation should allow confirming the proposed dosage adaptation and extending these recommendations to the entire intensive care pediatric population. External evaluation of phenobarbital published population pharmacokinetic model of Marsot et al. was realized in a new retrospective dataset of 35 patients hospitalized in a pediatric intensive care unit. The published population pharmacokinetic model was implemented in nonmem 7.3. Predictive performance was assessed by quantifying bias and inaccuracy of model prediction. Normalized prediction distribution errors (NPDE) and visual predictive check (VPC) were also evaluated. A total of 35 infants were studied with a mean age of 33.5 weeks (range: 12 days-16 years) and a mean weight of 12.6 kg (range: 2.7-70.0 kg). The model predicted the observed phenobarbital concentrations with a reasonable bias and inaccuracy. The median prediction error was 3.03% (95% CI: -8.52 to 58.12%), and the median absolute prediction error was 26.20% (95% CI: 13.07-75.59%). No trends in NPDE and VPC were observed. The model previously proposed by Marsot et al. in neonates hospitalized in intensive care unit was externally validated for IV infusion administration. The model-based dosing regimen was extended in all pediatric intensive care unit to optimize treatment. Due to inter- and intravariability in pharmacokinetic model, this dosing regimen should be combined with therapeutic drug monitoring. © 2017 Société Française de Pharmacologie et de Thérapeutique.

  6. Predictive Control Based upon State Space Models

    Directory of Open Access Journals (Sweden)

    Jens G. Balchen

    1989-04-01

    Full Text Available Repetitive online computation of the control vector by solving the optimal control problem of a non-linear multivariable process with arbitrary performance indices is investigated. Two different methods are considered in the search for an optimal, parameterized control vector: Pontryagin's Maximum Principle and optimization by using the performance index and its gradient directly. Unfortunately, solving this optimization problem has turned out to be a rather time-consuming task which has resulted in a time delay that cannot be accepted when the actual process is exposed to rapidly-varying disturbances. However, an instantaneous feedback strategy operating in parallel with the original control aogorithm was found to be able to cope with this problem.

  7. Probability-based collaborative filtering model for predicting gene–disease associations

    OpenAIRE

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-01-01

    Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. Methods We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our mo...

  8. 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

  9. Both Reaction Time and Accuracy Measures of Intraindividual Variability Predict Cognitive Performance in Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Björn U. Christ

    2018-04-01

    Full Text Available Dementia researchers around the world prioritize the urgent need for sensitive measurement tools that can detect cognitive and functional change at the earliest stages of Alzheimer's disease (AD. Sensitive indicators of underlying neural pathology assist in the early detection of cognitive change and are thus important for the evaluation of early-intervention clinical trials. One method that may be particularly well-suited to help achieve this goal involves the quantification of intraindividual variability (IIV in cognitive performance. The current study aimed to directly compare two methods of estimating IIV (fluctuations in accuracy-based scores vs. those in latency-based scores to predict cognitive performance in AD. Specifically, we directly compared the relative sensitivity of reaction time (RT—and accuracy-based estimates of IIV to cognitive compromise. The novelty of the present study, however, centered on the patients we tested [a group of patients with Alzheimer's disease (AD] and the outcome measures we used (a measure of general cognitive function and a measure of episodic memory function. Hence, we compared intraindividual standard deviations (iSDs from two RT tasks and three accuracy-based memory tasks in patients with possible or probable Alzheimer's dementia (n = 23 and matched healthy controls (n = 25. The main analyses modeled the relative contributions of RT vs. accuracy-based measures of IIV toward the prediction of performance on measures of (a overall cognitive functioning, and (b episodic memory functioning. Results indicated that RT-based IIV measures are superior predictors of neurocognitive impairment (as indexed by overall cognitive and memory performance than accuracy-based IIV measures, even after adjusting for the timescale of measurement. However, one accuracy-based IIV measure (derived from a recognition memory test also differentiated patients with AD from controls, and significantly predicted episodic memory

  10. Copula-based prediction of economic movements

    Science.gov (United States)

    García, J. E.; González-López, V. A.; Hirsh, I. D.

    2016-06-01

    In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

  11. Comparison of Different Approaches to Predict the Performance of Pumps As Turbines (PATs

    Directory of Open Access Journals (Sweden)

    Mauro Venturini

    2018-04-01

    Full Text Available This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs. The considered approaches are four, i.e., one physics-based simulation model (“white box” model, two “gray box” models, which integrate theory on turbomachines with specific data correlations, and one “black box” model. More in detail, the modeling approaches are: (1 a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2 a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3 the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4 an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53–5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed.

  12. Prediction of pipeline corrosion rate based on grey Markov models

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

    2009-01-01

    Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)

  13. Prediction of Cognitive Performance and Subjective Sleepiness Using a Model of Arousal Dynamics.

    Science.gov (United States)

    Postnova, Svetlana; Lockley, Steven W; Robinson, Peter A

    2018-04-01

    A model of arousal dynamics is applied to predict objective performance and subjective sleepiness measures, including lapses and reaction time on a visual Performance Vigilance Test (vPVT), performance on a mathematical addition task (ADD), and the Karolinska Sleepiness Scale (KSS). The arousal dynamics model is comprised of a physiologically based flip-flop switch between the wake- and sleep-active neuronal populations and a dynamic circadian oscillator, thus allowing prediction of sleep propensity. Published group-level experimental constant routine (CR) and forced desynchrony (FD) data are used to calibrate the model to predict performance and sleepiness. Only the studies using dim light (performance measures during CR and FD protocols, with sleep-wake cycles ranging from 20 to 42.85 h and a 2:1 wake-to-sleep ratio. New metrics relating model outputs to performance and sleepiness data are developed and tested against group average outcomes from 7 (vPVT lapses), 5 (ADD), and 8 (KSS) experimental protocols, showing good quantitative and qualitative agreement with the data (root mean squared error of 0.38, 0.19, and 0.35, respectively). The weights of the homeostatic and circadian effects are found to be different between the measures, with KSS having stronger homeostatic influence compared with the objective measures of performance. Using FD data in addition to CR data allows us to challenge the model in conditions of both acute sleep deprivation and structured circadian misalignment, ensuring that the role of the circadian and homeostatic drives in performance is properly captured.

  14. Adaptive learning compressive tracking based on Markov location prediction

    Science.gov (United States)

    Zhou, Xingyu; Fu, Dongmei; Yang, Tao; Shi, Yanan

    2017-03-01

    Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.

  15. Churn prediction based on text mining and CRM data analysis

    OpenAIRE

    Schatzmann, Anders; Heitz, Christoph; Münch, Thomas

    2014-01-01

    Within quantitative marketing, churn prediction on a single customer level has become a major issue. An extensive body of literature shows that, today, churn prediction is mainly based on structured CRM data. However, in the past years, more and more digitized customer text data has become available, originating from emails, surveys or scripts of phone calls. To date, this data source remains vastly untapped for churn prediction, and corresponding methods are rarely described in literature. ...

  16. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  17. Structured Performance Analysis for Component Based Systems

    OpenAIRE

    Salmi , N.; Moreaux , Patrice; Ioualalen , M.

    2012-01-01

    International audience; The Component Based System (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. % This paper proposes a compositional method for modeling and structured performance analysis of CBS. Modeling is based on Stochastic Well-formed...

  18. Power Load Prediction Based on Fractal Theory

    OpenAIRE

    Jian-Kai, Liang; Cattani, Carlo; Wan-Qing, Song

    2015-01-01

    The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and...

  19. Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

    Science.gov (United States)

    Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

    2013-10-01

    Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  20. Mining key elements for severe convection prediction based on CNN

    Science.gov (United States)

    Liu, Ming; Pan, Ning; Zhang, Changan; Sha, Hongzhou; Zhang, Bolei; Liu, Liang; Zhang, Meng

    2017-04-01

    the new machine-learning method via CNN models. Based on the analysis of those experimental results and case studies, the proposed new method have below benefits for the severe convection prediction: (1) helping forecasters to narrow down the scope of analysis and saves lead-time for those high-impact severe convection; (2) performing huge amount of weather big data by machine learning methods rather relying on traditional theory and knowledge, which provide new method to explore and quantify the severe convective weathers; (3) providing machine learning based end-to-end analysis and processing ability with considerable scalability on data volumes, and accomplishing the analysis work without human intervention.

  1. 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

  2. Texas cracking performance prediction, simulation, and binder recommendation.

    Science.gov (United States)

    2014-10-01

    Recent studies show some mixes with softer binders used outside of Texas (e.g., Minnesotas Cold Weather Road Research Facility mixes) have both good rutting and cracking performance. However, the current binder performance grading (PG) system fail...

  3. Do workaholism and work engagement predict employee well-being and performance in opposite directions?

    Science.gov (United States)

    Shimazu, Akihito; Schaufeli, Wilmar B; Kubota, Kazumi; Kawakami, Norito

    2012-01-01

    This study investigated the distinctiveness between workaholism and work engagement by examining their longitudinal relationships (measurement interval=7 months) with well-being and performance in a sample of 1,967 Japanese employees from various occupations. Based on a previous cross-sectional study (Shimazu & Schaufeli, 2009), we expected that workaholism predicts future unwell-being (i.e., high ill-health and low life satisfaction) and poor job performance, whereas work engagement predicts future well-being (i.e., low ill-health and high life satisfaction) and superior job performance. T1-T2 changes in ill-health, life satisfaction and job performance were measured as residual scores that were then included in the structural equation model. Results showed that workaholism and work engagement were weakly and positively related to each other. In addition, workaholism was related to an increase in ill-health and to a decrease in life satisfaction. In contrast, work engagement was related to a decrease in ill-health and to increases in both life satisfaction and job performance. These findings suggest that workaholism and work engagement are two different kinds of concepts that are oppositely related to well-being and performance.

  4. 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'

  5. Architectural Development and Performance Analysis of a Primary Data Cache with Read Miss Address Prediction Capability

    National Research Council Canada - National Science Library

    Christensen, Kathryn

    1998-01-01

    .... The Predictive Read Cache (PRC) further improves the overall memory hierarchy performance by tracking the data read miss patterns of memory accesses, developing a prediction for the next access and prefetching the data into the faster cache memory...

  6. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...

  7. Moment based model predictive control for systems with additive uncertainty

    NARCIS (Netherlands)

    Saltik, M.B.; Ozkan, L.; Weiland, S.; Ludlage, J.H.A.

    2017-01-01

    In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We

  8. Holland Type as a Moderator of Personality-Performance Predictions.

    Science.gov (United States)

    Fritzsche, Barbara A.; McIntire, Sandra A.; Yost, Amy Powell

    2002-01-01

    Data from 559 undergraduates provided modest evidence that Holland's taxonomy of work environments moderated the relationship between personality and performance. The traits of agreeableness and conscientiousness were better predictors of performance in certain environments. The important relationship between personality and performance may be…

  9. Annotation-Based Whole Genomic Prediction and Selection

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Do, Duy Ngoc; Janss, Luc

    Genomic selection is widely used in both animal and plant species, however, it is performed with no input from known genomic or biological role of genetic variants and therefore is a black box approach in a genomic era. This study investigated the role of different genomic regions and detected QTLs...... in their contribution to estimated genomic variances and in prediction of genomic breeding values by applying SNP annotation approaches to feed efficiency. Ensembl Variant Predictor (EVP) and Pig QTL database were used as the source of genomic annotation for 60K chip. Genomic prediction was performed using the Bayes...... classes. Predictive accuracy was 0.531, 0.532, 0.302, and 0.344 for DFI, RFI, ADG and BF, respectively. The contribution per SNP to total genomic variance was similar among annotated classes across different traits. Predictive performance of SNP classes did not significantly differ from randomized SNP...

  10. 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.

  11. 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.

  12. Flow Simulation and Performance Prediction of Centrifugal Pumps ...

    African Journals Online (AJOL)

    With the aid of computational fluid dynamics, the complex internal flows in water pump impellers can be well predicted, thus facilitating the product development process of pumps. In this paper a commercial CFD code was used to solve the governing equations of the flow field. A 2-D simulation of turbulent fluid flow is ...

  13. Next-Term Student Performance Prediction: A Recommender Systems Approach

    Science.gov (United States)

    Sweeney, Mack; Rangwala, Huzefa; Lester, Jaime; Johri, Aditya

    2016-01-01

    An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success,…

  14. Predicting Student Performance in a Collaborative Learning Environment

    Science.gov (United States)

    Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol

    2015-01-01

    Student models for adaptive systems may not model collaborative learning optimally. Past research has either focused on modeling individual learning or for collaboration, has focused on group dynamics or group processes without predicting learning. In the current paper, we adjust the Additive Factors Model (AFM), a standard logistic regression…

  15. Selection procedures in sports: Improving predictions of athletes’ future performance

    NARCIS (Netherlands)

    den Hartigh, Jan Rudolf; Niessen, Anna; Frencken, Wouter; Meijer, Rob R.

    The selection of athletes has been a central topic in sports sciences for decades. Yet, little consideration has been given to the theoretical underpinnings and predictive validity of the procedures. In this paper, we evaluate current selection procedures in sports given what we know from the

  16. Performance prediction model for distributed applications on multicore clusters

    CSIR Research Space (South Africa)

    Khanyile, NP

    2012-07-01

    Full Text Available discusses some of the short comings of this law in the current age. We propose a theoretical model for predicting the behavior of a distributed algorithm given the network restrictions of the cluster used. The paper focuses on the impact of latency...

  17. Inverted base pavements: construction and performance

    KAUST Repository

    Papadopoulos, Efthymios

    2017-05-11

    Inverted base pavements involve a well-compacted granular aggregate base built between a thin asphalt concrete layer and a cement-treated base. Inverted base pavements can be constructed using conventional equipment and procedures but require proper quality control. This study reviews the extensive South African experience and case histories in the USA. Accumulating evidence suggests that inverted base pavements are a viable alternative and can outperform conventional pavements at a lower cost. Inverted base pavements rely on the complementary interaction between layers. The cement-treated base provides a stiff foundation for efficient compaction and constrains the deformation of the stress-sensitive granular aggregate base. The thin asphalt surface layer deforms as a membrane and develops low tensile stress. Additional large-scale field tests should be conducted to assess the performance of inverted base pavement designs in a wide range of conditions relevant to the USA.

  18. Yield performance and stability of CMS-based triticale hybrids.

    Science.gov (United States)

    Mühleisen, Jonathan; Piepho, Hans-Peter; Maurer, Hans Peter; Reif, Jochen Christoph

    2015-02-01

    CMS-based triticale hybrids showed only marginal midparent heterosis for grain yield and lower dynamic yield stability compared to inbred lines. Hybrids of triticale (×Triticosecale Wittmack) are expected to possess outstanding yield performance and increased dynamic yield stability. The objectives of the present study were to (1) examine the optimum choice of the biometrical model to compare yield stability of hybrids versus lines, (2) investigate whether hybrids exhibit a more pronounced grain yield performance and yield stability, and (3) study optimal strategies to predict yield stability of hybrids. Thirteen female and seven male parental lines and their 91 factorial hybrids as well as 30 commercial lines were evaluated for grain yield in up to 20 environments. Hybrids were produced using a cytoplasmic male sterility (CMS)-inducing cytoplasm that originated from Triticumtimopheevii Zhuk. We found that the choice of the biometrical model can cause contrasting results and concluded that a group-by-environment interaction term should be added to the model when estimating stability variance of hybrids and lines. midparent heterosis for grain yield was on average 3 % with a range from -15.0 to 11.5 %. No hybrid outperformed the best inbred line. Hybrids had, on average, lower dynamic yield stability compared to the inbred lines. Grain yield performance of hybrids could be predicted based on midparent values and general combining ability (GCA)-predicted values. In contrast, stability variance of hybrids could be predicted only based on GCA-predicted values. We speculated that negative effects of the used CMS cytoplasm might be the reason for the low performance and yield stability of the hybrids. For this purpose a detailed study on the reasons for the drawback of the currently existing CMS system in triticale is urgently required comprising also the search of potentially alternative hybridization systems.

  19. Performance of immunological response in predicting virological failure.

    Science.gov (United States)

    Ingole, Nayana; Mehta, Preeti; Pazare, Amar; Paranjpe, Supriya; Sarkate, Purva

    2013-03-01

    In HIV-infected individuals on antiretroviral therapy (ART), the decision on when to switch from first-line to second-line therapy is dictated by treatment failure, and this can be measured in three ways: clinically, immunologically, and virologically. While viral load (VL) decreases and CD4 cell increases typically occur together after starting ART, discordant responses may be seen. Hence the current study was designed to determine the immunological and virological response to ART and to evaluate the utility of immunological response to predict virological failure. All treatment-naive HIV-positive individuals aged >18 years who were eligible for ART were enrolled and assessed at baseline, 6 months, and 12 months clinically and by CD4 cell count and viral load estimations. The patients were categorized as showing concordant favorable (CF), immunological only (IO), virological only (VO), and concordant unfavorable responses (CU). The efficiency of immunological failure to predict virological failure was analyzed across various levels of virological failure (VL>50, >500, and >5,000 copies/ml). At 6 months, 87(79.81%), 7(5.5%), 13 (11.92%), and 2 (1.83%) patients and at 12 months 61(69.3%), 9(10.2%), 16 (18.2%), and 2 (2.3%) patients had CF, IO, VO, and CU responses, respectively. Immunological failure criteria had a very low sensitivity (11.1-40%) and positive predictive value (8.3-25%) to predict virological failure. Immunological criteria do not accurately predict virological failure resulting in significant misclassification of therapeutic responses. There is an urgent need for inclusion of viral load testing in the initiation and monitoring of ART.

  20. Assessing Discriminative Performance at External Validation of Clinical Prediction Models.

    Directory of Open Access Journals (Sweden)

    Daan Nieboer

    Full Text Available External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting.We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1 the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2 the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury.The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples and heterogeneous in scenario 2 (in 17%-39% of simulated samples. Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2.The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.

  1. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    Science.gov (United States)

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  2. Slope Deformation Prediction Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei JIA

    2013-07-01

    Full Text Available This paper principally studies the prediction of slope deformation based on Support Vector Machine (SVM. In the prediction process,explore how to reconstruct the phase space. The geological body’s displacement data obtained from chaotic time series are used as SVM’s training samples. Slope displacement caused by multivariable coupling is predicted by means of single variable. Results show that this model is of high fitting accuracy and generalization, and provides reference for deformation prediction in slope engineering.

  3. Balancing Model Performance and Simplicity to Predict Postoperative Primary Care Blood Pressure Elevation.

    Science.gov (United States)

    Schonberger, Robert B; Dai, Feng; Brandt, Cynthia A; Burg, Matthew M

    2015-09-01

    Because of uncertainty regarding the reliability of perioperative blood pressures and traditional notions downplaying the role of anesthesiologists in longitudinal patient care, there is no consensus for anesthesiologists to recommend postoperative primary care blood pressure follow-up for patients presenting for surgery with an increased blood pressure. The decision of whom to refer should ideally be based on a predictive model that balances performance with ease-of-use. If an acceptable decision rule was developed, a new practice paradigm integrating the surgical encounter into broader public health efforts could be tested, with the goal of reducing long-term morbidity from hypertension among surgical patients. Using national data from US veterans receiving surgical care, we determined the prevalence of poorly controlled outpatient clinic blood pressures ≥140/90 mm Hg, based on the mean of up to 4 readings in the year after surgery. Four increasingly complex logistic regression models were assessed to predict this outcome. The first included the mean of 2 preoperative blood pressure readings; other models progressively added a broad array of demographic and clinical data. After internal validation, the C-statistics and the Net Reclassification Index between the simplest and most complex models were assessed. The performance characteristics of several simple blood pressure referral thresholds were then calculated. Among 215,621 patients, poorly controlled outpatient clinic blood pressure was present postoperatively in 25.7% (95% confidence interval [CI], 25.5%-25.9%) including 14.2% (95% CI, 13.9%-14.6%) of patients lacking a hypertension history. The most complex prediction model demonstrated statistically significant, but clinically marginal, improvement in discrimination over a model based on preoperative blood pressure alone (C-statistic, 0.736 [95% CI, 0.734-0.739] vs 0.721 [95% CI, 0.718-0.723]; P for difference 1 of 4 patients (95% CI, 25

  4. A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

    Science.gov (United States)

    Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Chen, Yiping; Zhuang, Zhaowen; Cheng, Yongqiang; Deng, Bin; Wang, Liandong; Zeng, Yonghu; Gao, Lei

    2014-01-01

    This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good. PMID:24967605

  5. Nitride fuels irradiation performance data base

    International Nuclear Information System (INIS)

    Brozak, D.E.; Thomas, J.K.; Peddicord, K.L.

    1987-01-01

    An irradiation performance data base for nitride fuels has been developed from an extensive literature search and review that emphasized uranium nitride, but also included performance data for mixed nitrides [(U,Pu)N] and carbonitrides [(U,Pu)C,N] to increase the quantity and depth of pin data available. This work represents a very extensive effort to systematically collect and organize irradiation data for nitride-based fuels. The data base has many potential applications. First, it can facilitate parametric studies of nitride-based fuels to be performed using a wide range of pin designs and operating conditions. This should aid in the identification of important parameters and design requirements for multimegawatt and SP-100 fuel systems. Secondly, the data base can be used to evaluate fuel performance models. For detailed studies, it can serve as a guide to selecting a small group of pin specimens for extensive characterization. Finally, the data base will serve as an easily accessible and expandable source of irradiation performance information for nitride fuels

  6. Protein-Based Urine Test Predicts Kidney Transplant Outcomes

    Science.gov (United States)

    ... News Releases News Release Thursday, August 22, 2013 Protein-based urine test predicts kidney transplant outcomes NIH- ... supporting development of noninvasive tests. Levels of a protein in the urine of kidney transplant recipients can ...

  7. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions.

    Science.gov (United States)

    Ochoa, David; García-Gutiérrez, Ponciano; Juan, David; Valencia, Alfonso; Pazos, Florencio

    2013-01-27

    A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

  8. 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...

  9. Predicting response to incretin-based therapy

    Directory of Open Access Journals (Sweden)

    Agrawal N

    2011-04-01

    Full Text Available Sanjay Kalra1, Bharti Kalra2, Rakesh Sahay3, Navneet Agrawal41Department of Endocrinology, 2Department of Diabetology, Bharti Hospital, Karnal, India; 3Department of Endocrinology, Osmania Medical College, Hyderabad, India; 4Department of Medicine, GR Medical College, Gwalior, IndiaAbstract: There are two important incretin hormones, glucose-dependent insulin tropic polypeptide (GIP and glucagon-like peptide-1 (GLP-1. The biological activities of GLP-1 include stimulation of glucose-dependent insulin secretion and insulin biosynthesis, inhibition of glucagon secretion and gastric emptying, and inhibition of food intake. GLP-1 appears to have a number of additional effects in the gastrointestinal tract and central nervous system. Incretin based therapy includes GLP-1 receptor agonists like human GLP-1 analogs (liraglutide and exendin-4 based molecules (exenatide, as well as DPP-4 inhibitors like sitagliptin, vildagliptin and saxagliptin. Most of the published studies showed a significant reduction in HbA1c using these drugs. A critical analysis of reported data shows that the response rate in terms of target achievers of these drugs is average. One of the first actions identified for GLP-1 was the glucose-dependent stimulation of insulin secretion from islet cell lines. Following the detection of GLP-1 receptors on islet beta cells, a large body of evidence has accumulated illustrating that GLP-1 exerts multiple actions on various signaling pathways and gene products in the ß cell. GLP-1 controls glucose homeostasis through well-defined actions on the islet ß cell via stimulation of insulin secretion and preservation and expansion of ß cell mass. In summary, there are several factors determining the response rate to incretin therapy. Currently minimal clinical data is available to make a conclusion. Key factors appear to be duration of diabetes, obesity, presence of autonomic neuropathy, resting energy expenditure, plasma glucagon levels and

  10. Influence of covariate distribution on the predictive performance of pharmacokinetic models in paediatric research

    Science.gov (United States)

    Piana, Chiara; Danhof, Meindert; Della Pasqua, Oscar

    2014-01-01

    Aims The accuracy of model-based predictions often reported in paediatric research has not been thoroughly characterized. The aim of this exercise is therefore to evaluate the role of covariate distributions when a pharmacokinetic model is used for simulation purposes. Methods Plasma concentrations of a hypothetical drug were simulated in a paediatric population using a pharmacokinetic model in which body weight was correlated with clearance and volume of distribution. Two subgroups of children were then selected from the overall population according to a typical study design, in which pre-specified body weight ranges (10–15 kg and 30–40 kg) were used as inclusion criteria. The simulated data sets were then analyzed using non-linear mixed effects modelling. Model performance was assessed by comparing the accuracy of AUC predictions obtained for each subgroup, based on the model derived from the overall population and by extrapolation of the model parameters across subgroups. Results Our findings show that systemic exposure as well as pharmacokinetic parameters cannot be accurately predicted from the pharmacokinetic model obtained from a population with a different covariate range from the one explored during model building. Predictions were accurate only when a model was used for prediction in a subgroup of the initial population. Conclusions In contrast to current practice, the use of pharmacokinetic modelling in children should be limited to interpolations within the range of values observed during model building. Furthermore, the covariate point estimate must be kept in the model even when predictions refer to a subset different from the original population. PMID:24433411

  11. Knowledge Tracing and Prediction of Future Trainee Performance

    National Research Council Canada - National Science Library

    Jastrzembski, Tiffany S; Gluck, Kevin A; Gunzelmann, Glenn

    2006-01-01

    ...). This model represents the system's estimate of the student's current knowledge or skill level, established from a performance history. Knowledge tracing (Aleven & Koedinger, 2002; Anderson, Conrad, & Corbett, 1989...

  12. Deep-Learning-Based Approach for Prediction of Algal Blooms

    Directory of Open Access Journals (Sweden)

    Feng Zhang

    2016-10-01

    Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

  13. Modeling of Complex Life Cycle Prediction Based on Cell Division

    Directory of Open Access Journals (Sweden)

    Fucheng Zhang

    2017-01-01

    Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

  14. An approach for prediction of petroleum production facility performance considering Arctic influence factors

    International Nuclear Information System (INIS)

    Gao Xueli; Barabady, Javad; Markeset, Tore

    2010-01-01

    As the oil and gas (O and G) industry is increasing the focus on petroleum exploration and development in the Arctic region, it is becoming increasingly important to design exploration and production facilities to suit the local operating conditions. The cold and harsh climate, the long distance from customer and suppliers' markets, and the sensitive environment may have considerable influence on the choice of design solutions and production performance characteristics such as throughput capacity, reliability, availability, maintainability, and supportability (RAMS) as well as operational and maintenance activities. Due to this, data and information collected for similar systems used in a normal climate may not be suitable. Hence, it is important to study and develop methods for prediction of the production performance characteristics during the design and operation phases. The aim of this paper is to present an approach for prediction of the production performance for oil and gas production facilities considering influencing factors in Arctic conditions. The proportional repair model (PRM) is developed in order to predict repair rate in Arctic conditions. The model is based on the proportional hazard model (PHM). A simple case study is used to demonstrate how the proposed approach can be applied.

  15. Evaluation of a Nutrition Model in Predicting Performance of Vietnamese Cattle

    Directory of Open Access Journals (Sweden)

    David Parsons

    2012-09-01

    level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known.

  16. Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients

    Directory of Open Access Journals (Sweden)

    Petra Povalej Brzan

    2017-04-01

    Full Text Available Background Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients. Methods The California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881. Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results. Results Area under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation. Discussion The results show positive influence of adding information from historical hospitalization records on predictive performance using all

  17. Distance matrix-based approach to protein structure prediction.

    Science.gov (United States)

    Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

    2009-03-01

    Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the

  18. Comparison of short term rainfall forecasts for model based flow prediction in urban drainage systems

    DEFF Research Database (Denmark)

    Thorndahl, Søren; Poulsen, Troels Sander; Bøvith, Thomas

    2012-01-01

    Forecast based flow prediction in drainage systems can be used to implement real time control of drainage systems. This study compares two different types of rainfall forecasts – a radar rainfall extrapolation based nowcast model and a numerical weather prediction model. The models are applied...... performance of the system is found using the radar nowcast for the short leadtimes and weather model for larger lead times....

  19. 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.

  20. Review and evaluation of performance measures for survival prediction models in external validation settings

    Directory of Open Access Journals (Sweden)

    M. Shafiqur Rahman

    2017-04-01

    Full Text Available Abstract Background When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. Methods An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Results Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell’s concordance measure which tended to increase as censoring increased. Conclusions We recommend that Uno’s concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller’s measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston’s D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive

  1. An auxiliary optimization method for complex public transit route network based on link prediction

    Science.gov (United States)

    Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian

    2018-02-01

    Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.

  2. 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.

  3. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

    Directory of Open Access Journals (Sweden)

    Hao Li

    2017-01-01

    Full Text Available Predicting the performance of solar water heater (SWH is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

  4. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  5. Prediction of polymer flooding performance using an analytical method

    International Nuclear Information System (INIS)

    Tan Czek Hoong; Mariyamni Awang; Foo Kok Wai

    2001-01-01

    The study investigated the applicability of an analytical method developed by El-Khatib in polymer flooding. Results from a simulator UTCHEM and experiments were compared with the El-Khatib prediction method. In general, by assuming a constant viscosity polymer injection, the method gave much higher recovery values than the simulation runs and the experiments. A modification of the method gave better correlation, albeit only oil production. Investigation is continuing on modifying the method so that a better overall fit can be obtained for polymer flooding. (Author)

  6. IT infrastructure and competitive aggressiveness in explaining and predicting performance

    NARCIS (Netherlands)

    Ajamieh, Aseel; Benitez, Jose; Braojos, Jessica; Gelhard, Carsten Volker

    2016-01-01

    While prior Information Systems and Operations Management literature emphasizes the role of both the firm's IT infrastructure and the general degree of competition as antecedents of firm performance, the organizational capabilities that mediate these important relationships remain undetermined.

  7. Utilizing Lab Tests to Predict Asphalt Concrete Overlay Performance

    Science.gov (United States)

    2017-12-01

    A series of five experimental projects and three demonstration projects were constructed to better understand the performance of pavement overlays using various levels of asphalt binder replacement (ABR) from reclaimed asphalt pavement (RAP), recycle...

  8. 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 presentation describes the capabilities of three-dimensional thermal power model of advanced stirling radioisotope generator (ASRG). The performance of the ASRG is presented for different scenario, such as Venus flyby with or without the auxiliary cooling system.

  9. SKread predicts handwriting performance in patients with low vision.

    Science.gov (United States)

    Downes, Ken; Walker, Laura L; Fletcher, Donald C

    2015-06-01

    To assess whether performance on the Smith-Kettlewell Reading (SKread) test is a reliable predictor of handwriting performance in patients with low vision. Cross-sectional study. Sixty-six patients at their initial low-vision rehabilitation evaluation. The patients completed all components of a routine low-vision appointment including logMAR acuity, performed the SKread test, and performed a handwriting task. Patients were timed while performing each task and their accuracy was recorded. The handwriting task was performed by having patients write 5 5-letter words into sets of boxes where each letter is separated by a box. The boxes were 15 × 15 mm, and accuracy was scored with 50 points possible from 25 letters: 1 point for each letter within the confines of a box and 1 point if the letter was legible. Correlation analysis was then performed. Median age of participants was 84 (range 54-97) years. Fifty-seven patients (86%) had age-related macular degeneration or some other maculopathy, whereas 9 patients (14%) had visual impairment from media opacity or neurologic impairment. Median Early Treatment Diabetic Retinopathy Study acuity was 20/133 (range 20/22 to 20/1000), and median logMAR acuity was 0.82 (range 0.04-1.70). SKread errors per block correlated with logMAR acuity (r = 0.6), and SKread time per block correlated with logMAR acuity (r = 0.51). SKread errors per block correlated with handwriting task time/accuracy ratio (r = 0.61). SKread time per block correlated with handwriting task time/accuracy ratio (r = 0.7). LogMAR acuity score correlated with handwriting task time/accuracy ratio (r = 0.42). All p values were handwriting performance in patients with low vision better than logMAR acuity. Copyright © 2015 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

  10. Working memory in children predicts performance on a gambling task.

    Science.gov (United States)

    Audusseau, Jean; Juhel, Jacques

    2015-01-01

    The authors investigated whether working memory (WM) plays a significant role in the development of decision making in children, operationalized by the Children's Gambling Task (CGT). A total of 105 children aged 6-7, 8-9, and 10-11 years old carried out the CGT. Children aged 6-7 years old were found to have a lower performance than older children, which shows that the CGT is sensitive to participant's age. The hypothesis that WM plays a significant role in decision making was then tested following two approaches: (a) an experimental approach, comparing between groups the performance on the CGT in a control condition (the CGT only was administered) to that in a double task condition (participants had to carry out a recall task in addition to the CGT); (b) an interindividual approach, probing the relationship between CGT performance and performance on tasks measuring WM efficiency. The between-groups approach evidenced a better performance in the control group. Moreover, the interindividual approach showed that the higher the participants' WM efficiency was, the higher their performance in the CGT was. Taken together, these two approaches yield converging results that support the hypothesis that WM plays a significant role in decision making in children.

  11. NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction.

    Science.gov (United States)

    Pardoe, Heath R; Kuzniecky, Ruben

    2018-01-01

    The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance. The NAPR framework allows external users to estimate the age of individual subjects using cortical thickness maps derived from their own locally processed T1-weighted whole brain MRI scans. As a demonstration of the NAPR approach, we have developed two age prediction models that were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367, age range 6-89 years). The provided age prediction models were trained using (i) relevance vector machines and (ii) Gaussian processes machine learning methods applied to cortical thickness surfaces obtained using Freesurfer v5.3. We believe that this transparent approach to out-of-sample evaluation and comparison of neuroimaging age prediction models will facilitate the development of improved age prediction models and allow for robust evaluation of the clinical utility of these methods.

  12. Dimensionless Numerical Approaches for the Performance Prediction of Marine Waterjet Propulsion Units

    Directory of Open Access Journals (Sweden)

    Marco Altosole

    2012-01-01

    Full Text Available One of the key issues at early design stage of a high-speed craft is the selection and the performance prediction of the propulsion system because at this stage only few information about the vessel are available. The objective of this work is precisely to provide the designer, in the case of waterjet propelled craft, with a simple and reliable calculation tool, able to predict the waterjet working points in design and off-design conditions, allowing to investigate several propulsive options during the ship design process. In the paper two original dimensionless numerical procedures, one referred to jet units for naval applications and the other more suitable for planing boats, are presented. The first procedure is based on a generalized performance map for mixed flow pumps, derived from the analysis of several waterjet pumps by applying similitude principles of the hydraulic machines. The second approach, validated by some comparisons with current waterjet installations, is based on a complete physical approach, from which a set of non-dimensional waterjet characteristics has been drawn by the authors. The presented application examples show the validity and the degree of accuracy of the proposed methodologies for the performance evaluation of waterjet propulsion systems.

  13. 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

  14. Corrosion models for predictions of performance of high-level radioactive-waste containers

    Energy Technology Data Exchange (ETDEWEB)

    Farmer, J.C.; McCright, R.D. [Lawrence Livermore National Lab., CA (United States); Gdowski, G.E. [KMI Energy Services, Livermore, CA (United States)

    1991-11-01

    The present plan for disposal of high-level radioactive waste in the US is to seal it in containers before emplacement in a geologic repository. A proposed site at Yucca Mountain, Nevada, is being evaluated for its suitability as a geologic repository. The containers will probably be made of either an austenitic or a copper-based alloy. Models of alloy degradation are being used to predict the long-term performance of the containers under repository conditions. The models are of uniform oxidation and corrosion, localized corrosion, and stress corrosion cracking, and are applicable to worst-case scenarios of container degradation. This paper reviews several of the models.

  15. Testing Theories of Recognition Memory by Predicting Performance Across Paradigms

    Science.gov (United States)

    Smith, David G.; Duncan, Matthew J. J.

    2004-01-01

    Signal-detection theory (SDT) accounts of recognition judgments depend on the assumption that recognition decisions result from a single familiarity-based process. However, fits of a hybrid SDT model, called dual-process theory (DPT), have provided evidence for the existence of a second, recollection-based process. In 2 experiments, the authors…

  16. A Realistic Seizure Prediction Study Based on Multiclass SVM.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

    2017-05-01

    A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

  17. Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

    Science.gov (United States)

    Daigle, Matthew John; Goebel, Kai Frank

    2010-01-01

    Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.

  18. Predicting Subsequent Task Performance From Goal Motivation and Goal Failure

    Directory of Open Access Journals (Sweden)

    Laura Catherine Healy

    2015-07-01

    Full Text Available Recent research has demonstrated that the cognitive processes associated with goal pursuit can continue to interfere with unrelated tasks when a goal is unfulfilled. Drawing from the self-regulation and goal-striving literatures, the present study explored the impact of goal failure on subsequent cognitive and physical task performance. Furthermore, we examined if the autonomous or controlled motivation underpinning goal striving moderates the responses to goal failure. Athletes (75 male, 59 female, Mage = 19.90 years, SDage = 3.50 completed a cycling trial with the goal of covering a given distance in 8 minutes. Prior to the trial, their motivation was primed using a video. During the trial they were provided with manipulated performance feedback, thus creating conditions of goal success or failure. No differences emerged in the responses to goal failure between the primed motivation or performance feedback conditions. We make recommendations for future research into how individuals can deal with failure in goal striving.

  19. Predicted performance of an integrated modular engine system

    Science.gov (United States)

    Binder, Michael; Felder, James L.

    1993-01-01

    Space vehicle propulsion systems are traditionally comprised of a cluster of discrete engines, each with its own set of turbopumps, valves, and a thrust chamber. The Integrated Modular Engine (IME) concept proposes a vehicle propulsion system comprised of multiple turbopumps, valves, and thrust chambers which are all interconnected. The IME concept has potential advantages in fault-tolerance, weight, and operational efficiency compared with the traditional clustered engine configuration. The purpose of this study is to examine the steady-state performance of an IME system with various components removed to simulate fault conditions. An IME configuration for a hydrogen/oxygen expander cycle propulsion system with four sets of turbopumps and eight thrust chambers has been modeled using the Rocket Engine Transient Simulator (ROCETS) program. The nominal steady-state performance is simulated, as well as turbopump thrust chamber and duct failures. The impact of component failures on system performance is discussed in the context of the system's fault tolerant capabilities.

  20. 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...

  1. Ligand and structure-based classification models for Prediction of P-glycoprotein inhibitors

    DEFF Research Database (Denmark)

    Klepsch, Freya; Poongavanam, Vasanthanathan; Ecker, Gerhard Franz

    2014-01-01

    an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and non-inhibitors, correctly predicting 73/75 % of the external test set compounds. Classification based on the docking experiments using the scoring function Chem...

  2. Text Comprehension Mediates Morphological Awareness, Syntactic Processing, and Working Memory in Predicting Chinese Written Composition Performance

    Science.gov (United States)

    Guan, Connie Qun; Ye, Feifei; Wagner, Richard K.; Meng, Wanjin; Leong, Che Kan

    2014-01-01

    The goal of the present study was to test opposing views about four issues concerning predictors of individual differences in Chinese written composition: (a) Whether morphological awareness, syntactic processing, and working memory represent distinct and measureable constructs in Chinese or are just manifestations of general language ability; (b) whether they are important predictors of Chinese written composition, and if so, the relative magnitudes and independence of their predictive relations; (c) whether observed predictive relations are mediated by text comprehension; and (d) whether these relations vary or are developmentally invariant across three years of writing development. Based on analyses of the performance of students in grades 4 (n = 246), 5 (n = 242) and 6 (n = 261), the results supported morphological awareness, syntactic processing, and working memory as distinct yet correlated abilities that made independent contributions to predicting Chinese written composition, with working memory as the strongest predictor. However, predictive relations were mediated by text comprehension. The final model accounted for approximately 75 percent of the variance in Chinese written composition. The results were largely developmentally invariant across the three grades from which participants were drawn. PMID:25530630

  3. Application of the aberration ring test (ARTEMIS) to determine lens quality and predict its lithographic performance

    Science.gov (United States)

    Moers, Marco H. P.; van der Laan, Hans; Zellenrath, Mark; de Boeij, Wim; Beaudry, Neil A.; Cummings, Kevin D.; van Zwol, Adriaan; Brecht, Arthur; Willekers, Rob

    2001-09-01

    ARTEMISTM (Aberration Ring Test Exposed at Multiple Illumination Settings) is a technique to determine in-situ, full-field, low and high order lens aberrations. In this paper we are analyzing the ARTEMISTM data of PAS5500/750TM DUV Step & Scan systems and its use as a lithographic prediction tool. ARTEMISTM is capable of determining Zernike coefficients up to Z25 with a 3(sigma) reproducibility range from 1.5 to 4.5 nm depending on the aberration type. 3D electric field simulations, that take the extended geometry of the phase shift feature into account, have been used for an improved treatment of the extraction of the spherical Zernike coefficients. Knowledge of the extracted Zernike coefficients allows an accurate prediction of the lithographic performance of the scanner system. This ability is demonstrated for a two bar pattern and an isolation pattern. The RMS difference between the ARTEMISTM-based lithographic prediction and the lithographic measurement is 2.5 nm for the two bar pattern and 3 nm for the isolation pattern. The 3(sigma) reproducibility of the prediction for the two bar pattern is 2.5 nm and 1 nm for the isolation pattern. This is better than the reproducibility of the lithographic measurements themselves.

  4. Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

    Science.gov (United States)

    Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

    2015-06-25

    Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

  5. Performance prediction of industrial centrifuges using scale-down models.

    Science.gov (United States)

    Boychyn, M; Yim, S S S; Bulmer, M; More, J; Bracewell, D G; Hoare, M

    2004-12-01

    Computational fluid dynamics was used to model the high flow forces found in the feed zone of a multichamber-bowl centrifuge and reproduce these in a small, high-speed rotating disc device. Linking the device to scale-down centrifugation, permitted good estimation of the performance of various continuous-flow centrifuges (disc stack, multichamber bowl, CARR Powerfuge) for shear-sensitive protein precipitates. Critically, the ultra scale-down centrifugation process proved to be a much more accurate predictor of production multichamber-bowl performance than was the pilot centrifuge.

  6. Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions

    Science.gov (United States)

    Miller, Christopher J.

    2011-01-01

    A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.

  7. Performance analysis of a potassium-base AMTEC cell

    International Nuclear Information System (INIS)

    Huang, C.; Hendricks, T.J.; Hunt, T.K.

    1998-01-01

    Sodium-BASE Alkali-Metal-Thermal-to-Electric-Conversion (AMTEC) cells have been receiving increased attention and funding from the Department of Energy, NASA and the United States Air Force. Recently, sodium-BASE (Na-BASE) AMTEC cells were selected for the Advanced Radioisotope Power System (ARPS) program for the next generation of deep-space missions and spacecraft. Potassium-BASE (K-BASE) AMTEC cells have not received as much attention to date, even though the vapor pressure of potassium is higher than that of sodium at the same temperature. So that, K-BASE AMTEC cells with potentially higher open circuit voltage and higher power output than Na-BASE AMTEC cells are possible. Because the surface tension of potassium is about half of the surface tension of sodium at the same temperature, the artery and evaporator design in a potassium AMTEC cell has much more challenging pore size requirements than designs using sodium. This paper uses a flexible thermal/fluid/electrical model to predict the performance of a K-BASE AMTEC cell. Pore sizes in the artery of K-BASE AMTEC cells must be smaller by an order of magnitude than in Na-BASE AMTEC cells. The performance of a K-BASE AMTEC cell was higher than a Na-BASE AMTEC cell at low voltages/high currents. K-BASE AMTEC cells also have the potential of much better electrode performance, thereby creating another avenue for potentially better performance in K-BASE AMTEC cells

  8. Risk-based plant performance indicators

    International Nuclear Information System (INIS)

    Boccio, J.L.; Azarm, M.A.; Hall, R.E.

    1991-01-01

    Tasked by the 1979 President's Commission on the Accident at Three Mile Island, the U.S. nuclear power industry has put into place a performance indicator program as one means for showing a demonstrable record of achievement. Largely through the efforts of the Institute of Nuclear Power Operations (INPO), plant performance data has, since 1983, been collected and analyzed to aid utility management in measuring their plants' performance progress. The U.S. Nuclear Regulatory Commission (NRC) has also developed a set of performance indicators. This program, conducted by NRC's Office for the Analysis and Evaluation of Operational Data (AEOD), is structured to present information on plant operational performance in a manner that could enhance the staff's ability to recognize changes in the safety performance. Both organizations recognized that performance indicators have limitations and could be subject to misinterpretation and misuse with the potential for an adverse impact on safety. This paper reports on performance indicators presently in use, e.g., unplanned automatic scrams, unplanned safety system actuation, safety system failures, etc., which are logically related to safety. But, a reliability/risk-based method for evaluating either individual indicators or an aggregated set of indicators is not yet available

  9. 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.

  10. Performance of Multiple Risk Assessment Tools to Predict Mortality for Adult Respiratory Distress Syndrome with Extracorporeal Membrane Oxygenation Therapy: An External Validation Study Based on Chinese Single-center Data

    Directory of Open Access Journals (Sweden)

    Lei Huang

    2016-01-01

    Conclusions: The RESP, APCHAE II, and SOFA scorings systems show good predictive value for intra-hospital survival of ARDS patients treated with ECMO in our single-center evaluation. Future validation should include a larger study with either more patients' data at single-center or by integration of domestic multi-center data. Development of a scoring system with national characteristics might be warranted.

  11. Performance-Based Evaluation and School Librarians

    Science.gov (United States)

    Church, Audrey P.

    2015-01-01

    Evaluation of instructional personnel is standard procedure in our Pre-K-12 public schools, and its purpose is to document educator effectiveness. With Race to the Top and No Child Left Behind waivers, states are required to implement performance-based evaluations that demonstrate student academic progress. This three-year study describes the…

  12. Overview of Performance Based Practical Design

    Science.gov (United States)

    2018-03-01

    DOI: https://doi.org/10.13023/KTC.RR.2018.03 State transportation agencies (STAs) have increasingly turned to practical design and performance based practical design(PBPD) to inform project development and implementation and to reduce project cos...

  13. A Performance-Based Instructional Theory

    Science.gov (United States)

    Lawson, Tom E.

    1974-01-01

    The rationale for a performanced- based instructional theory has arisen from significant advances during the past several years in instructional psychology. Four major areas of concern are: analysis of subject-matter content in terms of performance competencies, diagnosis of pre-instructional behavior, formulation of an instructional…

  14. Mixed movements/performance-based drawing

    DEFF Research Database (Denmark)

    Brabrand, Helle

    2011-01-01

    Mixed Movements is a research project engaged in performance-based architectural drawing. As one in a series working with architectonic implementation in relation to body and movements, the actual project relates body-movement and dynamic drawing and presents the material as interactive ‘space-time-tables’....

  15. Performance-Based Rewards and Work Stress

    Science.gov (United States)

    Ganster, Daniel C.; Kiersch, Christa E.; Marsh, Rachel E.; Bowen, Angela

    2011-01-01

    Even though reward systems play a central role in the management of organizations, their impact on stress and the well-being of workers is not well understood. We review the literature linking performance-based reward systems to various indicators of employee stress and well-being. Well-controlled experiments in field settings suggest that certain…

  16. Performance-based Pareto optimal design

    NARCIS (Netherlands)

    Sariyildiz, I.S.; Bittermann, M.S.; Ciftcioglu, O.

    2008-01-01

    A novel approach for performance-based design is presented, where Pareto optimality is pursued. Design requirements may contain linguistic information, which is difficult to bring into computation or make consistent their impartial estimations from case to case. Fuzzy logic and soft computing are

  17. Translation Ambiguity but Not Word Class Predicts Translation Performance

    Science.gov (United States)

    Prior, Anat; Kroll, Judith F.; Macwhinney, Brian

    2013-01-01

    We investigated the influence of word class and translation ambiguity on cross-linguistic representation and processing. Bilingual speakers of English and Spanish performed translation production and translation recognition tasks on nouns and verbs in both languages. Words either had a single translation or more than one translation. Translation…

  18. 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

  19. Predicting Performance Under Acute Stress : The Role of Individual Characteristics

    NARCIS (Netherlands)

    Delahaij, R.; Dam, K. van; Gaillard, A.W.K.; Soeters, J.

    2011-01-01

    This prospective study examined how differences in coping style, coping self-efficacy, and metacognitive awareness influence coping behavior and performance during a realistic acute stressful exercise in 2 military samples (n = 122 and n = 132). Results showed that coping self-efficacy and coping

  20. Goal orientations predict academic performance beyond intelligence and personality

    NARCIS (Netherlands)

    Steinmayr, R.; Bipp, T.; Spinath, B.

    2011-01-01

    Goal orientations are thought to be an important predictor of scholastic achievement. The present paper investigated the joint influence of goal orientations, intelligence, and personality on school performance in a sample of N = 520 11th and 12th graders (303 female; mean age M = 16.94 years).

  1. Fine-motor skills testing and prediction of endovascular performance

    DEFF Research Database (Denmark)

    Bech, Bo; Lönn, Lars; Schroeder, Torben V

    2013-01-01

    Performing endovascular procedures requires good control of fine-motor digital movements and hand-eye coordination. Objective assessment of such skills is difficult. Trainees acquire control of catheter/wire movements at various paces. However, little is known to what extent talent plays for novice...

  2. Predicting timing performance of advanced mechatronics control systems

    NARCIS (Netherlands)

    Voeten, J.P.M.; Hendriks, T.; Theelen, B.D.; Schuddemat, J.; Tabingh Suermondt, W.; Gemei, J.; Kotterink, C.; Huet, van J.; Eichler, G.; Kuepper, A.; Schau, V.; Fouchal, H.; Unger, H.

    2011-01-01

    Embedded control is a key product technology differentiator for many high-tech industries, including ASML. The strong increase in complexity of embedded control systems, combined with the occurrence of late changes in control requirements, results in many timing performance problems showing up only

  3. Students' Metacomprehension Knowledge: Components That Predict Comprehension Performance

    Science.gov (United States)

    Zabrucky, Karen M.; Moore, DeWayne; Agler, Lin-Miao Lin; Cummings, Andrea M.

    2015-01-01

    In the present study, we assessed students' metacomprehension knowledge and examined the components of knowledge most related to comprehension of expository texts. We used the Revised Metacomprehension Scale (RMCS) to investigate the relations between students' metacomprehension knowledge and comprehension performance. Students who evaluated and…

  4. Prediction-based Dynamic Energy Management in Wireless Sensor Networks

    Science.gov (United States)

    Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei

    2007-01-01

    Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

  5. Prediction-based Dynamic Energy Management in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Dao-Wei Bi

    2007-03-01

    Full Text Available Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

  6. Adult age differences in predicting memory performance: the effects of normative information and task experience.

    Science.gov (United States)

    McDonald-Miszczak, L; Hunter, M A; Hultsch, D F

    1994-03-01

    Two experiments addressed the effects of task information and experience on younger and older adults' ability to predict their memory for words. The first study examined the effects of normative task information on subjects' predictions for 30-word lists across three trials. The second study looked at the effects of making predictions and recalling either an easy (15) or a difficult (45) word list prior to making predictions and recalling a moderately difficult (30) word list. The results from both studies showed that task information and experience affected subjects' predictions and that elderly adults predicted their performance more accurately than younger adults.

  7. Mean platelet volume (MPV) predicts middle distance running performance.

    Science.gov (United States)

    Lippi, Giuseppe; Salvagno, Gian Luca; Danese, Elisa; Skafidas, Spyros; Tarperi, Cantor; Guidi, Gian Cesare; Schena, Federico

    2014-01-01

    Running economy and performance in middle distance running depend on several physiological factors, which include anthropometric variables, functional characteristics, training volume and intensity. Since little information is available about hematological predictors of middle distance running time, we investigated whether some hematological parameters may be associated with middle distance running performance in a large sample of recreational runners. The study population consisted in 43 amateur runners (15 females, 28 males; median age 47 years), who successfully concluded a 21.1 km half-marathon at 75-85% of their maximal aerobic power (VO2max). Whole blood was collected 10 min before the run started and immediately thereafter, and hematological testing was completed within 2 hours after sample collection. The values of lymphocytes and eosinophils exhibited a significant decrease compared to pre-run values, whereas those of mean corpuscular volume (MCV), platelets, mean platelet volume (MPV), white blood cells (WBCs), neutrophils and monocytes were significantly increased after the run. In univariate analysis, significant associations with running time were found for pre-run values of hematocrit, hemoglobin, mean corpuscular hemoglobin (MCH), red blood cell distribution width (RDW), MPV, reticulocyte hemoglobin concentration (RetCHR), and post-run values of MCH, RDW, MPV, monocytes and RetCHR. In multivariate analysis, in which running time was entered as dependent variable whereas age, sex, blood lactate, body mass index, VO2max, mean training regimen and the hematological parameters significantly associated with running performance in univariate analysis were entered as independent variables, only MPV values before and after the trial remained significantly associated with running time. After adjustment for platelet count, the MPV value before the run (p = 0.042), but not thereafter (p = 0.247), remained significantly associated with running

  8. Mean platelet volume (MPV predicts middle distance running performance.

    Directory of Open Access Journals (Sweden)

    Giuseppe Lippi

    Full Text Available Running economy and performance in middle distance running depend on several physiological factors, which include anthropometric variables, functional characteristics, training volume and intensity. Since little information is available about hematological predictors of middle distance running time, we investigated whether some hematological parameters may be associated with middle distance running performance in a large sample of recreational runners.The study population consisted in 43 amateur runners (15 females, 28 males; median age 47 years, who successfully concluded a 21.1 km half-marathon at 75-85% of their maximal aerobic power (VO2max. Whole blood was collected 10 min before the run started and immediately thereafter, and hematological testing was completed within 2 hours after sample collection.The values of lymphocytes and eosinophils exhibited a significant decrease compared to pre-run values, whereas those of mean corpuscular volume (MCV, platelets, mean platelet volume (MPV, white blood cells (WBCs, neutrophils and monocytes were significantly increased after the run. In univariate analysis, significant associations with running time were found for pre-run values of hematocrit, hemoglobin, mean corpuscular hemoglobin (MCH, red blood cell distribution width (RDW, MPV, reticulocyte hemoglobin concentration (RetCHR, and post-run values of MCH, RDW, MPV, monocytes and RetCHR. In multivariate analysis, in which running time was entered as dependent variable whereas age, sex, blood lactate, body mass index, VO2max, mean training regimen and the hematological parameters significantly associated with running performance in univariate analysis were entered as independent variables, only MPV values before and after the trial remained significantly associated with running time. After adjustment for platelet count, the MPV value before the run (p = 0.042, but not thereafter (p = 0.247, remained significantly associated with running

  9. Machine learning-based methods for prediction of linear B-cell epitopes.

    Science.gov (United States)

    Wang, Hsin-Wei; Pai, Tun-Wen

    2014-01-01

    B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

  10. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  11. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data

    Directory of Open Access Journals (Sweden)

    Milan Vukićević

    2014-01-01

    Full Text Available Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.

  12. Performance-based inspection and maintenance strategies

    International Nuclear Information System (INIS)

    Vesely, W.E.

    1995-01-01

    Performance-based inspection and maintenance strategies utilize measures of equipment performance to help guide inspection and maintenance activities. A relevant measure of performance for safety system components is component unavailability. The component unavailability can also be input into a plant risk model such as a Probabilistic Risk Assessment (PRA) to determine the associated plant risk performance. Based on the present and projected unavailability performance, or the present and projected risk performance, the effectiveness of current maintenance activities can be evaluated and this information can be used to plan future maintenance activities. A significant amount of information other than downtimes or failure times is collected or can be collected when an inspection or maintenance is conducted which can be used to estimate the component unavailability. This information generally involves observations on the condition or state of the component or component piecepart. The information can be detailed such as the amount of corrosion buildup or can be general such as the general state of the component described as ' high degradation', ' moderate degradation', or ' low degradation'. Much of the information collected in maintenance logs is qualitative and fuzzy. As part of an NRC Research program on performance-based engineering modeling, approaches have been developed to apply Fuzzy Set Theory to information collected on the state of the component to determine the implied component or component piecepart unavailability. Demonstrations of the applications of Fuzzy Set Theory are presented utilizing information from plant maintenance logs. The demonstrations show the power of Fuzzy Set Theory in translating engineering information to reliability and risk implications

  13. 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.

  14. Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations

    Directory of Open Access Journals (Sweden)

    Jian Ou

    2017-03-01

    Full Text Available The extensive applications of multi-function radars (MFRs have presented a great challenge to the technologies of radar countermeasures (RCMs and electronic intelligence (ELINT. The recently proposed cognitive electronic warfare (CEW provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR. With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

  15. Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations.

    Science.gov (United States)

    Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

    2017-03-19

    The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

  16. A Bayesian Performance Prediction Model for Mathematics Education: A Prototypical Approach for Effective Group Composition

    Science.gov (United States)

    Bekele, Rahel; McPherson, Maggie

    2011-01-01

    This research work presents a Bayesian Performance Prediction Model that was created in order to determine the strength of personality traits in predicting the level of mathematics performance of high school students in Addis Ababa. It is an automated tool that can be used to collect information from students for the purpose of effective group…

  17. Approach to performance based regulation development

    International Nuclear Information System (INIS)

    Spogen, L.R.; Cleland, L.L.

    1977-06-01

    An approach to the development of performance based regulations (PBR's) is described. Initially, a framework is constructed that consists of a function hierarchy and associated measures. The function at the top of the hierarchy is described in terms of societal objectives. Decomposition of this function into subordinate functions and their subsequent decompositions yield the function hierarchy. ''Bottom'' functions describe the roles of system components. When measures are identified for the performance of each function and means of aggregating performances to higher levels are established, the framework may be employed for developing PBR's. Consideration of system flexibility and performance uncertainty guide in determining the hierarchical level at which regulations are formulated. Ease of testing compliance is also a factor. To show the viability of the approach, the framework developed by Lawrence Livermore Laboratory for the Nuclear Regulatory Commission for evaluation of material control systems at fixed facilities is presented

  18. 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.

  19. SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

    Science.gov (United States)

    Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

    2015-01-01

    Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

  20. Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks

    Directory of Open Access Journals (Sweden)

    Jeng-Fung Chen

    2014-10-01

    Full Text Available Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS and Cuckoo Optimization Algorithm (COA is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.

  1. Research of performance prediction to energy on hydraulic turbine

    International Nuclear Information System (INIS)

    Quan, H; Li, R N; Li, Q F; Han, W; Su, Q M

    2012-01-01

    Refer to the low specific speed Francis turbine blade design principle and double-suction pump structure. Then, design a horizontal double-channel hydraulic turbine Francis. Through adding different guide vane airfoil and and no guide vane airfoil on the hydraulic conductivity components to predict hydraulic turbine energy and using Fluent software to numerical simulation that the operating conditions and point. The results show that the blade pressure surface and suction surface pressure is low when the hydraulic turbine installation is added standard positive curvature of the guide vane and modified positive curvature of guide vane. Therefore, the efficiency of energy recovery is low. However, the pressure of negative curvature guide vane and symmetric guide vane added on hydraulic turbine installations is larger than that of the former ones, and it is conducive to working of runner. With the decreasing of guide vane opening, increasing of inlet angle, flow state gets significantly worse. Then, others obvious phenomena are that the reflux and horizontal flow appeared in blade pressure surface. At the same time, the vortex was formed in Leaf Road, leading to the loss of energy. Through analyzing the distribution of pressure, velocity, flow lines of over-current flow in the the back hydraulic conductivity components in above programs we can known that the hydraulic turbine installation added guide vane is more reasonable than without guide vanes, it is conducive to improve efficiency of energy conversion.

  2. A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

    Science.gov (United States)

    Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

    2017-10-01

    The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

  3. Grammar-based feature generation for time-series prediction

    CERN Document Server

    De Silva, Anthony Mihirana

    2015-01-01

    This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

  4. Discrimination-Aware Classifiers for Student Performance Prediction

    Science.gov (United States)

    Luo, Ling; Koprinska, Irena; Liu, Wei

    2015-01-01

    In this paper we consider discrimination-aware classification of educational data. Mining and using rules that distinguish groups of students based on sensitive attributes such as gender and nationality may lead to discrimination. It is desirable to keep the sensitive attributes during the training of a classifier to avoid information loss but…

  5. The effect of genealogy-based haplotypes on genomic prediction

    DEFF Research Database (Denmark)

    Edriss, Vahid; Fernando, Rohan L.; Su, Guosheng

    2013-01-01

    on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. Methods A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using...... local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (pi) of the haplotype covariates had zero effect......, i.e. a Bayesian mixture method. Results About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some...

  6. Algorithms and Methods for High-Performance Model Predictive Control

    DEFF Research Database (Denmark)

    Frison, Gianluca

    routines employed in the numerical tests. The main focus of this thesis is on linear MPC problems. In this thesis, both the algorithms and their implementation are equally important. About the implementation, a novel implementation strategy for the dense linear algebra routines in embedded optimization...... is proposed, aiming at improving the computational performance in case of small matrices. About the algorithms, they are built on top of the proposed linear algebra, and they are tailored to exploit the high-level structure of the MPC problems, with special care on reducing the computational complexity....

  7. 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

  8. Performance of the FV3-powered Next Generation Global Prediction System for Harvey and Irma, and a vision for a "beyond weather timescale" prediction system for long-range hurricane track and intensity predictions

    Science.gov (United States)

    Lin, S. J.; Bender, M.; Harris, L.; Hazelton, A.

    2017-12-01

    The performance of a GFDL developed FV3-based Next Generation Global Prediction System (NGGPS) for Harvey and Irma will be reported. We will report on aspects of track and intensity errors (vs operational models), heavy precipitation (Harvey), rapid intensification, and simulated structure (in comparison with ground based radar), and point to a need of a future long-range (from day-5 up to 30 days) physically based ensemble hurricane prediction system for providing useful information to the forecasters, beyond the usual weather timescale.

  9. Screw Remaining Life Prediction Based on Quantum Genetic Algorithm and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Xiaochen Zhang

    2017-01-01

    Full Text Available To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA and support vector machine (SVM is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and isometric mapping (Isomap, the sensitive feature vectors are obtained and stored in database. Meanwhile, the sensitive feature vectors are randomly chosen from the database and constitute training samples and testing samples. Then the optimal kernel function parameter and penalty factor of SVM are searched with the method of QGA. Finally, the training samples are used to train optimized SVM while testing samples are adopted to test the prediction accuracy of the trained SVM so the screw remaining life prediction model can be got. The experiment results show that the screw remaining life prediction model could effectively predict screw remaining life.

  10. 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

  11. Prediction of SFL Interruption Performance from the Results of Arc Simulation during High-Current Phase

    Science.gov (United States)

    Lee, Jong-Chul; Lee, Won-Ho; Kim, Woun-Jea

    2015-09-01

    The design and development procedures of SF6 gas circuit breakers are still largely based on trial and error through testing although the development costs go higher every year. The computation cannot cover the testing satisfactorily because all the real processes arc not taken into account. But the knowledge of the arc behavior and the prediction of the thermal-flow inside the interrupters by numerical simulations are more useful than those by experiments due to the difficulties to obtain physical quantities experimentally and the reduction of computational costs in recent years. In this paper, in order to get further information into the interruption process of a SF6 self-blast interrupter, which is based on a combination of thermal expansion and the arc rotation principle, gas flow simulations with a CFD-arc modeling are performed during the whole switching process such as high-current period, pre-current zero period, and current-zero period. Through the complete work, the pressure-rise and the ramp of the pressure inside the chamber before current zero as well as the post-arc current after current zero should be a good criterion to predict the short-line fault interruption performance of interrupters.

  12. Investigations of internal turbulent flows in a low-head tubular pump and its performance predictions

    International Nuclear Information System (INIS)

    Tang, X L; Chen, X S; Wang, F J; Yang, W; Wu, Y L

    2012-01-01

    Based on the RANS equations, standard k−ε turbulence model and SIMPLE algorithm, the internal turbulent flows in a low-head tubular pump were simulated by using the FLUENT software. Based on the predicted flow fields, the external performance curves including the head-discharge, efficiency-discharge and power-discharge curves were further obtained. The calculated results indicate that the internal flow pattern is smooth at the best efficiency point (BEP). When it works under off-design operating cases, the flow pattern inside the diffuser and the discharge passage is disorder, and at the same time, the hydraulic losses mainly come from the secondary flows. At large flow rates, the minimum static pressure near the inlet of the blade pressure surfaces due to the negative attack angle. At small flow rates, the minimum value happens near the inlet of the suction surfaces. At the BEP, the lowest static pressure appears in the region behind the suction surfaces inlet. The newly-designed model is validated by the comparisons between its predicted external performance and the experimental data of the JGM-3 model. This research provides some important references for the optimization of a low-head tubular pump.

  13. Investigation into the performance of different models for predicting stutter.

    Science.gov (United States)

    Bright, Jo-Anne; Curran, James M; Buckleton, John S

    2013-07-01

    In this paper we have examined five possible models for the behaviour of the stutter ratio, SR. These were two log-normal models, two gamma models, and a two-component normal mixture model. A two-component normal mixture model was chosen with different behaviours of variance; at each locus SR was described with two distributions, both with the same mean. The distributions have difference variances: one for the majority of the observations and a second for the less well-behaved ones. We apply each model to a set of known single source Identifiler™, NGM SElect™ and PowerPlex(®) 21 DNA profiles to show the applicability of our findings to different data sets. SR determined from the single source profiles were compared to the calculated SR after application of the models. The model performance was tested by calculating the log-likelihoods and comparing the difference in Akaike information criterion (AIC). The two-component normal mixture model systematically outperformed all others, despite the increase in the number of parameters. This model, as well as performing well statistically, has intuitive appeal for forensic biologists and could be implemented in an expert system with a continuous method for DNA interpretation. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  14. Predicting performance in competitive apnea diving, part II: dynamic apnoea.

    Science.gov (United States)

    Schagatay, Erika

    2010-03-01

    Part I of this series of articles identified the main physiological factors defining the limits of static apnea, while this paper reviews the factors involved when physical work is added in the dynamic distance disciplines, performed in shallow water in a swimming pool. Little scientific work has been done concerning the prerequisites and limitations of swimming with or without fins whilst breath holding to extreme limits. Apneic duration influences all competitive apnea disciplines, and can be prolonged by any means that increase gas storage or tolerance to asphyxia, or reduce metabolic rate, as reviewed in the first article. For horizontal underwater distance swimming, the main challenge is to restrict metabolism despite the work, and to direct blood flow only to areas where demand is greatest, to allow sustained function. Here, work economy, local tissue energy and oxygen stores and the anaerobic capacity of the muscles are key components. Improvements in swimming techniques and, especially in swimming with fins, equipment have already contributed to enhanced performance and may do so further. High lactate levels observed after competition swims suggest a high anaerobic component, and muscle hypoxia could ultimately limit muscle work and swimming distance. However, the frequency of syncope, especially in swimming without fins, suggests that cerebral oxygenation may often be compromised before this occurs. In these pool disciplines, safety is high and the dive can be interrupted by the competitor or safety diver within seconds. The safety routines in place during pool competitions are described.

  15. Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

    DEFF Research Database (Denmark)

    Asgarpour, Masoud; Sørensen, John Dalsgaard

    2018-01-01

    The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation...... monitoring, fault prediction and predictive maintenance of offshore wind components is defined. The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution...

  16. Bayesian based Prognostic Model for Predictive Maintenance of Offshore Wind Farms

    DEFF Research Database (Denmark)

    Asgarpour, Masoud; Sørensen, John Dalsgaard

    2018-01-01

    monitoring, fault prediction and predictive maintenance of offshore wind components is defined. The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution......The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation...

  17. On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

    Science.gov (United States)

    Velarde, P.; Valverde, L.; Maestre, J. M.; Ocampo-Martinez, C.; Bordons, C.

    2017-03-01

    In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.

  18. Validation of a simple method for predicting the disinfection performance in a flow-through contactor.

    Science.gov (United States)

    Pfeiffer, Valentin; Barbeau, Benoit

    2014-02-01

    Despite its shortcomings, the T10 method introduced by the United States Environmental Protection Agency (USEPA) in 1989 is currently the method most frequently used in North America to calculate disinfection performance. Other methods (e.g., the Integrated Disinfection Design Framework, IDDF) have been advanced as replacements, and more recently, the USEPA suggested the Extended T10 and Extended CSTR (Continuous Stirred-Tank Reactor) methods to improve the inactivation calculations within ozone contactors. To develop a method that fully considers the hydraulic behavior of the contactor, two models (Plug Flow with Dispersion and N-CSTR) were successfully fitted with five tracer tests results derived from four Water Treatment Plants and a pilot-scale contactor. A new method based on the N-CSTR model was defined as the Partially Segregated (Pseg) method. The predictions from all the methods mentioned were compared under conditions of poor and good hydraulic performance, low and high disinfectant decay, and different levels of inactivation. These methods were also compared with experimental results from a chlorine pilot-scale contactor used for Escherichia coli inactivation. The T10 and Extended T10 methods led to large over- and under-estimations. The Segregated Flow Analysis (used in the IDDF) also considerably overestimated the inactivation under high disinfectant decay. Only the Extended CSTR and Pseg methods produced realistic and conservative predictions in all cases. Finally, a simple implementation procedure of the Pseg method was suggested for calculation of disinfection performance. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Study on performance prediction and energy saving of indirect evaporative cooling system

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Seong Yeon; Kim, Tae Ho; Kim, Myung Ho [Dept. of Mechanical Design Engineering, Chungnam National University, Daejeon (Korea, Republic of)

    2015-09-15

    The purpose of this study is to predict the performance of an indirect evaporative cooling system, and to evaluate its energy saving effect when applied to the exhaust heat recovery system of an air-handling unit. We derive the performance correlation of the indirect evaporative cooling system using a plastic heat exchanger based on experimental data obtained in various conditions. We predict the variations in the performance of the system for various return and outdoor air conditioning systems using the obtained correlation. We also analyze the energy saving of the system realized by the exhaust heat recovery using the typical meteorological data for several cities in Korea. The average utilization rate of the sensible cooling system for the exhaust heat recovery is 44.3% during summer, while that of the evaporative cooling system is 96.7%. The energy saving of the evaporative cooling system is much higher compared to the sensible cooling system, and was about 3.89 times the value obtained in Seoul.

  20. Predicting Performance: A Comparison of University Supervisors' Predictions and Teacher Candidates' Scores on a Teaching Performance Assessment

    Science.gov (United States)

    Sandholtz, Judith Haymore; Shea, Lauren M.

    2012-01-01

    The implementation of teaching performance assessments has prompted a range of concerns. Some educators question whether these assessments provide information beyond what university supervisors gain through their formative evaluations and classroom observations of candidates. This research examines the relationship between supervisors' predictions…

  1. Development of a modified equilibrium model for biomass pilot-scale fluidized bed gasifier performance predictions

    International Nuclear Information System (INIS)

    Rodriguez-Alejandro, David A.; Nam, Hyungseok; Maglinao, Amado L.; Capareda, Sergio C.; Aguilera-Alvarado, Alberto F.

    2016-01-01

    The objective of this work is to develop a thermodynamic model considering non-stoichiometric restrictions. The model validation was done from experimental works using a bench-scale fluidized bed gasifier with wood chips, dairy manure, and sorghum. The model was used for a further parametric study to predict the performance of a pilot-scale fluidized biomass gasifier. The Gibbs free energy minimization was applied to the modified equilibrium model considering a heat loss to the surroundings, carbon efficiency, and two non-equilibrium factors based on empirical correlations of ER and gasification temperature. The model was in a good agreement with RMS <4 for the produced gas. The parametric study ranges were 0.01 < ER < 0.99 and 500 °C < T < 900 °C to predict syngas concentrations and its LHV (lower heating value) for the optimization. Higher aromatics in tar were contained in WC gasification compared to manure gasification. A wood gasification tar simulation was produced to predict the amount of tars at specific conditions. The operating conditions for the highest quality syngas were reconciled experimentally with three biomass wastes using a fluidized bed gasifier. The thermodynamic model was used to predict the gasification performance at conditions beyond the actual operation. - Highlights: • Syngas from experimental gasification was used to create a non-equilibrium model. • Different types of biomass (HTS, DM, and WC) were used for gasification modelling. • Different tar compositions were identified with a simulation of tar yields. • The optimum operating conditions were found through the developed model.

  2. Plasma property and performance prediction for mercury ion thrusters

    Science.gov (United States)

    Longhurst, G. R.; Wilbur, P. J.

    1979-01-01

    The discharge chambers of mercury ion thrusters are modelled so the principal effects and processes which govern discharge plasma properties and thruster performance are described. The conservation relations for mass, charge and energy when applied to the Maxwellian electron population in the ion production region yield equations which may be made one-dimensional by the proper choice of coordinates. Solutions to these equations with the appropriate boundary conditions give electron density and temperature profiles which agree reasonably well with measurements. It is then possible to estimate plasma properties from thruster design data and those operating parameters which are directly controllable. By varying the operating parameter inputs to the computer code written to solve these equations, perfromance curves are obtained which agree quite well with measurements.

  3. Predicting neuropsychological test performance on the basis of temporal orientation.

    Science.gov (United States)

    Ryan, Joseph J; Glass, Laura A; Bartels, Jared M; Bergner, CariAnn M; Paolo, Anthony M

    2009-05-01

    Temporal orientation is often disrupted in the context of psychiatric or neurological disease; tests assessing this function are included in most mental status examinations. The present study examined the relationship between scores on the Temporal Orientation Scale (TOS) and performance on a battery of tests that assess memory, language, and cognitive functioning in a sample of patients with Alzheimer's disease (N = 55). Pearson-product moment correlations showed that, in all but two instances, the TOS was significantly correlated with each neuropsychological measure, p values < or = .05. Also, severely disoriented (i.e., TOS score < or = -8) patients were consistently 'impaired' on memory tests but not on tests of language and general cognitive functioning.

  4. Prediction of the Effect of Vortex Generators on Airfoil Performance

    International Nuclear Information System (INIS)

    Sørensen, Niels N; Zahle, F; Bak, C; Vronsky, T

    2014-01-01

    Vortex Generators (VGs) are widely used by the wind turbine industry, to control the flow over blade sections. The present work describes a computational fluid dynamic procedure that can handle a geometrical resolved VG on an airfoil section. After describing the method, it is applied to two different airfoils at a Reynolds number of 3 million, the FFA- W3-301 and FFA-W3-360, respectively. The computations are compared with wind tunnel measurements from the Stuttgart Laminar Wind Tunnel with respect to lift and drag variation as function of angle of attack. Even though the method does not exactly capture the measured performance, it can be used to compare different VG setups qualitatively with respect to chord- wise position, inter and intra-spacing and inclination of the VGs already in the design phase

  5. Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance.

    Science.gov (United States)

    Smith, Lauren N; Makam, Anil N; Darden, Douglas; Mayo, Helen; Das, Sandeep R; Halm, Ethan A; Nguyen, Oanh Kieu

    2018-01-01

    Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown. We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models. Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions

  6. Serum 25-hydroxyvitamin D predicts cognitive performance in adults

    Directory of Open Access Journals (Sweden)

    Darwish H

    2015-08-01

    Full Text Available Hala Darwish,1 Pia Zeinoun,2 Husam Ghusn,3,4 Brigitte Khoury,2 Hani Tamim,5 Samia J Khoury6 1Hariri School of Nursing, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; 2Psychiatry Department, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; 3Internal Medicine Department, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; 4Geriatrics Department, Ain Wazein Hospital, El Chouf, Lebanon; 5Clinical Research Institute, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; 6Neurology Department, Faculty of Medicine, American University of Beirut, Beirut, Lebanon Background: Vitamin D is an endogenous hormone known to regulate calcium levels in the body and plays a role in cognitive performance. Studies have shown an association between vitamin D deficiency and cognitive impairment in older adults. Lebanon has a high 25-hydroxyvitamin D (25(OHD deficiency prevalence across all age groups. Methods: In this cross-sectional study, we explored the cognitive performance and serum 25(OHD levels using an electrochemoluminescent immunoassay in 254 older (>60 years as well as younger (30–60 years adults. Subjects’ characteristics, including age, years of education, wearing of veil, alcohol consumption, smoking, and physical exercise, were collected. Participants were screened for depression prior to cognitive screening using the Montreal Cognitive Assessment Arabic version. Visuospatial memory was tested using the Rey Complex Figure Test and Recognition Trial, and speed of processing was assessed using the Symbol Digit Modalities test. Results: Pearson’s correlation and stepwise linear regression analyses showed that a low vitamin D level was associated with greater risk of cognitive impairment in older as well as younger adults. Conclusion: These findings suggest that correction of vitamin D needs to be explored as an intervention to prevent cognitive impairment. Prospective

  7. Static Formation Temperature Prediction Based on Bottom Hole Temperature

    Directory of Open Access Journals (Sweden)

    Changwei Liu

    2016-08-01

    Full Text Available Static formation temperature (SFT is required to determine the thermophysical properties and production parameters in geothermal and oil reservoirs. However, it is not easy to determine SFT by both experimental and physical methods. In this paper, a mathematical approach to predicting SFT, based on a new model describing the relationship between bottom hole temperature (BHT and shut-in time, has been proposed. The unknown coefficients of the model were derived from the least squares fit by the particle swarm optimization (PSO algorithm. Additionally, the ability to predict SFT using a few BHT data points (such as the first three, four, or five points of a data set was evaluated. The accuracy of the proposed method to predict SFT was confirmed by a deviation percentage less than ±4% and a high regression coefficient R2 (>0.98. The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells.

  8. Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting BRCA1/2 mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium.

    Science.gov (United States)

    Fischer, Christine; Kuchenbäcker, Karoline; Engel, Christoph; Zachariae, Silke; Rhiem, Kerstin; Meindl, Alfons; Rahner, Nils; Dikow, Nicola; Plendl, Hansjörg; Debatin, Irmgard; Grimm, Tiemo; Gadzicki, Dorothea; Flöttmann, Ricarda; Horvath, Judit; Schröck, Evelin; Stock, Friedrich; Schäfer, Dieter; Schwaab, Ira; Kartsonaki, Christiana; Mavaddat, Nasim; Schlegelberger, Brigitte; Antoniou, Antonis C; Schmutzler, Rita

    2013-06-01

    Risk prediction models are widely used in clinical genetic counselling. Despite their frequent use, the genetic risk models BOADICEA, BRCAPRO, IBIS and extended Claus model (eCLAUS), used to estimate BRCA1/2 mutation carrier probabilities, have never been comparatively evaluated in a large sample from central Europe. Additionally, a novel version of BOADICEA that incorporates tumour pathology information has not yet been validated. Using data from 7352 German families we estimated BRCA1/2 carrier probabilities under each model and compared their discrimination and calibration. The incremental value of using pathology information in BOADICEA was assessed in a subsample of 4928 pedigrees with available data on breast tumour molecular markers oestrogen receptor, progesterone receptor and human epidermal growth factor 2. BRCAPRO (area under receiver operating characteristic curve (AUC)=0.80 (95% CI 0.78 to 0.81)) and BOADICEA (AUC=0.79 (0.78-0.80)), had significantly higher diagnostic accuracy than IBIS and eCLAUS (p<0.001). The AUC increased when pathology information was used in BOADICEA: AUC=0.81 (95% CI 0.80 to 0.83, p<0.001). At carrier thresholds of 10% and 15%, the net reclassification index was +3.9% and +5.4%, respectively, when pathology was included in the model. Overall, calibration was best for BOADICEA and worst for eCLAUS. With eCLAUS, twice as many mutation carriers were predicted than observed. Our results support the use of BRCAPRO and BOADICEA for decision making regarding genetic testing for BRCA1/2 mutations. However, model calibration has to be improved for this population. eCLAUS should not be used for estimating mutation carrier probabilities in clinical settings. Whenever possible, breast tumour molecular marker information should be taken into account.

  9. Multi performance option in direct displacement based design

    Directory of Open Access Journals (Sweden)

    Muljati Ima

    2017-01-01

    Full Text Available Compare to traditional method, direct displacement based design (DDBD offers the more rational design choice due to its compatibility with performance based design which is controlled by the targeted displacement in design. The objectives of this study are: 1 to explore the performance of DDBD for design Level-1, -2 and -3; 2 to determine the most appropriate design level based on material efficiency and damage risk; and 3 to verify the chosen design in order to check its performance under small-, moderate- and severe earthquake. As case study, it uses regular concrete frame structures consists of fourand eight-story with typical plan, located in low- and high-risk seismicity area. The study shows that design Level-2 (repairable damage is the most appropriate choice. Nonlinear time history analysis is run for each case study in order to verify their performance based on parameter: story drift, damage indices, and plastic mechanism. It can be concluded that DDBD performed very well in predicting seismic demand of the observed structures. Design Level-2 can be chosen as the most appropriate design level. Structures are in safe plastic mechanism under all level of seismicity although some plastic hinges formed at some unexpected locations.

  10. A Study of Performance in Low-Power Tokamak Reactor with Integrated Predictive Modeling Code

    International Nuclear Information System (INIS)

    Pianroj, Y.; Onjun, T.; Suwanna, S.; Picha, R.; Poolyarat, N.

    2009-07-01

    Full text: A fusion hybrid or a small fusion power output with low power tokamak reactor is presented as another useful application of nuclear fusion. Such tokamak can be used for fuel breeding, high-level waste transmutation, hydrogen production at high temperature, and testing of nuclear fusion technology components. In this work, an investigation of the plasma performance in a small fusion power output design is carried out using the BALDUR predictive integrated modeling code. The simulations of the plasma performance in this design are carried out using the empirical-based Mixed Bohm/gyro Bohm (B/gB) model, whereas the pedestal temperature model is based on magnetic and flow shear (δ α ρ ζ 2 ) stabilization pedestal width scaling. The preliminary results using this core transport model show that the central ion and electron temperatures are rather pessimistic. To improve the performance, the optimization approach are carried out by varying some parameters, such as plasma current and power auxiliary heating, which results in some improvement of plasma performance

  11. Parallel protein secondary structure prediction based on neural networks.

    Science.gov (United States)

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  12. 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.

  13. Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?

    Science.gov (United States)

    Kramer, Andrew A; Higgins, Thomas L; Zimmerman, Jack E

    2015-02-01

    To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences. Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort. Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013. Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions. None. We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more

  14. A High Performance COTS Based Computer Architecture

    Science.gov (United States)

    Patte, Mathieu; Grimoldi, Raoul; Trautner, Roland

    2014-08-01

    Using Commercial Off The Shelf (COTS) electronic components for space applications is a long standing idea. Indeed the difference in processing performance and energy efficiency between radiation hardened components and COTS components is so important that COTS components are very attractive for use in mass and power constrained systems. However using COTS components in space is not straightforward as one must account with the effects of the space environment on the COTS components behavior. In the frame of the ESA funded activity called High Performance COTS Based Computer, Airbus Defense and Space and its subcontractor OHB CGS have developed and prototyped a versatile COTS based architecture for high performance processing. The rest of the paper is organized as follows: in a first section we will start by recapitulating the interests and constraints of using COTS components for space applications; then we will briefly describe existing fault mitigation architectures and present our solution for fault mitigation based on a component called the SmartIO; in the last part of the paper we will describe the prototyping activities executed during the HiP CBC project.

  15. The European computer model for optronic system performance prediction (ECOMOS)

    Science.gov (United States)

    Keßler, Stefan; Bijl, Piet; Labarre, Luc; Repasi, Endre; Wittenstein, Wolfgang; Bürsing, Helge

    2017-10-01

    ECOMOS is a multinational effort within the framework of an EDA Project Arrangement. Its aim is to provide a generally accepted and harmonized European computer model for computing nominal Target Acquisition (TA) ranges of optronic imagers operating in the Visible or thermal Infrared (IR). The project involves close co-operation of defence and security industry and public research institutes from France, Germany, Italy, The Netherlands and Sweden. ECOMOS uses and combines well-accepted existing European tools to build up a strong competitive position. This includes two TA models: the analytical TRM4 model and the image-based TOD model. In addition, it uses the atmosphere model MATISSE. In this paper, the central idea of ECOMOS is exposed. The overall software structure and the underlying models are shown and elucidated. The status of the project development is given as well as a short discussion of validation tests and an outlook on the future potential of simulation for sensor assessment.

  16. Learning to avoid spiders: fear predicts performance, not competence.

    Science.gov (United States)

    Luo, Xijia; Becker, Eni S; Rinck, Mike

    2018-01-05

    We used an immersive virtual environment to examine avoidance learning in spider-fearful participants. In 3 experiments, participants were asked to repeatedly lift one of 3 virtual boxes, under which either a toy car or a spider appeared and then approached the participant. Participants were not told that the probability of encountering a spider differed across boxes. When the difference was large (Exps. 1 and 2), spider-fearfuls learned to avoid spiders by lifting the few-spiders-box more often and the many-spiders-box less often than non-fearful controls did. However, they hardly managed to do so when the probability differences were small (Exp. 3), and they did not escape from threat more quickly (Exp. 2). In contrast to the observed performance differences, spider-fearfuls and non-fearfuls showed equal competence, that is comparable post-experimental knowledge about the probability to encounter spiders under the 3 boxes. The limitations and implications of the present study are discussed.

  17. Working memory capacity predicts conflict-task performance.

    Science.gov (United States)

    Gulbinaite, Rasa; Johnson, Addie

    2014-01-01

    The relationship between the ability to maintain task goals and working memory capacity (WMC) is firmly established, but evidence for WMC-related differences in conflict processing is mixed. We investigated whether WMC (measured using two complex-span tasks) mediates differences in adjustments of cognitive control in response to conflict. Participants performed a Simon task in which congruent and incongruent trials were equiprobable, but in which the proportion of congruency repetitions (congruent trials followed by congruent trials or incongruent trials followed by incongruent trials) and thus the need for trial-by-trial adjustments in cognitive control varied by block. The overall Simon effect did not depend on WMC capacity. However, for the low-WMC participants the Simon effect decreased as the proportion of congruency repetitions decreased, whereas for the high- and average-WMC participants it was relatively constant across conditions. Distribution analysis of the Simon effect showed more evidence for the inhibition of stimulus location in the low- than in the high-WMC participants, especially when the proportion of congruency repetitions was low. We hypothesize that low-WMC individuals exhibit more interference from task-irrelevant information due to weaker preparatory control prior to stimulus presentation and, thus, stronger reliance on reactive recruitment of cognitive control.

  18. Investigation and Prediction of RF Window Performance in APT Accelerators

    International Nuclear Information System (INIS)

    Humphries, S. Jr.

    1997-01-01

    The work described in this report was performed between November 1996 and May 1997 in support of the APT (Accelerator Production of Tritium) Program at Los Alamos National Laboratory. The goal was to write and to test computer programs for charged particle orbits in RF fields. The well-documented programs were written in portable form and compiled for standard personal computers for easy distribution to LANL researchers. They will be used in several APT applications including the following. Minimization of multipactor effects in the moderate β superconducting linac cavities under design for the APT accelerator. Investigation of suppression techniques for electron multipactoring in high-power RF feedthroughs. Modeling of the response of electron detectors for the protection of high power RF vacuum windows. In the contract period two new codes, Trak-RF and WaveSim, were completed and several critical benchmark etests were carried out. Trak-RF numerically tracks charged particle orbits in combined electrostatic, magnetostatic and electromagnetic fields. WaveSim determines frequency-domain RF field solutions and provides a key input to Trak-RF. The two-dimensional programs handle planar or cylindrical geometries. They have several unique characteristics

  19. Use of Neural Networks for modeling and predicting boiler's operating performance

    International Nuclear Information System (INIS)

    Kljajić, Miroslav; Gvozdenac, Dušan; Vukmirović, Srdjan

    2012-01-01

    The need for high boiler operating performance requires the application of improved techniques for the rational use of energy. The analysis presented is guided by an effort to find possibilities for ways energy resources can be used wisely to secure a more efficient final energy supply. However, the biggest challenges are related to the variety and stochastic nature of influencing factors. The paper presents a method for modeling, assessing, and predicting the efficiency of boilers based on measured operating performance. The method utilizes a neural network approach to analyze and predict boiler efficiency and also to discover possibilities for enhancing efficiency. The analysis is based on energy surveys of 65 randomly selected boilers located at over 50 sites in the northern province of Serbia. These surveys included a representative range of industrial, public and commercial users of steam and hot water. The sample covered approximately 25% of all boilers in the province and yielded reliable and relevant results. By creating a database combined with soft computing assistance a wide range of possibilities are created for identifying and assessing factors of influence and making a critical evaluation of practices used on the supply side as a source of identified inefficiency. -- Highlights: ► We develop the model for assessing and predicting efficiency of boilers. ► The method implies the use of Artificial Neural Network approach for analysis. ► The results obtained correspond well to collected and measured data. ► Findings confirm and present good abilities of preventive or proactive approach. ► Analysis reveals and specifies opportunities for increasing efficiency of boilers.

  20. Correlation of Amine Swingbed On-Orbit CO2 Performance with a Hardware Independent Predictive Model

    Science.gov (United States)

    Papale, William; Sweterlitsch, Jeffery

    2015-01-01

    The Amine Swingbed Payload is an experimental system deployed on the International Space Station (ISS) that includes a two-bed, vacuum regenerated, amine-based carbon dioxide (CO2) removal subsystem as the principal item under investigation. The aminebased subsystem, also described previously in various publications as CAMRAS 3, was originally designed, fabricated and tested by Hamilton Sundstrand Space Systems International, Inc. (HSSSI) and delivered to NASA in November 2008. The CAMRAS 3 unit was subsequently designed into a flight payload experiment in 2010 and 2011, with flight test integration activities accomplished on-orbit between January 2012 and March 2013. Payload activation was accomplished in May 2013 followed by a 1000 hour experimental period. The experimental nature of the Payload and the interaction with the dynamic ISS environment present unique scientific and engineering challenges, in particular to the verification and validation of the expected Payload CO2 removal performance. A modeling and simulation approach that incorporates principles of chemical reaction engineering has been developed for the amine-based system to predict the dynamic cabin CO2 partial pressure with given inputs of sorbent bed size, process air flow, operating temperature, half-cycle time, CO2 generation rate, cabin volume and the magnitude of vacuum available. Simulation runs using the model to predict ambient CO2 concentrations show good correlation to on-orbit performance measurements and ISS dynamic concentrations for the assumed operating conditions. The dynamic predictive modelling could benefit operational planning to help ensure ISS CO2 concentrations are maintained below prescribed limits and for the Orion vehicle to simulate various operating conditions, scenarios and transients.