TCP4-dependent induction of CONSTANS transcription requires GIGANTEA in photoperiodic flowering in Arabidopsis

PubMed Central

Shim, Jae Sung; Song, Yong Hun; Laboy Cintrón, Dianne; Koyama, Tomotsugu; Ohme-Takagi, Masaru; Pruneda-Paz, Jose L.; Kay, Steve A.; MacCoss, Michael J.

2017-01-01

Photoperiod is one of the most reliable environmental cues for plants to regulate flowering timing. In Arabidopsis thaliana, CONSTANS (CO) transcription factor plays a central role in regulating photoperiodic flowering. In contrast to posttranslational regulation of CO protein, still little was known about CO transcriptional regulation. Here we show that the CINCINNATA (CIN) clade of class II TEOSINTE BRANCHED 1/ CYCLOIDEA/ PROLIFERATING CELL NUCLEAR ANTIGEN FACTOR (TCP) proteins act as CO activators. Our yeast one-hybrid analysis revealed that class II CIN-TCPs, including TCP4, bind to the CO promoter. TCP4 induces CO expression around dusk by directly associating with the CO promoter in vivo. In addition, TCP4 binds to another flowering regulator, GIGANTEA (GI), in the nucleus, and induces CO expression in a GI-dependent manner. The physical association of TCP4 with the CO promoter was reduced in the gi mutant, suggesting that GI may enhance the DNA-binding ability of TCP4. Our tandem affinity purification coupled with mass spectrometry (TAP-MS) analysis identified all class II CIN-TCPs as the components of the in vivo TCP4 complex, and the gi mutant did not alter the composition of the TCP4 complex. Taken together, our results demonstrate a novel function of CIN-TCPs as photoperiodic flowering regulators, which may contribute to coordinating plant development with flowering regulation. PMID:28628608

TCP4-dependent induction of CONSTANS transcription requires GIGANTEA in photoperiodic flowering in Arabidopsis.

PubMed

Kubota, Akane; Ito, Shogo; Shim, Jae Sung; Johnson, Richard S; Song, Yong Hun; Breton, Ghislain; Goralogia, Greg S; Kwon, Michael S; Laboy Cintrón, Dianne; Koyama, Tomotsugu; Ohme-Takagi, Masaru; Pruneda-Paz, Jose L; Kay, Steve A; MacCoss, Michael J; Imaizumi, Takato

2017-06-01

Photoperiod is one of the most reliable environmental cues for plants to regulate flowering timing. In Arabidopsis thaliana, CONSTANS (CO) transcription factor plays a central role in regulating photoperiodic flowering. In contrast to posttranslational regulation of CO protein, still little was known about CO transcriptional regulation. Here we show that the CINCINNATA (CIN) clade of class II TEOSINTE BRANCHED 1/ CYCLOIDEA/ PROLIFERATING CELL NUCLEAR ANTIGEN FACTOR (TCP) proteins act as CO activators. Our yeast one-hybrid analysis revealed that class II CIN-TCPs, including TCP4, bind to the CO promoter. TCP4 induces CO expression around dusk by directly associating with the CO promoter in vivo. In addition, TCP4 binds to another flowering regulator, GIGANTEA (GI), in the nucleus, and induces CO expression in a GI-dependent manner. The physical association of TCP4 with the CO promoter was reduced in the gi mutant, suggesting that GI may enhance the DNA-binding ability of TCP4. Our tandem affinity purification coupled with mass spectrometry (TAP-MS) analysis identified all class II CIN-TCPs as the components of the in vivo TCP4 complex, and the gi mutant did not alter the composition of the TCP4 complex. Taken together, our results demonstrate a novel function of CIN-TCPs as photoperiodic flowering regulators, which may contribute to coordinating plant development with flowering regulation.

Assessment of atmospheric impacts of biomass open burning in Kalimantan, Borneo during 2004

NASA Astrophysics Data System (ADS)

Mahmud, Mastura

2013-10-01

Biomass burning from the combustion of agricultural wastes and forest materials is one of the major sources of air pollution. The objective of the study is to investigate the major contribution of the biomass open burning events in the island of Borneo, Indonesia to the degradation of air quality in equatorial Southeast Asia. A total of 10173 active fire counts were detected by the MODIS Aqua satellite during August 2004, and consequently, elevated the PM10 concentration levels at six air quality stations in the state of Sarawak, in east Malaysia, which is located in northwestern Borneo. The PM10 concentrations measured on a daily basis were above the 50 μg m-3 criteria as stipulated by the World Health Organization Air Quality Guidelines for most of the month, and exceeded the 24-h Recommended Malaysian Air Quality Guidelines of 150 μg m-3 on three separate periods from the 13th to the 30th August 2004. The average correlation between the ground level PM10 concentrations and the satellite derived aerosol optical depth (AOD) of 0.3 at several ground level air quality stations, implied the moderate relationship between the aerosols over the depth of the entire column of atmosphere and the ground level suspended particulate matter. Multiple regression for meteorological parameters such as rainfall, windspeed, visibility, mean temperature, relative humidity at two stations in Sarawak and active fire counts that were located near the centre of fire activities were only able to explain for 61% of the total variation in the AOD. The trajectory analysis of the low level mesoscale meteorological conditions simulated by the TAPM model illustrated the influence of the sea and land breezes within the lowest part of the planetary boundary layer, embedded within the prevailing monsoonal southwesterlies, in circulating the aged and new air particles within Sarawak.

The Five Key Questions of Human Performance Modeling.

PubMed

Wu, Changxu

2018-01-01

Via building computational (typically mathematical and computer simulation) models, human performance modeling (HPM) quantifies, predicts, and maximizes human performance, human-machine system productivity and safety. This paper describes and summarizes the five key questions of human performance modeling: 1) Why we build models of human performance; 2) What the expectations of a good human performance model are; 3) What the procedures and requirements in building and verifying a human performance model are; 4) How we integrate a human performance model with system design; and 5) What the possible future directions of human performance modeling research are. Recent and classic HPM findings are addressed in the five questions to provide new thinking in HPM's motivations, expectations, procedures, system integration and future directions.

Model Performance Evaluation and Scenario Analysis (MPESA) Tutorial

EPA Science Inventory

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit m...

Integrating Reliability Analysis with a Performance Tool

NASA Technical Reports Server (NTRS)

Nicol, David M.; Palumbo, Daniel L.; Ulrey, Michael

1995-01-01

A large number of commercial simulation tools support performance oriented studies of complex computer and communication systems. Reliability of these systems, when desired, must be obtained by remodeling the system in a different tool. This has obvious drawbacks: (1) substantial extra effort is required to create the reliability model; (2) through modeling error the reliability model may not reflect precisely the same system as the performance model; (3) as the performance model evolves one must continuously reevaluate the validity of assumptions made in that model. In this paper we describe an approach, and a tool that implements this approach, for integrating a reliability analysis engine into a production quality simulation based performance modeling tool, and for modeling within such an integrated tool. The integrated tool allows one to use the same modeling formalisms to conduct both performance and reliability studies. We describe how the reliability analysis engine is integrated into the performance tool, describe the extensions made to the performance tool to support the reliability analysis, and consider the tool's performance.

Estimating thermal performance curves from repeated field observations

USGS Publications Warehouse

Childress, Evan; Letcher, Benjamin H.

2017-01-01

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Designing Performance Measurement For Supply Chain's Actors And Regulator Using Scale Balanced Scorecard And Data Envelopment Analysis

NASA Astrophysics Data System (ADS)

Kusrini, Elisa; Subagyo; Aini Masruroh, Nur

2016-01-01

This research is a sequel of the author's earlier conducted researches in the fields of designing of integrated performance measurement between supply chain's actors and regulator. In the previous paper, the design of performance measurement is done by combining Balanced Scorecard - Supply Chain Operation Reference - Regulator Contribution model and Data Envelopment Analysis. This model referred as B-S-Rc-DEA model. The combination has the disadvantage that all the performance variables have the same weight. This paper investigates whether by giving weight to performance variables will produce more sensitive performance measurement in detecting performance improvement. Therefore, this paper discusses the development of the model B-S-Rc-DEA by giving weight to its performance'variables. This model referred as Scale B-S-Rc-DEA model. To illustrate the model of development, some samples from small medium enterprises of leather craft industry supply chain in province of Yogyakarta, Indonesia are used in this research. It is found that Scale B-S-Rc-DEA model is more sensitive to detecting performance improvement than B-S- Rc-DEA model.

Translation from UML to Markov Model: A Performance Modeling Framework

NASA Astrophysics Data System (ADS)

Khan, Razib Hayat; Heegaard, Poul E.

Performance engineering focuses on the quantitative investigation of the behavior of a system during the early phase of the system development life cycle. Bearing this on mind, we delineate a performance modeling framework of the application for communication system that proposes a translation process from high level UML notation to Continuous Time Markov Chain model (CTMC) and solves the model for relevant performance metrics. The framework utilizes UML collaborations, activity diagrams and deployment diagrams to be used for generating performance model for a communication system. The system dynamics will be captured by UML collaboration and activity diagram as reusable specification building blocks, while deployment diagram highlights the components of the system. The collaboration and activity show how reusable building blocks in the form of collaboration can compose together the service components through input and output pin by highlighting the behavior of the components and later a mapping between collaboration and system component identified by deployment diagram will be delineated. Moreover the UML models are annotated to associate performance related quality of service (QoS) information which is necessary for solving the performance model for relevant performance metrics through our proposed framework. The applicability of our proposed performance modeling framework in performance evaluation is delineated in the context of modeling a communication system.

The effects of teacher anxiety and modeling on the acquisition of a science teaching skill and concomitant student performance

NASA Astrophysics Data System (ADS)

Koran, John J., Jr.; Koran, Mary Lou

In a study designed to explore the effects of teacher anxiety and modeling on acquisition of a science teaching skill and concomitant student performance, 69 preservice secondary teachers and 295 eighth grade students were randomly assigned to microteaching sessions. Prior to microteaching, teachers were given an anxiety test, then randomly assigned to one of three treatments; a transcript model, a protocol model, or a control condition. Subsequently both teacher and student performance was assessed using written and behavioral measures. Analysis of variance indicated that subjects in the two modeling treatments significantly exceeded performance of control group subjects on all measures of the dependent variable, with the protocol model being generally superior to the transcript model. The differential effects of the modeling treatments were further reflected in student performance. Regression analysis of aptitude-treatment interactions indicated that teacher anxiety scores interacted significantly with instructional treatments, with high anxiety teachers performing best in the protocol modeling treatment. Again, this interaction was reflected in student performance, where students taught by highly anxious teachers performed significantly better when their teachers had received the protocol model. These results were discussed in terms of teacher concerns and a memory model of the effects of anxiety on performance.

Temporal diagnostic analysis of the SWAT model to detect dominant periods of poor model performance

NASA Astrophysics Data System (ADS)

Guse, Björn; Reusser, Dominik E.; Fohrer, Nicola

2013-04-01

Hydrological models generally include thresholds and non-linearities, such as snow-rain-temperature thresholds, non-linear reservoirs, infiltration thresholds and the like. When relating observed variables to modelling results, formal methods often calculate performance metrics over long periods, reporting model performance with only few numbers. Such approaches are not well suited to compare dominating processes between reality and model and to better understand when thresholds and non-linearities are driving model results. We present a combination of two temporally resolved model diagnostic tools to answer when a model is performing (not so) well and what the dominant processes are during these periods. We look at the temporal dynamics of parameter sensitivities and model performance to answer this question. For this, the eco-hydrological SWAT model is applied in the Treene lowland catchment in Northern Germany. As a first step, temporal dynamics of parameter sensitivities are analyzed using the Fourier Amplitude Sensitivity test (FAST). The sensitivities of the eight model parameters investigated show strong temporal variations. High sensitivities were detected for two groundwater (GW_DELAY, ALPHA_BF) and one evaporation parameters (ESCO) most of the time. The periods of high parameter sensitivity can be related to different phases of the hydrograph with dominances of the groundwater parameters in the recession phases and of ESCO in baseflow and resaturation periods. Surface runoff parameters show high parameter sensitivities in phases of a precipitation event in combination with high soil water contents. The dominant parameters give indication for the controlling processes during a given period for the hydrological catchment. The second step included the temporal analysis of model performance. For each time step, model performance was characterized with a "finger print" consisting of a large set of performance measures. These finger prints were clustered into four reoccurring patterns of typical model performance, which can be related to different phases of the hydrograph. Overall, the baseflow cluster has the lowest performance. By combining the periods with poor model performance with the dominant model components during these phases, the groundwater module was detected as the model part with the highest potential for model improvements. The detection of dominant processes in periods of poor model performance enhances the understanding of the SWAT model. Based on this, concepts how to improve the SWAT model structure for the application in German lowland catchment are derived.

Comparison of modeling methods to predict the spatial distribution of deep-sea coral and sponge in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Rooper, Christopher N.; Zimmermann, Mark; Prescott, Megan M.

2017-08-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska's marine waters, and are associated with many different species of fishes and invertebrates. These ecosystems are vulnerable to the effects of commercial fishing activities and climate change. We compared four commonly used species distribution models (general linear models, generalized additive models, boosted regression trees and random forest models) and an ensemble model to predict the presence or absence and abundance of six groups of benthic invertebrate taxa in the Gulf of Alaska. All four model types performed adequately on training data for predicting presence and absence, with regression forest models having the best overall performance measured by the area under the receiver-operating-curve (AUC). The models also performed well on the test data for presence and absence with average AUCs ranging from 0.66 to 0.82. For the test data, ensemble models performed the best. For abundance data, there was an obvious demarcation in performance between the two regression-based methods (general linear models and generalized additive models), and the tree-based models. The boosted regression tree and random forest models out-performed the other models by a wide margin on both the training and testing data. However, there was a significant drop-off in performance for all models of invertebrate abundance ( 50%) when moving from the training data to the testing data. Ensemble model performance was between the tree-based and regression-based methods. The maps of predictions from the models for both presence and abundance agreed very well across model types, with an increase in variability in predictions for the abundance data. We conclude that where data conforms well to the modeled distribution (such as the presence-absence data and binomial distribution in this study), the four types of models will provide similar results, although the regression-type models may be more consistent with biological theory. For data with highly zero-inflated distributions and non-normal distributions such as the abundance data from this study, the tree-based methods performed better. Ensemble models that averaged predictions across the four model types, performed better than the GLM or GAM models but slightly poorer than the tree-based methods, suggesting ensemble models might be more robust to overfitting than tree methods, while mitigating some of the disadvantages in predictive performance of regression methods.

Differentiation and Exploration of Model MACP for HE VER 1.0 on Prototype Performance Measurement Application for Higher Education

NASA Astrophysics Data System (ADS)

El Akbar, R. Reza; Anshary, Muhammad Adi Khairul; Hariadi, Dennis

2018-02-01

Model MACP for HE ver.1. Is a model that describes how to perform measurement and monitoring performance for Higher Education. Based on a review of the research related to the model, there are several parts of the model component to develop in further research, so this research has four main objectives. The first objective is to differentiate the CSF (critical success factor) components in the previous model, the two key KPI (key performance indicators) exploration in the previous model, the three based on the previous objective, the new and more detailed model design. The final goal is the fourth designed prototype application for performance measurement in higher education, based on a new model created. The method used is explorative research method and application design using prototype method. The results of this study are first, forming a more detailed new model for measurement and monitoring of performance in higher education, differentiation and exploration of the Model MACP for HE Ver.1. The second result compiles a dictionary of college performance measurement by re-evaluating the existing indicators. The third result is the design of prototype application of performance measurement in higher education.

Rotorcraft Performance Model (RPM) for use in AEDT.

DOT National Transportation Integrated Search

2015-11-01

This report documents a rotorcraft performance model for use in the FAAs Aviation Environmental Design Tool. The new rotorcraft performance model is physics-based. This new model replaces the existing helicopter trajectory modeling methods in the ...

Development, Testing, and Validation of a Model-Based Tool to Predict Operator Responses in Unexpected Workload Transitions

NASA Technical Reports Server (NTRS)

Sebok, Angelia; Wickens, Christopher; Sargent, Robert

2015-01-01

One human factors challenge is predicting operator performance in novel situations. Approaches such as drawing on relevant previous experience, and developing computational models to predict operator performance in complex situations, offer potential methods to address this challenge. A few concerns with modeling operator performance are that models need to realistic, and they need to be tested empirically and validated. In addition, many existing human performance modeling tools are complex and require that an analyst gain significant experience to be able to develop models for meaningful data collection. This paper describes an effort to address these challenges by developing an easy to use model-based tool, using models that were developed from a review of existing human performance literature and targeted experimental studies, and performing an empirical validation of key model predictions.

Research and development on performance models of thermal imaging systems

NASA Astrophysics Data System (ADS)

Wang, Ji-hui; Jin, Wei-qi; Wang, Xia; Cheng, Yi-nan

2009-07-01

Traditional ACQUIRE models perform the discrimination tasks of detection (target orientation, recognition and identification) for military target based upon minimum resolvable temperature difference (MRTD) and Johnson criteria for thermal imaging systems (TIS). Johnson criteria is generally pessimistic for performance predict of sampled imager with the development of focal plane array (FPA) detectors and digital image process technology. Triangle orientation discrimination threshold (TOD) model, minimum temperature difference perceived (MTDP)/ thermal range model (TRM3) Model and target task performance (TTP) metric have been developed to predict the performance of sampled imager, especially TTP metric can provides better accuracy than the Johnson criteria. In this paper, the performance models above are described; channel width metrics have been presented to describe the synthesis performance including modulate translate function (MTF) channel width for high signal noise to ration (SNR) optoelectronic imaging systems and MRTD channel width for low SNR TIS; the under resolvable questions for performance assessment of TIS are indicated; last, the development direction of performance models for TIS are discussed.

Getting to the Heart of Performance.

ERIC Educational Resources Information Center

Stock, Byron

1996-01-01

Human performance technology (HPT) models are compared. One model groups performance factors by their relation to the performer (internal or external). A second model categorizes factors by which organizational level has the most control over them (executive, managerial, or individual). A third model considers rational and emotional intelligences;…

Predicting nitrogen loading with land-cover composition: how can watershed size affect model performance?

PubMed

Zhang, Tao; Yang, Xiaojun

2013-01-01

Watershed-wide land-cover proportions can be used to predict the in-stream non-point source pollutant loadings through regression modeling. However, the model performance can vary greatly across different study sites and among various watersheds. Existing literature has shown that this type of regression modeling tends to perform better for large watersheds than for small ones, and that such a performance variation has been largely linked with different interwatershed landscape heterogeneity levels. The purpose of this study is to further examine the previously mentioned empirical observation based on a set of watersheds in the northern part of Georgia (USA) to explore the underlying causes of the variation in model performance. Through the combined use of the neutral landscape modeling approach and a spatially explicit nutrient loading model, we tested whether the regression model performance variation over the watershed groups ranging in size is due to the different watershed landscape heterogeneity levels. We adopted three neutral landscape modeling criteria that were tied with different similarity levels in watershed landscape properties and used the nutrient loading model to estimate the nitrogen loads for these neutral watersheds. Then we compared the regression model performance for the real and neutral landscape scenarios, respectively. We found that watershed size can affect the regression model performance both directly and indirectly. Along with the indirect effect through interwatershed heterogeneity, watershed size can directly affect the model performance over the watersheds varying in size. We also found that the regression model performance can be more significantly affected by other physiographic properties shaping nitrogen delivery effectiveness than the watershed land-cover heterogeneity. This study contrasts with many existing studies because it goes beyond hypothesis formulation based on empirical observations and into hypothesis testing to explore the fundamental mechanism.

Model performance evaluation (validation and calibration) in model-based studies of therapeutic interventions for cardiovascular diseases : a review and suggested reporting framework.

PubMed

Haji Ali Afzali, Hossein; Gray, Jodi; Karnon, Jonathan

2013-04-01

Decision analytic models play an increasingly important role in the economic evaluation of health technologies. Given uncertainties around the assumptions used to develop such models, several guidelines have been published to identify and assess 'best practice' in the model development process, including general modelling approach (e.g., time horizon), model structure, input data and model performance evaluation. This paper focuses on model performance evaluation. In the absence of a sufficient level of detail around model performance evaluation, concerns regarding the accuracy of model outputs, and hence the credibility of such models, are frequently raised. Following presentation of its components, a review of the application and reporting of model performance evaluation is presented. Taking cardiovascular disease as an illustrative example, the review investigates the use of face validity, internal validity, external validity, and cross model validity. As a part of the performance evaluation process, model calibration is also discussed and its use in applied studies investigated. The review found that the application and reporting of model performance evaluation across 81 studies of treatment for cardiovascular disease was variable. Cross-model validation was reported in 55 % of the reviewed studies, though the level of detail provided varied considerably. We found that very few studies documented other types of validity, and only 6 % of the reviewed articles reported a calibration process. Considering the above findings, we propose a comprehensive model performance evaluation framework (checklist), informed by a review of best-practice guidelines. This framework provides a basis for more accurate and consistent documentation of model performance evaluation. This will improve the peer review process and the comparability of modelling studies. Recognising the fundamental role of decision analytic models in informing public funding decisions, the proposed framework should usefully inform guidelines for preparing submissions to reimbursement bodies.

GASP- General Aviation Synthesis Program. Volume 6: Performance

NASA Technical Reports Server (NTRS)

Hague, D.

1978-01-01

Aircraft performance modeling requires consideration of propulsion, aerodynamics, and weight characteristics. Eleven subroutines used in modeling aircraft performance are presented and their interactions considered. Manuals for performance model users and programmers are included.

Model Performance Evaluation and Scenario Analysis (MPESA)

EPA Pesticide Factsheets

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

Ku-Band rendezvous radar performance computer simulation model

NASA Technical Reports Server (NTRS)

Magnusson, H. G.; Goff, M. F.

1984-01-01

All work performed on the Ku-band rendezvous radar performance computer simulation model program since the release of the preliminary final report is summarized. Developments on the program fall into three distinct categories: (1) modifications to the existing Ku-band radar tracking performance computer model; (2) the addition of a highly accurate, nonrealtime search and acquisition performance computer model to the total software package developed on this program; and (3) development of radar cross section (RCS) computation models for three additional satellites. All changes in the tracking model involved improvements in the automatic gain control (AGC) and the radar signal strength (RSS) computer models. Although the search and acquisition computer models were developed under the auspices of the Hughes Aircraft Company Ku-Band Integrated Radar and Communications Subsystem program office, they have been supplied to NASA as part of the Ku-band radar performance comuter model package. Their purpose is to predict Ku-band acquisition performance for specific satellite targets on specific missions. The RCS models were developed for three satellites: the Long Duration Exposure Facility (LDEF) spacecraft, the Solar Maximum Mission (SMM) spacecraft, and the Space Telescopes.

Ku-Band rendezvous radar performance computer simulation model

NASA Astrophysics Data System (ADS)

Magnusson, H. G.; Goff, M. F.

1984-06-01

All work performed on the Ku-band rendezvous radar performance computer simulation model program since the release of the preliminary final report is summarized. Developments on the program fall into three distinct categories: (1) modifications to the existing Ku-band radar tracking performance computer model; (2) the addition of a highly accurate, nonrealtime search and acquisition performance computer model to the total software package developed on this program; and (3) development of radar cross section (RCS) computation models for three additional satellites. All changes in the tracking model involved improvements in the automatic gain control (AGC) and the radar signal strength (RSS) computer models. Although the search and acquisition computer models were developed under the auspices of the Hughes Aircraft Company Ku-Band Integrated Radar and Communications Subsystem program office, they have been supplied to NASA as part of the Ku-band radar performance comuter model package. Their purpose is to predict Ku-band acquisition performance for specific satellite targets on specific missions. The RCS models were developed for three satellites: the Long Duration Exposure Facility (LDEF) spacecraft, the Solar Maximum Mission (SMM) spacecraft, and the Space Telescopes.

Fuel-efficient cruise performance model for general aviation piston engine airplanes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parkinson, R.C.H.

1982-01-01

The uses and limitations of typical Pilot Operating Handbook cruise performance data, for constructing cruise performance models suitable for maximizing specific range, are first examined. These data are found to be inadequate for constructing such models. A new model of General Aviation piston-prop airplane cruise performance is then developed. This model consists of two subsystem models: the airframe-propeller-atmosphere subsystem model; and the engine-atmosphere subsystem model. The new model facilitates maximizing specific range; and by virtue of its simplicity and low volume data storage requirements, appears suitable for airborne microprocessor implementation.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Reference Manual for the System Advisor Model's Wind Power Performance Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freeman, J.; Jorgenson, J.; Gilman, P.

2014-08-01

This manual describes the National Renewable Energy Laboratory's System Advisor Model (SAM) wind power performance model. The model calculates the hourly electrical output of a single wind turbine or of a wind farm. The wind power performance model requires information about the wind resource, wind turbine specifications, wind farm layout (if applicable), and costs. In SAM, the performance model can be coupled to one of the financial models to calculate economic metrics for residential, commercial, or utility-scale wind projects. This manual describes the algorithms used by the wind power performance model, which is available in the SAM user interface andmore » as part of the SAM Simulation Core (SSC) library, and is intended to supplement the user documentation that comes with the software.« less

A fuel-efficient cruise performance model for general aviation piston engine airplanes. Ph.D. Thesis. Final Report

NASA Technical Reports Server (NTRS)

Parkinson, R. C. H.

1983-01-01

A fuel-efficient cruise performance model which facilitates maximizing the specific range of General Aviation airplanes powered by spark-ignition piston engines and propellers is presented. Airplanes of fixed design only are considered. The uses and limitations of typical Pilot Operating Handbook cruise performance data, for constructing cruise performance models suitable for maximizing specific range, are first examined. These data are found to be inadequate for constructing such models. A new model of General Aviation piston-prop airplane cruise performance is then developed. This model consists of two subsystem models: the airframe-propeller-atmosphere subsystem model; and the engine-atmosphere subsystem model. The new model facilitates maximizing specific range; and by virtue of its implicity and low volume data storge requirements, appears suitable for airborne microprocessor implementation.

Model for Predicting the Performance of Planetary Suit Hip Bearing Designs

NASA Technical Reports Server (NTRS)

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

2012-01-01

Designing a space suit is very complex and often requires difficult trade-offs between performance, cost, mass, and system complexity. During the development period of the suit numerous design iterations need to occur before the hardware meets human performance requirements. Using computer models early in the design phase of hardware development is advantageous, by allowing virtual prototyping to take place. A virtual design environment allows designers to think creatively, exhaust design possibilities, and study design impacts on suit and human performance. A model of the rigid components of the Mark III Technology Demonstrator Suit (planetary-type space suit) and a human manikin were created and tested in a virtual environment. The performance of the Mark III hip bearing model was first developed and evaluated virtually by comparing the differences in mobility performance between the nominal bearing configurations and modified bearing configurations. Suited human performance was then simulated with the model and compared to actual suited human performance data using the same bearing configurations. The Mark III hip bearing model was able to visually represent complex bearing rotations and the theoretical volumetric ranges of motion in three dimensions. The model was also able to predict suited human hip flexion and abduction maximums to within 10% of the actual suited human subject data, except for one modified bearing condition in hip flexion which was off by 24%. Differences between the model predictions and the human subject performance data were attributed to the lack of joint moment limits in the model, human subject fitting issues, and the limited suit experience of some of the subjects. The results demonstrate that modeling space suit rigid segments is a feasible design tool for evaluating and optimizing suited human performance. Keywords: space suit, design, modeling, performance

Summary of photovoltaic system performance models

NASA Technical Reports Server (NTRS)

Smith, J. H.; Reiter, L. J.

1984-01-01

A detailed overview of photovoltaics (PV) performance modeling capabilities developed for analyzing PV system and component design and policy issues is provided. A set of 10 performance models are selected which span a representative range of capabilities from generalized first order calculations to highly specialized electrical network simulations. A set of performance modeling topics and characteristics is defined and used to examine some of the major issues associated with photovoltaic performance modeling. Each of the models is described in the context of these topics and characteristics to assess its purpose, approach, and level of detail. The issues are discussed in terms of the range of model capabilities available and summarized in tabular form for quick reference. The models are grouped into categories to illustrate their purposes and perspectives.

A model for evaluating the social performance of construction waste management

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yuan Hongping, E-mail: hpyuan2005@gmail.com

Highlights: Black-Right-Pointing-Pointer Scant attention is paid to social performance of construction waste management (CWM). Black-Right-Pointing-Pointer We develop a model for assessing the social performance of CWM. Black-Right-Pointing-Pointer With the model, the social performance of CWM can be quantitatively simulated. - Abstract: It has been determined by existing literature that a lot of research efforts have been made to the economic performance of construction waste management (CWM), but less attention is paid to investigation of the social performance of CWM. This study therefore attempts to develop a model for quantitatively evaluating the social performance of CWM by using a system dynamicsmore » (SD) approach. Firstly, major variables affecting the social performance of CWM are identified and a holistic system for assessing the social performance of CWM is formulated in line with feedback relationships underlying these variables. The developed system is then converted into a SD model through the software iThink. An empirical case study is finally conducted to demonstrate application of the model. Results of model validation indicate that the model is robust and reasonable to reflect the situation of the real system under study. Findings of the case study offer helpful insights into effectively promoting the social performance of CWM of the project investigated. Furthermore, the model exhibits great potential to function as an experimental platform for dynamically evaluating effects of management measures on improving the social performance of CWM of construction projects.« less

CPMIP: measurements of real computational performance of Earth system models in CMIP6

NASA Astrophysics Data System (ADS)

Balaji, Venkatramani; Maisonnave, Eric; Zadeh, Niki; Lawrence, Bryan N.; Biercamp, Joachim; Fladrich, Uwe; Aloisio, Giovanni; Benson, Rusty; Caubel, Arnaud; Durachta, Jeffrey; Foujols, Marie-Alice; Lister, Grenville; Mocavero, Silvia; Underwood, Seth; Wright, Garrett

2017-01-01

A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weak-scaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance. Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models. codes present particular challenges to computational performance. We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering. codes present particular challenges to computational performance. We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

PubMed

Rácz, A; Bajusz, D; Héberger, K

2015-01-01

Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

Human Performance Models of Pilot Behavior

NASA Technical Reports Server (NTRS)

Foyle, David C.; Hooey, Becky L.; Byrne, Michael D.; Deutsch, Stephen; Lebiere, Christian; Leiden, Ken; Wickens, Christopher D.; Corker, Kevin M.

2005-01-01

Five modeling teams from industry and academia were chosen by the NASA Aviation Safety and Security Program to develop human performance models (HPM) of pilots performing taxi operations and runway instrument approaches with and without advanced displays. One representative from each team will serve as a panelist to discuss their team s model architecture, augmentations and advancements to HPMs, and aviation-safety related lessons learned. Panelists will discuss how modeling results are influenced by a model s architecture and structure, the role of the external environment, specific modeling advances and future directions and challenges for human performance modeling in aviation.

Photovoltaic performance models - A report card

NASA Technical Reports Server (NTRS)

Smith, J. H.; Reiter, L. R.

1985-01-01

Models for the analysis of photovoltaic (PV) systems' designs, implementation policies, and economic performance, have proliferated while keeping pace with rapid changes in basic PV technology and extensive empirical data compiled for such systems' performance. Attention is presently given to the results of a comparative assessment of ten well documented and widely used models, which range in complexity from first-order approximations of PV system performance to in-depth, circuit-level characterizations. The comparisons were made on the basis of the performance of their subsystem, as well as system, elements. The models fall into three categories in light of their degree of aggregation into subsystems: (1) simplified models for first-order calculation of system performance, with easily met input requirements but limited capability to address more than a small variety of design considerations; (2) models simulating PV systems in greater detail, encompassing types primarily intended for either concentrator-incorporating or flat plate collector PV systems; and (3) models not specifically designed for PV system performance modeling, but applicable to aspects of electrical system design. Models ignoring subsystem failure or degradation are noted to exclude operating and maintenance characteristics as well.

Evaluating Models of Human Performance: Safety-Critical Systems Applications

NASA Technical Reports Server (NTRS)

Feary, Michael S.

2012-01-01

This presentation is part of panel discussion on Evaluating Models of Human Performance. The purpose of this panel is to discuss the increasing use of models in the world today and specifically focus on how to describe and evaluate models of human performance. My presentation will focus on discussions of generating distributions of performance, and the evaluation of different strategies for humans performing tasks with mixed initiative (Human-Automation) systems. I will also discuss issues with how to provide Human Performance modeling data to support decisions on acceptability and tradeoffs in the design of safety critical systems. I will conclude with challenges for the future.

Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

NASA Astrophysics Data System (ADS)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

Validating models of target acquisition performance in the dismounted soldier context

NASA Astrophysics Data System (ADS)

Glaholt, Mackenzie G.; Wong, Rachel K.; Hollands, Justin G.

2018-04-01

The problem of predicting real-world operator performance with digital imaging devices is of great interest within the military and commercial domains. There are several approaches to this problem, including: field trials with imaging devices, laboratory experiments using imagery captured from these devices, and models that predict human performance based on imaging device parameters. The modeling approach is desirable, as both field trials and laboratory experiments are costly and time-consuming. However, the data from these experiments is required for model validation. Here we considered this problem in the context of dismounted soldiering, for which detection and identification of human targets are essential tasks. Human performance data were obtained for two-alternative detection and identification decisions in a laboratory experiment in which photographs of human targets were presented on a computer monitor and the images were digitally magnified to simulate range-to-target. We then compared the predictions of different performance models within the NV-IPM software package: Targeting Task Performance (TTP) metric model and the Johnson model. We also introduced a modification to the TTP metric computation that incorporates an additional correction for target angular size. We examined model predictions using NV-IPM default values for a critical model constant, V50, and we also considered predictions when this value was optimized to fit the behavioral data. When using default values, certain model versions produced a reasonably close fit to the human performance data in the detection task, while for the identification task all models substantially overestimated performance. When using fitted V50 values the models produced improved predictions, though the slopes of the performance functions were still shallow compared to the behavioral data. These findings are discussed in relation to the models' designs and parameters, and the characteristics of the behavioral paradigm.

Analytical Performance Modeling and Validation of Intel’s Xeon Phi Architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chunduri, Sudheer; Balaprakash, Prasanna; Morozov, Vitali

Modeling the performance of scientific applications on emerging hardware plays a central role in achieving extreme-scale computing goals. Analytical models that capture the interaction between applications and hardware characteristics are attractive because even a reasonably accurate model can be useful for performance tuning before the hardware is made available. In this paper, we develop a hardware model for Intel’s second-generation Xeon Phi architecture code-named Knights Landing (KNL) for the SKOPE framework. We validate the KNL hardware model by projecting the performance of mini-benchmarks and application kernels. The results show that our KNL model can project the performance with prediction errorsmore » of 10% to 20%. The hardware model also provides informative recommendations for code transformations and tuning.« less

Performability modeling with continuous accomplishment sets

NASA Technical Reports Server (NTRS)

Meyer, J. F.

1979-01-01

A general modeling framework that permits the definition, formulation, and evaluation of performability is described. It is shown that performability relates directly to system effectiveness, and is a proper generalization of both performance and reliability. A hierarchical modeling scheme is used to formulate the capability function used to evaluate performability. The case in which performance variables take values in a continuous accomplishment set is treated explicitly.

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

NASA Astrophysics Data System (ADS)

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.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

PubMed

Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

PubMed Central

Yates, Katherine L.; Mellin, Camille; Caley, M. Julian; Radford, Ben T.; Meeuwig, Jessica J.

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability. PMID:27333202

Work domain constraints for modelling surgical performance.

PubMed

Morineau, Thierry; Riffaud, Laurent; Morandi, Xavier; Villain, Jonathan; Jannin, Pierre

2015-10-01

Three main approaches can be identified for modelling surgical performance: a competency-based approach, a task-based approach, both largely explored in the literature, and a less known work domain-based approach. The work domain-based approach first describes the work domain properties that constrain the agent's actions and shape the performance. This paper presents a work domain-based approach for modelling performance during cervical spine surgery, based on the idea that anatomical structures delineate the surgical performance. This model was evaluated through an analysis of junior and senior surgeons' actions. Twenty-four cervical spine surgeries performed by two junior and two senior surgeons were recorded in real time by an expert surgeon. According to a work domain-based model describing an optimal progression through anatomical structures, the degree of adjustment of each surgical procedure to a statistical polynomial function was assessed. Each surgical procedure showed a significant suitability with the model and regression coefficient values around 0.9. However, the surgeries performed by senior surgeons fitted this model significantly better than those performed by junior surgeons. Analysis of the relative frequencies of actions on anatomical structures showed that some specific anatomical structures discriminate senior from junior performances. The work domain-based modelling approach can provide an overall statistical indicator of surgical performance, but in particular, it can highlight specific points of interest among anatomical structures that the surgeons dwelled on according to their level of expertise.

Advanced flight design systems subsystem performance models. Sample model: Environmental analysis routine library

NASA Technical Reports Server (NTRS)

Parker, K. C.; Torian, J. G.

1980-01-01

A sample environmental control and life support model performance analysis using the environmental analysis routines library is presented. An example of a complete model set up and execution is provided. The particular model was synthesized to utilize all of the component performance routines and most of the program options.

Verification of a VRF Heat Pump Computer Model in EnergyPlus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nigusse, Bereket; Raustad, Richard

2013-06-15

This paper provides verification results of the EnergyPlus variable refrigerant flow (VRF) heat pump computer model using manufacturer's performance data. The paper provides an overview of the VRF model, presents the verification methodology, and discusses the results. The verification provides quantitative comparison of full and part-load performance to manufacturer's data in cooling-only and heating-only modes of operation. The VRF heat pump computer model uses dual range bi-quadratic performance curves to represent capacity and Energy Input Ratio (EIR) as a function of indoor and outdoor air temperatures, and dual range quadratic performance curves as a function of part-load-ratio for modeling part-loadmore » performance. These performance curves are generated directly from manufacturer's published performance data. The verification compared the simulation output directly to manufacturer's performance data, and found that the dual range equation fit VRF heat pump computer model predicts the manufacturer's performance data very well over a wide range of indoor and outdoor temperatures and part-load conditions. The predicted capacity and electric power deviations are comparbale to equation-fit HVAC computer models commonly used for packaged and split unitary HVAC equipment.« less

COBRA ATD minefield detection model initial performance analysis

NASA Astrophysics Data System (ADS)

Holmes, V. Todd; Kenton, Arthur C.; Hilton, Russell J.; Witherspoon, Ned H.; Holloway, John H., Jr.

2000-08-01

A statistical performance analysis of the USMC Coastal Battlefield Reconnaissance and Analysis (COBRA) Minefield Detection (MFD) Model has been performed in support of the COBRA ATD Program under execution by the Naval Surface Warfare Center/Dahlgren Division/Coastal Systems Station . This analysis uses the Veridian ERIM International MFD model from the COBRA Sensor Performance Evaluation and Computational Tools for Research Analysis modeling toolbox and a collection of multispectral mine detection algorithm response distributions for mines and minelike clutter objects. These mine detection response distributions were generated form actual COBRA ATD test missions over littoral zone minefields. This analysis serves to validate both the utility and effectiveness of the COBRA MFD Model as a predictive MFD performance too. COBRA ATD minefield detection model algorithm performance results based on a simulate baseline minefield detection scenario are presented, as well as result of a MFD model algorithm parametric sensitivity study.

A critical evaluation of various turbulence models as applied to internal fluid flows

NASA Technical Reports Server (NTRS)

Nallasamy, M.

1985-01-01

Models employed in the computation of turbulent flows are described and their application to internal flows is evaluated by examining the predictions of various turbulence models in selected flow configurations. The main conclusions are: (1) the k-epsilon model is used in a majority of all the two-dimensional flow calculations reported in the literature; (2) modified forms of the k-epsilon model improve the performance for flows with streamline curvature and heat transfer; (3) for flows with swirl, the k-epsilon model performs rather poorly; the algebraic stress model performs better in this case; and (4) for flows with regions of secondary flow (noncircular duct flows), the algebraic stress model performs fairly well for fully developed flow, for developing flow, the algebraic stress model performance is not good; a Reynolds stress model should be used. False diffusion and inlet boundary conditions are discussed. Countergradient transport and its implications in turbulence modeling is mentioned. Two examples of recirculating flow predictions obtained using PHOENICS code are discussed. The vortex method, large eddy simulation (modeling of subgrid scale Reynolds stresses), and direct simulation, are considered. Some recommendations for improving the model performance are made. The need for detailed experimental data in flows with strong curvature is emphasized.

The Real World Significance of Performance Prediction

ERIC Educational Resources Information Center

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…

A Spectral Evaluation of Models Performances in Mediterranean Oak Woodlands

NASA Astrophysics Data System (ADS)

Vargas, R.; Baldocchi, D. D.; Abramowitz, G.; Carrara, A.; Correia, A.; Kobayashi, H.; Papale, D.; Pearson, D.; Pereira, J.; Piao, S.; Rambal, S.; Sonnentag, O.

2009-12-01

Ecosystem processes are influenced by climatic trends at multiple temporal scales including diel patterns and other mid-term climatic modes, such as interannual and seasonal variability. Because interactions between biophysical components of ecosystem processes are complex, it is important to test how models perform in frequency (e.g. hours, days, weeks, months, years) and time (i.e. day of the year) domains in addition to traditional tests of annual or monthly sums. Here we present a spectral evaluation using wavelet time series analysis of model performance in seven Mediterranean Oak Woodlands that encompass three deciduous and four evergreen sites. We tested the performance of five models (CABLE, ORCHIDEE, BEPS, Biome-BGC, and JULES) on measured variables of gross primary production (GPP) and evapotranspiration (ET). In general, model performance fails at intermediate periods (e.g. weeks to months) likely because these models do not represent the water pulse dynamics that influence GPP and ET at these Mediterranean systems. To improve the performance of a model it is critical to identify first where and when the model fails. Only by identifying where a model fails we can improve the model performance and use them as prognostic tools and to generate further hypotheses that can be tested by new experiments and measurements.

Model Performance Evaluation and Scenario Analysis ...

EPA Pesticide Factsheets

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors. The performance measures include error analysis, coefficient of determination, Nash-Sutcliffe efficiency, and a new weighted rank method. These performance metrics only provide useful information about the overall model performance. Note that MPESA is based on the separation of observed and simulated time series into magnitude and sequence components. The separation of time series into magnitude and sequence components and the reconstruction back to time series provides diagnostic insights to modelers. For example, traditional approaches lack the capability to identify if the source of uncertainty in the simulated data is due to the quality of the input data or the way the analyst adjusted the model parameters. This report presents a suite of model diagnostics that identify if mismatches between observed and simulated data result from magnitude or sequence related errors. MPESA offers graphical and statistical options that allow HSPF users to compare observed and simulated time series and identify the parameter values to adjust or the input data to modify. The scenario analysis part of the too

Palm: Easing the Burden of Analytical Performance Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tallent, Nathan R.; Hoisie, Adolfy

2014-06-01

Analytical (predictive) application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult because they must be both accurate and concise. To ease the burden of performance modeling, we developed Palm, a modeling tool that combines top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. To express insight, Palm defines a source code modeling annotation language. By coordinating models and source code, Palm's models are `first-class' and reproducible. Unlike prior work, Palm formally links models, functions, and measurements. As a result, Palm (a) uses functions to either abstract or express complexitymore » (b) generates hierarchical models (representing an application's static and dynamic structure); and (c) automatically incorporates measurements to focus attention, represent constant behavior, and validate models. We discuss generating models for three different applications.« less

The use of neural network technology to model swimming performance.

PubMed

Silva, António José; Costa, Aldo Manuel; Oliveira, Paulo Moura; Reis, Victor Machado; Saavedra, José; Perl, Jurgen; Rouboa, Abel; Marinho, Daniel Almeida

2007-01-01

to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).

Calibration of PMIS pavement performance prediction models.

DOT National Transportation Integrated Search

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...

Telerobotic system performance measurement - Motivation and methods

NASA Technical Reports Server (NTRS)

Kondraske, George V.; Khoury, George J.

1992-01-01

A systems performance-based strategy for modeling and conducting experiments relevant to the design and performance characterization of telerobotic systems is described. A developmental testbed consisting of a distributed telerobotics network and initial efforts to implement the strategy described is presented. Consideration is given to the general systems performance theory (GSPT) to tackle human performance problems as a basis for: measurement of overall telerobotic system (TRS) performance; task decomposition; development of a generic TRS model; and the characterization of performance of subsystems comprising the generic model. GSPT employs a resource construct to model performance and resource economic principles to govern the interface of systems to tasks. It provides a comprehensive modeling/measurement strategy applicable to complex systems including both human and artificial components. Application is presented within the framework of a distributed telerobotics network as a testbed. Insight into the design of test protocols which elicit application-independent data is described.

Evaluation of risk scores for risk stratification of acute coronary syndromes in the Myocardial Infarction National Audit Project (MINAP) database.

PubMed

Gale, C P; Manda, S O M; Weston, C F; Birkhead, J S; Batin, P D; Hall, A S

2009-03-01

To compare the discriminative performance of the PURSUIT, GUSTO-1, GRACE, SRI and EMMACE risk models, assess their performance among risk supergroups and evaluate the EMMACE risk model over the wider spectrum of acute coronary syndrome (ACS). Observational study of a national registry. All acute hospitals in England and Wales. 100 686 cases of ACS between 2003 and 2005. Model performance (C-index) in predicting the likelihood of death over the time period for which they were designed. The C-index, or area under the receiver-operating curve, range 0-1, is a measure of the discriminative performance of a model. The C-indexes were: PURSUIT C-index 0.79 (95% confidence interval 0.78 to 0.80); GUSTO-1 0.80 (0.79 to 0.81); GRACE in-hospital 0.80 (0.80 to 0.81); GRACE 6-month 0.80 (0.79 to 0.80); SRI 0.79 (0.78 to 0.80); and EMMACE 0.78 (0.77 to 0.78). EMMACE maintained its ability to discriminate 30-day mortality throughout different ACS diagnoses. Recalibration of the model offered no notable improvement in performance over the original risk equation. For all models the discriminative performance was reduced in patients with diabetes, chronic renal failure or angina. The five ACS risk models maintained their discriminative performance in a large unselected English and Welsh ACS population, but performed less well in higher-risk supergroups. Simpler risk models had comparable performance to more complex risk models. The EMMACE risk score performed well across the wider spectrum of ACS diagnoses.

An analytic performance model of disk arrays and its application

NASA Technical Reports Server (NTRS)

Lee, Edward K.; Katz, Randy H.

1991-01-01

As disk arrays become widely used, tools for understanding and analyzing their performance become increasingly important. In particular, performance models can be invaluable in both configuring and designing disk arrays. Accurate analytic performance models are desirable over other types of models because they can be quickly evaluated, are applicable under a wide range of system and workload parameters, and can be manipulated by a range of mathematical techniques. Unfortunately, analytical performance models of disk arrays are difficult to formulate due to the presence of queuing and fork-join synchronization; a disk array request is broken up into independent disk requests which must all complete to satisfy the original request. We develop, validate, and apply an analytic performance model for disk arrays. We derive simple equations for approximating their utilization, response time, and throughput. We then validate the analytic model via simulation and investigate the accuracy of each approximation used in deriving the analytical model. Finally, we apply the analytical model to derive an equation for the optimal unit of data striping in disk arrays.

A national framework for flood forecasting model assessment for use in operations and investment planning over England and Wales

NASA Astrophysics Data System (ADS)

Moore, Robert J.; Wells, Steven C.; Cole, Steven J.

2016-04-01

It has been common for flood forecasting systems to be commissioned at a catchment or regional level in response to local priorities and hydrological conditions, leading to variety in system design and model choice. As systems mature and efficiencies of national management are sought, there can be a drive towards system rationalisation, gaining an overview of model performance and consideration of simplification through model-type convergence. Flood forecasting model assessments, whilst overseen at a national level, may be commissioned and managed at a catchment and regional level, take a variety of forms and be large in number. This presents a challenge when an integrated national assessment is required to guide operational use of flood forecasts and plan future investment in flood forecasting models and supporting hydrometric monitoring. This contribution reports on how a nationally consistent framework for flood forecasting model performance has been developed to embrace many past, ongoing and future assessments for local river systems by engineering consultants across England & Wales. The outcome is a Performance Summary for every site model assessed which, on a single page, contains relevant catchment information for context, a selection of overlain forecast and observed hydrographs and a set of performance statistics with associated displays of novel condensed form. One display provides performance comparison with other models that may exist for the site. The performance statistics include skill scores for forecasting events (flow/level threshold crossings) of differing severity/rarity, indicating their probability and likely timing, which have real value in an operational setting. The local models assessed can be of any type and span rainfall-runoff (conceptual and transfer function) and flow routing (hydrological and hydrodynamic) forms. Also accommodated by the framework is the national G2G (Grid-to-Grid) distributed hydrological model, providing area-wide coverage across the fluvial rivers of England and Wales, which can be assessed at gauged sites. Thus the performance of the national G2G model forecasts can be directly compared with that from the local models. The Performance Summary for each site model is complemented by a national spatial analysis of model performance stratified by model-type, geographical region and forecast lead-time. The map displays provide an extensive evidence-base that can be interrogated, through a Flood Forecasting Model Performance web portal, to reveal fresh insights into comparative performance across locations, lead-times and models. This work was commissioned by the Environment Agency in partnership with Natural Resources Wales and the Flood Forecasting Centre for England and Wales.

Regionalized PM2.5 Community Multiscale Air Quality model performance evaluation across a continuous spatiotemporal domain.

PubMed

Reyes, Jeanette M; Xu, Yadong; Vizuete, William; Serre, Marc L

2017-01-01

The regulatory Community Multiscale Air Quality (CMAQ) model is a means to understanding the sources, concentrations and regulatory attainment of air pollutants within a model's domain. Substantial resources are allocated to the evaluation of model performance. The Regionalized Air quality Model Performance (RAMP) method introduced here explores novel ways of visualizing and evaluating CMAQ model performance and errors for daily Particulate Matter ≤ 2.5 micrometers (PM2.5) concentrations across the continental United States. The RAMP method performs a non-homogenous, non-linear, non-homoscedastic model performance evaluation at each CMAQ grid. This work demonstrates that CMAQ model performance, for a well-documented 2001 regulatory episode, is non-homogeneous across space/time. The RAMP correction of systematic errors outperforms other model evaluation methods as demonstrated by a 22.1% reduction in Mean Square Error compared to a constant domain wide correction. The RAMP method is able to accurately reproduce simulated performance with a correlation of r = 76.1%. Most of the error coming from CMAQ is random error with only a minority of error being systematic. Areas of high systematic error are collocated with areas of high random error, implying both error types originate from similar sources. Therefore, addressing underlying causes of systematic error will have the added benefit of also addressing underlying causes of random error.

The Critical Power Model as a Potential Tool for Anti-doping

PubMed Central

Puchowicz, Michael J.; Mizelman, Eliran; Yogev, Assaf; Koehle, Michael S.; Townsend, Nathan E.; Clarke, David C.

2018-01-01

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation. PMID:29928234

A model for evaluating the social performance of construction waste management.

PubMed

Yuan, Hongping

2012-06-01

It has been determined by existing literature that a lot of research efforts have been made to the economic performance of construction waste management (CWM), but less attention is paid to investigation of the social performance of CWM. This study therefore attempts to develop a model for quantitatively evaluating the social performance of CWM by using a system dynamics (SD) approach. Firstly, major variables affecting the social performance of CWM are identified and a holistic system for assessing the social performance of CWM is formulated in line with feedback relationships underlying these variables. The developed system is then converted into a SD model through the software iThink. An empirical case study is finally conducted to demonstrate application of the model. Results of model validation indicate that the model is robust and reasonable to reflect the situation of the real system under study. Findings of the case study offer helpful insights into effectively promoting the social performance of CWM of the project investigated. Furthermore, the model exhibits great potential to function as an experimental platform for dynamically evaluating effects of management measures on improving the social performance of CWM of construction projects. Copyright © 2012 Elsevier Ltd. All rights reserved.

Improving Climate Projections Using "Intelligent" Ensembles

NASA Technical Reports Server (NTRS)

Baker, Noel C.; Taylor, Patrick C.

2015-01-01

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Advanced Performance Modeling with Combined Passive and Active Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dovrolis, Constantine; Sim, Alex

2015-04-15

To improve the efficiency of resource utilization and scheduling of scientific data transfers on high-speed networks, the "Advanced Performance Modeling with combined passive and active monitoring" (APM) project investigates and models a general-purpose, reusable and expandable network performance estimation framework. The predictive estimation model and the framework will be helpful in optimizing the performance and utilization of networks as well as sharing resources with predictable performance for scientific collaborations, especially in data intensive applications. Our prediction model utilizes historical network performance information from various network activity logs as well as live streaming measurements from network peering devices. Historical network performancemore » information is used without putting extra load on the resources by active measurement collection. Performance measurements collected by active probing is used judiciously for improving the accuracy of predictions.« less

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Performance model for grid-connected photovoltaic inverters.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boyson, William Earl; Galbraith, Gary M.; King, David L.

2007-09-01

This document provides an empirically based performance model for grid-connected photovoltaic inverters used for system performance (energy) modeling and for continuous monitoring of inverter performance during system operation. The versatility and accuracy of the model were validated for a variety of both residential and commercial size inverters. Default parameters for the model can be obtained from manufacturers specification sheets, and the accuracy of the model can be further refined using measurements from either well-instrumented field measurements in operational systems or using detailed measurements from a recognized testing laboratory. An initial database of inverter performance parameters was developed based on measurementsmore » conducted at Sandia National Laboratories and at laboratories supporting the solar programs of the California Energy Commission.« less

Blocking performance of the hose model and the pipe model for VPN service provisioning over WDM optical networks

NASA Astrophysics Data System (ADS)

Wang, Haibo; Swee Poo, Gee

2004-08-01

We study the provisioning of virtual private network (VPN) service over WDM optical networks. For this purpose, we investigate the blocking performance of the hose model versus the pipe model for the provisioning. Two techniques are presented: an analytical queuing model and a discrete event simulation. The queuing model is developed from the multirate reduced-load approximation technique. The simulation is done with the OPNET simulator. Several experimental situations were used. The blocking probabilities calculated from the two approaches show a close match, indicating that the multirate reduced-load approximation technique is capable of predicting the blocking performance for the pipe model and the hose model in WDM networks. A comparison of the blocking behavior of the two models shows that the hose model has superior blocking performance as compared with pipe model. By and large, the blocking probability of the hose model is better than that of the pipe model by a few orders of magnitude, particularly at low load regions. The flexibility of the hose model allowing for the sharing of resources on a link among all connections accounts for its superior performance.

Flight assessment of the onboard propulsion system model for the Performance Seeking Control algorithm on an F-15 aircraft

NASA Technical Reports Server (NTRS)

Orme, John S.; Schkolnik, Gerard S.

1995-01-01

Performance Seeking Control (PSC), an onboard, adaptive, real-time optimization algorithm, relies upon an onboard propulsion system model. Flight results illustrated propulsion system performance improvements as calculated by the model. These improvements were subject to uncertainty arising from modeling error. Thus to quantify uncertainty in the PSC performance improvements, modeling accuracy must be assessed. A flight test approach to verify PSC-predicted increases in thrust (FNP) and absolute levels of fan stall margin is developed and applied to flight test data. Application of the excess thrust technique shows that increases of FNP agree to within 3 percent of full-scale measurements for most conditions. Accuracy to these levels is significant because uncertainty bands may now be applied to the performance improvements provided by PSC. Assessment of PSC fan stall margin modeling accuracy was completed with analysis of in-flight stall tests. Results indicate that the model overestimates the stall margin by between 5 to 10 percent. Because PSC achieves performance gains by using available stall margin, this overestimation may represent performance improvements to be recovered with increased modeling accuracy. Assessment of thrust and stall margin modeling accuracy provides a critical piece for a comprehensive understanding of PSC's capabilities and limitations.

Results of tests performed on the Acoustic Quiet Flow Facility Three-Dimensional Model Tunnel: Report on the Modified D.S.M.A. Design

NASA Technical Reports Server (NTRS)

Barna, P. S.

1996-01-01

Numerous tests were performed on the original Acoustic Quiet Flow Facility Three-Dimensional Model Tunnel, scaled down from the full-scale plans. Results of tests performed on the original scale model tunnel were reported in April 1995, which clearly showed that this model was lacking in performance. Subsequently this scale model was modified to attempt to possibly improve the tunnel performance. The modifications included: (a) redesigned diffuser; (b) addition of a collector; (c) addition of a Nozzle-Diffuser; (d) changes in location of vent-air. Tests performed on the modified tunnel showed a marked improvement in performance amounting to a nominal increase of pressure recovery in the diffuser from 34 percent to 54 percent. Results obtained in the tests have wider application. They may also be applied to other tunnels operating with an open test section not necessarily having similar geometry as the model under consideration.

Closed-form solutions of performability. [modeling of a degradable buffer/multiprocessor system

NASA Technical Reports Server (NTRS)

Meyer, J. F.

1981-01-01

Methods which yield closed form performability solutions for continuous valued variables are developed. The models are similar to those employed in performance modeling (i.e., Markovian queueing models) but are extended so as to account for variations in structure due to faults. In particular, the modeling of a degradable buffer/multiprocessor system is considered whose performance Y is the (normalized) average throughput rate realized during a bounded interval of time. To avoid known difficulties associated with exact transient solutions, an approximate decomposition of the model is employed permitting certain submodels to be solved in equilibrium. These solutions are then incorporated in a model with fewer transient states and by solving the latter, a closed form solution of the system's performability is obtained. In conclusion, some applications of this solution are discussed and illustrated, including an example of design optimization.

A Community Health Worker "logic model": towards a theory of enhanced performance in low- and middle-income countries.

PubMed

Naimoli, Joseph F; Frymus, Diana E; Wuliji, Tana; Franco, Lynne M; Newsome, Martha H

2014-10-02

There has been a resurgence of interest in national Community Health Worker (CHW) programs in low- and middle-income countries (LMICs). A lack of strong research evidence persists, however, about the most efficient and effective strategies to ensure optimal, sustained performance of CHWs at scale. To facilitate learning and research to address this knowledge gap, the authors developed a generic CHW logic model that proposes a theoretical causal pathway to improved performance. The logic model draws upon available research and expert knowledge on CHWs in LMICs. Construction of the model entailed a multi-stage, inductive, two-year process. It began with the planning and implementation of a structured review of the existing research on community and health system support for enhanced CHW performance. It continued with a facilitated discussion of review findings with experts during a two-day consultation. The process culminated with the authors' review of consultation-generated documentation, additional analysis, and production of multiple iterations of the model. The generic CHW logic model posits that optimal CHW performance is a function of high quality CHW programming, which is reinforced, sustained, and brought to scale by robust, high-performing health and community systems, both of which mobilize inputs and put in place processes needed to fully achieve performance objectives. Multiple contextual factors can influence CHW programming, system functioning, and CHW performance. The model is a novel contribution to current thinking about CHWs. It places CHW performance at the center of the discussion about CHW programming, recognizes the strengths and limitations of discrete, targeted programs, and is comprehensive, reflecting the current state of both scientific and tacit knowledge about support for improving CHW performance. The model is also a practical tool that offers guidance for continuous learning about what works. Despite the model's limitations and several challenges in translating the potential for learning into tangible learning, the CHW generic logic model provides a solid basis for exploring and testing a causal pathway to improved performance.

The Audience Performs: A Phenomenological Model for Criticism of Oral Interpretation Performance.

ERIC Educational Resources Information Center

Langellier, Kristin M.

Richard Lanigan's phenomenology of human communication is applicable to the development of a model for critiquing oral interpretation performance. This phenomenological model takes conscious experience of the relationship of a person and the lived-world as its data base, and assumes a phenomenology of performance which creates text in the triadic…

Benchmarking hydrological model predictive capability for UK River flows and flood peaks.

NASA Astrophysics Data System (ADS)

Lane, Rosanna; Coxon, Gemma; Freer, Jim; Wagener, Thorsten

2017-04-01

Data and hydrological models are now available for national hydrological analyses. However, hydrological model performance varies between catchments, and lumped, conceptual models are not able to produce adequate simulations everywhere. This study aims to benchmark hydrological model performance for catchments across the United Kingdom within an uncertainty analysis framework. We have applied four hydrological models from the FUSE framework to 1128 catchments across the UK. These models are all lumped models and run at a daily timestep, but differ in the model structural architecture and process parameterisations, therefore producing different but equally plausible simulations. We apply FUSE over a 20 year period from 1988-2008, within a GLUE Monte Carlo uncertainty analyses framework. Model performance was evaluated for each catchment, model structure and parameter set using standard performance metrics. These were calculated both for the whole time series and to assess seasonal differences in model performance. The GLUE uncertainty analysis framework was then applied to produce simulated 5th and 95th percentile uncertainty bounds for the daily flow time-series and additionally the annual maximum prediction bounds for each catchment. The results show that the model performance varies significantly in space and time depending on catchment characteristics including climate, geology and human impact. We identify regions where models are systematically failing to produce good results, and present reasons why this could be the case. We also identify regions or catchment characteristics where one model performs better than others, and have explored what structural component or parameterisation enables certain models to produce better simulations in these catchments. Model predictive capability was assessed for each catchment, through looking at the ability of the models to produce discharge prediction bounds which successfully bound the observed discharge. These results improve our understanding of the predictive capability of simple conceptual hydrological models across the UK and help us to identify where further effort is needed to develop modelling approaches to better represent different catchment and climate typologies.

Development of an Integrated Performance Measurement (PM) Model for Pharmaceutical Industry

PubMed Central

Shabaninejad, Hosein; Mirsalehian, Mohammad Hossein; Mehralian, Gholamhossein

2014-01-01

With respect to special characteristics of pharmaceutical industry and lack of reported performance measure, this study tries to design an integrated PM model for pharmaceutical companies. For generating this model; we first identified the key performance indicators (KPIs) and the key result indicators (KRIs) of a typical pharmaceutical company. Then, based on experts᾽ opinions, the identified indicators were ranked with respect to their importance, and the most important of them were selected to be used in the proposed model; In this model, we identified 25 KPIs and 12 KRIs. Although, this model is mostly appropriate to measure the performances of pharmaceutical companies, it can be also used to measure the performances of other industries with some modifications. We strongly recommend pharmaceutical managers to link these indicators with their payment and reward system, which can dramatically affect the performance of employees, and consequently their organization`s success. PMID:24711848

Models for evaluating the performability of degradable computing systems

NASA Technical Reports Server (NTRS)

Wu, L. T.

1982-01-01

Recent advances in multiprocessor technology established the need for unified methods to evaluate computing systems performance and reliability. In response to this modeling need, a general modeling framework that permits the modeling, analysis and evaluation of degradable computing systems is considered. Within this framework, several user oriented performance variables are identified and shown to be proper generalizations of the traditional notions of system performance and reliability. Furthermore, a time varying version of the model is developed to generalize the traditional fault tree reliability evaluation methods of phased missions.

Risk prediction models of breast cancer: a systematic review of model performances.

PubMed

Anothaisintawee, Thunyarat; Teerawattananon, Yot; Wiratkapun, Chollathip; Kasamesup, Vijj; Thakkinstian, Ammarin

2012-05-01

The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.

Performance measurement and modeling of component applications in a high performance computing environment : a case study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Robert C.; Ray, Jaideep; Malony, A.

2003-11-01

We present a case study of performance measurement and modeling of a CCA (Common Component Architecture) component-based application in a high performance computing environment. We explore issues peculiar to component-based HPC applications and propose a performance measurement infrastructure for HPC based loosely on recent work done for Grid environments. A prototypical implementation of the infrastructure is used to collect data for a three components in a scientific application and construct performance models for two of them. Both computational and message-passing performance are addressed.

Modeling the Direct and Indirect Determinants of Different Types of Individual Job Performance

DTIC Science & Technology

2008-06-01

cognitions , and self-regulation). A different model was found to describe the process depending on whether the performance dimension was an element of...performing the behaviors they indicated they intended to perform, and assembled a battery of existing instruments to measure cognitive ability, personality...model came from the task performance dimension. For this dimension, knowledge, skill, cognitive choice aspects of motivation, and self-regulation

Implementing Lumberjacks and Black Swans Into Model-Based Tools to Support Human-Automation Interaction.

PubMed

Sebok, Angelia; Wickens, Christopher D

2017-03-01

The objectives were to (a) implement theoretical perspectives regarding human-automation interaction (HAI) into model-based tools to assist designers in developing systems that support effective performance and (b) conduct validations to assess the ability of the models to predict operator performance. Two key concepts in HAI, the lumberjack analogy and black swan events, have been studied extensively. The lumberjack analogy describes the effects of imperfect automation on operator performance. In routine operations, an increased degree of automation supports performance, but in failure conditions, increased automation results in more significantly impaired performance. Black swans are the rare and unexpected failures of imperfect automation. The lumberjack analogy and black swan concepts have been implemented into three model-based tools that predict operator performance in different systems. These tools include a flight management system, a remotely controlled robotic arm, and an environmental process control system. Each modeling effort included a corresponding validation. In one validation, the software tool was used to compare three flight management system designs, which were ranked in the same order as predicted by subject matter experts. The second validation compared model-predicted operator complacency with empirical performance in the same conditions. The third validation compared model-predicted and empirically determined time to detect and repair faults in four automation conditions. The three model-based tools offer useful ways to predict operator performance in complex systems. The three tools offer ways to predict the effects of different automation designs on operator performance.

A contrast-sensitive channelized-Hotelling observer to predict human performance in a detection task using lumpy backgrounds and Gaussian signals

NASA Astrophysics Data System (ADS)

Park, Subok; Badano, Aldo; Gallas, Brandon D.; Myers, Kyle J.

2007-03-01

Previously, a non-prewhitening matched filter (NPWMF) incorporating a model for the contrast sensitivity of the human visual system was introduced for modeling human performance in detection tasks with different viewing angles and white-noise backgrounds by Badano et al. But NPWMF observers do not perform well detection tasks involving complex backgrounds since they do not account for random backgrounds. A channelized-Hotelling observer (CHO) using difference-of-Gaussians (DOG) channels has been shown to track human performance well in detection tasks using lumpy backgrounds. In this work, a CHO with DOG channels, incorporating the model of the human contrast sensitivity, was developed similarly. We call this new observer a contrast-sensitive CHO (CS-CHO). The Barten model was the basis of our human contrast sensitivity model. A scalar was multiplied to the Barten model and varied to control the thresholding effect of the contrast sensitivity on luminance-valued images and hence the performance-prediction ability of the CS-CHO. The performance of the CS-CHO was compared to the average human performance from the psychophysical study by Park et al., where the task was to detect a known Gaussian signal in non-Gaussian distributed lumpy backgrounds. Six different signal-intensity values were used in this study. We chose the free parameter of our model to match the mean human performance in the detection experiment at the strongest signal intensity. Then we compared the model to the human at five different signal-intensity values in order to see if the performance of the CS-CHO matched human performance. Our results indicate that the CS-CHO with the chosen scalar for the contrast sensitivity predicts human performance closely as a function of signal intensity.

Rapid performance modeling and parameter regression of geodynamic models

NASA Astrophysics Data System (ADS)

Brown, J.; Duplyakin, D.

2016-12-01

Geodynamic models run in a parallel environment have many parameters with complicated effects on performance and scientifically-relevant functionals. Manually choosing an efficient machine configuration and mapping out the parameter space requires a great deal of expert knowledge and time-consuming experiments. We propose an active learning technique based on Gaussion Process Regression to automatically select experiments to map out the performance landscape with respect to scientific and machine parameters. The resulting performance model is then used to select optimal experiments for improving the accuracy of a reduced order model per unit of computational cost. We present the framework and evaluate its quality and capability using popular lithospheric dynamics models.

Modeling and simulation of continuous wave velocity radar based on third-order DPLL

NASA Astrophysics Data System (ADS)

Di, Yan; Zhu, Chen; Hong, Ma

2015-02-01

Second-order digital phase-locked-loop (DPLL) is widely used in traditional Continuous wave (CW) velocity radar with poor performance in high dynamic conditions. Using the third-order DPLL can improve the performance. Firstly, the echo signal model of CW radar is given. Secondly, theoretical derivations of the tracking performance in different velocity conditions are given. Finally, simulation model of CW radar is established based on Simulink tool. Tracking performance of the two kinds of DPLL in different acceleration and jerk conditions is studied by this model. The results show that third-order PLL has better performance in high dynamic conditions. This model provides a platform for further research of CW radar.

Modeling Human Steering Behavior During Path Following in Teleoperation of Unmanned Ground Vehicles.

PubMed

Mirinejad, Hossein; Jayakumar, Paramsothy; Ersal, Tulga

2018-04-01

This paper presents a behavioral model representing the human steering performance in teleoperated unmanned ground vehicles (UGVs). Human steering performance in teleoperation is considerably different from the performance in regular onboard driving situations due to significant communication delays in teleoperation systems and limited information human teleoperators receive from the vehicle sensory system. Mathematical models capturing the teleoperation performance are a key to making the development and evaluation of teleoperated UGV technologies fully simulation based and thus more rapid and cost-effective. However, driver models developed for the typical onboard driving case do not readily address this need. To fill the gap, this paper adopts a cognitive model that was originally developed for a typical highway driving scenario and develops a tuning strategy that adjusts the model parameters in the absence of human data to reflect the effect of various latencies and UGV speeds on driver performance in a teleoperated path-following task. Based on data collected from a human subject test study, it is shown that the tuned model can predict both the trend of changes in driver performance for different driving conditions and the best steering performance of human subjects in all driving conditions considered. The proposed model with the tuning strategy has a satisfactory performance in predicting human steering behavior in the task of teleoperated path following of UGVs. The established model is a suited candidate to be used in place of human drivers for simulation-based studies of UGV mobility in teleoperation systems.

Switching performance of OBS network model under prefetched real traffic

NASA Astrophysics Data System (ADS)

Huang, Zhenhua; Xu, Du; Lei, Wen

2005-11-01

Optical Burst Switching (OBS) [1] is now widely considered as an efficient switching technique in building the next generation optical Internet .So it's very important to precisely evaluate the performance of the OBS network model. The performance of the OBS network model is variable in different condition, but the most important thing is that how it works under real traffic load. In the traditional simulation models, uniform traffics are usually generated by simulation software to imitate the data source of the edge node in the OBS network model, and through which the performance of the OBS network is evaluated. Unfortunately, without being simulated by real traffic, the traditional simulation models have several problems and their results are doubtable. To deal with this problem, we present a new simulation model for analysis and performance evaluation of the OBS network, which uses prefetched IP traffic to be data source of the OBS network model. The prefetched IP traffic can be considered as real IP source of the OBS edge node and the OBS network model has the same clock rate with a real OBS system. So it's easy to conclude that this model is closer to the real OBS system than the traditional ones. The simulation results also indicate that this model is more accurate to evaluate the performance of the OBS network system and the results of this model are closer to the actual situation.

A cost-performance model for ground-based optical communications receiving telescopes

NASA Technical Reports Server (NTRS)

Lesh, J. R.; Robinson, D. L.

1986-01-01

An analytical cost-performance model for a ground-based optical communications receiving telescope is presented. The model considers costs of existing telescopes as a function of diameter and field of view. This, coupled with communication performance as a function of receiver diameter and field of view, yields the appropriate telescope cost versus communication performance curve.

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

Effects of video modeling on treatment integrity of behavioral interventions.

PubMed

Digennaro-Reed, Florence D; Codding, Robin; Catania, Cynthia N; Maguire, Helena

2010-01-01

We examined the effects of individualized video modeling on the accurate implementation of behavioral interventions using a multiple baseline design across 3 teachers. During video modeling, treatment integrity improved above baseline levels; however, teacher performance remained variable. The addition of verbal performance feedback increased treatment integrity to 100% for all participants, and performance was maintained 1 week later. Teachers found video modeling to be more socially acceptable with performance feedback than alone, but rated both positively.

Performance and Architecture Lab Modeling Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-06-19

Analytical application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult. Furthermore, models are frequently expressed in forms that are hard to distribute and validate. The Performance and Architecture Lab Modeling tool, or Palm, is a modeling tool designed to make application modeling easier. Palm provides a source code modeling annotation language. Not only does the modeling language divide the modeling task into sub problems, it formally links an application's source code with its model. This link is important because a model's purpose is to capture application behavior. Furthermore, this linkmore » makes it possible to define rules for generating models according to source code organization. Palm generates hierarchical models according to well-defined rules. Given an application, a set of annotations, and a representative execution environment, Palm will generate the same model. A generated model is a an executable program whose constituent parts directly correspond to the modeled application. Palm generates models by combining top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. A model's hierarchy is defined by static and dynamic source code structure. Because Palm coordinates models and source code, Palm's models are 'first-class' and reproducible. Palm automates common modeling tasks. For instance, Palm incorporates measurements to focus attention, represent constant behavior, and validate models. Palm's workflow is as follows. The workflow's input is source code annotated with Palm modeling annotations. The most important annotation models an instance of a block of code. Given annotated source code, the Palm Compiler produces executables and the Palm Monitor collects a representative performance profile. The Palm Generator synthesizes a model based on the static and dynamic mapping of annotations to program behavior. The model -- an executable program -- is a hierarchical composition of annotation functions, synthesized functions, statistics for runtime values, and performance measurements.« less

The Role of Integrated Modeling in the Design and Verification of the James Webb Space Telescope

NASA Technical Reports Server (NTRS)

Mosier, Gary E.; Howard, Joseph M.; Johnston, John D.; Parrish, Keith A.; Hyde, T. Tupper; McGinnis, Mark A.; Bluth, Marcel; Kim, Kevin; Ha, Kong Q.

2004-01-01

The James Web Space Telescope (JWST) is a large, infrared-optimized space telescope scheduled for launch in 2011. System-level verification of critical optical performance requirements will rely on integrated modeling to a considerable degree. In turn, requirements for accuracy of the models are significant. The size of the lightweight observatory structure, coupled with the need to test at cryogenic temperatures, effectively precludes validation of the models and verification of optical performance with a single test in 1-g. Rather, a complex series of steps are planned by which the components of the end-to-end models are validated at various levels of subassembly, and the ultimate verification of optical performance is by analysis using the assembled models. This paper describes the critical optical performance requirements driving the integrated modeling activity, shows how the error budget is used to allocate and track contributions to total performance, and presents examples of integrated modeling methods and results that support the preliminary observatory design. Finally, the concepts for model validation and the role of integrated modeling in the ultimate verification of observatory are described.

No doubt about it: when doubtful role models undermine men's and women's math performance under threat.

PubMed

Marx, David M; Monroe, Allyce H; Cole, Chris E; Gilbert, Patricia N

2013-01-01

Past work has shown that female role models are effective buffers against stereotype threat. The present research examines the boundary conditions of this role model effect. Specifically, we argue that female role models should avoid expressing doubt about their math abilities; otherwise they may cease to buffer women from stereotype threat. For men, a non-doubtful male role model should be seen as threatening, thus harming performance. A doubtful male role model, however, should be seen as non-threatening, thus allowing men to perform up to their ability in math. To test this reasoning, men and women were exposed to either an outgroup or ingroup role model who either expressed doubt or did not. Participants then took a math exam under stereotype threat conditions. As expected, doubtful ingroup role models hurt women, but helped men's performance. Outgroup role models' expressed doubt had no differential effect on performance. We also show that expressions of doubt take on a different meaning when expressed by a female rather than a male role model.

Proactive Supply Chain Performance Management with Predictive Analytics

PubMed Central

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

Proactive supply chain performance management with predictive analytics.

PubMed

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.

A Bayesian hierarchical diffusion model decomposition of performance in Approach–Avoidance Tasks

PubMed Central

Krypotos, Angelos-Miltiadis; Beckers, Tom; Kindt, Merel; Wagenmakers, Eric-Jan

2015-01-01

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally for individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. PMID:25491372

Study on the CO2 electric driven fixed swash plate type compressor for eco-friendly vehicles

NASA Astrophysics Data System (ADS)

Nam, Donglim; Kim, Kitae; Lee, Jehie; Kwon, Yunki; Lee, Geonho

2017-08-01

The purpose of this study is to experiment and to performance analysis about the electric-driven fixed swash plate compressor using alternate refrigerant(R744). Comprehensive simulation model for an electric driven compressor using CO2 for eco-friendly vehicle is presented. This model consists of compression model and dynamic model. The compression model included valve dynamics, leakage, and heat transfer models. And the dynamic model included frictional loss between piston ring and cylinder wall, frictional loss between shoe and swash plate, frictional loss of bearings, and electric efficiency. Especially, because the efficiency of an electric parts(motor and inverter) in the compressor affects the loss of the compressor, the dynamo test was performed. We made the designed compressor, and tested the performance of the compressor about the variety pressure conditions. Also we compared the performance analysis result and performance test result.

An updated geospatial liquefaction model for global application

USGS Publications Warehouse

Zhu, Jing; Baise, Laurie G.; Thompson, Eric M.

2017-01-01

We present an updated geospatial approach to estimation of earthquake-induced liquefaction from globally available geospatial proxies. Our previous iteration of the geospatial liquefaction model was based on mapped liquefaction surface effects from four earthquakes in Christchurch, New Zealand, and Kobe, Japan, paired with geospatial explanatory variables including slope-derived VS30, compound topographic index, and magnitude-adjusted peak ground acceleration from ShakeMap. The updated geospatial liquefaction model presented herein improves the performance and the generality of the model. The updates include (1) expanding the liquefaction database to 27 earthquake events across 6 countries, (2) addressing the sampling of nonliquefaction for incomplete liquefaction inventories, (3) testing interaction effects between explanatory variables, and (4) overall improving model performance. While we test 14 geospatial proxies for soil density and soil saturation, the most promising geospatial parameters are slope-derived VS30, modeled water table depth, distance to coast, distance to river, distance to closest water body, and precipitation. We found that peak ground velocity (PGV) performs better than peak ground acceleration (PGA) as the shaking intensity parameter. We present two models which offer improved performance over prior models. We evaluate model performance using the area under the curve under the Receiver Operating Characteristic (ROC) curve (AUC) and the Brier score. The best-performing model in a coastal setting uses distance to coast but is problematic for regions away from the coast. The second best model, using PGV, VS30, water table depth, distance to closest water body, and precipitation, performs better in noncoastal regions and thus is the model we recommend for global implementation.

The Use of Neural Network Technology to Model Swimming Performance

PubMed Central

Silva, António José; Costa, Aldo Manuel; Oliveira, Paulo Moura; Reis, Victor Machado; Saavedra, José; Perl, Jurgen; Rouboa, Abel; Marinho, Daniel Almeida

2007-01-01

The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons) and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females) of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics) and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron) with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances) is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports. Key pointsThe non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance). PMID:24149233

Performer-centric Interface Design.

ERIC Educational Resources Information Center

McGraw, Karen L.

1995-01-01

Describes performer-centric interface design and explains a model-based approach for conducting performer-centric analysis and design. Highlights include design methodology, including cognitive task analysis; creating task scenarios; creating the presentation model; creating storyboards; proof of concept screens; object models and icons;…

EVALUATION OF THE COMMUNITY MULTISCALE AIR QUALITY (CMAQ) MODEL VERSION 4.5: UNCERTAINTIES AND SENSITIVITIES IMPACTING MODEL PERFORMANCE: PART II - PARTICULATE MATTER

EPA Science Inventory

This paper presents an analysis of the CMAQ v4.5 model performance for particulate matter and its chemical components for the simulated year 2001. This is part two is two part series of papers that examines the model performance of CMAQ v4.5.

Evaluation of annual, global seismicity forecasts, including ensemble models

NASA Astrophysics Data System (ADS)

Taroni, Matteo; Zechar, Jeremy; Marzocchi, Warner

2013-04-01

In 2009, the Collaboratory for the Study of the Earthquake Predictability (CSEP) initiated a prototype global earthquake forecast experiment. Three models participated in this experiment for 2009, 2010 and 2011—each model forecast the number of earthquakes above magnitude 6 in 1x1 degree cells that span the globe. Here we use likelihood-based metrics to evaluate the consistency of the forecasts with the observed seismicity. We compare model performance with statistical tests and a new method based on the peer-to-peer gambling score. The results of the comparisons are used to build ensemble models that are a weighted combination of the individual models. Notably, in these experiments the ensemble model always performs significantly better than the single best-performing model. Our results indicate the following: i) time-varying forecasts, if not updated after each major shock, may not provide significant advantages with respect to time-invariant models in 1-year forecast experiments; ii) the spatial distribution seems to be the most important feature to characterize the different forecasting performances of the models; iii) the interpretation of consistency tests may be misleading because some good models may be rejected while trivial models may pass consistency tests; iv) a proper ensemble modeling seems to be a valuable procedure to get the best performing model for practical purposes.

Preliminary evaluation of the Community Multiscale Air Quality model for 2002 over the Southeastern United States.

PubMed

Morris, Ralph E; McNally, Dennis E; Tesche, Thomas W; Tonnesen, Gail; Boylan, James W; Brewer, Patricia

2005-11-01

The Visibility Improvement State and Tribal Association of the Southeast (VISTAS) is one of five Regional Planning Organizations that is charged with the management of haze, visibility, and other regional air quality issues in the United States. The VISTAS Phase I work effort modeled three episodes (January 2002, July 1999, and July 2001) to identify the optimal model configuration(s) to be used for the 2002 annual modeling in Phase II. Using model configurations recommended in the Phase I analysis, 2002 annual meteorological (Mesoscale Meterological Model [MM5]), emissions (Sparse Matrix Operator Kernal Emissions [SMOKE]), and air quality (Community Multiscale Air Quality [CMAQ]) simulations were performed on a 36-km grid covering the continental United States and a 12-km grid covering the Eastern United States. Model estimates were then compared against observations. This paper presents the results of the preliminary CMAQ model performance evaluation for the initial 2002 annual base case simulation. Model performance is presented for the Eastern United States using speciated fine particle concentration and wet deposition measurements from several monitoring networks. Initial results indicate fairly good performance for sulfate with fractional bias values generally within +/-20%. Nitrate is overestimated in the winter by approximately +50% and underestimated in the summer by more than -100%. Organic carbon exhibits a large summer underestimation bias of approximately -100% with much improved performance seen in the winter with a bias near zero. Performance for elemental carbon is reasonable with fractional bias values within +/- 40%. Other fine particulate (soil) and coarse particular matter exhibit large (80-150%) overestimation in the winter but improved performance in the summer. The preliminary 2002 CMAQ runs identified several areas of enhancements to improve model performance, including revised temporal allocation factors for ammonia emissions to improve nitrate performance and addressing missing processes in the secondary organic aerosol module to improve OC performance.

Influence of Cleats-Surface Interaction on the Performance and Risk of Injury in Soccer: A Systematic Review

PubMed Central

Macedo, Rui; Montes, António Mesquita

2017-01-01

Objective To review the influence of cleats-surface interaction on the performance and risk of injury in soccer athletes. Design Systematic review. Data Sources Scopus, Web of science, PubMed, and B-on. Eligibility Criteria Full experimental and original papers, written in English that studied the influence of soccer cleats on sports performance and injury risk in artificial or natural grass. Results Twenty-three articles were included in this review: nine related to performance and fourteen to injury risk. On artificial grass, the soft ground model on dry and wet conditions and the turf model in wet conditions are related to worse performance. Compared to rounded studs, bladed ones improve performance during changes of directions in both natural and synthetic grass. Cleat models presenting better traction on the stance leg improve ball velocity while those presenting a homogeneous pressure across the foot promote better kicking accuracy. Bladed studs can be considered less secure by increasing plantar pressure on lateral border. The turf model decrease peak plantar pressure compared to other studded models. Conclusion The soft ground model provides lower performance especially on artificial grass, while the turf model provides a high protective effect in both fields. PMID:28684897

Influence of Cleats-Surface Interaction on the Performance and Risk of Injury in Soccer: A Systematic Review.

PubMed

Silva, Diogo C F; Santos, Rubim; Vilas-Boas, João Paulo; Macedo, Rui; Montes, António Mesquita; Sousa, Andreia S P

2017-01-01

To review the influence of cleats-surface interaction on the performance and risk of injury in soccer athletes. Systematic review. Scopus, Web of science, PubMed, and B-on. Full experimental and original papers, written in English that studied the influence of soccer cleats on sports performance and injury risk in artificial or natural grass. Twenty-three articles were included in this review: nine related to performance and fourteen to injury risk. On artificial grass, the soft ground model on dry and wet conditions and the turf model in wet conditions are related to worse performance. Compared to rounded studs, bladed ones improve performance during changes of directions in both natural and synthetic grass. Cleat models presenting better traction on the stance leg improve ball velocity while those presenting a homogeneous pressure across the foot promote better kicking accuracy. Bladed studs can be considered less secure by increasing plantar pressure on lateral border. The turf model decrease peak plantar pressure compared to other studded models. The soft ground model provides lower performance especially on artificial grass, while the turf model provides a high protective effect in both fields.

Integrated performance and reliability specification for digital avionics systems

NASA Technical Reports Server (NTRS)

Brehm, Eric W.; Goettge, Robert T.

1995-01-01

This paper describes an automated tool for performance and reliability assessment of digital avionics systems, called the Automated Design Tool Set (ADTS). ADTS is based on an integrated approach to design assessment that unifies traditional performance and reliability views of system designs, and that addresses interdependencies between performance and reliability behavior via exchange of parameters and result between mathematical models of each type. A multi-layer tool set architecture has been developed for ADTS that separates the concerns of system specification, model generation, and model solution. Performance and reliability models are generated automatically as a function of candidate system designs, and model results are expressed within the system specification. The layered approach helps deal with the inherent complexity of the design assessment process, and preserves long-term flexibility to accommodate a wide range of models and solution techniques within the tool set structure. ADTS research and development to date has focused on development of a language for specification of system designs as a basis for performance and reliability evaluation. A model generation and solution framework has also been developed for ADTS, that will ultimately encompass an integrated set of analytic and simulated based techniques for performance, reliability, and combined design assessment.

Planetary Suit Hip Bearing Model for Predicting Design vs. Performance

NASA Technical Reports Server (NTRS)

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

2011-01-01

Designing a planetary suit is very complex and often requires difficult trade-offs between performance, cost, mass, and system complexity. In order to verifying that new suit designs meet requirements, full prototypes must eventually be built and tested with human subjects. Using computer models early in the design phase of new hardware development can be advantageous, allowing virtual prototyping to take place. Having easily modifiable models of the suit hard sections may reduce the time it takes to make changes to the hardware designs and then to understand their impact on suit and human performance. A virtual design environment gives designers the ability to think outside the box and exhaust design possibilities before building and testing physical prototypes with human subjects. Reductions in prototyping and testing may eventually reduce development costs. This study is an attempt to develop computer models of the hard components of the suit with known physical characteristics, supplemented with human subject performance data. Objectives: The primary objective was to develop an articulating solid model of the Mark III hip bearings to be used for evaluating suit design performance of the hip joint. Methods: Solid models of a planetary prototype (Mark III) suit s hip bearings and brief section were reverse-engineered from the prototype. The performance of the models was then compared by evaluating the mobility performance differences between the nominal hardware configuration and hardware modifications. This was accomplished by gathering data from specific suited tasks. Subjects performed maximum flexion and abduction tasks while in a nominal suit bearing configuration and in three off-nominal configurations. Performance data for the hip were recorded using state-of-the-art motion capture technology. Results: The results demonstrate that solid models of planetary suit hard segments for use as a performance design tool is feasible. From a general trend perspective, 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.

Application of Support Vector Machine to Forex Monitoring

NASA Astrophysics Data System (ADS)

Kamruzzaman, Joarder; Sarker, Ruhul A.

Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

Modeling the Learner in Computer-Assisted Instruction

ERIC Educational Resources Information Center

Fletcher, J. D.

1975-01-01

This paper briefly reviews relevant work in four areas: 1) quantitative models of memory; 2) regression models of performance; 3) automation models of performance; and 4) artificial intelligence. (Author/HB)

Evidence for the involvement of a nonlexical route in the repetition of familiar words: A comparison of single and dual route models of auditory repetition.

PubMed

Hanley, J Richard; Dell, Gary S; Kay, Janice; Baron, Rachel

2004-03-01

In this paper, we attempt to simulate the picture naming and auditory repetition performance of two patients reported by Hanley, Kay, and Edwards (2002), who were matched for picture naming score but who differed significantly in their ability to repeat familiar words. In Experiment 1, we demonstrate that the model of naming and repetition put forward by Foygel and Dell (2000) is better able to accommodate this pattern of performance than the model put forward by Dell, Schwartz, Martin, Saffran, and Gagnon (1997). Nevertheless, Foygel and Dell's model underpredicted the repetition performance of both patients. In Experiment 2, we attempt to simulate their performance using a new dual route model of repetition in which Foygel and Dell's model is augmented by an additional nonlexical repetition pathway. The new model provided a more accurate fit to the real-word repetition performance of both patients. It is argued that the results provide support for dual route models of auditory repetition.

Performance Models for the Spike Banded Linear System Solver

DOE PAGES

Manguoglu, Murat; Saied, Faisal; Sameh, Ahmed; ...

2011-01-01

With availability of large-scale parallel platforms comprised of tens-of-thousands of processors and beyond, there is significant impetus for the development of scalable parallel sparse linear system solvers and preconditioners. An integral part of this design process is the development of performance models capable of predicting performance and providing accurate cost models for the solvers and preconditioners. There has been some work in the past on characterizing performance of the iterative solvers themselves. In this paper, we investigate the problem of characterizing performance and scalability of banded preconditioners. Recent work has demonstrated the superior convergence properties and robustness of banded preconditioners,more » compared to state-of-the-art ILU family of preconditioners as well as algebraic multigrid preconditioners. Furthermore, when used in conjunction with efficient banded solvers, banded preconditioners are capable of significantly faster time-to-solution. Our banded solver, the Truncated Spike algorithm is specifically designed for parallel performance and tolerance to deep memory hierarchies. Its regular structure is also highly amenable to accurate performance characterization. Using these characteristics, we derive the following results in this paper: (i) we develop parallel formulations of the Truncated Spike solver, (ii) we develop a highly accurate pseudo-analytical parallel performance model for our solver, (iii) we show excellent predication capabilities of our model – based on which we argue the high scalability of our solver. Our pseudo-analytical performance model is based on analytical performance characterization of each phase of our solver. These analytical models are then parameterized using actual runtime information on target platforms. An important consequence of our performance models is that they reveal underlying performance bottlenecks in both serial and parallel formulations. All of our results are validated on diverse heterogeneous multiclusters – platforms for which performance prediction is particularly challenging. Finally, we provide predict the scalability of the Spike algorithm using up to 65,536 cores with our model. In this paper we extend the results presented in the Ninth International Symposium on Parallel and Distributed Computing.« less

Multitasking TORT under UNICOS: Parallel performance models and measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barnett, A.; Azmy, Y.Y.

1999-09-27

The existing parallel algorithms in the TORT discrete ordinates code were updated to function in a UNICOS environment. A performance model for the parallel overhead was derived for the existing algorithms. The largest contributors to the parallel overhead were identified and a new algorithm was developed. A parallel overhead model was also derived for the new algorithm. The results of the comparison of parallel performance models were compared to applications of the code to two TORT standard test problems and a large production problem. The parallel performance models agree well with the measured parallel overhead.

Multitasking TORT Under UNICOS: Parallel Performance Models and Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azmy, Y.Y.; Barnett, D.A.

1999-09-27

The existing parallel algorithms in the TORT discrete ordinates were updated to function in a UNI-COS environment. A performance model for the parallel overhead was derived for the existing algorithms. The largest contributors to the parallel overhead were identified and a new algorithm was developed. A parallel overhead model was also derived for the new algorithm. The results of the comparison of parallel performance models were compared to applications of the code to two TORT standard test problems and a large production problem. The parallel performance models agree well with the measured parallel overhead.

Development of task network models of human performance in microgravity

NASA Technical Reports Server (NTRS)

Diaz, Manuel F.; Adam, Susan

1992-01-01

This paper discusses the utility of task-network modeling for quantifying human performance variability in microgravity. The data are gathered for: (1) improving current methodologies for assessing human performance and workload in the operational space environment; (2) developing tools for assessing alternative system designs; and (3) developing an integrated set of methodologies for the evaluation of performance degradation during extended duration spaceflight. The evaluation entailed an analysis of the Remote Manipulator System payload-grapple task performed on many shuttle missions. Task-network modeling can be used as a tool for assessing and enhancing human performance in man-machine systems, particularly for modeling long-duration manned spaceflight. Task-network modeling can be directed toward improving system efficiency by increasing the understanding of basic capabilities of the human component in the system and the factors that influence these capabilities.

Virginia Higher Education Performance Funding Model.

ERIC Educational Resources Information Center

Virginia State Council of Higher Education, Richmond.

This report reviews the proposed Virginia Higher Education Performance Funding Model. It includes an overview of the proposed funding model, examples of likely funding scenarios (including determination of block grants, assumptions underlying performance funding for four-year and two-year institutions); information on deregulation/decentralization…

Modeling and Performance Simulation of the Mass Storage Network Environment

NASA Technical Reports Server (NTRS)

Kim, Chan M.; Sang, Janche

2000-01-01

This paper describes the application of modeling and simulation in evaluating and predicting the performance of the mass storage network environment. Network traffic is generated to mimic the realistic pattern of file transfer, electronic mail, and web browsing. The behavior and performance of the mass storage network and a typical client-server Local Area Network (LAN) are investigated by modeling and simulation. Performance characteristics in throughput and delay demonstrate the important role of modeling and simulation in network engineering and capacity planning.

Mental models of audit and feedback in primary care settings.

PubMed

Hysong, Sylvia J; Smitham, Kristen; SoRelle, Richard; Amspoker, Amber; Hughes, Ashley M; Haidet, Paul

2018-05-30

Audit and feedback has been shown to be instrumental in improving quality of care, particularly in outpatient settings. The mental model individuals and organizations hold regarding audit and feedback can moderate its effectiveness, yet this has received limited study in the quality improvement literature. In this study we sought to uncover patterns in mental models of current feedback practices within high- and low-performing healthcare facilities. We purposively sampled 16 geographically dispersed VA hospitals based on high and low performance on a set of chronic and preventive care measures. We interviewed up to 4 personnel from each location (n = 48) to determine the facility's receptivity to audit and feedback practices. Interview transcripts were analyzed via content and framework analysis to identify emergent themes. We found high variability in the mental models of audit and feedback, which we organized into positive and negative themes. We were unable to associate mental models of audit and feedback with clinical performance due to high variance in facility performance over time. Positive mental models exhibit perceived utility of audit and feedback practices in improving performance; whereas, negative mental models did not. Results speak to the variability of mental models of feedback, highlighting how facilities perceive current audit and feedback practices. Findings are consistent with prior research  in that variability in feedback mental models is associated with lower performance.; Future research should seek to empirically link mental models revealed in this paper to high and low levels of clinical performance.

Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

NASA Astrophysics Data System (ADS)

Li, Weihua; Sankarasubramanian, A.

2012-12-01

Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

High dimensional biological data retrieval optimization with NoSQL technology.

PubMed

Wang, Shicai; Pandis, Ioannis; Wu, Chao; He, Sijin; Johnson, David; Emam, Ibrahim; Guitton, Florian; Guo, Yike

2014-01-01

High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data.

High dimensional biological data retrieval optimization with NoSQL technology

PubMed Central

2014-01-01

Background High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. Results In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. Conclusions The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data. PMID:25435347

Consistent Evolution of Software Artifacts and Non-Functional Models

DTIC Science & Technology

2014-11-14

induce bad software performance)? 15. SUBJECT TERMS EOARD, Nano particles, Photo-Acoustic Sensors, Model-Driven Engineering ( MDE ), Software Performance...Università degli Studi dell’Aquila, Via Vetoio, 67100 L’Aquila, Italy Email: vittorio.cortellessa@univaq.it Web : http: // www. di. univaq. it/ cortelle/ Phone...Model-Driven Engineering ( MDE ), Software Performance Engineering (SPE), Change Propagation, Performance Antipatterns. For sake of readability of the

Middle-School Science Students' Scientific Modelling Performances Across Content Areas and Within a Learning Progression

NASA Astrophysics Data System (ADS)

Bamberger, Yael M.; Davis, Elizabeth A.

2013-01-01

This paper focuses on students' ability to transfer modelling performances across content areas, taking into consideration their improvement of content knowledge as a result of a model-based instruction. Sixty-five sixth grade students of one science teacher in an urban public school in the Midwestern USA engaged in scientific modelling practices that were incorporated into a curriculum focused on the nature of matter. Concept-process models were embedded in the curriculum, as well as emphasis on meta-modelling knowledge and modelling practices. Pre-post test items that required drawing scientific models of smell, evaporation, and friction were analysed. The level of content understanding was coded and scored, as were the following elements of modelling performance: explanation, comparativeness, abstraction, and labelling. Paired t-tests were conducted to analyse differences in students' pre-post tests scores on content knowledge and on each element of the modelling performances. These are described in terms of the amount of transfer. Students significantly improved in their content knowledge for the smell and the evaporation models, but not for the friction model, which was expected as that topic was not taught during the instruction. However, students significantly improved in some of their modelling performances for all the three models. This improvement serves as evidence that the model-based instruction can help students acquire modelling practices that they can apply in a new content area.

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

PubMed

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.

Integrated Main Propulsion System Performance Reconstruction Process/Models

NASA Technical Reports Server (NTRS)

Lopez, Eduardo; Elliott, Katie; Snell, Steven; Evans, Michael

2013-01-01

The Integrated Main Propulsion System (MPS) Performance Reconstruction process provides the MPS post-flight data files needed for postflight reporting to the project integration management and key customers to verify flight performance. This process/model was used as the baseline for the currently ongoing Space Launch System (SLS) work. The process utilizes several methodologies, including multiple software programs, to model integrated propulsion system performance through space shuttle ascent. It is used to evaluate integrated propulsion systems, including propellant tanks, feed systems, rocket engine, and pressurization systems performance throughout ascent based on flight pressure and temperature data. The latest revision incorporates new methods based on main engine power balance model updates to model higher mixture ratio operation at lower engine power levels.

Evaluating Organic Aerosol Model Performance: Impact of two Embedded Assumptions

NASA Astrophysics Data System (ADS)

Jiang, W.; Giroux, E.; Roth, H.; Yin, D.

2004-05-01

Organic aerosols are important due to their abundance in the polluted lower atmosphere and their impact on human health and vegetation. However, modeling organic aerosols is a very challenging task because of the complexity of aerosol composition, structure, and formation processes. Assumptions and their associated uncertainties in both models and measurement data make model performance evaluation a truly demanding job. Although some assumptions are obvious, others are hidden and embedded, and can significantly impact modeling results, possibly even changing conclusions about model performance. This paper focuses on analyzing the impact of two embedded assumptions on evaluation of organic aerosol model performance. One assumption is about the enthalpy of vaporization widely used in various secondary organic aerosol (SOA) algorithms. The other is about the conversion factor used to obtain ambient organic aerosol concentrations from measured organic carbon. These two assumptions reflect uncertainties in the model and in the ambient measurement data, respectively. For illustration purposes, various choices of the assumed values are implemented in the evaluation process for an air quality model based on CMAQ (the Community Multiscale Air Quality Model). Model simulations are conducted for the Lower Fraser Valley covering Southwest British Columbia, Canada, and Northwest Washington, United States, for a historical pollution episode in 1993. To understand the impact of the assumed enthalpy of vaporization on modeling results, its impact on instantaneous organic aerosol yields (IAY) through partitioning coefficients is analysed first. The analysis shows that utilizing different enthalpy of vaporization values causes changes in the shapes of IAY curves and in the response of SOA formation capability of reactive organic gases to temperature variations. These changes are then carried into the air quality model and cause substantial changes in the organic aerosol modeling results. In another aspect, using different assumed factors to convert measured organic carbon to organic aerosol concentrations cause substantial variations in the processed ambient data themselves, which are normally used as performance targets for model evaluations. The combination of uncertainties in the modeling results and in the moving performance targets causes major uncertainties in the final conclusion about the model performance. Without further information, the best thing that a modeler can do is to choose a combination of the assumed values from the sensible parameter ranges available in the literature, based on the best match of the modeling results with the processed measurement data. However, the best match of the modeling results with the processed measurement data may not necessarily guarantee that the model itself is rigorous and the model performance is robust. Conclusions on the model performance can only be reached with sufficient understanding of the uncertainties and their impact.

Current practices in pavement performance modeling project 08-03 (C07) : task 4 report final summary of findings.

DOT National Transportation Integrated Search

2010-02-26

In anticipation of developing pavement performance models as part of a proposed pavement management : system, the Pennsylvania Department of Transportation (PennDOT) initiated a study in 2009 to investigate : performance modeling activities and condi...

ESPVI 4.0 ELECTROSTATIS PRECIPITATOR V-1 AND PERFORMANCE MODEL: USER'S MANUAL

EPA Science Inventory

The manual is the companion document for the microcomputer program ESPVI 4.0, Electrostatic Precipitation VI and Performance Model. The program was developed to provide a user- friendly interface to an advanced model of electrostatic precipitation (ESP) performance. The program i...

Modeling of video compression effects on target acquisition performance

NASA Astrophysics Data System (ADS)

Cha, Jae H.; Preece, Bradley; Espinola, Richard L.

2009-05-01

The effect of video compression on image quality was investigated from the perspective of target acquisition performance modeling. Human perception tests were conducted recently at the U.S. Army RDECOM CERDEC NVESD, measuring identification (ID) performance on simulated military vehicle targets at various ranges. These videos were compressed with different quality and/or quantization levels utilizing motion JPEG, motion JPEG2000, and MPEG-4 encoding. To model the degradation on task performance, the loss in image quality is fit to an equivalent Gaussian MTF scaled by the Structural Similarity Image Metric (SSIM). Residual compression artifacts are treated as 3-D spatio-temporal noise. This 3-D noise is found by taking the difference of the uncompressed frame, with the estimated equivalent blur applied, and the corresponding compressed frame. Results show good agreement between the experimental data and the model prediction. This method has led to a predictive performance model for video compression by correlating various compression levels to particular blur and noise input parameters for NVESD target acquisition performance model suite.

Active imaging system performance model for target acquisition

NASA Astrophysics Data System (ADS)

Espinola, Richard L.; Teaney, Brian; Nguyen, Quang; Jacobs, Eddie L.; Halford, Carl E.; Tofsted, David H.

2007-04-01

The U.S. Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate has developed a laser-range-gated imaging system performance model for the detection, recognition, and identification of vehicle targets. The model is based on the established US Army RDECOM CERDEC NVESD sensor performance models of the human system response through an imaging system. The Java-based model, called NVLRG, accounts for the effect of active illumination, atmospheric attenuation, and turbulence effects relevant to LRG imagers, such as speckle and scintillation, and for the critical sensor and display components. This model can be used to assess the performance of recently proposed active SWIR systems through various trade studies. This paper will describe the NVLRG model in detail, discuss the validation of recent model components, present initial trade study results, and outline plans to validate and calibrate the end-to-end model with field data through human perception testing.

Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

NASA Astrophysics Data System (ADS)

Ma, Feng; Wei, Yu; Huang, Dengshi; Chen, Yixiang

2014-07-01

In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

Using satellite observations in performance evaluation for regulatory air quality modeling: Comparison with ground-level measurements

NASA Astrophysics Data System (ADS)

Odman, M. T.; Hu, Y.; Russell, A.; Chai, T.; Lee, P.; Shankar, U.; Boylan, J.

2012-12-01

Regulatory air quality modeling, such as State Implementation Plan (SIP) modeling, requires that model performance meets recommended criteria in the base-year simulations using period-specific, estimated emissions. The goal of the performance evaluation is to assure that the base-year modeling accurately captures the observed chemical reality of the lower troposphere. Any significant deficiencies found in the performance evaluation must be corrected before any base-case (with typical emissions) and future-year modeling is conducted. Corrections are usually made to model inputs such as emission-rate estimates or meteorology and/or to the air quality model itself, in modules that describe specific processes. Use of ground-level measurements that follow approved protocols is recommended for evaluating model performance. However, ground-level monitoring networks are spatially sparse, especially for particulate matter. Satellite retrievals of atmospheric chemical properties such as aerosol optical depth (AOD) provide spatial coverage that can compensate for the sparseness of ground-level measurements. Satellite retrievals can also help diagnose potential model or data problems in the upper troposphere. It is possible to achieve good model performance near the ground, but have, for example, erroneous sources or sinks in the upper troposphere that may result in misleading and unrealistic responses to emission reductions. Despite these advantages, satellite retrievals are rarely used in model performance evaluation, especially for regulatory modeling purposes, due to the high uncertainty in retrievals associated with various contaminations, for example by clouds. In this study, 2007 was selected as the base year for SIP modeling in the southeastern U.S. Performance of the Community Multiscale Air Quality (CMAQ) model, at a 12-km horizontal resolution, for this annual simulation is evaluated using both recommended ground-level measurements and non-traditional satellite retrievals. Evaluation results are assessed against recommended criteria and peer studies in the literature. Further analysis is conducted, based upon these assessments, to discover likely errors in model inputs and potential deficiencies in the model itself. Correlations as well as differences in input errors and model deficiencies revealed by ground-level measurements versus satellite observations are discussed. Additionally, sensitivity analyses are employed to investigate errors in emission-rate estimates using either ground-level measurements or satellite retrievals, and the results are compared against each other considering observational uncertainties. Recommendations are made for how to effectively utilize satellite retrievals in regulatory air quality modeling.

Predicting detection performance with model observers: Fourier domain or spatial domain?

PubMed

Chen, Baiyu; Yu, Lifeng; Leng, Shuai; Kofler, James; Favazza, Christopher; Vrieze, Thomas; McCollough, Cynthia

2016-02-27

The use of Fourier domain model observer is challenged by iterative reconstruction (IR), because IR algorithms are nonlinear and IR images have noise texture different from that of FBP. A modified Fourier domain model observer, which incorporates nonlinear noise and resolution properties, has been proposed for IR and needs to be validated with human detection performance. On the other hand, the spatial domain model observer is theoretically applicable to IR, but more computationally intensive than the Fourier domain method. The purpose of this study is to compare the modified Fourier domain model observer to the spatial domain model observer with both FBP and IR images, using human detection performance as the gold standard. A phantom with inserts of various low contrast levels and sizes was repeatedly scanned 100 times on a third-generation, dual-source CT scanner at 5 dose levels and reconstructed using FBP and IR algorithms. The human detection performance of the inserts was measured via a 2-alternative-forced-choice (2AFC) test. In addition, two model observer performances were calculated, including a Fourier domain non-prewhitening model observer and a spatial domain channelized Hotelling observer. The performance of these two mode observers was compared in terms of how well they correlated with human observer performance. Our results demonstrated that the spatial domain model observer correlated well with human observers across various dose levels, object contrast levels, and object sizes. The Fourier domain observer correlated well with human observers using FBP images, but overestimated the detection performance using IR images.

The model for Fundamentals of Endovascular Surgery (FEVS) successfully defines the competent endovascular surgeon.

PubMed

Duran, Cassidy; Estrada, Sean; O'Malley, Marcia; Sheahan, Malachi G; Shames, Murray L; Lee, Jason T; Bismuth, Jean

2015-12-01

Fundamental skills testing is now required for certification in general surgery. No model for assessing fundamental endovascular skills exists. Our objective was to develop a model that tests the fundamental endovascular skills and differentiates competent from noncompetent performance. The Fundamentals of Endovascular Surgery model was developed in silicon and virtual-reality versions. Twenty individuals (with a range of experience) performed four tasks on each model in three separate sessions. Tasks on the silicon model were performed under fluoroscopic guidance, and electromagnetic tracking captured motion metrics for catheter tip position. Image processing captured tool tip position and motion on the virtual model. Performance was evaluated using a global rating scale, blinded video assessment of error metrics, and catheter tip movement and position. Motion analysis was based on derivations of speed and position that define proficiency of movement (spectral arc length, duration of submovement, and number of submovements). Performance was significantly different between competent and noncompetent interventionalists for the three performance measures of motion metrics, error metrics, and global rating scale. The mean error metric score was 6.83 for noncompetent individuals and 2.51 for the competent group (P < .0001). Median global rating scores were 2.25 for the noncompetent group and 4.75 for the competent users (P < .0001). The Fundamentals of Endovascular Surgery model successfully differentiates competent and noncompetent performance of fundamental endovascular skills based on a series of objective performance measures. This model could serve as a platform for skills testing for all trainees. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Predicting detection performance with model observers: Fourier domain or spatial domain?

PubMed Central

Chen, Baiyu; Yu, Lifeng; Leng, Shuai; Kofler, James; Favazza, Christopher; Vrieze, Thomas; McCollough, Cynthia

2016-01-01

The use of Fourier domain model observer is challenged by iterative reconstruction (IR), because IR algorithms are nonlinear and IR images have noise texture different from that of FBP. A modified Fourier domain model observer, which incorporates nonlinear noise and resolution properties, has been proposed for IR and needs to be validated with human detection performance. On the other hand, the spatial domain model observer is theoretically applicable to IR, but more computationally intensive than the Fourier domain method. The purpose of this study is to compare the modified Fourier domain model observer to the spatial domain model observer with both FBP and IR images, using human detection performance as the gold standard. A phantom with inserts of various low contrast levels and sizes was repeatedly scanned 100 times on a third-generation, dual-source CT scanner at 5 dose levels and reconstructed using FBP and IR algorithms. The human detection performance of the inserts was measured via a 2-alternative-forced-choice (2AFC) test. In addition, two model observer performances were calculated, including a Fourier domain non-prewhitening model observer and a spatial domain channelized Hotelling observer. The performance of these two mode observers was compared in terms of how well they correlated with human observer performance. Our results demonstrated that the spatial domain model observer correlated well with human observers across various dose levels, object contrast levels, and object sizes. The Fourier domain observer correlated well with human observers using FBP images, but overestimated the detection performance using IR images. PMID:27239086

Why Bother to Calibrate? Model Consistency and the Value of Prior Information

NASA Astrophysics Data System (ADS)

Hrachowitz, Markus; Fovet, Ophelie; Ruiz, Laurent; Euser, Tanja; Gharari, Shervan; Nijzink, Remko; Savenije, Hubert; Gascuel-Odoux, Chantal

2015-04-01

Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus ways are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this study the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by 4 calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce 20 hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter-balanced by using prior information about the system to impose "prior constraints", inferred from expert knowledge and to ensure a model which behaves well with respect to the modeller's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model set-up exhibited increased performance in the independent test period and skill to reproduce all 20 signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if efficiently counter-balanced by available prior constraints, can increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert-knowledge driven strategy of constraining models.

Why Bother and Calibrate? Model Consistency and the Value of Prior Information.

NASA Astrophysics Data System (ADS)

Hrachowitz, M.; Fovet, O.; Ruiz, L.; Euser, T.; Gharari, S.; Nijzink, R.; Freer, J. E.; Savenije, H.; Gascuel-Odoux, C.

2014-12-01

Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus ways are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this study the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by 4 calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce 20 hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter-balanced by using prior information about the system to impose "prior constraints", inferred from expert knowledge and to ensure a model which behaves well with respect to the modeller's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model set-up exhibited increased performance in the independent test period and skill to reproduce all 20 signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if efficiently counter-balanced by available prior constraints, can increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert-knowledge driven strategy of constraining models.

Process consistency in models: The importance of system signatures, expert knowledge, and process complexity

NASA Astrophysics Data System (ADS)

Hrachowitz, M.; Fovet, O.; Ruiz, L.; Euser, T.; Gharari, S.; Nijzink, R.; Freer, J.; Savenije, H. H. G.; Gascuel-Odoux, C.

2014-09-01

Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus, ways are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this study, the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by four calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce a suite of hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter-balanced by "prior constraints," inferred from expert knowledge to ensure a model which behaves well with respect to the modeler's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model setup exhibited increased performance in the independent test period and skill to better reproduce all tested signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if counter-balanced by prior constraints, can significantly increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert-knowledge-driven strategy of constraining models.

Model Performance Evaluation and Scenario Analysis (MPESA) Tutorial

EPA Pesticide Factsheets

The model performance evaluation consists of metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors.

Analytic Guided-Search Model of Human Performance Accuracy in Target- Localization Search Tasks

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Beutter, Brent R.; Stone, Leland S.

2000-01-01

Current models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.

An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows.

PubMed

Brun-Lafleur, L; Cutullic, E; Faverdin, P; Delaby, L; Disenhaus, C

2013-08-01

To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rates.

A novel spatial performance metric for robust pattern optimization of distributed hydrological models

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, C.; Koch, J.

2017-12-01

Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing platforms. We see great potential of spaef across environmental disciplines dealing with spatially distributed modelling.

Electric Propulsion System Modeling for the Proposed Prometheus 1 Mission

NASA Technical Reports Server (NTRS)

Fiehler, Douglas; Dougherty, Ryan; Manzella, David

2005-01-01

The proposed Prometheus 1 spacecraft would utilize nuclear electric propulsion to propel the spacecraft to its ultimate destination where it would perform its primary mission. As part of the Prometheus 1 Phase A studies, system models were developed for each of the spacecraft subsystems that were integrated into one overarching system model. The Electric Propulsion System (EPS) model was developed using data from the Prometheus 1 electric propulsion technology development efforts. This EPS model was then used to provide both performance and mass information to the Prometheus 1 system model for total system trades. Development of the EPS model is described, detailing both the performance calculations as well as its evolution over the course of Phase A through three technical baselines. Model outputs are also presented, detailing the performance of the model and its direct relationship to the Prometheus 1 technology development efforts. These EP system model outputs are also analyzed chronologically showing the response of the model development to the four technical baselines during Prometheus 1 Phase A.

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

A performance model for GPUs with caches

DOE PAGES

Dao, Thanh Tuan; Kim, Jungwon; Seo, Sangmin; ...

2014-06-24

To exploit the abundant computational power of the world's fastest supercomputers, an even workload distribution to the typically heterogeneous compute devices is necessary. While relatively accurate performance models exist for conventional CPUs, accurate performance estimation models for modern GPUs do not exist. This paper presents two accurate models for modern GPUs: a sampling-based linear model, and a model based on machine-learning (ML) techniques which improves the accuracy of the linear model and is applicable to modern GPUs with and without caches. We first construct the sampling-based linear model to predict the runtime of an arbitrary OpenCL kernel. Based on anmore » analysis of NVIDIA GPUs' scheduling policies we determine the earliest sampling points that allow an accurate estimation. The linear model cannot capture well the significant effects that memory coalescing or caching as implemented in modern GPUs have on performance. We therefore propose a model based on ML techniques that takes several compiler-generated statistics about the kernel as well as the GPU's hardware performance counters as additional inputs to obtain a more accurate runtime performance estimation for modern GPUs. We demonstrate the effectiveness and broad applicability of the model by applying it to three different NVIDIA GPU architectures and one AMD GPU architecture. On an extensive set of OpenCL benchmarks, on average, the proposed model estimates the runtime performance with less than 7 percent error for a second-generation GTX 280 with no on-chip caches and less than 5 percent for the Fermi-based GTX 580 with hardware caches. On the Kepler-based GTX 680, the linear model has an error of less than 10 percent. On an AMD GPU architecture, Radeon HD 6970, the model estimates with 8 percent of error rates. As a result, the proposed technique outperforms existing models by a factor of 5 to 6 in terms of accuracy.« less

Performance Optimizing Adaptive Control with Time-Varying Reference Model Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Hashemi, Kelley E.

2017-01-01

This paper presents a new adaptive control approach that involves a performance optimization objective. The control synthesis involves the design of a performance optimizing adaptive controller from a subset of control inputs. The resulting effect of the performance optimizing adaptive controller is to modify the initial reference model into a time-varying reference model which satisfies the performance optimization requirement obtained from an optimal control problem. The time-varying reference model modification is accomplished by the real-time solutions of the time-varying Riccati and Sylvester equations coupled with the least-squares parameter estimation of the sensitivities of the performance metric. The effectiveness of the proposed method is demonstrated by an application of maneuver load alleviation control for a flexible aircraft.

Modeling the Performance of Direct-Detection Doppler Lidar Systems in Real Atmospheres

NASA Technical Reports Server (NTRS)

McGill, Matthew J.; Hart, William D.; McKay, Jack A.; Spinhirne, James D.

1999-01-01

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems has assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar systems: the double-edge and the multi-channel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only about 10-20% compared to nighttime performance, provided a proper solar filter is included in the instrument design.

Modeling the performance of direct-detection Doppler lidar systems including cloud and solar background variability.

PubMed

McGill, M J; Hart, W D; McKay, J A; Spinhirne, J D

1999-10-20

Previous modeling of the performance of spaceborne direct-detection Doppler lidar systems assumed extremely idealized atmospheric models. Here we develop a technique for modeling the performance of these systems in a more realistic atmosphere, based on actual airborne lidar observations. The resulting atmospheric model contains cloud and aerosol variability that is absent in other simulations of spaceborne Doppler lidar instruments. To produce a realistic simulation of daytime performance, we include solar radiance values that are based on actual measurements and are allowed to vary as the viewing scene changes. Simulations are performed for two types of direct-detection Doppler lidar system: the double-edge and the multichannel techniques. Both systems were optimized to measure winds from Rayleigh backscatter at 355 nm. Simulations show that the measurement uncertainty during daytime is degraded by only approximately 10-20% compared with nighttime performance, provided that a proper solar filter is included in the instrument design.

The simplest acquisition protocol is sometimes the best protocol: performing and learning a 1:2 bimanual coordination task.

PubMed

Panzer, Stefan; Kennedy, Deanna; Wang, Chaoyi; Shea, Charles H

2018-02-01

An experiment was conducted to determine if the performance and learning of a multi-frequency (1:2) coordination pattern between the limbs are enhanced when a model is provided prior to each acquisition trial. Research has indicated very effective performance of a wide variety of bimanual coordination tasks when Lissajous plots with goal templates are provided, but this research has also found that participants become dependent on this information and perform quite poorly when it is withdrawn. The present experiment was designed to test three forms of modeling (Lissajous with template, Lissajous without template, and limb model), but in each situations, the model was presented prior to practice and not available during the performance of the task. This was done to decrease dependency on the model and increase the development of an internal reference of correctness that could be applied on test trials. A control condition was also collected, where a metronome was used to guide the movement. Following less than 7 min of practice, participants in the three modeling conditions performed the first test block very effectively; however, performance of the control condition was quite poor. Note that Test 1 was performed under the same conditions as used during acquisition. Test 2 was conducted with no augmented information provided prior to or during the performance of the task. Only participants in the limb model condition were able to maintain performance on Test 2. The findings suggest that a very simple intuitive display can provide the necessary information to form an effective internal representation of the coordination pattern which can be used guide performance when the augmented display is withdrawn.

Estuarine modeling: Does a higher grid resolution improve model performance?

EPA Science Inventory

Ecological models are useful tools to explore cause effect relationships, test hypothesis and perform management scenarios. A mathematical model, the Gulf of Mexico Dissolved Oxygen Model (GoMDOM), has been developed and applied to the Louisiana continental shelf of the northern ...

Propagation of uncertainty in nasal spray in vitro performance models using Monte Carlo simulation: Part II. Error propagation during product performance modeling.

PubMed

Guo, Changning; Doub, William H; Kauffman, John F

2010-08-01

Monte Carlo simulations were applied to investigate the propagation of uncertainty in both input variables and response measurements on model prediction for nasal spray product performance design of experiment (DOE) models in the first part of this study, with an initial assumption that the models perfectly represent the relationship between input variables and the measured responses. In this article, we discard the initial assumption, and extended the Monte Carlo simulation study to examine the influence of both input variable variation and product performance measurement variation on the uncertainty in DOE model coefficients. The Monte Carlo simulations presented in this article illustrate the importance of careful error propagation during product performance modeling. Our results show that the error estimates based on Monte Carlo simulation result in smaller model coefficient standard deviations than those from regression methods. This suggests that the estimated standard deviations from regression may overestimate the uncertainties in the model coefficients. Monte Carlo simulations provide a simple software solution to understand the propagation of uncertainty in complex DOE models so that design space can be specified with statistically meaningful confidence levels. (c) 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Thermal model of attic systems with radiant barriers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wilkes, K.E.

This report summarizes the first phase of a project to model the thermal performance of radiant barriers. The objective of this phase of the project was to develop a refined model for the thermal performance of residential house attics, with and without radiant barriers, and to verify the model by comparing its predictions against selected existing experimental thermal performance data. Models for the thermal performance of attics with and without radiant barriers have been developed and implemented on an IBM PC/AT computer. The validity of the models has been tested by comparing their predictions with ceiling heat fluxes measured inmore » a number of laboratory and field experiments on attics with and without radiant barriers. Cumulative heat flows predicted by the models were usually within about 5 to 10 percent of measured values. In future phases of the project, the models for attic/radiant barrier performance will be coupled with a whole-house model and further comparisons with experimental data will be made. Following this, the models will be utilized to provide an initial assessment of the energy savings potential of radiant barriers in various configurations and under various climatic conditions. 38 refs., 14 figs., 22 tabs.« less

LADAR Performance Simulations with a High Spectral Resolution Atmospheric Transmittance and Radiance Model-LEEDR

DTIC Science & Technology

2012-03-01

such as FASCODE is accomplished. The assessment is limited by the correctness of the models used; validating the models is beyond the scope of this...comparisons with other models and validation against data sets (Snell et al. 2000). 2.3.2 Previous Research Several LADAR simulations have been produced...performance models would better capture the atmosphere physics and climatological effects on these systems. Also, further validation needs to be performed

Modeling the effects of contrast enhancement on target acquisition performance

NASA Astrophysics Data System (ADS)

Du Bosq, Todd W.; Fanning, Jonathan D.

2008-04-01

Contrast enhancement and dynamic range compression are currently being used to improve the performance of infrared imagers by increasing the contrast between the target and the scene content, by better utilizing the available gray levels either globally or locally. This paper assesses the range-performance effects of various contrast enhancement algorithms for target identification with well contrasted vehicles. Human perception experiments were performed to determine field performance using contrast enhancement on the U.S. Army RDECOM CERDEC NVESD standard military eight target set using an un-cooled LWIR camera. The experiments compare the identification performance of observers viewing linearly scaled images and various contrast enhancement processed images. Contrast enhancement is modeled in the US Army thermal target acquisition model (NVThermIP) by changing the scene contrast temperature. The model predicts improved performance based on any improved target contrast, regardless of feature saturation or enhancement. To account for the equivalent blur associated with each contrast enhancement algorithm, an additional effective MTF was calculated and added to the model. The measured results are compared with the predicted performance based on the target task difficulty metric used in NVThermIP.

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Railroad Performance Model

DOT National Transportation Integrated Search

1977-10-01

This report describes an operational, though preliminary, version of the Railroad Performance Model, which is a computer simulation model of the nation's railroad system. The ultimate purpose of this model is to predict the effect of changes in gover...

Monte Carlo simulation of Ray-Scan 64 PET system and performance evaluation using GATE toolkit

NASA Astrophysics Data System (ADS)

Li, Suying; Zhang, Qiushi; Vuletic, Ivan; Xie, Zhaoheng; Yang, Kun; Ren, Qiushi

2017-02-01

In this study, we aimed to develop a GATE model for the simulation of Ray-Scan 64 PET scanner and model its performance characteristics. A detailed implementation of system geometry and physical process were included in the simulation model. Then we modeled the performance characteristics of Ray-Scan 64 PET system for the first time, based on National Electrical Manufacturers Association (NEMA) NU-2 2007 protocols and validated the model against experimental measurement, including spatial resolution, sensitivity, counting rates and noise equivalent count rate (NECR). Moreover, an accurate dead time module was investigated to simulate the counting rate performance. Overall results showed reasonable agreement between simulation and experimental data. The validation results showed the reliability and feasibility of the GATE model to evaluate major performance of Ray-Scan 64 PET system. It provided a useful tool for a wide range of research applications.

A comprehensive model for diagnosing the causes of individual medical performance problems: skills, knowledge, internal, past and external factors (SKIPE).

PubMed

Norfolk, Tim; Siriwardena, A Niroshan

2013-01-01

This discussion paper describes a new and comprehensive model for diagnosing the causes of individual medical performance problems: SKIPE (skills, knowledge, internal, past and external factors). This builds on a previous paper describing a unifying theory of clinical practice, the RDM-p model, which captures the primary skill sets required for effective medical performance (relationship, diagnostics and management), and the professionalism that needs to underpin them. The SKIPE model is currently being used, in conjunction with the RDM-p model, for the in-depth assessment and management of doctors whose performance is a cause for concern.

Applicability of common stomatal conductance models in maize under varying soil moisture conditions.

PubMed

Wang, Qiuling; He, Qijin; Zhou, Guangsheng

2018-07-01

In the context of climate warming, the varying soil moisture caused by precipitation pattern change will affect the applicability of stomatal conductance models, thereby affecting the simulation accuracy of carbon-nitrogen-water cycles in ecosystems. We studied the applicability of four common stomatal conductance models including Jarvis, Ball-Woodrow-Berry (BWB), Ball-Berry-Leuning (BBL) and unified stomatal optimization (USO) models based on summer maize leaf gas exchange data from a soil moisture consecutive decrease manipulation experiment. The results showed that the USO model performed best, followed by the BBL model, BWB model, and the Jarvis model performed worst under varying soil moisture conditions. The effects of soil moisture made a difference in the relative performance among the models. By introducing a water response function, the performance of the Jarvis, BWB, and USO models improved, which decreased the normalized root mean square error (NRMSE) by 15.7%, 16.6% and 3.9%, respectively; however, the performance of the BBL model was negative, which increased the NRMSE by 5.3%. It was observed that the models of Jarvis, BWB, BBL and USO were applicable within different ranges of soil relative water content (i.e., 55%-65%, 56%-67%, 37%-79% and 37%-95%, respectively) based on the 95% confidence limits. Moreover, introducing a water response function, the applicability of the Jarvis and BWB models improved. The USO model performed best with or without introducing the water response function and was applicable under varying soil moisture conditions. Our results provide a basis for selecting appropriate stomatal conductance models under drought conditions. Copyright © 2018 Elsevier B.V. All rights reserved.

Performance of the SEAPROG prognosis variant of the forest vegetation simulator.

Treesearch

Michael H. McClellan; Frances E. Biles

2003-01-01

This paper reports the first phase of a recent effort to evaluate the performance and use of the FVS-SEAPROG vegetation growth model. In this paper, we present our evaluation of SEAPROG’s performance in modeling the growth of even-aged stands regenerated by clearcutting, windthrow, or fire. We evaluated the model by comparing model predictions to observed values from...

Controlling flexible structures with second order actuator dynamics

NASA Technical Reports Server (NTRS)

Inman, Daniel J.; Umland, Jeffrey W.; Bellos, John

1989-01-01

The control of flexible structures for those systems with actuators that are modeled by second order dynamics is examined. Two modeling approaches are investigated. First a stability and performance analysis is performed using a low order finite dimensional model of the structure. Secondly, a continuum model of the flexible structure to be controlled, coupled with lumped parameter second order dynamic models of the actuators performing the control is used. This model is appropriate in the modeling of the control of a flexible panel by proof-mass actuators as well as other beam, plate and shell like structural numbers. The model is verified with experimental measurements.

Strategies for concurrent processing of complex algorithms in data driven architectures

NASA Technical Reports Server (NTRS)

Som, Sukhamoy; Stoughton, John W.; Mielke, Roland R.

1990-01-01

Performance modeling and performance enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures are discussed. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called algorithm to architecture mapping model (ATAMM). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

An Empirical Study of a Solo Performance Assessment Model

ERIC Educational Resources Information Center

Russell, Brian E.

2015-01-01

The purpose of this study was to test a hypothesized model of solo music performance assessment. Specifically, this study investigates the influence of technique and musical expression on perceptions of overall performance quality. The Aural Musical Performance Quality (AMPQ) measure was created to measure overall performance quality, technique,…

Error Detection Processes during Observational Learning

ERIC Educational Resources Information Center

Badets, Arnaud; Blandin, Yannick; Wright, David L.; Shea, Charles H.

2006-01-01

The purpose of this experiment was to determine whether a faded knowledge of results (KR) frequency during observation of a model's performance enhanced error detection capabilities. During the observation phase, participants observed a model performing a timing task and received KR about the model's performance on each trial or on one of two…

A Model of Metacognition, Achievement Goal Orientation, Learning Style and Self-Efficacy

ERIC Educational Resources Information Center

Coutinho, Savia A.; Neuman, George

2008-01-01

Structural equation modelling was used to test a model integrating achievement goal orientation, learning style, self-efficacy and metacognition into a single framework that explained and predicted variation in performance. Self-efficacy was the strongest predictor of performance. Metacognition was a weak predictor of performance. Deep processing…

A predictive model of flight crew performance in automated air traffic control and flight management operations

DOT National Transportation Integrated Search

1995-01-01

Prepared ca. 1995. This paper describes Air-MIDAS, a model of pilot performance in interaction with varied levels of automation in flight management operations. The model was used to predict the performance of a two person flight crew responding to c...

Predicting Document Retrieval System Performance: An Expected Precision Measure.

ERIC Educational Resources Information Center

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…

A modified F-test for evaluating model performance by including both experimental and simulation uncertainties

USDA-ARS?s Scientific Manuscript database

Experimental and simulation uncertainties have not been included in many of the statistics used in assessing agricultural model performance. The objectives of this study were to develop an F-test that can be used to evaluate model performance considering experimental and simulation uncertainties, an...

Electrochemical carbon dioxide concentrator subsystem math model. [for manned space station

NASA Technical Reports Server (NTRS)

Marshall, R. D.; Carlson, J. N.; Schubert, F. H.

1974-01-01

A steady state computer simulation model has been developed to describe the performance of a total six man, self-contained electrochemical carbon dioxide concentrator subsystem built for the space station prototype. The math model combines expressions describing the performance of the electrochemical depolarized carbon dioxide concentrator cells and modules previously developed with expressions describing the performance of the other major CS-6 components. The model is capable of accurately predicting CS-6 performance over EDC operating ranges and the computer simulation results agree with experimental data obtained over the prediction range.

Damage modeling and statistical analysis of optics damage performance in MJ-class laser systems.

PubMed

Liao, Zhi M; Raymond, B; Gaylord, J; Fallejo, R; Bude, J; Wegner, P

2014-11-17

Modeling the lifetime of a fused silica optic is described for a multiple beam, MJ-class laser system. This entails combining optic processing data along with laser shot data to account for complete history of optic processing and shot exposure. Integrating with online inspection data allows for the construction of a performance metric to describe how an optic performs with respect to the model. This methodology helps to validate the damage model as well as allows strategic planning and identifying potential hidden parameters that are affecting the optic's performance.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Theoretical performance model for single image depth from defocus.

PubMed

Trouvé-Peloux, Pauline; Champagnat, Frédéric; Le Besnerais, Guy; Idier, Jérôme

2014-12-01

In this paper we present a performance model for depth estimation using single image depth from defocus (SIDFD). Our model is based on an original expression of the Cramér-Rao bound (CRB) in this context. We show that this model is consistent with the expected behavior of SIDFD. We then study the influence on the performance of the optical parameters of a conventional camera such as the focal length, the aperture, and the position of the in-focus plane (IFP). We derive an approximate analytical expression of the CRB away from the IFP, and we propose an interpretation of the SIDFD performance in this domain. Finally, we illustrate the predictive capacity of our performance model on experimental data comparing several settings of a consumer camera.

Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini

2014-12-01

The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies-both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the choice of the wavelets in multiscale model evaluation. It was found that the proposed wavelet-based performance measures, namely the MNSC (Multiscale Nash-Sutcliffe Criteria) and MNRMSE (Multiscale Normalized Root Mean Square Error), are a more reliable measure than traditional performance measures such as the Nash-Sutcliffe Criteria (NSC), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Further, the proposed methodology can be used to: i) compare different hydrological models (both physical and statistical models), and ii) help in model calibration.

Validation of the internalization of the Model Minority Myth Measure (IM-4) and its link to academic performance and psychological adjustment among Asian American adolescents.

PubMed

Yoo, Hyung Chol; Miller, Matthew J; Yip, Pansy

2015-04-01

There is limited research examining psychological correlates of a uniquely racialized experience of the model minority stereotype faced by Asian Americans. The present study examined the factor structure and fit of the only published measure of the internalization of the model minority myth, the Internalization of the Model Minority Myth Measure (IM-4; Yoo et al., 2010), with a sample of 155 Asian American high school adolescents. We also examined the link between internalization of the model minority myth types (i.e., myth associated with achievement and myth associated with unrestricted mobility) and psychological adjustment (i.e., affective distress, somatic distress, performance difficulty, academic expectations stress), and the potential moderating effect of academic performance (cumulative grade point average). Results suggested the 2-factor model of the IM-4 had an acceptable fit to the data and supported the factor structure using confirmatory factor analyses. Internalizing the model minority myth of achievement related positively to academic expectations stress; however, internalizing the model minority myth of unrestricted mobility related negatively to academic expectations stress, both controlling for gender and academic performance. Finally, academic performance moderated the model minority myth associated with unrestricted mobility and affective distress link and the model minority myth associated with achievement and performance difficulty link. These findings highlight the complex ways in which the model minority myth relates to psychological outcomes. (c) 2015 APA, all rights reserved).

Nonlinearity analysis of measurement model for vision-based optical navigation system

NASA Astrophysics Data System (ADS)

Li, Jianguo; Cui, Hutao; Tian, Yang

2015-02-01

In the autonomous optical navigation system based on line-of-sight vector observation, nonlinearity of measurement model is highly correlated with the navigation performance. By quantitatively calculating the degree of nonlinearity of the focal plane model and the unit vector model, this paper focuses on determining which optical measurement model performs better. Firstly, measurement equations and measurement noise statistics of these two line-of-sight measurement models are established based on perspective projection co-linearity equation. Then the nonlinear effects of measurement model on the filter performance are analyzed within the framework of the Extended Kalman filter, also the degrees of nonlinearity of two measurement models are compared using the curvature measure theory from differential geometry. Finally, a simulation of star-tracker-based attitude determination is presented to confirm the superiority of the unit vector measurement model. Simulation results show that the magnitude of curvature nonlinearity measurement is consistent with the filter performance, and the unit vector measurement model yields higher estimation precision and faster convergence properties.

Validation of a national hydrological model

NASA Astrophysics Data System (ADS)

McMillan, H. K.; Booker, D. J.; Cattoën, C.

2016-10-01

Nationwide predictions of flow time-series are valuable for development of policies relating to environmental flows, calculating reliability of supply to water users, or assessing risk of floods or droughts. This breadth of model utility is possible because various hydrological signatures can be derived from simulated flow time-series. However, producing national hydrological simulations can be challenging due to strong environmental diversity across catchments and a lack of data available to aid model parameterisation. A comprehensive and consistent suite of test procedures to quantify spatial and temporal patterns in performance across various parts of the hydrograph is described and applied to quantify the performance of an uncalibrated national rainfall-runoff model of New Zealand. Flow time-series observed at 485 gauging stations were used to calculate Nash-Sutcliffe efficiency and percent bias when simulating between-site differences in daily series, between-year differences in annual series, and between-site differences in hydrological signatures. The procedures were used to assess the benefit of applying a correction to the modelled flow duration curve based on an independent statistical analysis. They were used to aid understanding of climatological, hydrological and model-based causes of differences in predictive performance by assessing multiple hypotheses that describe where and when the model was expected to perform best. As the procedures produce quantitative measures of performance, they provide an objective basis for model assessment that could be applied when comparing observed daily flow series with competing simulated flow series from any region-wide or nationwide hydrological model. Model performance varied in space and time with better scores in larger and medium-wet catchments, and in catchments with smaller seasonal variations. Surprisingly, model performance was not sensitive to aquifer fraction or rain gauge density.

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.

PubMed

Najah, A; El-Shafie, A; Karim, O A; El-Shafie, Amr H

2014-02-01

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.

Development of a model to assess environmental performance, concerning HSE-MS principles.

PubMed

Abbaspour, M; Hosseinzadeh Lotfi, F; Karbassi, A R; Roayaei, E; Nikoomaram, H

2010-06-01

The main objective of the present study was to develop a valid and appropriate model to evaluate companies' efficiency and environmental performance, concerning health, safety, and environmental management system principles. The proposed model overcomes the shortcomings of the previous models developed in this area. This model has been designed on the basis of a mathematical method known as Data Envelopment Analysis (DEA). In order to differentiate high-performing companies from weak ones, one of DEA nonradial models named as enhanced Russell graph efficiency measure has been applied. Since some of the environmental performance indicators cannot be controlled by companies' managers, it was necessary to develop the model in a way that it could be applied when discretionary and/or nondiscretionary factors were involved. The model, then, has been modified on a real case that comprised 12 oil and gas general contractors. The results showed the relative efficiency, inefficiency sources, and the rank of contractors.

Comparative study of turbulence models in predicting hypersonic inlet flows

NASA Technical Reports Server (NTRS)

Kapoor, Kamlesh; Anderson, Bernhard H.; Shaw, Robert J.

1992-01-01

A numerical study was conducted to analyze the performance of different turbulence models when applied to the hypersonic NASA P8 inlet. Computational results from the PARC2D code, which solves the full two-dimensional Reynolds-averaged Navier-Stokes equation, were compared with experimental data. The zero-equation models considered for the study were the Baldwin-Lomax model, the Thomas model, and a combination of the Baldwin-Lomax and Thomas models; the two-equation models considered were the Chien model, the Speziale model (both low Reynolds number), and the Launder and Spalding model (high Reynolds number). The Thomas model performed best among the zero-equation models, and predicted good pressure distributions. The Chien and Speziale models compared wery well with the experimental data, and performed better than the Thomas model near the walls.

Comparative study of turbulence models in predicting hypersonic inlet flows

NASA Technical Reports Server (NTRS)

Kapoor, Kamlesh; Anderson, Bernhard H.; Shaw, Robert J.

1992-01-01

A numerical study was conducted to analyze the performance of different turbulence models when applied to the hypersonic NASA P8 inlet. Computational results from the PARC2D code, which solves the full two-dimensional Reynolds-averaged Navier-Stokes equation, were compared with experimental data. The zero-equation models considered for the study were the Baldwin-Lomax model, the Thomas model, and a combination of the Baldwin-Lomax and Thomas models; the two-equation models considered were the Chien model, the Speziale model (both low Reynolds number), and the Launder and Spalding model (high Reynolds number). The Thomas model performed best among the zero-equation models, and predicted good pressure distributions. The Chien and Speziale models compared very well with the experimental data, and performed better than the Thomas model near the walls.

Maritime Platform Sleep and Performance Study: Evaluating the SAFTE Model for Maritime Workplace Application

DTIC Science & Technology

2012-06-01

SLEEP AND PERFORMANCE STUDY: EVALUATING THE SAFTE MODEL FOR MARITIME WORKPLACE APPLICATION by Stephanie A. T. Brown June 2012 Thesis...REPORT DATE June 2012 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE Maritime Platform Sleep and Performance Study...Evaluating the SAFTE Model for Maritime Workplace Application 5. FUNDING NUMBERS 6. AUTHOR(S) Stephanie A. T. Brown 7. PERFORMING ORGANIZATION

High Performance, Robust Control of Flexible Space Structures: MSFC Center Director's Discretionary Fund

NASA Technical Reports Server (NTRS)

Whorton, M. S.

1998-01-01

Many spacecraft systems have ambitious objectives that place stringent requirements on control systems. Achievable performance is often limited because of difficulty of obtaining accurate models for flexible space structures. To achieve sufficiently high performance to accomplish mission objectives may require the ability to refine the control design model based on closed-loop test data and tune the controller based on the refined model. A control system design procedure is developed based on mixed H2/H(infinity) optimization to synthesize a set of controllers explicitly trading between nominal performance and robust stability. A homotopy algorithm is presented which generates a trajectory of gains that may be implemented to determine maximum achievable performance for a given model error bound. Examples show that a better balance between robustness and performance is obtained using the mixed H2/H(infinity) design method than either H2 or mu-synthesis control design. A second contribution is a new procedure for closed-loop system identification which refines parameters of a control design model in a canonical realization. Examples demonstrate convergence of the parameter estimation and improved performance realized by using the refined model for controller redesign. These developments result in an effective mechanism for achieving high-performance control of flexible space structures.

Temporal evolution modeling of hydraulic and water quality performance of permeable pavements

NASA Astrophysics Data System (ADS)

Huang, Jian; He, Jianxun; Valeo, Caterina; Chu, Angus

2016-02-01

A mathematical model for predicting hydraulic and water quality performance in both the short- and long-term is proposed based on field measurements for three types of permeable pavements: porous asphalt (PA), porous concrete (PC), and permeable inter-locking concrete pavers (PICP). The model was applied to three field-scale test sites in Calgary, Alberta, Canada. The model performance was assessed in terms of hydraulic parameters including time to peak, peak flow and water balance and a water quality variable (the removal rate of total suspended solids). A total of 20 simulated storm events were used for model calibration and verification processes. The proposed model can simulate the outflow hydrographs with a coefficient of determination (R2) ranging from 0.762 to 0.907, and normalized root-mean-square deviation (NRMSD) ranging from 13.78% to 17.83%. Comparison of the time to peak flow, peak flow, runoff volume and TSS removal rates between the measured and modeled values in model verification phase had a maximum difference of 11%. The results demonstrate that the proposed model is capable of capturing the temporal dynamics of the pavement performance. Therefore, the model has great potential as a practical modeling tool for permeable pavement design and performance assessment.

An ICAI architecture for troubleshooting in complex, dynamic systems

NASA Technical Reports Server (NTRS)

Fath, Janet L.; Mitchell, Christine M.; Govindaraj, T.

1990-01-01

Ahab, an intelligent computer-aided instruction (ICAI) program, illustrates an architecture for simulator-based ICAI programs to teach troubleshooting in complex, dynamic environments. The architecture posits three elements of a computerized instructor: the task model, the student model, and the instructional module. The task model is a prescriptive model of expert performance that uses symptomatic and topographic search strategies to provide students with directed problem-solving aids. The student model is a descriptive model of student performance in the context of the task model. This student model compares the student and task models, critiques student performance, and provides interactive performance feedback. The instructional module coordinates information presented by the instructional media, the task model, and the student model so that each student receives individualized instruction. Concept and metaconcept knowledge that supports these elements is contained in frames and production rules, respectively. The results of an experimental evaluation are discussed. They support the hypothesis that training with an adaptive online system built using the Ahab architecture produces better performance than training using simulator practice alone, at least with unfamiliar problems. It is not sufficient to develop an expert strategy and present it to students using offline materials. The training is most effective if it adapts to individual student needs.

Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level.

PubMed

Ouyang, Huei-Tau

2017-08-01

Accurate inundation level forecasting during typhoon invasion is crucial for organizing response actions such as the evacuation of people from areas that could potentially flood. This paper explores the ability of nonlinear autoregressive neural networks with exogenous inputs (NARX) to predict inundation levels induced by typhoons. Two types of NARX architecture were employed: series-parallel (NARX-S) and parallel (NARX-P). Based on cross-correlation analysis of rainfall and water-level data from historical typhoon records, 10 NARX models (five of each architecture type) were constructed. The forecasting ability of each model was assessed by considering coefficient of efficiency (CE), relative time shift error (RTS), and peak water-level error (PE). The results revealed that high CE performance could be achieved by employing more model input variables. Comparisons of the two types of model demonstrated that the NARX-S models outperformed the NARX-P models in terms of CE and RTS, whereas both performed exceptionally in terms of PE and without significant difference. The NARX-S and NARX-P models with the highest overall performance were identified and their predictions were compared with those of traditional ARX-based models. The NARX-S model outperformed the ARX-based models in all three indexes, whereas the NARX-P model exhibited comparable CE performance and superior RTS and PE performance.

Simulation and performance of brushless dc motor actuators

NASA Astrophysics Data System (ADS)

Gerba, A., Jr.

1985-12-01

The simulation model for a Brushless D.C. Motor and the associated commutation power conditioner transistor model are presented. The necessary conditions for maximum power output while operating at steady-state speed and sinusoidally distributed air-gap flux are developed. Comparison of simulated model with the measured performance of a typical motor are done both on time response waveforms and on average performance characteristics. These preliminary results indicate good agreement. Plans for model improvement and testing of a motor-driven positioning device for model evaluation are outlined.

Do great teams think alike? An examination of team mental models and their impact on team performance.

PubMed

Gardner, Aimee K; Scott, Daniel J; AbdelFattah, Kareem R

2017-05-01

Team mental models represent the shared understanding of team members within their relevant environment. Thus, team mental models should have a substantial impact on a team's ability to engage in purposeful and coordinated action. We sought to examine the impact of shared team mental models on team performance and to investigate if team mental models increase over time as teams continue to work together. New surgery interns were assigned randomly to 1 of 10 teams. Each team participated in one unique simulation every day for 5 days, each followed by video-based debriefing with a facilitator. Participants also completed independently a concept similarity tool validated previously in nonmedical team literature to assess team mental models. All performances were video recorded and evaluated with a scenario-specific team performance tool by a single, blinded junior surgeon under an institutional review board-approved protocol. Changes in performance and team mental models over time were assessed with paired samples t tests. Regression analysis was used to examine the extent to which team mental models predicted team performance. Thirty interns (age 27; 77% men) participated in the training program. Percentage of items achieved (x¯ ± SD) on the performance evaluation was 39 ± 20, 51 ± 14, 22 ± 17, 63 ± 14, and 77 ± 25 for Days 1-5, respectively. Team mental models were 30 ± 5, 28 ± 6, 27 ± 8, 26 ± 7, and 25 ± 6 for Days 1-5 respectively, such that larger values corresponded to greater differences in team mental models. Paired sample t tests indicated that both average performance and team mental models similarity improved from the first to last day (P < .01, P < .05, respectively). Additionally, regression analyses indicated that team mental models predicted team performance on Days 2-5 (all P < .05) but not on the first day of simulations. These results demonstrate that greater sharing of team mental models among the teams leads to better team performance. Additionally, the increase in team mental models over time suggests that engaging in team-based simulation may catalyze the process by which surgery teams are able to develop shared knowledge. Copyright © 2016 Elsevier Inc. All rights reserved.

A comprehensive mechanistic model for upward two-phase flow in wellbores

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sylvester, N.D.; Sarica, C.; Shoham, O.

1994-05-01

A comprehensive model is formulated to predict the flow behavior for upward two-phase flow. This model is composed of a model for flow-pattern prediction and a set of independent mechanistic models for predicting such flow characteristics as holdup and pressure drop in bubble, slug, and annular flow. The comprehensive model is evaluated by using a well data bank made up of 1,712 well cases covering a wide variety of field data. Model performance is also compared with six commonly used empirical correlations and the Hasan-Kabir mechanistic model. Overall model performance is in good agreement with the data. In comparison withmore » other methods, the comprehensive model performed the best.« less

ExaSAT: An exascale co-design tool for performance modeling

DOE PAGES

Unat, Didem; Chan, Cy; Zhang, Weiqun; ...

2015-02-09

One of the emerging challenges to designing HPC systems is understanding and projecting the requirements of exascale applications. In order to determine the performance consequences of different hardware designs, analytic models are essential because they can provide fast feedback to the co-design centers and chip designers without costly simulations. However, current attempts to analytically model program performance typically rely on the user manually specifying a performance model. Here we introduce the ExaSAT framework that automates the extraction of parameterized performance models directly from source code using compiler analysis. The parameterized analytic model enables quantitative evaluation of a broad range ofmore » hardware design trade-offs and software optimizations on a variety of different performance metrics, with a primary focus on data movement as a metric. Finally, we demonstrate the ExaSAT framework’s ability to perform deep code analysis of a proxy application from the Department of Energy Combustion Co-design Center to illustrate its value to the exascale co-design process. ExaSAT analysis provides insights into the hardware and software trade-offs and lays the groundwork for exploring a more targeted set of design points using cycle-accurate architectural simulators.« less

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

A framework for performance measurement in university using extended network data envelopment analysis (DEA) structures

NASA Astrophysics Data System (ADS)

Kashim, Rosmaini; Kasim, Maznah Mat; Rahman, Rosshairy Abd

2015-12-01

Measuring university performance is essential for efficient allocation and utilization of educational resources. In most of the previous studies, performance measurement in universities emphasized the operational efficiency and resource utilization without investigating the university's ability to fulfill the needs of its stakeholders and society. Therefore, assessment of the performance of university should be separated into two stages namely efficiency and effectiveness. In conventional DEA analysis, a decision making unit (DMU) or in this context, a university is generally treated as a black-box which ignores the operation and interdependence of the internal processes. When this happens, the results obtained would be misleading. Thus, this paper suggest an alternative framework for measuring the overall performance of a university by incorporating both efficiency and effectiveness and applies network DEA model. The network DEA models are recommended because this approach takes into account the interrelationship between the processes of efficiency and effectiveness in the system. This framework also focuses on the university structure which is expanded from the hierarchical to form a series of horizontal relationship between subordinate units by assuming both intermediate unit and its subordinate units can generate output(s). Three conceptual models are proposed to evaluate the performance of a university. An efficiency model is developed at the first stage by using hierarchical network model. It is followed by an effectiveness model which take output(s) from the hierarchical structure at the first stage as a input(s) at the second stage. As a result, a new overall performance model is proposed by combining both efficiency and effectiveness models. Thus, once this overall model is realized and utilized, the university's top management can determine the overall performance of each unit more accurately and systematically. Besides that, the result from the network DEA model can give a superior benchmarking power over the conventional models.

A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region

NASA Astrophysics Data System (ADS)

He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

2014-02-01

Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach

PubMed Central

Kneifel, Joshua; Webb, David

2016-01-01

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 estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF. PMID:27956756

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach.

PubMed

Kneifel, Joshua; Webb, David

2016-09-01

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 estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF.

Commercial absorption chiller models for evaluation of control strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koeppel, E.A.; Klein, S.A.; Mitchell, J.W.

1995-08-01

A steady-state computer simulation model of a direct fired double-effect water-lithium bromide absorption chiller in the parallel-flow configuration was developed from first principles. Unknown model parameters such as heat transfer coefficients were determined by matching the model`s calculated state points and coefficient of performance (COP) against nominal full-load operating data and COPs obtained from a manufacturer`s catalog. The model compares favorably with the manufacturer`s performance ratings for varying water circuit (chilled and cooling) temperatures at full load conditions and for chiller part-load performance. The model was used (1) to investigate the effect of varying the water circuit flow rates withmore » the chiller load and (2) to optimize chiller part-load performance with respect to the distribution and flow of the weak solution.« less

On the use and the performance of software reliability growth models

NASA Technical Reports Server (NTRS)

Keiller, Peter A.; Miller, Douglas R.

1991-01-01

We address the problem of predicting future failures for a piece of software. The number of failures occurring during a finite future time interval is predicted from the number failures observed during an initial period of usage by using software reliability growth models. Two different methods for using the models are considered: straightforward use of individual models, and dynamic selection among models based on goodness-of-fit and quality-of-prediction criteria. Performance is judged by the relative error of the predicted number of failures over future finite time intervals relative to the number of failures eventually observed during the intervals. Six of the former models and eight of the latter are evaluated, based on their performance on twenty data sets. Many open questions remain regarding the use and the performance of software reliability growth models.

Do repeated assessments of performance status improve predictions for risk of death among patients with cancer? A population-based cohort study.

PubMed

Su, Jiandong; Barbera, Lisa; Sutradhar, Rinku

2015-06-01

Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancer patients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death. To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis. This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. Our study consisted of 15,487 cancer patients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis. When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status. © The Author(s) 2015.

Modeling and Evaluating Pilot Performance in NextGen: Review of and Recommendations Regarding Pilot Modeling Efforts, Architectures, and Validation Studies

NASA Technical Reports Server (NTRS)

Wickens, Christopher; Sebok, Angelia; Keller, John; Peters, Steve; Small, Ronald; Hutchins, Shaun; Algarin, Liana; Gore, Brian Francis; Hooey, Becky Lee; Foyle, David C.

2013-01-01

NextGen operations are associated with a variety of changes to the national airspace system (NAS) including changes to the allocation of roles and responsibilities among operators and automation, the use of new technologies and automation, additional information presented on the flight deck, and the entire concept of operations (ConOps). In the transition to NextGen airspace, aviation and air operations designers need to consider the implications of design or system changes on human performance and the potential for error. To ensure continued safety of the NAS, it will be necessary for researchers to evaluate design concepts and potential NextGen scenarios well before implementation. One approach for such evaluations is through human performance modeling. Human performance models (HPMs) provide effective tools for predicting and evaluating operator performance in systems. HPMs offer significant advantages over empirical, human-in-the-loop testing in that (1) they allow detailed analyses of systems that have not yet been built, (2) they offer great flexibility for extensive data collection, (3) they do not require experimental participants, and thus can offer cost and time savings. HPMs differ in their ability to predict performance and safety with NextGen procedures, equipment and ConOps. Models also vary in terms of how they approach human performance (e.g., some focus on cognitive processing, others focus on discrete tasks performed by a human, while others consider perceptual processes), and in terms of their associated validation efforts. The objectives of this research effort were to support the Federal Aviation Administration (FAA) in identifying HPMs that are appropriate for predicting pilot performance in NextGen operations, to provide guidance on how to evaluate the quality of different models, and to identify gaps in pilot performance modeling research, that could guide future research opportunities. This research effort is intended to help the FAA evaluate pilot modeling efforts and select the appropriate tools for future modeling efforts to predict pilot performance in NextGen operations.

Bayesian Maximum Entropy Integration of Ozone Observations and Model Predictions: A National Application.

PubMed

Xu, Yadong; Serre, Marc L; Reyes, Jeanette; Vizuete, William

2016-04-19

To improve ozone exposure estimates for ambient concentrations at a national scale, we introduce our novel Regionalized Air Quality Model Performance (RAMP) approach to integrate chemical transport model (CTM) predictions with the available ozone observations using the Bayesian Maximum Entropy (BME) framework. The framework models the nonlinear and nonhomoscedastic relation between air pollution observations and CTM predictions and for the first time accounts for variability in CTM model performance. A validation analysis using only noncollocated data outside of a validation radius rv was performed and the R(2) between observations and re-estimated values for two daily metrics, the daily maximum 8-h average (DM8A) and the daily 24-h average (D24A) ozone concentrations, were obtained with the OBS scenario using ozone observations only in contrast with the RAMP and a Constant Air Quality Model Performance (CAMP) scenarios. We show that, by accounting for the spatial and temporal variability in model performance, our novel RAMP approach is able to extract more information in terms of R(2) increase percentage, with over 12 times for the DM8A and over 3.5 times for the D24A ozone concentrations, from CTM predictions than the CAMP approach assuming that model performance does not change across space and time.

Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models

USGS Publications Warehouse

Watling, James I.; Brandt, Laura A.; Bucklin, David N.; Fujisaki, Ikuko; Mazzotti, Frank J.; Romañach, Stephanie; Speroterra, Carolina

2015-01-01

Species distribution models (SDMs) are widely used in basic and applied ecology, making it important to understand sources and magnitudes of uncertainty in SDM performance and predictions. We analyzed SDM performance and partitioned variance among prediction maps for 15 rare vertebrate species in the southeastern USA using all possible combinations of seven potential sources of uncertainty in SDMs: algorithms, climate datasets, model domain, species presences, variable collinearity, CO2 emissions scenarios, and general circulation models. The choice of modeling algorithm was the greatest source of uncertainty in SDM performance and prediction maps, with some additional variation in performance associated with the comprehensiveness of the species presences used for modeling. Other sources of uncertainty that have received attention in the SDM literature such as variable collinearity and model domain contributed little to differences in SDM performance or predictions in this study. Predictions from different algorithms tended to be more variable at northern range margins for species with more northern distributions, which may complicate conservation planning at the leading edge of species' geographic ranges. The clear message emerging from this work is that researchers should use multiple algorithms for modeling rather than relying on predictions from a single algorithm, invest resources in compiling a comprehensive set of species presences, and explicitly evaluate uncertainty in SDM predictions at leading range margins.

Hypnosis in sport: an Isomorphic Model.

PubMed

Robazza, C; Bortoli, L

1994-10-01

Hypnosis in sport can be applied according to an Isomorphic Model. Active-alert hypnosis is induced before or during practice whereas traditional hypnosis is induced after practice to establish connections between the two experiences. The fundamental goals are to (a) develop mental skills important to both motor and hypnotic performance, (b) supply a wide range of motor and hypnotic bodily experiences important to performance, and (c) induce alert hypnosis before or during performance. The model is based on the assumption that hypnosis and motor performance share common skills modifiable through training. Similarities between hypnosis and peak performance in the model are also considered. Some predictions are important from theoretical and practical points of view.

Structural, Thermal, and Optical Performance (STOP) Modeling and Results for the James Webb Space Telescope Integrated Science Instrument Module

NASA Technical Reports Server (NTRS)

Gracey, Renee; Bartoszyk, Andrew; Cofie, Emmanuel; Comber, Brian; Hartig, George; Howard, Joseph; Sabatke, Derek; Wenzel, Greg; Ohl, Raymond

2016-01-01

The James Webb Space Telescope includes the Integrated Science Instrument Module (ISIM) element that contains four science instruments (SI) including a Guider. We performed extensive structural, thermal, and optical performance(STOP) modeling in support of all phases of ISIM development. In this paper, we focus on modeling and results associated with test and verification. ISIMs test program is bound by ground environments, mostly notably the 1g and test chamber thermal environments. This paper describes STOP modeling used to predict ISIM system performance in 0g and at various on-orbit temperature environments. The predictions are used to project results obtained during testing to on-orbit performance.

Design logistics performance measurement model of automotive component industry for srengthening competitiveness of dealing AEC 2015

NASA Astrophysics Data System (ADS)

Amran, T. G.; Janitra Yose, Mindy

2018-03-01

As the free trade Asean Economic Community (AEC) causes the tougher competition, it is important that Indonesia’s automotive industry have high competitiveness as well. A model of logistics performance measurement was designed as an evaluation tool for automotive component companies to improve their logistics performance in order to compete in AEC. The design of logistics performance measurement model was based on the Logistics Scorecard perspectives, divided into two stages: identifying the logistics business strategy to get the KPI and arranging the model. 23 KPI was obtained. The measurement result can be taken into consideration of determining policies to improve the performance logistics competitiveness.

Minimum resolvable power contrast model

NASA Astrophysics Data System (ADS)

Qian, Shuai; Wang, Xia; Zhou, Jingjing

2018-01-01

Signal-to-noise ratio and MTF are important indexs to evaluate the performance of optical systems. However,whether they are used alone or joint assessment cannot intuitively describe the overall performance of the system. Therefore, an index is proposed to reflect the comprehensive system performance-Minimum Resolvable Radiation Performance Contrast (MRP) model. MRP is an evaluation model without human eyes. It starts from the radiance of the target and the background, transforms the target and background into the equivalent strips,and considers attenuation of the atmosphere, the optical imaging system, and the detector. Combining with the signal-to-noise ratio and the MTF, the Minimum Resolvable Radiation Performance Contrast is obtained. Finally the detection probability model of MRP is given.

Applying model abstraction techniques to optimize monitoring networks for detecting subsurface contaminant transport

USDA-ARS?s Scientific Manuscript database

Improving strategies for monitoring subsurface contaminant transport includes performance comparison of competing models, developed independently or obtained via model abstraction. Model comparison and parameter discrimination involve specific performance indicators selected to better understand s...

Measuring the Performance and Intelligence of Systems: Proceedings of the 2002 PerMIS Workshop

NASA Technical Reports Server (NTRS)

Messina, E. R.; Meystel, A. M.

2002-01-01

Contents include the following: Performance Metrics; Performance of Multiple Agents; Performance of Mobility Systems; Performance of Planning Systems; General Discussion Panel 1; Uncertainty of Representation I; Performance of Robots in Hazardous Domains; Modeling Intelligence; Modeling of Mind; Measuring Intelligence; Grouping: A Core Procedure of Intelligence; Uncertainty in Representation II; Towards Universal Planning/Control Systems.

Exposure to Psychological Aggression at Work and Job Performance: The Mediating Role of Job Attitudes and Personal Health

PubMed Central

Schat, Aaron; Frone, Michael R.

2011-01-01

Despite the growing literature on workplace aggression and the importance of employee performance at work, few studies have examined the relation between workplace aggression and job performance. The purpose of this study was to investigate the relations between psychological aggression at work and two forms of job performance (task performance and contextual performance) and potential mediators of these relations. Based on Conservation of Resources theory and prior research, a model was developed and tested in which overall job attitudes (i.e., job satisfaction and organizational commitment) and overall personal health (i.e., physical and psychological health) fully mediate the relations between exposure to psychological aggression at work and both task performance and contextual performance. Data were obtained from a national probability sample of US workers (N = 2376) and the model was tested using structural equation modelling. The results supported the hypothesized model, demonstrating that exposure to psychological aggression at work negatively predicted both task performance and contextual performance, and that these relations were explained by decrements in job attitudes and health associated with exposure to psychological aggression at work. PMID:21643471

Exposure to Psychological Aggression at Work and Job Performance: The Mediating Role of Job Attitudes and Personal Health.

PubMed

Schat, Aaron; Frone, Michael R

2011-01-01

Despite the growing literature on workplace aggression and the importance of employee performance at work, few studies have examined the relation between workplace aggression and job performance. The purpose of this study was to investigate the relations between psychological aggression at work and two forms of job performance (task performance and contextual performance) and potential mediators of these relations. Based on Conservation of Resources theory and prior research, a model was developed and tested in which overall job attitudes (i.e., job satisfaction and organizational commitment) and overall personal health (i.e., physical and psychological health) fully mediate the relations between exposure to psychological aggression at work and both task performance and contextual performance. Data were obtained from a national probability sample of US workers (N = 2376) and the model was tested using structural equation modelling. The results supported the hypothesized model, demonstrating that exposure to psychological aggression at work negatively predicted both task performance and contextual performance, and that these relations were explained by decrements in job attitudes and health associated with exposure to psychological aggression at work.

Modeling Students' Problem Solving Performance in the Computer-Based Mathematics Learning Environment

ERIC Educational Resources Information Center

Lee, Young-Jin

2017-01-01

Purpose: The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment. Design/methodology/approach: Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can…

A Systemic Cause Analysis Model for Human Performance Technicians

ERIC Educational Resources Information Center

Sostrin, Jesse

2011-01-01

This article presents a systemic, research-based cause analysis model for use in the field of human performance technology (HPT). The model organizes the most prominent barriers to workplace learning and performance into a conceptual framework that explains and illuminates the architecture of these barriers that exist within the fabric of everyday…

Performance Technology--Not a One-Size-Fits-All Profession

ERIC Educational Resources Information Center

Dierkes, Sunda V.

2012-01-01

The current debate over whether to choose just one universal human performance technology (HPT) model, in particular Langdon's language of work (LOW) model, promises a shared understanding among HPT professionals, credibility for the HPT profession, and a return on investment of time and effort in developing performance models over more than 70…

Preparation for implementation of the mechanistic-empirical pavement design guide in Michigan, part 3 : local calibration and validation of the pavement-ME performance models.

DOT National Transportation Integrated Search

2014-11-01

The main objective of Part 3 was to locally calibrate and validate the mechanistic-empirical pavement : design guide (Pavement-ME) performance models to Michigan conditions. The local calibration of the : performance models in the Pavement-ME is a ch...

University Library Strategy Development: A Conceptual Model of Researcher Performance to Inform Service Delivery

ERIC Educational Resources Information Center

Maddox, Alexia; Zhao, Linlin

2017-01-01

This case study presents a conceptual model of researcher performance developed by Deakin University Library, Australia. The model aims to organize research performance data into meaningful researcher profiles, referred to as researcher typologies, which support the demonstration of research impact and value. Three dimensions shaping researcher…

Design and performance evaluation of a simplified dynamic model for combined sewer overflows in pumped sewer systems

NASA Astrophysics Data System (ADS)

van Daal-Rombouts, Petra; Sun, Siao; Langeveld, Jeroen; Bertrand-Krajewski, Jean-Luc; Clemens, François

2016-07-01

Optimisation or real time control (RTC) studies in wastewater systems increasingly require rapid simulations of sewer systems in extensive catchments. To reduce the simulation time calibrated simplified models are applied, with the performance generally based on the goodness of fit of the calibration. In this research the performance of three simplified and a full hydrodynamic (FH) model for two catchments are compared based on the correct determination of CSO event occurrences and of the total discharged volumes to the surface water. Simplified model M1 consists of a rainfall runoff outflow (RRO) model only. M2 combines the RRO model with a static reservoir model for the sewer behaviour. M3 comprises the RRO model and a dynamic reservoir model. The dynamic reservoir characteristics were derived from FH model simulations. It was found that M2 and M3 are able to describe the sewer behaviour of the catchments, contrary to M1. The preferred model structure depends on the quality of the information (geometrical database and monitoring data) available for the design and calibration of the model. Finally, calibrated simplified models are shown to be preferable to uncalibrated FH models when performing optimisation or RTC studies.

Geospace Environment Modeling 2008-2009 Challenge: Ground Magnetic Field Perturbations

NASA Technical Reports Server (NTRS)

Pulkkinen, A.; Kuznetsova, M.; Ridley, A.; Raeder, J.; Vapirev, A.; Weimer, D.; Weigel, R. S.; Wiltberger, M.; Millward, G.; Rastatter, L.;

New model performance index for engineering design of control systems

NASA Technical Reports Server (NTRS)

1970-01-01

Performance index includes a model representing linear control-system design specifications. Based on a geometric criterion for approximation of the model by the actual system, the index can be interpreted directly in terms of the desired system response model without actually having the model's time response.

42 CFR § 512.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2017-10-01

...) HEALTH CARE INFRASTRUCTURE AND MODEL PROGRAMS EPISODE PAYMENT MODEL General Provisions § 512.2... model means the model testing CR incentive payments for CR/ICR service use made in accordance with... performance year means one of the years in which the CR incentive payment model is being tested. Performance...

Metrics for evaluating performance and uncertainty of Bayesian network models

Treesearch

Bruce G. Marcot

2012-01-01

This paper presents a selected set of existing and new metrics for gauging Bayesian network model performance and uncertainty. Selected existing and new metrics are discussed for conducting model sensitivity analysis (variance reduction, entropy reduction, case file simulation); evaluating scenarios (influence analysis); depicting model complexity (numbers of model...

Performance modeling for large database systems

NASA Astrophysics Data System (ADS)

Schaar, Stephen; Hum, Frank; Romano, Joe

1997-02-01

One of the unique approaches Science Applications International Corporation took to meet performance requirements was to start the modeling effort during the proposal phase of the Interstate Identification Index/Federal Bureau of Investigations (III/FBI) project. The III/FBI Performance Model uses analytical modeling techniques to represent the III/FBI system. Inputs to the model include workloads for each transaction type, record size for each record type, number of records for each file, hardware envelope characteristics, engineering margins and estimates for software instructions, memory, and I/O for each transaction type. The model uses queuing theory to calculate the average transaction queue length. The model calculates a response time and the resources needed for each transaction type. Outputs of the model include the total resources needed for the system, a hardware configuration, and projected inherent and operational availability. The III/FBI Performance Model is used to evaluate what-if scenarios and allows a rapid response to engineering change proposals and technical enhancements.

A new training model for robot-assisted urethrovesical anastomosis and posterior muscle-fascial reconstruction: the Verona training technique.

PubMed

Cacciamani, G; De Marco, V; Siracusano, S; De Marchi, D; Bizzotto, L; Cerruto, M A; Motton, G; Porcaro, A B; Artibani, W

2017-06-01

A training model is usually needed to teach robotic surgical technique successfully. In this way, an ideal training model should mimic as much as possible the "in vivo" procedure and allow several consecutive surgical simulations. The goal of this study was to create a "wet lab" model suitable for RARP training programs, providing the simulation of the posterior fascial reconstruction. The second aim was to compare the original "Venezuelan" chicken model described by Sotelo to our training model. Our training model consists of performing an anastomosis, reproducing the surgical procedure in "vivo" as in RARP, between proventriculus and the proximal portion of the esophagus. A posterior fascial reconstruction simulating Rocco's stitch is performed between the tissues located under the posterior surface of the esophagus and the tissue represented by the serosa of the proventriculus. From 2014 to 2015, during 6 different full-immersion training courses, thirty-four surgeons performed the urethrovesical anastomosis using our model and the Sotelo's one. After the training period, each surgeon was asked to fill out a non-validated questionnaire to perform an evaluation of the differences between the two training models. Our model was judged the best model, in terms of similarity with urethral tissue and similarity with the anatomic unit urethra-pelvic wall. Our training model as reported by all trainees is easily reproducible and anatomically comparable with the urethrovesical anastomosis as performed during radical prostatectomy in humans. It is suitable for performing posterior fascial reconstruction reported by Rocco. In this context, our surgical training model could be routinely proposed in all robotic training courses to develop specific expertise in urethrovesical anastomosis with the reproducibility of the Rocco stitch.

Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.

PubMed

Jampathong, Nampet; Laopaiboon, Malinee; Rattanakanokchai, Siwanon; Pattanittum, Porjai

2018-03-09

Prognostic models have been increasingly developed to predict complete recovery in ischemic stroke. However, questions arise about the performance characteristics of these models. The aim of this study was to systematically review and synthesize performance of existing prognostic models for complete recovery in ischemic stroke. We searched journal publications indexed in PUBMED, SCOPUS, CENTRAL, ISI Web of Science and OVID MEDLINE from inception until 4 December, 2017, for studies designed to develop and/or validate prognostic models for predicting complete recovery in ischemic stroke patients. Two reviewers independently examined titles and abstracts, and assessed whether each study met the pre-defined inclusion criteria and also independently extracted information about model development and performance. We evaluated validation of the models by medians of the area under the receiver operating characteristic curve (AUC) or c-statistic and calibration performance. We used a random-effects meta-analysis to pool AUC values. We included 10 studies with 23 models developed from elderly patients with a moderately severe ischemic stroke, mainly in three high income countries. Sample sizes for each study ranged from 75 to 4441. Logistic regression was the only analytical strategy used to develop the models. The number of various predictors varied from one to 11. Internal validation was performed in 12 models with a median AUC of 0.80 (95% CI 0.73 to 0.84). One model reported good calibration. Nine models reported external validation with a median AUC of 0.80 (95% CI 0.76 to 0.82). Four models showed good discrimination and calibration on external validation. The pooled AUC of the two validation models of the same developed model was 0.78 (95% CI 0.71 to 0.85). The performance of the 23 models found in the systematic review varied from fair to good in terms of internal and external validation. Further models should be developed with internal and external validation in low and middle income countries.

Performance evaluation of air quality models for predicting PM10 and PM2.5 concentrations at urban traffic intersection during winter period.

PubMed

Gokhale, Sharad; Raokhande, Namita

2008-05-01

There are several models that can be used to evaluate roadside air quality. The comparison of the operational performance of different models pertinent to local conditions is desirable so that the model that performs best can be identified. Three air quality models, namely the 'modified General Finite Line Source Model' (M-GFLSM) of particulates, the 'California Line Source' (CALINE3) model, and the 'California Line Source for Queuing & Hot Spot Calculations' (CAL3QHC) model have been identified for evaluating the air quality at one of the busiest traffic intersections in the city of Guwahati. These models have been evaluated statistically with the vehicle-derived airborne particulate mass emissions in two sizes, i.e. PM10 and PM2.5, the prevailing meteorology and the temporal distribution of the measured daily average PM10 and PM2.5 concentrations in wintertime. The study has shown that the CAL3QHC model would make better predictions compared to other models for varied meteorology and traffic conditions. The detailed study reveals that the agreements between the measured and the modeled PM10 and PM2.5 concentrations have been reasonably good for CALINE3 and CAL3QHC models. Further detailed analysis shows that the CAL3QHC model performed well compared to the CALINE3. The monthly performance measures have also led to the similar results. These two models have also outperformed for a class of wind speed velocities except for low winds (<1 m s(-1)), for which, the M-GFLSM model has shown the tendency of better performance for PM10. Nevertheless, the CAL3QHC model has outperformed for both the particulate sizes and for all the wind classes, which therefore can be optional for air quality assessment at urban traffic intersections.

Input/output models for general aviation piston-prop aircraft fuel economy

NASA Technical Reports Server (NTRS)

Sweet, L. M.

1982-01-01

A fuel efficient cruise performance model for general aviation piston engine airplane was tested. The following equations were made: (1) for the standard atmosphere; (2) airframe-propeller-atmosphere cruise performance; and (3) naturally aspirated engine cruise performance. Adjustments are made to the compact cruise performance model as follows: corrected quantities, corrected performance plots, algebraic equations, maximize R with or without constraints, and appears suitable for airborne microprocessor implementation. The following hardwares are recommended: ignition timing regulator, fuel-air mass ration controller, microprocessor, sensors and displays.

High Performance Programming Using Explicit Shared Memory Model on Cray T3D1

NASA Technical Reports Server (NTRS)

Simon, Horst D.; Saini, Subhash; Grassi, Charles

1994-01-01

The Cray T3D system is the first-phase system in Cray Research, Inc.'s (CRI) three-phase massively parallel processing (MPP) program. This system features a heterogeneous architecture that closely couples DEC's Alpha microprocessors and CRI's parallel-vector technology, i.e., the Cray Y-MP and Cray C90. An overview of the Cray T3D hardware and available programming models is presented. Under Cray Research adaptive Fortran (CRAFT) model four programming methods (data parallel, work sharing, message-passing using PVM, and explicit shared memory model) are available to the users. However, at this time data parallel and work sharing programming models are not available to the user community. The differences between standard PVM and CRI's PVM are highlighted with performance measurements such as latencies and communication bandwidths. We have found that the performance of neither standard PVM nor CRI s PVM exploits the hardware capabilities of the T3D. The reasons for the bad performance of PVM as a native message-passing library are presented. This is illustrated by the performance of NAS Parallel Benchmarks (NPB) programmed in explicit shared memory model on Cray T3D. In general, the performance of standard PVM is about 4 to 5 times less than obtained by using explicit shared memory model. This degradation in performance is also seen on CM-5 where the performance of applications using native message-passing library CMMD on CM-5 is also about 4 to 5 times less than using data parallel methods. The issues involved (such as barriers, synchronization, invalidating data cache, aligning data cache etc.) while programming in explicit shared memory model are discussed. Comparative performance of NPB using explicit shared memory programming model on the Cray T3D and other highly parallel systems such as the TMC CM-5, Intel Paragon, Cray C90, IBM-SP1, etc. is presented.

Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events

NASA Astrophysics Data System (ADS)

Ficchì, Andrea; Perrin, Charles; Andréassian, Vazken

2016-07-01

Hydro-climatic data at short time steps are considered essential to model the rainfall-runoff relationship, especially for short-duration hydrological events, typically flash floods. Also, using fine time step information may be beneficial when using or analysing model outputs at larger aggregated time scales. However, the actual gain in prediction efficiency using short time-step data is not well understood or quantified. In this paper, we investigate the extent to which the performance of hydrological modelling is improved by short time-step data, using a large set of 240 French catchments, for which 2400 flood events were selected. Six-minute rain gauge data were available and the GR4 rainfall-runoff model was run with precipitation inputs at eight different time steps ranging from 6 min to 1 day. Then model outputs were aggregated at seven different reference time scales ranging from sub-hourly to daily for a comparative evaluation of simulations at different target time steps. Three classes of model performance behaviour were found for the 240 test catchments: (i) significant improvement of performance with shorter time steps; (ii) performance insensitivity to the modelling time step; (iii) performance degradation as the time step becomes shorter. The differences between these groups were analysed based on a number of catchment and event characteristics. A statistical test highlighted the most influential explanatory variables for model performance evolution at different time steps, including flow auto-correlation, flood and storm duration, flood hydrograph peakedness, rainfall-runoff lag time and precipitation temporal variability.

Measured and estimated performance of a fleet of shaded photovoltaic systems with string and module-level inverters

DOE PAGES

MacAlpine, Sara; Deline, Chris; Dobos, Aron

2017-03-16

Shade obstructions can significantly impact the performance of photovoltaic (PV) systems. Although there are many models for partially shaded PV arrays, there is a lack of information available regarding their accuracy and uncertainty when compared with actual field performance. This work assesses the recorded performance of 46 residential PV systems, equipped with either string-level or module-level inverters, under a variety of shading conditions. We compare their energy production data to annual PV performance predictions, with a focus on the practical models developed here for National Renewable Energy Laboratory's system advisor model software. This includes assessment of shade extent on eachmore » PV system by using traditional onsite surveys and newer 3D obstruction modelling. The electrical impact of shade is modelled by either a nonlinear performance model or assumption of linear impact with shade extent, depending on the inverter type. When applied to the fleet of residential PV systems, performance is predicted with median annual bias errors of 2.5% or less, for systems with up to 20% estimated shading loss. The partial shade models are not found to add appreciable uncertainty to annual predictions of energy production for this fleet of systems but do introduce a monthly root-mean-square error of approximately 4%-9% due to seasonal effects. Here the use of a detailed 3D model results in similar or improved accuracy over site survey methods, indicating that, with proper description of shade obstructions, modelling of partially shaded PV arrays can be done completely remotely, potentially saving time and cost.« less

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Model-as-a-service (MaaS) using the cloud service innovation platform (CSIP)

USDA-ARS?s Scientific Manuscript database

Cloud infrastructures for modelling activities such as data processing, performing environmental simulations, or conducting model calibrations/optimizations provide a cost effective alternative to traditional high performance computing approaches. Cloud-based modelling examples emerged into the more...

Model surgery with a passive robot arm for orthognathic surgery planning.

PubMed

Theodossy, Tamer; Bamber, Mohammad Anwar

2003-11-01

The aims of the study were to assess the degree of accuracy of model surgery performed manually using the Eastman technique and to compare it with model surgery performed with the aid of a robot arm. Twenty-one patients undergoing orthognathic surgery gave consent for this study. They were divided into 2 groups based on the model surgery technique used. Group A (52%) had model surgery performed manually, whereas group B (48%) had their model surgery performed using the robot arm. Patients' maxillary casts were measured before and after model surgery, and results were compared with those for the original treatment plan in horizontal (x-axis), vertical (y-axis), and transverse (z-axis) planes. Statistical analysis using Mann-Whitney U test for x- and y-axis and independent sample t test for z-axis have shown significant differences between both groups in x-axis (P =.024) and y-axis (P =.01) but not in z-axis (P =.776). Model surgery performed with the aid of a robot arm is significantly more accurate in anteroposterior and vertical planes than is manual model surgery. Robot arm has an important role to play in orthognathic surgery planning and in determining the biometrics of orthognathic surgical change at the model surgery stage.

Risk assessment model for development of advanced age-related macular degeneration.

PubMed

Klein, Michael L; Francis, Peter J; Ferris, Frederick L; Hamon, Sara C; Clemons, Traci E

2011-12-01

To design a risk assessment model for development of advanced age-related macular degeneration (AMD) incorporating phenotypic, demographic, environmental, and genetic risk factors. We evaluated longitudinal data from 2846 participants in the Age-Related Eye Disease Study. At baseline, these individuals had all levels of AMD, ranging from none to unilateral advanced AMD (neovascular or geographic atrophy). Follow-up averaged 9.3 years. We performed a Cox proportional hazards analysis with demographic, environmental, phenotypic, and genetic covariates and constructed a risk assessment model for development of advanced AMD. Performance of the model was evaluated using the C statistic and the Brier score and externally validated in participants in the Complications of Age-Related Macular Degeneration Prevention Trial. The final model included the following independent variables: age, smoking history, family history of AMD (first-degree member), phenotype based on a modified Age-Related Eye Disease Study simple scale score, and genetic variants CFH Y402H and ARMS2 A69S. The model did well on performance measures, with very good discrimination (C statistic = 0.872) and excellent calibration and overall performance (Brier score at 5 years = 0.08). Successful external validation was performed, and a risk assessment tool was designed for use with or without the genetic component. We constructed a risk assessment model for development of advanced AMD. The model performed well on measures of discrimination, calibration, and overall performance and was successfully externally validated. This risk assessment tool is available for online use.

The effect of observing novice and expert performance on acquisition of surgical skills on a robotic platform

PubMed Central

Harris, David J.; Vine, Samuel J.; Wilson, Mark R.; McGrath, John S.; LeBel, Marie-Eve

2017-01-01

Background Observational learning plays an important role in surgical skills training, following the traditional model of learning from expertise. Recent findings have, however, highlighted the benefit of observing not only expert performance but also error-strewn performance. The aim of this study was to determine which model (novice vs. expert) would lead to the greatest benefits when learning robotically assisted surgical skills. Methods 120 medical students with no prior experience of robotically-assisted surgery completed a ring-carrying training task on three occasions; baseline, post-intervention and at one-week follow-up. The observation intervention consisted of a video model performing the ring-carrying task, with participants randomly assigned to view an expert model, a novice model, a mixed expert/novice model or no observation (control group). Participants were assessed for task performance and surgical instrument control. Results There were significant group differences post-intervention, with expert and novice observation groups outperforming the control group, but there were no clear group differences at a retention test one week later. There was no difference in performance between the expert-observing and error-observing groups. Conclusions Similar benefits were found when observing the traditional expert model or the error-strewn model, suggesting that viewing poor performance may be as beneficial as viewing expertise in the early acquisition of robotic surgical skills. Further work is required to understand, then inform, the optimal curriculum design when utilising observational learning in surgical training. PMID:29141046

Modeling error analysis of stationary linear discrete-time filters

NASA Technical Reports Server (NTRS)

Patel, R.; Toda, M.

1977-01-01

The performance of Kalman-type, linear, discrete-time filters in the presence of modeling errors is considered. The discussion is limited to stationary performance, and bounds are obtained for the performance index, the mean-squared error of estimates for suboptimal and optimal (Kalman) filters. The computation of these bounds requires information on only the model matrices and the range of errors for these matrices. Consequently, a design can easily compare the performance of a suboptimal filter with that of the optimal filter, when only the range of errors in the elements of the model matrices is available.

Trichotomous goals of elementary school students learning English as a foreign language: a structural equation model.

PubMed

He, Tung-Hsien; Chang, Shan-Mao; Chen, Shu-Hui Eileen; Gou, Wen Johnny

2012-02-01

This study applied structural equation modeling (SEM) techniques to define the relations among trichotomous goals (mastery goals, performance-approach goals, and performance-avoidance goals), self-efficacy, use of metacognitive self-regulation strategies, positive belief in seeking help, and help-avoidance behavior. Elementary school students (N = 105), who were learning English as a foreign language, were surveyed using five self-report scales. The structural equation model showed that self-efficacy led to the adoption of mastery goals but discouraged the adoption of performance-approach goals and performance-avoidance goals. Furthermore, mastery goals increased the use of metacognitive self-regulation strategies, whereas performance-approach goals and performance-avoidance goals reduced their use. Mastery goals encouraged positive belief in help-seeking, but performance-avoidance goals decreased such belief. Finally, performance-avoidance goals directly led to help-avoidance behavior, whereas positive belief assumed a critical role in reducing help-avoidance. The established structural equation model illuminated the potential causal relations among these variables for the young learners in this study.

Performance Evaluation Model for Application Layer Firewalls.

PubMed

Xuan, Shichang; Yang, Wu; Dong, Hui; Zhang, Jiangchuan

2016-01-01

Application layer firewalls protect the trusted area network against information security risks. However, firewall performance may affect user experience. Therefore, performance analysis plays a significant role in the evaluation of application layer firewalls. This paper presents an analytic model of the application layer firewall, based on a system analysis to evaluate the capability of the firewall. In order to enable users to improve the performance of the application layer firewall with limited resources, resource allocation was evaluated to obtain the optimal resource allocation scheme in terms of throughput, delay, and packet loss rate. The proposed model employs the Erlangian queuing model to analyze the performance parameters of the system with regard to the three layers (network, transport, and application layers). Then, the analysis results of all the layers are combined to obtain the overall system performance indicators. A discrete event simulation method was used to evaluate the proposed model. Finally, limited service desk resources were allocated to obtain the values of the performance indicators under different resource allocation scenarios in order to determine the optimal allocation scheme. Under limited resource allocation, this scheme enables users to maximize the performance of the application layer firewall.

Transmutation Fuel Performance Code Thermal Model Verification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gregory K. Miller; Pavel G. Medvedev

2007-09-01

FRAPCON fuel performance code is being modified to be able to model performance of the nuclear fuels of interest to the Global Nuclear Energy Partnership (GNEP). The present report documents the effort for verification of the FRAPCON thermal model. It was found that, with minor modifications, FRAPCON thermal model temperature calculation agrees with that of the commercial software ABAQUS (Version 6.4-4). This report outlines the methodology of the verification, code input, and calculation results.

Distributed multi-criteria model evaluation and spatial association analysis

NASA Astrophysics Data System (ADS)

Scherer, Laura; Pfister, Stephan

2015-04-01

Model performance, if evaluated, is often communicated by a single indicator and at an aggregated level; however, it does not embrace the trade-offs between different indicators and the inherent spatial heterogeneity of model efficiency. In this study, we simulated the water balance of the Mississippi watershed using the Soil and Water Assessment Tool (SWAT). The model was calibrated against monthly river discharge at 131 measurement stations. Its time series were bisected to allow for subsequent validation at the same gauges. Furthermore, the model was validated against evapotranspiration which was available as a continuous raster based on remote sensing. The model performance was evaluated for each of the 451 sub-watersheds using four different criteria: 1) Nash-Sutcliffe efficiency (NSE), 2) percent bias (PBIAS), 3) root mean square error (RMSE) normalized to standard deviation (RSR), as well as 4) a combined indicator of the squared correlation coefficient and the linear regression slope (bR2). Conditions that might lead to a poor model performance include aridity, a very flat and steep relief, snowfall and dams, as indicated by previous research. In an attempt to explain spatial differences in model efficiency, the goodness of the model was spatially compared to these four phenomena by means of a bivariate spatial association measure which combines Pearson's correlation coefficient and Moran's index for spatial autocorrelation. In order to assess the model performance of the Mississippi watershed as a whole, three different averages of the sub-watershed results were computed by 1) applying equal weights, 2) weighting by the mean observed river discharge, 3) weighting by the upstream catchment area and the square root of the time series length. Ratings of model performance differed significantly in space and according to efficiency criterion. The model performed much better in the humid Eastern region than in the arid Western region which was confirmed by the high spatial association with the aridity index (ratio of mean annual precipitation to mean annual potential evapotranspiration). This association was still significant when controlling for slopes which manifested the second highest spatial association. In line with these findings, overall model efficiency of the entire Mississippi watershed appeared better when weighted with mean observed river discharge. Furthermore, the model received the highest rating with regards to PBIAS and was judged worst when considering NSE as the most comprehensive indicator. No universal performance indicator exists that considers all aspects of a hydrograph. Therefore, sound model evaluation must take into account multiple criteria. Since model efficiency varies in space which is masked by aggregated ratings spatially explicit model goodness should be communicated as standard praxis - at least as a measure of spatial variability of indicators. Furthermore, transparent documentation of the evaluation procedure also with regards to weighting of aggregated model performance is crucial but often lacking in published research. Finally, the high spatial association between model performance and aridity highlights the need to improve modelling schemes for arid conditions as priority over other aspects that might weaken model goodness.

Real-time individualization of the unified model of performance.

PubMed

Liu, Jianbo; Ramakrishnan, Sridhar; Laxminarayan, Srinivas; Balkin, Thomas J; Reifman, Jaques

2017-12-01

Existing mathematical models for predicting neurobehavioural performance are not suited for mobile computing platforms because they cannot adapt model parameters automatically in real time to reflect individual differences in the effects of sleep loss. We used an extended Kalman filter to develop a computationally efficient algorithm that continually adapts the parameters of the recently developed Unified Model of Performance (UMP) to an individual. The algorithm accomplishes this in real time as new performance data for the individual become available. We assessed the algorithm's performance by simulating real-time model individualization for 18 subjects subjected to 64 h of total sleep deprivation (TSD) and 7 days of chronic sleep restriction (CSR) with 3 h of time in bed per night, using psychomotor vigilance task (PVT) data collected every 2 h during wakefulness. This UMP individualization process produced parameter estimates that progressively approached the solution produced by a post-hoc fitting of model parameters using all data. The minimum number of PVT measurements needed to individualize the model parameters depended upon the type of sleep-loss challenge, with ~30 required for TSD and ~70 for CSR. However, model individualization depended upon the overall duration of data collection, yielding increasingly accurate model parameters with greater number of days. Interestingly, reducing the PVT sampling frequency by a factor of two did not notably hamper model individualization. The proposed algorithm facilitates real-time learning of an individual's trait-like responses to sleep loss and enables the development of individualized performance prediction models for use in a mobile computing platform. © 2017 European Sleep Research Society.

Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

NASA Astrophysics Data System (ADS)

Jothiprakash, V.; Magar, R. B.

2012-07-01

SummaryIn this study, artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training-testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.

Sensor fusion display evaluation using information integration models in enhanced/synthetic vision applications

NASA Technical Reports Server (NTRS)

Foyle, David C.

1993-01-01

Based on existing integration models in the psychological literature, an evaluation framework is developed to assess sensor fusion displays as might be implemented in an enhanced/synthetic vision system. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The pilot's performance with the sensor fusion image is compared to models' predictions based on the pilot's performance when viewing the original component sensor images prior to fusion. This allows for the determination as to when a sensor fusion system leads to: poorer performance than one of the original sensor displays, clearly an undesirable system in which the fused sensor system causes some distortion or interference; better performance than with either single sensor system alone, but at a sub-optimal level compared to model predictions; optimal performance compared to model predictions; or, super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays.

Extending BPM Environments of Your Choice with Performance Related Decision Support

NASA Astrophysics Data System (ADS)

Fritzsche, Mathias; Picht, Michael; Gilani, Wasif; Spence, Ivor; Brown, John; Kilpatrick, Peter

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

The bidirectional pathways between internalizing and externalizing problems and academic performance from 6 to 18 years.

PubMed

Van der Ende, Jan; Verhulst, Frank C; Tiemeier, Henning

2016-08-01

Internalizing and externalizing problems are associated with poor academic performance, both concurrently and longitudinally. Important questions are whether problems precede academic performance or vice versa, whether both internalizing and externalizing are associated with academic problems when simultaneously tested, and whether associations and their direction depend on the informant providing information. These questions were addressed in a sample of 816 children who were assessed four times. The children were 6-10 years at baseline and 14-18 years at the last assessment. Parent-reported internalizing and externalizing problems and teacher-reported academic performance were tested in cross-lagged models to examine bidirectional paths between these constructs. These models were compared with cross-lagged models testing paths between teacher-reported internalizing and externalizing problems and parent-reported academic performance. Both final models revealed similar pathways from mostly externalizing problems to academic performance. No paths emerged from internalizing problems to academic performance. Moreover, paths from academic performance to internalizing and externalizing problems were only found when teachers reported on children's problems and not for parent-reported problems. Additional model tests revealed that paths were observed in both childhood and adolescence. Externalizing problems place children at increased risk of poor academic performance and should therefore be the target for interventions.

The Teacher, the Physician and the Person: Exploring Causal Connections between Teaching Performance and Role Model Types Using Directed Acyclic Graphs

PubMed Central

Boerebach, Benjamin C. M.; Lombarts, Kiki M. J. M. H.; Scherpbier, Albert J. J.; Arah, Onyebuchi A.

2013-01-01

Background In fledgling areas of research, evidence supporting causal assumptions is often scarce due to the small number of empirical studies conducted. In many studies it remains unclear what impact explicit and implicit causal assumptions have on the research findings; only the primary assumptions of the researchers are often presented. This is particularly true for research on the effect of faculty’s teaching performance on their role modeling. Therefore, there is a need for robust frameworks and methods for transparent formal presentation of the underlying causal assumptions used in assessing the causal effects of teaching performance on role modeling. This study explores the effects of different (plausible) causal assumptions on research outcomes. Methods This study revisits a previously published study about the influence of faculty’s teaching performance on their role modeling (as teacher-supervisor, physician and person). We drew eight directed acyclic graphs (DAGs) to visually represent different plausible causal relationships between the variables under study. These DAGs were subsequently translated into corresponding statistical models, and regression analyses were performed to estimate the associations between teaching performance and role modeling. Results The different causal models were compatible with major differences in the magnitude of the relationship between faculty’s teaching performance and their role modeling. Odds ratios for the associations between teaching performance and the three role model types ranged from 31.1 to 73.6 for the teacher-supervisor role, from 3.7 to 15.5 for the physician role, and from 2.8 to 13.8 for the person role. Conclusions Different sets of assumptions about causal relationships in role modeling research can be visually depicted using DAGs, which are then used to guide both statistical analysis and interpretation of results. Since study conclusions can be sensitive to different causal assumptions, results should be interpreted in the light of causal assumptions made in each study. PMID:23936020

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

PubMed Central

Marias, Kostas; Lambregts, Doenja M. J.; Nikiforaki, Katerina; van Heeswijk, Miriam M.; Bakers, Frans C. H.; Beets-Tan, Regina G. H.

2017-01-01

Purpose The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior. PMID:28863161

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

PubMed

Manikis, Georgios C; Marias, Kostas; Lambregts, Doenja M J; Nikiforaki, Katerina; van Heeswijk, Miriam M; Bakers, Frans C H; Beets-Tan, Regina G H; Papanikolaou, Nikolaos

2017-01-01

The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

Indonesian Private University Lecturer Performance Improvement Model to Improve a Sustainable Organization Performance

ERIC Educational Resources Information Center

Suryaman

2018-01-01

Lecturer performance will affect the quality and carrying capacity of the sustainability of an organization, in this case the university. There are many models developed to measure the performance of teachers, but not much to discuss the influence of faculty performance itself towards sustainability of an organization. This study was conducted in…

Regime-based evaluation of cloudiness in CMIP5 models

NASA Astrophysics Data System (ADS)

Jin, Daeho; Oreopoulos, Lazaros; Lee, Dongmin

2017-01-01

The concept of cloud regimes (CRs) is used to develop a framework for evaluating the cloudiness of 12 fifth Coupled Model Intercomparison Project (CMIP5) models. Reference CRs come from existing global International Satellite Cloud Climatology Project (ISCCP) weather states. The evaluation is made possible by the implementation in several CMIP5 models of the ISCCP simulator generating in each grid cell daily joint histograms of cloud optical thickness and cloud top pressure. Model performance is assessed with several metrics such as CR global cloud fraction (CF), CR relative frequency of occurrence (RFO), their product [long-term average total cloud amount (TCA)], cross-correlations of CR RFO maps, and a metric of resemblance between model and ISCCP CRs. In terms of CR global RFO, arguably the most fundamental metric, the models perform unsatisfactorily overall, except for CRs representing thick storm clouds. Because model CR CF is internally constrained by our method, RFO discrepancies yield also substantial TCA errors. Our results support previous findings that CMIP5 models underestimate cloudiness. The multi-model mean performs well in matching observed RFO maps for many CRs, but is still not the best for this or other metrics. When overall performance across all CRs is assessed, some models, despite shortcomings, apparently outperform Moderate Resolution Imaging Spectroradiometer cloud observations evaluated against ISCCP like another model output. Lastly, contrasting cloud simulation performance against each model's equilibrium climate sensitivity in order to gain insight on whether good cloud simulation pairs with particular values of this parameter, yields no clear conclusions.

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

PubMed

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Gas Path On-line Fault Diagnostics Using a Nonlinear Integrated Model for Gas Turbine Engines

NASA Astrophysics Data System (ADS)

Lu, Feng; Huang, Jin-quan; Ji, Chun-sheng; Zhang, Dong-dong; Jiao, Hua-bin

2014-08-01

Gas turbine engine gas path fault diagnosis is closely related technology that assists operators in managing the engine units. However, the performance gradual degradation is inevitable due to the usage, and it result in the model mismatch and then misdiagnosis by the popular model-based approach. In this paper, an on-line integrated architecture based on nonlinear model is developed for gas turbine engine anomaly detection and fault diagnosis over the course of the engine's life. These two engine models have different performance parameter update rate. One is the nonlinear real-time adaptive performance model with the spherical square-root unscented Kalman filter (SSR-UKF) producing performance estimates, and the other is a nonlinear baseline model for the measurement estimates. The fault detection and diagnosis logic is designed to discriminate sensor fault and component fault. This integration architecture is not only aware of long-term engine health degradation but also effective to detect gas path performance anomaly shifts while the engine continues to degrade. Compared to the existing architecture, the proposed approach has its benefit investigated in the experiment and analysis.

An ambient agent model for analyzing managers' performance during stress

NASA Astrophysics Data System (ADS)

ChePa, Noraziah; Aziz, Azizi Ab; Gratim, Haned

2016-08-01

Stress at work have been reported everywhere. Work related performance during stress is a pattern of reactions that occurs when managers are presented with work demands that are not matched with their knowledge, skills, or abilities, and which challenge their ability to cope. Although there are many prior findings pertaining to explain the development of manager performance during stress, less attention has been given to explain the same concept through computational models. In such, a descriptive nature in psychological theories about managers' performance during stress can be transformed into a causal-mechanistic stage that explains the relationship between a series of observed phenomena. This paper proposed an ambient agent model for analyzing managers' performance during stress. Set of properties and variables are identified through past literatures to construct the model. Differential equations have been used in formalizing the model. Set of equations reflecting relations involved in the proposed model are presented. The proposed model is essential and can be encapsulated within an intelligent agent or robots that can be used to support managers during stress.

Design and performance characterization strategy using modeling for biofiltration control of odorous hydrogen sulfide.

PubMed

Martin, Ronald W; Mihelcic, James R; Crittenden, John C

2004-07-01

Biofilter, dynamic modeling software characterizing contaminant removal via biofiltration, was used in the preliminary design of a biofilter to treat odorous hydrogen sulfide (H2S). Steady-state model simulations were run to generate performance plots for various influent concentrations, loadings, residence times, media sizes, and temperatures. Although elimination capacity and removal efficiency frequently are used to characterize biofilter performance, effluent concentration can be used to characterize performance when treating to a target effluent concentration. Model simulations illustrate that, at a given temperature, a biofilter cannot reduce H2S emissions below a minimum value, no matter how large the biofilter or how long the residence time. However, a higher biofilter temperature results in lower effluent H2S concentrations. Because dynamic model simulations show that shock loading can significantly increase the effluent concentration above values predicted by the steady-state model simulations, it is recommended that, to consistently meet treatment objectives, dynamic feed conditions should be considered. This study illustrates that modeling can serve as a valuable tool in the design and performance optimization of biofilters.

Human performance modeling for system of systems analytics :soldier fatigue.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lawton, Craig R.; Campbell, James E.; Miller, Dwight Peter

2005-10-01

The military has identified Human Performance Modeling (HPM) as a significant requirement and challenge of future systems modeling and analysis initiatives as can be seen in the Department of Defense's (DoD) Defense Modeling and Simulation Office's (DMSO) Master Plan (DoD 5000.59-P 1995). To this goal, the military is currently spending millions of dollars on programs devoted to HPM in various military contexts. Examples include the Human Performance Modeling Integration (HPMI) program within the Air Force Research Laboratory, which focuses on integrating HPMs with constructive models of systems (e.g. cockpit simulations) and the Navy's Human Performance Center (HPC) established in Septembermore » 2003. Nearly all of these initiatives focus on the interface between humans and a single system. This is insufficient in the era of highly complex network centric SoS. This report presents research and development in the area of HPM in a system-of-systems (SoS). Specifically, this report addresses modeling soldier fatigue and the potential impacts soldier fatigue can have on SoS performance.« less

Systems cost/performance analysis (study 2.3). Volume 2: Systems cost/performance model. [unmanned automated payload programs and program planning

NASA Technical Reports Server (NTRS)

Campbell, B. H.

1974-01-01

A methodology which was developed for balanced designing of spacecraft subsystems and interrelates cost, performance, safety, and schedule considerations was refined. The methodology consists of a two-step process: the first step is one of selecting all hardware designs which satisfy the given performance and safety requirements, the second step is one of estimating the cost and schedule required to design, build, and operate each spacecraft design. Using this methodology to develop a systems cost/performance model allows the user of such a model to establish specific designs and the related costs and schedule. The user is able to determine the sensitivity of design, costs, and schedules to changes in requirements. The resulting systems cost performance model is described and implemented as a digital computer program.

Strategies for concurrent processing of complex algorithms in data driven architectures

NASA Technical Reports Server (NTRS)

Stoughton, John W.; Mielke, Roland R.; Som, Sukhamony

1990-01-01

The performance modeling and enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures is examined. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called ATAMM (Algorithm To Architecture Mapping Model). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Achievement Goals and Achievement Emotions: Testing a Model of Their Joint Relations with Academic Performance

ERIC Educational Resources Information Center

Pekrun, Reinhard; Elliot, Andrew J.; Maier, Markus A.

2009-01-01

The authors propose a theoretical model linking achievement goals and achievement emotions to academic performance. This model was tested in a prospective study with undergraduates (N = 213), using exam-specific assessments of both goals and emotions as predictors of exam performance in an introductory-level psychology course. The findings were…

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

The performance evaluation model of mining project founded on the weight optimization entropy value method

NASA Astrophysics Data System (ADS)

Mao, Chao; Chen, Shou

2017-01-01

According to the traditional entropy value method still have low evaluation accuracy when evaluating the performance of mining projects, a performance evaluation model of mineral project founded on improved entropy is proposed. First establish a new weight assignment model founded on compatible matrix analysis of analytic hierarchy process (AHP) and entropy value method, when the compatibility matrix analysis to achieve consistency requirements, if it has differences between subjective weights and objective weights, moderately adjust both proportions, then on this basis, the fuzzy evaluation matrix for performance evaluation. The simulation experiments show that, compared with traditional entropy and compatible matrix analysis method, the proposed performance evaluation model of mining project based on improved entropy value method has higher accuracy assessment.

Installation effects on performance of multiple model V/STOL lift fans

NASA Technical Reports Server (NTRS)

Diedrich, J. H.; Clough, N.; Lieblein, S.

1972-01-01

An experimental program was performed in which the individual performance of multiple VTOL model lift fans was measured. The model tested consisted of three 5.5 in. diameter tip-turbine driven model VTOL lift fans mounted chordwise in a two-dimensional wing to simulate a pod-type array. The performance data provided significant insight into possible thrust variations and losses caused by the presence of cover doors, adjacent fuselage panels, and adjacent fans. The effect of a partial loss of drive air supply (simulated gas generator failure) on fan performance was also investigated. The results of the tests demonstrated that lift fan installation variables and hardware can have a significant effect on the thrust of the individual fans.

Formulating Spatially Varying Performance in the Statistical Fusion Framework

PubMed Central

Landman, Bennett A.

2012-01-01

To date, label fusion methods have primarily relied either on global (e.g. STAPLE, globally weighted vote) or voxelwise (e.g. locally weighted vote) performance models. Optimality of the statistical fusion framework hinges upon the validity of the stochastic model of how a rater errs (i.e., the labeling process model). Hitherto, approaches have tended to focus on the extremes of potential models. Herein, we propose an extension to the STAPLE approach to seamlessly account for spatially varying performance by extending the performance level parameters to account for a smooth, voxelwise performance level field that is unique to each rater. This approach, Spatial STAPLE, provides significant improvements over state-of-the-art label fusion algorithms in both simulated and empirical data sets. PMID:22438513

Performance Analysis of GFDL's GCM Line-By-Line Radiative Transfer Model on GPU and MIC Architectures

NASA Astrophysics Data System (ADS)

Menzel, R.; Paynter, D.; Jones, A. L.

2017-12-01

Due to their relatively low computational cost, radiative transfer models in global climate models (GCMs) run on traditional CPU architectures generally consist of shortwave and longwave parameterizations over a small number of wavelength bands. With the rise of newer GPU and MIC architectures, however, the performance of high resolution line-by-line radiative transfer models may soon approach those of the physical parameterizations currently employed in GCMs. Here we present an analysis of the current performance of a new line-by-line radiative transfer model currently under development at GFDL. Although originally designed to specifically exploit GPU architectures through the use of CUDA, the radiative transfer model has recently been extended to include OpenMP in an effort to also effectively target MIC architectures such as Intel's Xeon Phi. Using input data provided by the upcoming Radiative Forcing Model Intercomparison Project (RFMIP, as part of CMIP 6), we compare model results and performance data for various model configurations and spectral resolutions run on both GPU and Intel Knights Landing architectures to analogous runs of the standard Oxford Reference Forward Model on traditional CPUs.

Stiffness modeling of compliant parallel mechanisms and applications in the performance analysis of a decoupled parallel compliant stage

NASA Astrophysics Data System (ADS)

Jiang, Yao; Li, Tie-Min; Wang, Li-Ping

2015-09-01

This paper investigates the stiffness modeling of compliant parallel mechanism (CPM) based on the matrix method. First, the general compliance matrix of a serial flexure chain is derived. The stiffness modeling of CPMs is next discussed in detail, considering the relative positions of the applied load and the selected displacement output point. The derived stiffness models have simple and explicit forms, and the input, output, and coupling stiffness matrices of the CPM can easily be obtained. The proposed analytical model is applied to the stiffness modeling and performance analysis of an XY parallel compliant stage with input and output decoupling characteristics. Then, the key geometrical parameters of the stage are optimized to obtain the minimum input decoupling degree. Finally, a prototype of the compliant stage is developed and its input axial stiffness, coupling characteristics, positioning resolution, and circular contouring performance are tested. The results demonstrate the excellent performance of the compliant stage and verify the effectiveness of the proposed theoretical model. The general stiffness models provided in this paper will be helpful for performance analysis, especially in determining coupling characteristics, and the structure optimization of the CPM.

Automated Design Space Exploration with Aspen

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spafford, Kyle L.; Vetter, Jeffrey S.

Architects and applications scientists often use performance models to explore a multidimensional design space of architectural characteristics, algorithm designs, and application parameters. With traditional performance modeling tools, these explorations forced users to first develop a performance model and then repeatedly evaluate and analyze the model manually. These manual investigations proved laborious and error prone. More importantly, the complexity of this traditional process often forced users to simplify their investigations. To address this challenge of design space exploration, we extend our Aspen (Abstract Scalable Performance Engineering Notation) language with three new language constructs: user-defined resources, parameter ranges, and a collection ofmore » costs in the abstract machine model. Then, we use these constructs to enable automated design space exploration via a nonlinear optimization solver. We show how four interesting classes of design space exploration scenarios can be derived from Aspen models and formulated as pure nonlinear programs. The analysis tools are demonstrated using examples based on Aspen models for a three-dimensional Fast Fourier Transform, the CoMD molecular dynamics proxy application, and the DARPA Streaming Sensor Challenge Problem. Our results show that this approach can compose and solve arbitrary performance modeling questions quickly and rigorously when compared to the traditional manual approach.« less

Automated Design Space Exploration with Aspen

DOE PAGES

Spafford, Kyle L.; Vetter, Jeffrey S.

2015-01-01

Architects and applications scientists often use performance models to explore a multidimensional design space of architectural characteristics, algorithm designs, and application parameters. With traditional performance modeling tools, these explorations forced users to first develop a performance model and then repeatedly evaluate and analyze the model manually. These manual investigations proved laborious and error prone. More importantly, the complexity of this traditional process often forced users to simplify their investigations. To address this challenge of design space exploration, we extend our Aspen (Abstract Scalable Performance Engineering Notation) language with three new language constructs: user-defined resources, parameter ranges, and a collection ofmore » costs in the abstract machine model. Then, we use these constructs to enable automated design space exploration via a nonlinear optimization solver. We show how four interesting classes of design space exploration scenarios can be derived from Aspen models and formulated as pure nonlinear programs. The analysis tools are demonstrated using examples based on Aspen models for a three-dimensional Fast Fourier Transform, the CoMD molecular dynamics proxy application, and the DARPA Streaming Sensor Challenge Problem. Our results show that this approach can compose and solve arbitrary performance modeling questions quickly and rigorously when compared to the traditional manual approach.« less

Performance Model and Sensitivity Analysis for a Solar Thermoelectric Generator

NASA Astrophysics Data System (ADS)

Rehman, Naveed Ur; Siddiqui, Mubashir Ali

2017-03-01

In this paper, a regression model for evaluating the performance of solar concentrated thermoelectric generators (SCTEGs) is established and the significance of contributing parameters is discussed in detail. The model is based on several natural, design and operational parameters of the system, including the thermoelectric generator (TEG) module and its intrinsic material properties, the connected electrical load, concentrator attributes, heat transfer coefficients, solar flux, and ambient temperature. The model is developed by fitting a response curve, using the least-squares method, to the results. The sample points for the model were obtained by simulating a thermodynamic model, also developed in this paper, over a range of values of input variables. These samples were generated employing the Latin hypercube sampling (LHS) technique using a realistic distribution of parameters. The coefficient of determination was found to be 99.2%. The proposed model is validated by comparing the predicted results with those in the published literature. In addition, based on the elasticity for parameters in the model, sensitivity analysis was performed and the effects of parameters on the performance of SCTEGs are discussed in detail. This research will contribute to the design and performance evaluation of any SCTEG system for a variety of applications.

Feedforward object-vision models only tolerate small image variations compared to human

PubMed Central

Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2014-01-01

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e., briefly presented masked stimuli with complex image variations), human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modeling. We show that this approach is not of significant help in solving the computational crux of object recognition (i.e., invariant object recognition) when the identity-preserving image variations become more complex. PMID:25100986

Review of Methods for Buildings Energy Performance Modelling

NASA Astrophysics Data System (ADS)

Krstić, Hrvoje; Teni, Mihaela

2017-10-01

Research presented in this paper gives a brief review of methods used for buildings energy performance modelling. This paper gives also a comprehensive review of the advantages and disadvantages of available methods as well as the input parameters used for modelling buildings energy performance. European Directive EPBD obliges the implementation of energy certification procedure which gives an insight on buildings energy performance via exiting energy certificate databases. Some of the methods for buildings energy performance modelling mentioned in this paper are developed by employing data sets of buildings which have already undergone an energy certification procedure. Such database is used in this paper where the majority of buildings in the database have already gone under some form of partial retrofitting - replacement of windows or installation of thermal insulation but still have poor energy performance. The case study presented in this paper utilizes energy certificates database obtained from residential units in Croatia (over 400 buildings) in order to determine the dependence between buildings energy performance and variables from database by using statistical dependencies tests. Building energy performance in database is presented with building energy efficiency rate (from A+ to G) which is based on specific annual energy needs for heating for referential climatic data [kWh/(m2a)]. Independent variables in database are surfaces and volume of the conditioned part of the building, building shape factor, energy used for heating, CO2 emission, building age and year of reconstruction. Research results presented in this paper give an insight in possibilities of methods used for buildings energy performance modelling. Further on it gives an analysis of dependencies between buildings energy performance as a dependent variable and independent variables from the database. Presented results could be used for development of new building energy performance predictive model.

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

PubMed

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

2016-01-01

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

Petascale computation performance of lightweight multiscale cardiac models using hybrid programming models.

PubMed

Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias

2011-01-01

Future multiscale and multiphysics models must use the power of high performance computing (HPC) systems to enable research into human disease, translational medical science, and treatment. Previously we showed that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message passing processes (e.g. the message passing interface (MPI)) with multithreading (e.g. OpenMP, POSIX pthreads). The objective of this work is to compare the performance of such hybrid programming models when applied to the simulation of a lightweight multiscale cardiac model. Our results show that the hybrid models do not perform favourably when compared to an implementation using only MPI which is in contrast to our results using complex physiological models. Thus, with regards to lightweight multiscale cardiac models, the user may not need to increase programming complexity by using a hybrid programming approach. However, considering that model complexity will increase as well as the HPC system size in both node count and number of cores per node, it is still foreseeable that we will achieve faster than real time multiscale cardiac simulations on these systems using hybrid programming models.

Facial Performance Transfer via Deformable Models and Parametric Correspondence.

PubMed

Asthana, Akshay; de la Hunty, Miles; Dhall, Abhinav; Goecke, Roland

2012-09-01

The issue of transferring facial performance from one person's face to another's has been an area of interest for the movie industry and the computer graphics community for quite some time. In recent years, deformable face models, such as the Active Appearance Model (AAM), have made it possible to track and synthesize faces in real time. Not surprisingly, deformable face model-based approaches for facial performance transfer have gained tremendous interest in the computer vision and graphics community. In this paper, we focus on the problem of real-time facial performance transfer using the AAM framework. We propose a novel approach of learning the mapping between the parameters of two completely independent AAMs, using them to facilitate the facial performance transfer in a more realistic manner than previous approaches. The main advantage of modeling this parametric correspondence is that it allows a "meaningful" transfer of both the nonrigid shape and texture across faces irrespective of the speakers' gender, shape, and size of the faces, and illumination conditions. We explore linear and nonlinear methods for modeling the parametric correspondence between the AAMs and show that the sparse linear regression method performs the best. Moreover, we show the utility of the proposed framework for a cross-language facial performance transfer that is an area of interest for the movie dubbing industry.

POPEYE: A production rule-based model of multitask supervisory control (POPCORN)

NASA Technical Reports Server (NTRS)

Townsend, James T.; Kadlec, Helena; Kantowitz, Barry H.

1988-01-01

Recent studies of relationships between subjective ratings of mental workload, performance, and human operator and task characteristics have indicated that these relationships are quite complex. In order to study the various relationships and place subjective mental workload within a theoretical framework, we developed a production system model for the performance component of the complex supervisory task called POPCORN. The production system model is represented by a hierarchial structure of goals and subgoals, and the information flow is controlled by a set of condition-action rules. The implementation of this production system, called POPEYE, generates computer simulated data under different task difficulty conditions which are comparable to those of human operators performing the task. This model is the performance aspect of an overall dynamic psychological model which we are developing to examine and quantify relationships between performance and psychological aspects in a complex environment.

Neural network submodel as an abstraction tool: relating network performance to combat outcome

NASA Astrophysics Data System (ADS)

Jablunovsky, Greg; Dorman, Clark; Yaworsky, Paul S.

2000-06-01

Simulation of Command and Control (C2) networks has historically emphasized individual system performance with little architectural context or credible linkage to `bottom- line' measures of combat outcomes. Renewed interest in modeling C2 effects and relationships stems from emerging network intensive operational concepts. This demands improved methods to span the analytical hierarchy between C2 system performance models and theater-level models. Neural network technology offers a modeling approach that can abstract the essential behavior of higher resolution C2 models within a campaign simulation. The proposed methodology uses off-line learning of the relationships between network state and campaign-impacting performance of a complex C2 architecture and then approximation of that performance as a time-varying parameter in an aggregated simulation. Ultimately, this abstraction tool offers an increased fidelity of C2 system simulation that captures dynamic network dependencies within a campaign context.

Performance characteristics of the Cooper PC-9 centrifugal compressor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foster, R.E.; Neely, R.F.

1988-06-30

Mathematical performance modeling of the PC-9 centrifugal compressor has been completed. Performance characteristics curves have never been obtained for them in test loops with the same degree of accuracy as for the uprated axial compressors and, consequently, computer modeling of the top cascade and purge cascades has been very difficult and of limited value. This compressor modeling work has been carried out in an attempt to generate data which would more accurately define the compressor's performance and would permit more accurate cascade modeling. A computer code, COMPAL, was used to mathematically model the PC-9 performance with variations in gas composition,more » flow ratios, pressure ratios, speed and temperature. The results of this effort, in the form of graphs, with information about the compressor and the code, are the subject of this report. Compressor characteristic curves are featured. 13 figs.« less

Systems, methods and computer-readable media for modeling cell performance fade of rechargeable electrochemical devices

DOEpatents

Gering, Kevin L

2013-08-27

A system includes an electrochemical cell, monitoring hardware, and a computing system. The monitoring hardware periodically samples performance characteristics of the electrochemical cell. The computing system determines cell information from the performance characteristics of the electrochemical cell. The computing system also develops a mechanistic level model of the electrochemical cell to determine performance fade characteristics of the electrochemical cell and analyzing the mechanistic level model to estimate performance fade characteristics over aging of a similar electrochemical cell. The mechanistic level model uses first constant-current pulses applied to the electrochemical cell at a first aging period and at three or more current values bracketing a first exchange current density. The mechanistic level model also is based on second constant-current pulses applied to the electrochemical cell at a second aging period and at three or more current values bracketing the second exchange current density.

Practical Techniques for Modeling Gas Turbine Engine Performance

NASA Technical Reports Server (NTRS)

Chapman, Jeffryes W.; Lavelle, Thomas M.; Litt, Jonathan S.

2016-01-01

The cost and risk associated with the design and operation of gas turbine engine systems has led to an increasing dependence on mathematical models. In this paper, the fundamentals of engine simulation will be reviewed, an example performance analysis will be performed, and relationships useful for engine control system development will be highlighted. The focus will be on thermodynamic modeling utilizing techniques common in industry, such as: the Brayton cycle, component performance maps, map scaling, and design point criteria generation. In general, these topics will be viewed from the standpoint of an example turbojet engine model; however, demonstrated concepts may be adapted to other gas turbine systems, such as gas generators, marine engines, or high bypass aircraft engines. The purpose of this paper is to provide an example of gas turbine model generation and system performance analysis for educational uses, such as curriculum creation or student reference.

Weather model performance on extreme rainfall events simulation's over Western Iberian Peninsula

NASA Astrophysics Data System (ADS)

Pereira, S. C.; Carvalho, A. C.; Ferreira, J.; Nunes, J. P.; Kaiser, J. J.; Rocha, A.

2012-08-01

This study evaluates the performance of the WRF-ARW numerical weather model in simulating the spatial and temporal patterns of an extreme rainfall period over a complex orographic region in north-central Portugal. The analysis was performed for the December month of 2009, during the Portugal Mainland rainy season. The heavy rainfall to extreme heavy rainfall periods were due to several low surface pressure's systems associated with frontal surfaces. The total amount of precipitation for December exceeded, in average, the climatological mean for the 1971-2000 time period in +89 mm, varying from 190 mm (south part of the country) to 1175 mm (north part of the country). Three model runs were conducted to assess possible improvements in model performance: (1) the WRF-ARW is forced with the initial fields from a global domain model (RunRef); (2) data assimilation for a specific location (RunObsN) is included; (3) nudging is used to adjust the analysis field (RunGridN). Model performance was evaluated against an observed hourly precipitation dataset of 15 rainfall stations using several statistical parameters. The WRF-ARW model reproduced well the temporal rainfall patterns but tended to overestimate precipitation amounts. The RunGridN simulation provided the best results but model performance of the other two runs was good too, so that the selected extreme rainfall episode was successfully reproduced.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

PubMed

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Model Performance of Water-Current Meters

USGS Publications Warehouse

Fulford, J.M.; ,

2002-01-01

The measurement of discharge in natural streams requires hydrographers to use accurate water-current meters that have consistent performance among meters of the same model. This paper presents the results of an investigation into the performance of four models of current meters - Price type-AA, Price pygmy, Marsh McBirney 2000 and Swoffer 2100. Tests for consistency and accuracy for six meters of each model are summarized. Variation of meter performance within a model is used as an indicator of consistency, and percent velocity error that is computed from a measured reference velocity is used as an indicator of meter accuracy. Velocities measured by each meter are also compared to the manufacturer's published or advertised accuracy limits. For the meters tested, the Price models werer found to be more accurate and consistent over the range of test velocities compared to the other models. The Marsh McBirney model usually measured within its accuracy specification. The Swoffer meters did not meet the stringent Swoffer accuracy limits for all the velocities tested.

Calculation of the Aerodynamic Behavior of the Tilt Rotor Aeroacoustic Model (TRAM) in the DNW

NASA Technical Reports Server (NTRS)

Johnson, Wayne

2001-01-01

Comparisons of measured and calculated aerodynamic behavior of a tiltrotor model are presented. The test of the Tilt Rotor Aeroacoustic Model (TRAM) with a single, 1/4-scale V- 22 rotor in the German-Dutch Wind Tunnel (DNW) provides an extensive set of aeroacoustic, performance, and structural loads data. The calculations were performed using the rotorcraft comprehensive analysis CAMRAD II. Presented are comparisons of measured and calculated performance and airloads for helicopter mode operation, as well as calculated induced and profile power. An aerodynamic and wake model and calculation procedure that reflects the unique geometry and phenomena of tiltrotors has been developed. There are major differences between this model and the corresponding aerodynamic and wake model that has been established for helicopter rotors. In general, good correlation between measured and calculated performance and airloads behavior has been shown. Two aspects of the analysis that clearly need improvement are the stall delay model and the trailed vortex formation model.

A comparison of linear and nonlinear statistical techniques in performance attribution.

PubMed

Chan, N H; Genovese, C R

2001-01-01

Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.

Evaluation of performance of distributed delay model for chemotherapy-induced myelosuppression.

PubMed

Krzyzanski, Wojciech; Hu, Shuhua; Dunlavey, Michael

2018-04-01

The distributed delay model has been introduced that replaces the transit compartments in the classic model of chemotherapy-induced myelosuppression with a convolution integral. The maturation of granulocyte precursors in the bone marrow is described by the gamma probability density function with the shape parameter (ν). If ν is a positive integer, the distributed delay model coincides with the classic model with ν transit compartments. The purpose of this work was to evaluate performance of the distributed delay model with particular focus on model deterministic identifiability in the presence of the shape parameter. The classic model served as a reference for comparison. Previously published white blood cell (WBC) count data in rats receiving bolus doses of 5-fluorouracil were fitted by both models. The negative two log-likelihood objective function (-2LL) and running times were used as major markers of performance. Local sensitivity analysis was done to evaluate the impact of ν on the pharmacodynamics response WBC. The ν estimate was 1.46 with 16.1% CV% compared to ν = 3 for the classic model. The difference of 6.78 in - 2LL between classic model and the distributed delay model implied that the latter performed significantly better than former according to the log-likelihood ratio test (P = 0.009), although the overall performance was modestly better. The running times were 1 s and 66.2 min, respectively. The long running time of the distributed delay model was attributed to computationally intensive evaluation of the convolution integral. The sensitivity analysis revealed that ν strongly influences the WBC response by controlling cell proliferation and elimination of WBCs from the circulation. In conclusion, the distributed delay model was deterministically identifiable from typical cytotoxic data. Its performance was modestly better than the classic model with significantly longer running time.

Evaluation of atmospheric nitrogen deposition model performance in the context of U.S. critical load assessments

NASA Astrophysics Data System (ADS)

Williams, Jason J.; Chung, Serena H.; Johansen, Anne M.; Lamb, Brian K.; Vaughan, Joseph K.; Beutel, Marc

2017-02-01

Air quality models are widely used to estimate pollutant deposition rates and thereby calculate critical loads and critical load exceedances (model deposition > critical load). However, model operational performance is not always quantified specifically to inform these applications. We developed a performance assessment approach designed to inform critical load and exceedance calculations, and applied it to the Pacific Northwest region of the U.S. We quantified wet inorganic N deposition performance of several widely-used air quality models, including five different Community Multiscale Air Quality Model (CMAQ) simulations, the Tdep model, and 'PRISM x NTN' model. Modeled wet inorganic N deposition estimates were compared to wet inorganic N deposition measurements at 16 National Trends Network (NTN) monitoring sites, and to annual bulk inorganic N deposition measurements at Mount Rainier National Park. Model bias (model - observed) and error (|model - observed|) were expressed as a percentage of regional critical load values for diatoms and lichens. This novel approach demonstrated that wet inorganic N deposition bias in the Pacific Northwest approached or exceeded 100% of regional diatom and lichen critical load values at several individual monitoring sites, and approached or exceeded 50% of critical loads when averaged regionally. Even models that adjusted deposition estimates based on deposition measurements to reduce bias or that spatially-interpolated measurement data, had bias that approached or exceeded critical loads at some locations. While wet inorganic N deposition model bias is only one source of uncertainty that can affect critical load and exceedance calculations, results demonstrate expressing bias as a percentage of critical loads at a spatial scale consistent with calculations may be a useful exercise for those performing calculations. It may help decide if model performance is adequate for a particular calculation, help assess confidence in calculation results, and highlight cases where a non-deterministic approach may be needed.

Performance of European chemistry transport models as function of horizontal resolution

NASA Astrophysics Data System (ADS)

Schaap, M.; Cuvelier, C.; Hendriks, C.; Bessagnet, B.; Baldasano, J. M.; Colette, A.; Thunis, P.; Karam, D.; Fagerli, H.; Graff, A.; Kranenburg, R.; Nyiri, A.; Pay, M. T.; Rouïl, L.; Schulz, M.; Simpson, D.; Stern, R.; Terrenoire, E.; Wind, P.

2015-07-01

Air pollution causes adverse effects on human health as well as ecosystems and crop yield and also has an impact on climate change trough short-lived climate forcers. To design mitigation strategies for air pollution, 3D Chemistry Transport Models (CTMs) have been developed to support the decision process. Increases in model resolution may provide more accurate and detailed information, but will cubically increase computational costs and pose additional challenges concerning high resolution input data. The motivation for the present study was therefore to explore the impact of using finer horizontal grid resolution for policy support applications of the European Monitoring and Evaluation Programme (EMEP) model within the Long Range Transboundary Air Pollution (LRTAP) convention. The goal was to determine the "optimum resolution" at which additional computational efforts do not provide increased model performance using presently available input data. Five regional CTMs performed four runs for 2009 over Europe at different horizontal resolutions. The models' responses to an increase in resolution are broadly consistent for all models. The largest response was found for NO2 followed by PM10 and O3. Model resolution does not impact model performance for rural background conditions. However, increasing model resolution improves the model performance at stations in and near large conglomerations. The statistical evaluation showed that the increased resolution better reproduces the spatial gradients in pollution regimes, but does not help to improve significantly the model performance for reproducing observed temporal variability. This study clearly shows that increasing model resolution is advantageous, and that leaving a resolution of 50 km in favour of a resolution between 10 and 20 km is practical and worthwhile. As about 70% of the model response to grid resolution is determined by the difference in the spatial emission distribution, improved emission allocation procedures at high spatial and temporal resolution are a crucial factor for further model resolution improvements.

Modeling Ni-Cd performance. Planned alterations to the Goddard battery model

NASA Technical Reports Server (NTRS)

Jagielski, J. M.

1986-01-01

The Goddard Space Flight Center (GSFC) currently has a preliminary computer model to simulate a Nickel Cadmium (Ni-Cd) performance. The basic methodology of the model was described in the paper entitled Fundamental Algorithms of the Goddard Battery Model. At present, the model is undergoing alterations to increase its efficiency, accuracy, and generality. A review of the present battery model is given, and the planned charges of the model are described.

(abstract) Simple Spreadsheet Thermal Models for Cryogenic Applications

NASA Technical Reports Server (NTRS)

Nash, A. E.

1994-01-01

Self consistent circuit analog thermal models, that can be run in commercial spreadsheet programs on personal computers, have been created to calculate the cooldown and steady state performance of cryogen cooled Dewars. The models include temperature dependent conduction and radiation effects. The outputs of the models provide temperature distribution and Dewar performance information. These models have been used to analyze the Cryogenic Telescope Test Facility (CTTF). The facility will be on line in early 1995 for its first user, the Infrared Telescope Technology Testbed (ITTT), for the Space Infrared Telescope Facility (SIRTF) at JPL. The model algorithm as well as a comparison of the model predictions and actual performance of this facility will be presented.

Simple Spreadsheet Thermal Models for Cryogenic Applications

NASA Technical Reports Server (NTRS)

Nash, Alfred

1995-01-01

Self consistent circuit analog thermal models that can be run in commercial spreadsheet programs on personal computers have been created to calculate the cooldown and steady state performance of cryogen cooled Dewars. The models include temperature dependent conduction and radiation effects. The outputs of the models provide temperature distribution and Dewar performance information. these models have been used to analyze the SIRTF Telescope Test Facility (STTF). The facility has been brought on line for its first user, the Infrared Telescope Technology Testbed (ITTT), for the Space Infrared Telescope Facility (SIRTF) at JPL. The model algorithm as well as a comparison between the models' predictions and actual performance of this facility will be presented.

KU-Band rendezvous radar performance computer simulation model

NASA Technical Reports Server (NTRS)

Griffin, J. W.

1980-01-01

The preparation of a real time computer simulation model of the KU band rendezvous radar to be integrated into the shuttle mission simulator (SMS), the shuttle engineering simulator (SES), and the shuttle avionics integration laboratory (SAIL) simulator is described. To meet crew training requirements a radar tracking performance model, and a target modeling method were developed. The parent simulation/radar simulation interface requirements, and the method selected to model target scattering properties, including an application of this method to the SPAS spacecraft are described. The radar search and acquisition mode performance model and the radar track mode signal processor model are examined and analyzed. The angle, angle rate, range, and range rate tracking loops are also discussed.

Mixed Phase Modeling in GlennICE with Application to Engine Icing

NASA Technical Reports Server (NTRS)

Wright, William B.; Jorgenson, Philip C. E.; Veres, Joseph P.

2011-01-01

A capability for modeling ice crystals and mixed phase icing has been added to GlennICE. Modifications have been made to the particle trajectory algorithm and energy balance to model this behavior. This capability has been added as part of a larger effort to model ice crystal ingestion in aircraft engines. Comparisons have been made to four mixed phase ice accretions performed in the Cox icing tunnel in order to calibrate an ice erosion model. A sample ice ingestion case was performed using the Energy Efficient Engine (E3) model in order to illustrate current capabilities. Engine performance characteristics were supplied using the Numerical Propulsion System Simulation (NPSS) model for this test case.

ATAMM enhancement and multiprocessor performance evaluation

NASA Technical Reports Server (NTRS)

Stoughton, John W.; Mielke, Roland R.; Som, Sukhamoy; Obando, Rodrigo; Malekpour, Mahyar R.; Jones, Robert L., III; Mandala, Brij Mohan V.

1991-01-01

ATAMM (Algorithm To Architecture Mapping Model) enhancement and multiprocessor performance evaluation is discussed. The following topics are included: the ATAMM model; ATAMM enhancement; ADM (Advanced Development Model) implementation of ATAMM; and ATAMM support tools.

The Social Responsibility Performance Outcomes Model: Building Socially Responsible Companies through Performance Improvement Outcomes.

ERIC Educational Resources Information Center

Hatcher, Tim

2000-01-01

Considers the role of performance improvement professionals and human resources development professionals in helping organizations realize the ethical and financial power of corporate social responsibility. Explains the social responsibility performance outcomes model, which incorporates the concepts of societal needs and outcomes. (LRW)

What Do HPT Consultants Do for Performance Analysis?

ERIC Educational Resources Information Center

Kang, Sung

2017-01-01

This study was conducted to contribute to the field of Human Performance Technology (HPT) through the validation of the performance analysis process of the International Society for Performance Improvement (ISPI) HPT model, the most representative and frequently utilized process model in the HPT field. The study was conducted using content…

Double multiple streamtube model with recent improvements

NASA Astrophysics Data System (ADS)

Paraschivoiu, I.; Delclaux, F.

1983-06-01

The objective of the present paper is to show the new capabilities of the double multiple streamtube (DMS) model for predicting the aerodynamic loads and performance of the Darrieus vertical-axis turbine. The original DMS model has been improved (DMSV model) by considering the variation in the upwind and downwind induced velocities as a function of the azimuthal angle for each streamtube. A comparison is made of the rotor performance for several blade geometries (parabola, catenary, troposkien, and Sandia shape). A new formulation is given for an approximate troposkien shape by considering the effect of the gravitational field. The effects of three NACA symmetrical profiles, 0012, 0015 and 0018, on the aerodynamic performance of the turbine are shown. Finally, a semiempirical dynamic-stall model has been incorporated and a better approximation obtained for modeling the local aerodynamic forces and performance for a Darrieus rotor.

Confirming the Value of Swimming-Performance Models for Adolescents.

PubMed

Dormehl, Shilo J; Robertson, Samuel J; Barker, Alan R; Williams, Craig A

2017-10-01

To evaluate the efficacy of existing performance models to assess the progression of male and female adolescent swimmers through a quantitative and qualitative mixed-methods approach. Fourteen published models were tested using retrospective data from an independent sample of Dutch junior national-level swimmers from when they were 12-18 y of age (n = 13). The degree of association by Pearson correlations was compared between the calculated differences from the models and quadratic functions derived from the Dutch junior national qualifying times. Swimmers were grouped based on their differences from the models and compared with their swimming histories that were extracted from questionnaires and follow-up interviews. Correlations of the deviations from both the models and quadratic functions derived from the Dutch qualifying times were all significant except for the 100-m breaststroke and butterfly and the 200-m freestyle for females (P < .05). In addition, the 100-m freestyle and backstroke for males and 200-m freestyle for males and females were almost directly proportional. In general, deviations from the models were accounted for by the swimmers' training histories. Higher levels of retrospective motivation appeared to be synonymous with higher-level career performance. This mixed-methods approach helped confirm the validity of the models that were found to be applicable to adolescent swimmers at all levels, allowing coaches to track performance and set goals. The value of the models in being able to account for the expected performance gains during adolescence enables quantification of peripheral factors that could affect performance.

Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps)

NASA Astrophysics Data System (ADS)

Réveillet, Marion; Six, Delphine; Vincent, Christian; Rabatel, Antoine; Dumont, Marie; Lafaysse, Matthieu; Morin, Samuel; Vionnet, Vincent; Litt, Maxime

2018-04-01

This study focuses on simulations of the seasonal and annual surface mass balance (SMB) of Saint-Sorlin Glacier (French Alps) for the period 1996-2015 using the detailed SURFEX/ISBA-Crocus snowpack model. The model is forced by SAFRAN meteorological reanalysis data, adjusted with automatic weather station (AWS) measurements to ensure that simulations of all the energy balance components, in particular turbulent fluxes, are accurately represented with respect to the measured energy balance. Results indicate good model performance for the simulation of summer SMB when using meteorological forcing adjusted with in situ measurements. Model performance however strongly decreases without in situ meteorological measurements. The sensitivity of the model to meteorological forcing indicates a strong sensitivity to wind speed, higher than the sensitivity to ice albedo. Compared to an empirical approach, the model exhibited better performance for simulations of snow and firn melting in the accumulation area and similar performance in the ablation area when forced with meteorological data adjusted with nearby AWS measurements. When such measurements were not available close to the glacier, the empirical model performed better. Our results suggest that simulations of the evolution of future mass balance using an energy balance model require very accurate meteorological data. Given the uncertainties in the temporal evolution of the relevant meteorological variables and glacier surface properties in the future, empirical approaches based on temperature and precipitation could be more appropriate for simulations of glaciers in the future.

Landslide model performance in a high resolution small-scale landscape

NASA Astrophysics Data System (ADS)

De Sy, V.; Schoorl, J. M.; Keesstra, S. D.; Jones, K. E.; Claessens, L.

2013-05-01

The frequency and severity of shallow landslides in New Zealand threatens life and property, both on- and off-site. The physically-based shallow landslide model LAPSUS-LS is tested for its performance in simulating shallow landslide locations induced by a high intensity rain event in a small-scale landscape. Furthermore, the effect of high resolution digital elevation models on the performance was tested. The performance of the model was optimised by calibrating different parameter values. A satisfactory result was achieved with a high resolution (1 m) DEM. Landslides, however, were generally predicted lower on the slope than mapped erosion scars. This discrepancy could be due to i) inaccuracies in the DEM or in other model input data such as soil strength properties; ii) relevant processes for this environmental context that are not included in the model; or iii) the limited validity of the infinite length assumption in the infinite slope stability model embedded in the LAPSUS-LS. The trade-off between a correct prediction of landslides versus stable cells becomes increasingly worse with coarser resolutions; and model performance decreases mainly due to altering slope characteristics. The optimal parameter combinations differ per resolution. In this environmental context the 1 m resolution topography resembles actual topography most closely and landslide locations are better distinguished from stable areas than for coarser resolutions. More gain in model performance could be achieved by adding landslide process complexities and parameter heterogeneity of the catchment.

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

NASA Astrophysics Data System (ADS)

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.

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.

PubMed

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Modeling of nitrate concentration in groundwater using artificial intelligence approach--a case study of Gaza coastal aquifer.

PubMed

Alagha, Jawad S; Said, Md Azlin Md; Mogheir, Yunes

2014-01-01

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.

The model of flood control using servqual method and importance performance analysis in Surakarta City – Indonesia

NASA Astrophysics Data System (ADS)

Titi Purwantini, V.; Sutanto, Yusuf

2018-05-01

This research is to create a model of flood control in the city of Surakarta using Servqual method and Importance Performance Analysis. Service quality is generally defined as the overall assessment of a service by the customersor the extent to which a service meets customer’s needs or expectations. The purpose of this study is to find the first model of flood control that is appropriate to the condition of the community. Surakarta This means looking for a model that can provide satisfactory service for the people of Surakarta who are in the location of the flood. The second is to find the right model to improve service performance of Surakarta City Government in serving the people in flood location. The method used to determine the satisfaction of the public on the quality of service is to see the difference in the quality of service expected by the community with the reality. This method is Servqual Method While to assess the performance of city government officials is by comparing the actual performance with the quality of services provided, this method is This means looking for a model that can provide satisfactory service for the people of Surakarta who are in the location of the flood.The second is to find the right model to improve service performance of Surakarta City Government in serving the people in flood location. The method used to determine the satisfaction of the public on the quality of service is to see the difference in the quality of service expected by the community with the reality. This method is Servqual Method While to assess the performance of city government officials is by comparing the actual performance with the quality of services provided, this method is Importance Performance Analysis. Samples were people living in flooded areas in the city of Surakarta. Result this research is Satisfaction = Responsiveness+ Realibility + Assurance + Empathy+ Tangible (Servqual Model) and Importance Performance Analysis is From Cartesian diagram can be made Flood Control Formula as follow: Food Control = High performance

Green-Ampt approximations: A comprehensive analysis

NASA Astrophysics Data System (ADS)

Ali, Shakir; Islam, Adlul; Mishra, P. K.; Sikka, Alok K.

2016-04-01

Green-Ampt (GA) model and its modifications are widely used for simulating infiltration process. Several explicit approximate solutions to the implicit GA model have been developed with varying degree of accuracy. In this study, performance of nine explicit approximations to the GA model is compared with the implicit GA model using the published data for broad range of soil classes and infiltration time. The explicit GA models considered are Li et al. (1976) (LI), Stone et al. (1994) (ST), Salvucci and Entekhabi (1994) (SE), Parlange et al. (2002) (PA), Barry et al. (2005) (BA), Swamee et al. (2012) (SW), Ali et al. (2013) (AL), Almedeij and Esen (2014) (AE), and Vatankhah (2015) (VA). Six statistical indicators (e.g., percent relative error, maximum absolute percent relative error, average absolute percent relative errors, percent bias, index of agreement, and Nash-Sutcliffe efficiency) and relative computer computation time are used for assessing the model performance. Models are ranked based on the overall performance index (OPI). The BA model is found to be the most accurate followed by the PA and VA models for variety of soil classes and infiltration periods. The AE, SW, SE, and LI model also performed comparatively better. Based on the overall performance index, the explicit models are ranked as BA > PA > VA > LI > AE > SE > SW > ST > AL. Results of this study will be helpful in selection of accurate and simple explicit approximate GA models for solving variety of hydrological problems.

Concurrent and convergent validity of the mobility- and multidimensional-hierarchical disability categorization models with physical performance in community older adults.

PubMed

Hu, Ming-Hsia; Yeh, Chih-Jun; Chen, Tou-Rong; Wang, Ching-Yi

2014-01-01

A valid, time-efficient and easy-to-use instrument is important for busy clinical settings, large scale surveys, or community screening use. The purpose of this study was to validate the mobility hierarchical disability categorization model (an abbreviated model) by investigating its concurrent validity with the multidimensional hierarchical disability categorization model (a comprehensive model) and triangulating both models with physical performance measures in older adults. 604 community-dwelling older adults of at least 60 years in age volunteered to participate. Self-reported function on mobility, instrumental activities of daily living (IADL) and activities of daily living (ADL) domains were recorded and then the disability status determined based on both the multidimensional hierarchical categorization model and the mobility hierarchical categorization model. The physical performance measures, consisting of grip strength and usual and fastest gait speeds (UGS, FGS), were collected on the same day. Both categorization models showed high correlation (γs = 0.92, p < 0.001) and agreement (kappa = 0.61, p < 0.0001). Physical performance measures demonstrated significant different group means among the disability subgroups based on both categorization models. The results of multiple regression analysis indicated that both models individually explain similar amount of variance on all physical performances, with adjustments for age, sex, and number of comorbidities. Our results found that the mobility hierarchical disability categorization model is a valid and time efficient tool for large survey or screening use.

Prioritizing public- private partnership models for public hospitals of iran based on performance indicators.

PubMed

Gholamzadeh Nikjoo, Raana; Jabbari Beyrami, Hossein; Jannati, Ali; Asghari Jaafarabadi, Mohammad

2012-01-01

The present study was conducted to scrutinize Public- Private Partnership (PPP) models in public hospitals of different countries based on performance indicators in order to se-lect appropriated models for Iran hospitals. In this mixed (quantitative-qualitative) study, systematic review and expert panel has been done to identify varied models of PPP as well as performance indicators. In the second step we prioritized performance indicator and PPP models based on selected performance indicators by Analytical Hierarchy process (AHP) technique. The data were analyzed by Excel 2007 and Expert Choice11 software's. In quality - effectiveness area, indicators like the rate of hospital infections (100%), hospital accidents prevalence rate (73%), pure rate of hospital mortality (63%), patient satisfaction percentage (53%), in accessibility equity area indicators such as average inpatient waiting time (100%) and average outpatient waiting time (74%), and in financial - efficiency area, indicators including average length of stay (100%), bed occupation ratio (99%), specific income to total cost ratio (97%) have been chosen to be the most key performance indicators. In the pri¬oritization of the PPP models clinical outsourcing, management, privatization, BOO (build, own, operate) and non-clinical outsourcing models, achieved high priority for various performance in¬dicator areas. This study had been provided the most common PPP options in the field of public hospitals and had gathered suitable evidences from experts for choosing appropriate PPP option for public hospitals. Effect of private sector presence in public hospital performance, based on which PPP options undertaken, will be different.

Prioritizing Public- Private Partnership Models for Public Hospitals of Iran Based on Performance Indicators

PubMed Central

Gholamzadeh Nikjoo, Raana; Jabbari Beyrami, Hossein; Jannati, Ali; Asghari Jaafarabadi, Mohammad

2012-01-01

Background: The present study was conducted to scrutinize Public- Private Partnership (PPP) models in public hospitals of different countries based on performance indicators in order to se-lect appropriated models for Iran hospitals. Methods: In this mixed (quantitative-qualitative) study, systematic review and expert panel has been done to identify varied models of PPP as well as performance indicators. In the second step we prioritized performance indicator and PPP models based on selected performance indicators by Analytical Hierarchy process (AHP) technique. The data were analyzed by Excel 2007 and Expert Choice11 software’s. Results: In quality – effectiveness area, indicators like the rate of hospital infections (100%), hospital accidents prevalence rate (73%), pure rate of hospital mortality (63%), patient satisfaction percentage (53%), in accessibility equity area indicators such as average inpatient waiting time (100%) and average outpatient waiting time (74%), and in financial – efficiency area, indicators including average length of stay (100%), bed occupation ratio (99%), specific income to total cost ratio (97%) have been chosen to be the most key performance indicators. In the pri¬oritization of the PPP models clinical outsourcing, management, privatization, BOO (build, own, operate) and non-clinical outsourcing models, achieved high priority for various performance in¬dicator areas. Conclusion: This study had been provided the most common PPP options in the field of public hospitals and had gathered suitable evidences from experts for choosing appropriate PPP option for public hospitals. Effect of private sector presence in public hospital performance, based on which PPP options undertaken, will be different. PMID:24688942

Incorporation of RAM techniques into simulation modeling

NASA Astrophysics Data System (ADS)

Nelson, S. C., Jr.; Haire, M. J.; Schryver, J. C.

1995-01-01

This work concludes that reliability, availability, and maintainability (RAM) analytical techniques can be incorporated into computer network simulation modeling to yield an important new analytical tool. This paper describes the incorporation of failure and repair information into network simulation to build a stochastic computer model to represent the RAM Performance of two vehicles being developed for the US Army: The Advanced Field Artillery System (AFAS) and the Future Armored Resupply Vehicle (FARV). The AFAS is the US Army's next generation self-propelled cannon artillery system. The FARV is a resupply vehicle for the AFAS. Both vehicles utilize automation technologies to improve the operational performance of the vehicles and reduce manpower. The network simulation model used in this work is task based. The model programmed in this application requirements a typical battle mission and the failures and repairs that occur during that battle. Each task that the FARV performs--upload, travel to the AFAS, refuel, perform tactical/survivability moves, return to logistic resupply, etc.--is modeled. Such a model reproduces a model reproduces operational phenomena (e.g., failures and repairs) that are likely to occur in actual performance. Simulation tasks are modeled as discrete chronological steps; after the completion of each task decisions are programmed that determine the next path to be followed. The result is a complex logic diagram or network. The network simulation model is developed within a hierarchy of vehicle systems, subsystems, and equipment and includes failure management subnetworks. RAM information and other performance measures are collected which have impact on design requirements. Design changes are evaluated through 'what if' questions, sensitivity studies, and battle scenario changes.

A strategic management model for evaluation of health, safety and environmental performance.

PubMed

Abbaspour, Majid; Toutounchian, Solmaz; Roayaei, Emad; Nassiri, Parvin

2012-05-01

Strategic health, safety, and environmental management system (HSE-MS) involves systematic and cooperative planning in each phase of the lifecycle of a project to ensure that interaction among the industry group, client, contractor, stakeholder, and host community exists with the highest level of health, safety, and environmental standard performances. Therefore, it seems necessary to assess the HSE-MS performance of contractor(s) by a comparative strategic management model with the aim of continuous improvement. The present Strategic Management Model (SMM) has been illustrated by a case study and the results show that the model is a suitable management tool for decision making in a contract environment, especially in oil and gas fields and based on accepted international standards within the framework of management deming cycle. To develop this model, a data bank has been created, which includes the statistical data calculated by converting the HSE performance qualitative data into quantitative values. Based on this fact, the structure of the model has been formed by defining HSE performance indicators according to the HSE-MS model. Therefore, 178 indicators have been selected which have been grouped into four attributes. Model output provides quantitative measures of HSE-MS performance as a percentage of an ideal level with maximum possible score for each attribute. Defining the strengths and weaknesses of the contractor(s) is another capability of this model. On the other hand, this model provides a ranking that could be used as the basis for decision making at the contractors' pre-qualification phase or during the execution of the project.

Case Studies Comparing System Advisor Model (SAM) Results to Real Performance Data: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blair, N.; Dobos, A.; Sather, N.

2012-06-01

NREL has completed a series of detailed case studies comparing the simulations of the System Advisor Model (SAM) and measured performance data or published performance expectations. These case studies compare PV measured performance data with simulated performance data using appropriate weather data. The measured data sets were primarily taken from NREL onsite PV systems and weather monitoring stations.

The Performance Blueprint: An Integrated Logic Model Developed To Enhance Performance Measurement Literacy: The Case of Performance-Based Contract Management.

ERIC Educational Resources Information Center

Longo, Paul J.

This study explored the mechanics of using an enhanced, comprehensive multipurpose logic model, the Performance Blueprint, as a means of building evaluation capacity, referred to in this paper as performance measurement literacy, to facilitate the attainment of both service-delivery oriented and community-oriented outcomes. The application of this…

Theoretical performance of hydrogen-bromine rechargeable SPE fuel cell

NASA Technical Reports Server (NTRS)

Savinell, Robert F.; Fritts, S. D.

1987-01-01

A mathematical model was formulated to describe the performance of a hydrogen-bromine fuel cell. Porous electrode theory was applied to the carbon felt flow-by electrode and was coupled to theory describing the solid polymer electrolyte (SPE) system. Parametric studies using the numerical solution to this model were performed to determine the effect of kinetic, mass transfer, and design parameters on the performance of the fuel cell. The results indicate that the cell performance is most sensitive to the transport properties of the SPE membrane. The model was also shown to be a useful tool for scale-up studies.

Optimization of Biomathematical Model Predictions for Cognitive Performance Impairment in Individuals: Accounting for Unknown Traits and Uncertain States in Homeostatic and Circadian Processes

PubMed Central

Van Dongen, Hans P. A.; Mott, Christopher G.; Huang, Jen-Kuang; Mollicone, Daniel J.; McKenzie, Frederic D.; Dinges, David F.

2007-01-01

Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings. Citation: Van Dongen HPA; Mott CG; Huang JK; Mollicone DJ; McKenzie FD; Dinges DF. Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes. SLEEP 2007;30(9):1129-1143. PMID:17910385

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Dependability and performability analysis

NASA Technical Reports Server (NTRS)

Trivedi, Kishor S.; Ciardo, Gianfranco; Malhotra, Manish; Sahner, Robin A.

1993-01-01

Several practical issues regarding specifications and solution of dependability and performability models are discussed. Model types with and without rewards are compared. Continuous-time Markov chains (CTMC's) are compared with (continuous-time) Markov reward models (MRM's) and generalized stochastic Petri nets (GSPN's) are compared with stochastic reward nets (SRN's). It is shown that reward-based models could lead to more concise model specifications and solution of a variety of new measures. With respect to the solution of dependability and performability models, three practical issues were identified: largeness, stiffness, and non-exponentiality, and a variety of approaches are discussed to deal with them, including some of the latest research efforts.

An in-depth review of photovoltaic system performance models

NASA Technical Reports Server (NTRS)

Smith, J. H.; Reiter, L. R.

1984-01-01

The features, strong points and shortcomings of 10 numerical models commonly applied to assessing photovoltaic performance are discussed. The models range in capabilities from first-order approximations to full circuit level descriptions. Account is taken, at times, of the cell and module characteristics, the orientation and geometry, array-level factors, the power-conditioning equipment, the overall plant performance, O and M effects, and site-specific factors. Areas of improvement and/or necessary extensions are identified for several of the models. Although the simplicity of a model was found not necessarily to affect the accuracy of the data generated, the use of any one model was dependent on the application.

The Development of Web-Based Collaborative Training Model for Enhancing Human Performances on ICT for Students in Banditpattanasilpa Institute

ERIC Educational Resources Information Center

Pumipuntu, Natawut; Kidrakarn, Pachoen; Chetakarn, Somchock

2015-01-01

This research aimed to develop the model of Web-based Collaborative (WBC) Training model for enhancing human performances on ICT for students in Banditpattanasilpa Institute. The research is divided into three phases: 1) investigating students and teachers' training needs on ICT web-based contents and performance, 2) developing a web-based…

Performance Improvement: Applying a Human Performance Model to Organizational Processes in a Military Training Environment

ERIC Educational Resources Information Center

Aaberg, Wayne; Thompson, Carla J.; West, Haywood V.; Swiergosz, Matthew J.

2009-01-01

This article provides a description and the results of a study that utilized the human performance (HP) model and methods to explore and analyze a training organization. The systemic and systematic practices of the HP model are applicable to military training organizations as well as civilian organizations. Implications of the study for future…

Why did the bear cross the road? Comparing the performance of multiple resistance surfaces and connectivity modeling methods

Treesearch

Samuel A. Cushman; Jesse S. Lewis; Erin L. Landguth

2014-01-01

There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway...

Teaching Students about Performance Anxiety: The Scratch Pad Pop-Up Model

ERIC Educational Resources Information Center

Robertson, Donald U.; Eisensmith, Kevin E.

2010-01-01

Performance anxiety is a common problem for many students and an issue that educators often address. This article examines a model of performance anxiety based on working memory and attentional processes. The model is described in a way that is easily understood by students of all ages. It is then used to classify methods of reducing performance…

A Structural Model of Self-Concept, Autonomous Motivation and Academic Performance in Cross-Cultural Perspective

ERIC Educational Resources Information Center

Ahmed, Wondimu; Bruinsma, Marjon

2006-01-01

The purpose of this study was to propose and test a motivational model of performance by integrating constructs from self-concept and self-determination theories and to explore cultural group differences in the model. To this end, self-report measures of global self-esteem, academic self-concept, academic motivation and academic performance were…

The Motivational Determinants of Task Performance in a Non-Industrial Milieu: A Modification and Extension of Vroom's Model.

ERIC Educational Resources Information Center

Mendel, Raymond M.; Dickinson, Terry L.

Vroom's cognitive model, which proposes to both explain and predict an individual's level of work productivity by drawing on the construct motivation, is discussed and three hypotheses generated: (1) that Vroom's model does predict performance in a non-industrial setting; (2) that it predicts self-perceived performance better than measures…

PROCRU: A model for analyzing flight crew procedures in approach to landing

NASA Technical Reports Server (NTRS)

Baron, S.; Zacharias, G.; Muraidharan, R.; Lancraft, R.

1982-01-01

A model for the human performance of approach and landing tasks that would provide a means for systematic exploration of questions concerning the impact of procedural and equipment design and the allocation of resources in the cockpit on performance and safety in approach-to-landing is discussed. A system model is needed that accounts for the interactions of crew, procedures, vehicle, approach geometry, and environment. The issues of interest revolve principally around allocation of tasks in the cockpit and crew performance with respect to the cognitive aspects of the tasks. The model must, therefore, deal effectively with information processing and decision-making aspects of human performance.

A model for sequential decoding overflow due to a noisy carrier reference. [communication performance prediction

NASA Technical Reports Server (NTRS)

Layland, J. W.

1974-01-01

An approximate analysis of the effect of a noisy carrier reference on the performance of sequential decoding is presented. The analysis uses previously developed techniques for evaluating noisy reference performance for medium-rate uncoded communications adapted to sequential decoding for data rates of 8 to 2048 bits/s. In estimating the ten to the minus fourth power deletion probability thresholds for Helios, the model agrees with experimental data to within the experimental tolerances. The computational problem involved in sequential decoding, carrier loop effects, the main characteristics of the medium-rate model, modeled decoding performance, and perspectives on future work are discussed.

Assessing the limitations of the Banister model in monitoring training

PubMed Central

Hellard, Philippe; Avalos, Marta; Lacoste, Lucien; Barale, Frédéric; Chatard, Jean-Claude; Millet, Grégoire P.

2006-01-01

The aim of this study was to carry out a statistical analysis of the Banister model to verify how useful it is in monitoring the training programmes of elite swimmers. The accuracy, the ill-conditioning and the stability of this model were thus investigated. Training loads of nine elite swimmers, measured over one season, were related to performances with the Banister model. Firstly, to assess accuracy, the 95% bootstrap confidence interval (95% CI) of parameter estimates and modelled performances were calculated. Secondly, to study ill-conditioning, the correlation matrix of parameter estimates was computed. Finally, to analyse stability, iterative computation was performed with the same data but minus one performance, chosen randomly. Performances were significantly related to training loads in all subjects (R2= 0.79 ± 0.13, P < 0.05) and the estimation procedure seemed to be stable. Nevertheless, the 95% CI of the most useful parameters for monitoring training were wide τa =38 (17, 59), τf =19 (6, 32), tn =19 (7, 35), tg =43 (25, 61). Furthermore, some parameters were highly correlated making their interpretation worthless. The study suggested possible ways to deal with these problems and reviewed alternative methods to model the training-performance relationships. PMID:16608765

Reduction in training time of a deep learning model in detection of lesions in CT

NASA Astrophysics Data System (ADS)

Makkinejad, Nazanin; Tajbakhsh, Nima; Zarshenas, Amin; Khokhar, Ashfaq; Suzuki, Kenji

2018-02-01

Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a "small" number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

[Comparison between administrative and clinical databases in the evaluation of cardiac surgery performance].

PubMed

Rosato, Stefano; D'Errigo, Paola; Badoni, Gabriella; Fusco, Danilo; Perucci, Carlo A; Seccareccia, Fulvia

2008-08-01

The availability of two contemporary sources of information about coronary artery bypass graft (CABG) interventions, allowed 1) to verify the feasibility of performing outcome evaluation studies using administrative data sources, and 2) to compare hospital performance obtainable using the CABG Project clinical database with hospital performance derived from the use of current administrative data. Interventions recorded in the CABG Project were linked to the hospital discharge record (HDR) administrative database. Only the linked records were considered for subsequent analyses (46% of the total CABG Project). A new selected population "clinical card-HDR" was then defined. Two independent risk-adjustment models were applied, each of them using information derived from one of the two different sources. Then, HDR information was supplemented with some patient preoperative conditions from the CABG clinical database. The two models were compared in terms of their adaptability to data. Hospital performances identified by the two different models and significantly different from the mean was compared. In only 4 of the 13 hospitals considered for analysis, the results obtained using the HDR model did not completely overlap with those obtained by the CABG model. When comparing statistical parameters of the HDR model and the HDR model + patient preoperative conditions, the latter showed the best adaptability to data. In this "clinical card-HDR" population, hospital performance assessment obtained using information from the clinical database is similar to that derived from the use of current administrative data. However, when risk-adjustment models built on administrative databases are supplemented with a few clinical variables, their statistical parameters improve and hospital performance assessment becomes more accurate.

Robust Planning for Effects-Based Operations

DTIC Science & Technology

2006-06-01

Algorithm ......................................... 34 2.6 Robust Optimization Literature ..................................... 36 2.6.1 Protecting Against...Model Formulation ...................... 55 3.1.5 Deterministic EBO Model Example and Performance ............. 59 3.1.6 Greedy Algorithm ...111 4.1.9 Conclusions on Robust EBO Model Performance .................... 116 4.2 Greedy Algorithm versus EBO Models

Intern Performance in Three Supervisory Models

ERIC Educational Resources Information Center

Womack, Sid T.; Hanna, Shellie L.; Callaway, Rebecca; Woodall, Peggy

2011-01-01

Differences in intern performance, as measured by a Praxis III-similar instrument were found between interns supervised in three supervisory models: Traditional triad model, cohort model, and distance supervision. Candidates in this study's particular form of distance supervision were not as effective as teachers as candidates in traditional-triad…

Modeling and performance data for heaving bouy wave energy converter with a compressible degree of freedom

DOE Data Explorer

Bacelli, Giorgio

2016-09-28

Modeling and performance data in Matlab data file (.mat) containing 3 structures (WEC model, simRes_sr and simRes_fix), and a pdf document describing the model, the simulations, and the analysis that has been carried out.

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H.; Moore, Kara A.; Zimmermann, Niklaus E.

2016-01-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasise several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. PMID:26872305

Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

NASA Astrophysics Data System (ADS)

Zaherpour, Jamal; Gosling, Simon N.; Mount, Nick; Müller Schmied, Hannes; Veldkamp, Ted I. E.; Dankers, Rutger; Eisner, Stephanie; Gerten, Dieter; Gudmundsson, Lukas; Haddeland, Ingjerd; Hanasaki, Naota; Kim, Hyungjun; Leng, Guoyong; Liu, Junguo; Masaki, Yoshimitsu; Oki, Taikan; Pokhrel, Yadu; Satoh, Yusuke; Schewe, Jacob; Wada, Yoshihide

2018-06-01

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

Benchmarking novel approaches for modelling species range dynamics.

PubMed

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

2016-08-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. © 2016 John Wiley & Sons Ltd.

Evaluation of the Community Multiscale Air Quality (CMAQ) Model Version 5.1

EPA Science Inventory

The AMAD will performed two CMAQ model simulations, one with the current publically available version of the CMAQ model (v5.0.2) and the other with the new version of the CMAQ model (v5.1). The results of each model simulation are compared to observations and the performance of t...

Evaluation of weighted regression and sample size in developing a taper model for loblolly pine

Treesearch

Kenneth L. Cormier; Robin M. Reich; Raymond L. Czaplewski; William A. Bechtold

1992-01-01

A stem profile model, fit using pseudo-likelihood weighted regression, was used to estimate merchantable volume of loblolly pine (Pinus taeda L.) in the southeast. The weighted regression increased model fit marginally, but did not substantially increase model performance. In all cases, the unweighted regression models performed as well as the...

Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning

ERIC Educational Resources Information Center

MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R.

2015-01-01

Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…

Dynamic aspects of voluntary turnover: an integrated approach to curvilinearity in the performance-turnover relationship.

PubMed

Becker, William J; Cropanzano, Russell

2011-03-01

Previous research pertaining to job performance and voluntary turnover has been guided by 2 distinct theoretical perspectives. First, the push-pull model proposes that there is a quadratic or curvilinear relationship existing between these 2 variables. Second, the unfolding model of turnover posits that turnover is a dynamic process and that a downward performance change may increase the likelihood of organizational separation. Drawing on decision theory, we propose and test an integrative framework. This approach incorporates both of these earlier models. Specifically, we argue that individuals are most likely to voluntarily exit when they are below-average performers who are also experiencing a downward performance change. Furthermore, the interaction between this downward change and performance partially accounts for the curvilinear relationship proposed by the push-pull model. Findings from a longitudinal field study supported this integrative theory. PsycINFO Database Record (c) 2011 APA, all rights reserved.

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Complex versus simple models: ion-channel cardiac toxicity prediction.

PubMed

Mistry, Hitesh B

2018-01-01

There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

Validating the ACE Model for Evaluating Student Performance Using a Teaching-Learning Process Based on Computational Modeling Systems

ERIC Educational Resources Information Center

Louzada, Alexandre Neves; Elia, Marcos da Fonseca; Sampaio, Fábio Ferrentini; Vidal, Andre Luiz Pestana

2014-01-01

The aim of this work is to adapt and test, in a Brazilian public school, the ACE model proposed by Borkulo for evaluating student performance as a teaching-learning process based on computational modeling systems. The ACE model is based on different types of reasoning involving three dimensions. In addition to adapting the model and introducing…

Modelling and Prediction of Spark-ignition Engine Power Performance Using Incremental Least Squares Support Vector Machines

NASA Astrophysics Data System (ADS)

Wong, Pak-kin; Vong, Chi-man; Wong, Hang-cheong; Li, Ke

2010-05-01

Modern automotive spark-ignition (SI) power performance usually refers to output power and torque, and they are significantly affected by the setup of control parameters in the engine management system (EMS). EMS calibration is done empirically through tests on the dynamometer (dyno) because no exact mathematical engine model is yet available. With an emerging nonlinear function estimation technique of Least squares support vector machines (LS-SVM), the approximate power performance model of a SI engine can be determined by training the sample data acquired from the dyno. A novel incremental algorithm based on typical LS-SVM is also proposed in this paper, so the power performance models built from the incremental LS-SVM can be updated whenever new training data arrives. With updating the models, the model accuracies can be continuously increased. The predicted results using the estimated models from the incremental LS-SVM are good agreement with the actual test results and with the almost same average accuracy of retraining the models from scratch, but the incremental algorithm can significantly shorten the model construction time when new training data arrives.

Modeling and performance improvement of the constant power regulator systems in variable displacement axial piston pump.

PubMed

Park, Sung Hwan; Lee, Ji Min; Kim, Jong Shik

2013-01-01

An irregular performance of a mechanical-type constant power regulator is considered. In order to find the cause of an irregular discharge flow at the cut-off pressure area, modeling and numerical simulations are performed to observe dynamic behavior of internal parts of the constant power regulator system for a swashplate-type axial piston pump. The commercial numerical simulation software AMESim is applied to model the mechanical-type regulator with hydraulic pump and simulate the performance of it. The validity of the simulation model of the constant power regulator system is verified by comparing simulation results with experiments. In order to find the cause of the irregular performance of the mechanical-type constant power regulator system, the behavior of main components such as the spool, sleeve, and counterbalance piston is investigated using computer simulation. The shape modification of the counterbalance piston is proposed to improve the undesirable performance of the mechanical-type constant power regulator. The performance improvement is verified by computer simulation using AMESim software.

A comparison of cognitive performance decreases during acute, progressive fatigue arising from different concurrent stressors.

PubMed

Fogt, Donovan L; Kalns, John E; Michael, Darren J

2010-12-01

Fatigue is known to impair cognitive performance, but it remains unclear whether concurrent common stressors affect cognitive performance similarly. We used the Stroop Color-Word Conflict Test to assess cognitive performance over 24 hours for four groups: control, sleep-deprived (SD), SD + energy deficit, and SD + energy deficit + fluid restricted. Fatigue levels were quantified using the Profile of Mood States (POMS) survey. Linear mixed-effects (LME) models allowed for testing of group-specific differences in cognitive performance while accounting for subject-level variation. Starting fatigue levels were similar among all groups, while 24-hour fatigue levels differed significantly. For each cognitive performance test, results were modeled separately. The simplest LME model contained a significant fixed-effects term for slope and intercept. Moreover, the simplest LME model used a single slope coefficient to fit data from all four groups, suggesting that loss in cognitive performance over a 24-hour duty cycle with respect to fatigue level is similar regardless of the cause.

A Study on the Self-Adaption Incentive Performance Salary

NASA Astrophysics Data System (ADS)

Zhang, Chuanming; Wang, Yang

In project managing, the performance salary management mode is often used to motivate project managers and other similar staff to improve performance or reduce the cost. But the engineering activities who own a lot of internal and external uncertain factors can not be known by the principle. It is difficult for to develop a suitable incentive target to project managers etch. This paper thinks that the manager self master the maximum of information on engineering activities. So this paper sets up an incentive model: the project managers themselves report performance objectives; owner gives the managers reward or punishment combined with their reported performance and actual performance. The model to ensure that the project manager is only accurate self reported its results to get the maximum profit. At the same time, it cans incentive managers to improve performance or reduce the cost. This paper focuses on setting up the model, analyzing the model parameters. And cite an example analyze them.

Using a Functional Simulation of Crisis Management to Test the C2 Agility Model Parameters on Key Performance Variables

DTIC Science & Technology

2013-06-01

1 18th ICCRTS Using a Functional Simulation of Crisis Management to Test the C2 Agility Model Parameters on Key Performance Variables...AND SUBTITLE Using a Functional Simulation of Crisis Management to Test the C2 Agility Model Parameters on Key Performance Variables 5a. CONTRACT...command in crisis management. C2 Agility Model Agility can be conceptualized at a number of different levels; for instance at the team

The performance of discrete models of low Reynolds number swimmers.

PubMed

Wang, Qixuan; Othmer, Hans G

2015-12-01

Swimming by shape changes at low Reynolds number is widely used in biology and understanding how the performance of movement depends on the geometric pattern of shape changes is important to understand swimming of microorganisms and in designing low Reynolds number swimming models. The simplest models of shape changes are those that comprise a series of linked spheres that can change their separation and/or their size. Herein we compare the performance of three models in which these modes are used in different ways.

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

NASA Technical Reports Server (NTRS)

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.

Development and Integration of Control System Models

NASA Technical Reports Server (NTRS)

Kim, Young K.

1998-01-01

The computer simulation tool, TREETOPS, has been upgraded and used at NASA/MSFC to model various complicated mechanical systems and to perform their dynamics and control analysis with pointing control systems. A TREETOPS model of Advanced X-ray Astrophysics Facility - Imaging (AXAF-1) dynamics and control system was developed to evaluate the AXAF-I pointing performance for Normal Pointing Mode. An optical model of Shooting Star Experiment (SSE) was also developed and its optical performance analysis was done using the MACOS software.

A Perspective on Computational Human Performance Models as Design Tools

NASA Technical Reports Server (NTRS)

Jones, Patricia M.

2010-01-01

The design of interactive systems, including levels of automation, displays, and controls, is usually based on design guidelines and iterative empirical prototyping. A complementary approach is to use computational human performance models to evaluate designs. An integrated strategy of model-based and empirical test and evaluation activities is particularly attractive as a methodology for verification and validation of human-rated systems for commercial space. This talk will review several computational human performance modeling approaches and their applicability to design of display and control requirements.

FLAME: A platform for high performance computing of complex systems, applied for three case studies

DOE PAGES

Kiran, Mariam; Bicak, Mesude; Maleki-Dizaji, Saeedeh; ...

2011-01-01

FLAME allows complex models to be automatically parallelised on High Performance Computing (HPC) grids enabling large number of agents to be simulated over short periods of time. Modellers are hindered by complexities of porting models on parallel platforms and time taken to run large simulations on a single machine, which FLAME overcomes. Three case studies from different disciplines were modelled using FLAME, and are presented along with their performance results on a grid.

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan.

PubMed

Chang, Hsien-Yen; Weiner, Jonathan P

2010-01-18

Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme. A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level. The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall. Given the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.

Independent external validation of predictive models for urinary dysfunction following external beam radiotherapy of the prostate: Issues in model development and reporting.

PubMed

Yahya, Noorazrul; Ebert, Martin A; Bulsara, Max; Kennedy, Angel; Joseph, David J; Denham, James W

2016-08-01

Most predictive models are not sufficiently validated for prospective use. We performed independent external validation of published predictive models for urinary dysfunctions following radiotherapy of the prostate. Multivariable models developed to predict atomised and generalised urinary symptoms, both acute and late, were considered for validation using a dataset representing 754 participants from the TROG 03.04-RADAR trial. Endpoints and features were harmonised to match the predictive models. The overall performance, calibration and discrimination were assessed. 14 models from four publications were validated. The discrimination of the predictive models in an independent external validation cohort, measured using the area under the receiver operating characteristic (ROC) curve, ranged from 0.473 to 0.695, generally lower than in internal validation. 4 models had ROC >0.6. Shrinkage was required for all predictive models' coefficients ranging from -0.309 (prediction probability was inverse to observed proportion) to 0.823. Predictive models which include baseline symptoms as a feature produced the highest discrimination. Two models produced a predicted probability of 0 and 1 for all patients. Predictive models vary in performance and transferability illustrating the need for improvements in model development and reporting. Several models showed reasonable potential but efforts should be increased to improve performance. Baseline symptoms should always be considered as potential features for predictive models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

New Integrated Modeling Capabilities: MIDAS' Recent Behavioral Enhancements

NASA Technical Reports Server (NTRS)

Gore, Brian F.; Jarvis, Peter A.

2005-01-01

The Man-machine Integration Design and Analysis System (MIDAS) is an integrated human performance modeling software tool that is based on mechanisms that underlie and cause human behavior. A PC-Windows version of MIDAS has been created that integrates the anthropometric character "Jack (TM)" with MIDAS' validated perceptual and attention mechanisms. MIDAS now models multiple simulated humans engaging in goal-related behaviors. New capabilities include the ability to predict situations in which errors and/or performance decrements are likely due to a variety of factors including concurrent workload and performance influencing factors (PIFs). This paper describes a new model that predicts the effects of microgravity on a mission specialist's performance, and its first application to simulating the task of conducting a Life Sciences experiment in space according to a sequential or parallel schedule of performance.

Model-Based Linkage Analysis of a Quantitative Trait.

PubMed

Song, Yeunjoo E; Song, Sunah; Schnell, Audrey H

2017-01-01

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

A new rate-dependent model for high-frequency tracking performance enhancement of piezoactuator system

NASA Astrophysics Data System (ADS)

Tian, Lizhi; Xiong, Zhenhua; Wu, Jianhua; Ding, Han

2017-05-01

Feedforward-feedback control is widely used in motion control of piezoactuator systems. Due to the phase lag caused by incomplete dynamics compensation, the performance of the composite controller is greatly limited at high frequency. This paper proposes a new rate-dependent model to improve the high-frequency tracking performance by reducing dynamics compensation error. The rate-dependent model is designed as a function of the input and input variation rate to describe the input-output relationship of the residual system dynamics which mainly performs as phase lag in a wide frequency band. Then the direct inversion of the proposed rate-dependent model is used to compensate the residual system dynamics. Using the proposed rate-dependent model as feedforward term, the open loop performance can be improved significantly at medium-high frequency. Then, combining the with feedback controller, the composite controller can provide enhanced close loop performance from low frequency to high frequency. At the frequency of 1 Hz, the proposed controller presents the same performance as previous methods. However, at the frequency of 900 Hz, the tracking error is reduced to be 30.7% of the decoupled approach.

Numerical Modeling of Pulse Detonation Rocket Engine Gasdynamics and Performance

NASA Technical Reports Server (NTRS)

2003-01-01

This paper presents viewgraphs on the numerical modeling of pulse detonation rocket engines (PDRE), with an emphasis on the Gasdynamics and performance analysis of these engines. The topics include: 1) Performance Analysis of PDREs; 2) Simplified PDRE Cycle; 3) Comparison of PDRE and Steady-State Rocket Engines (SSRE) Performance; 4) Numerical Modeling of Quasi 1-D Rocket Flows; 5) Specific PDRE Geometries Studied; 6) Time-Accurate Thrust Calculations; 7) PDRE Performance (Geometries A B C and D); 8) PDRE Blowdown Gasdynamics (Geom. A B C and D); 9) PDRE Geometry Performance Comparison; 10) PDRE Blowdown Time (Geom. A B C and D); 11) Specific SSRE Geometry Studied; 12) Effect of F-R Chemistry on SSRE Performance; 13) PDRE/SSRE Performance Comparison; 14) PDRE Performance Study; 15) Grid Resolution Study; and 16) Effect of F-R Chemistry on SSRE Exit Species Mole Fractions.

Regime-Based Evaluation of Cloudiness in CMIP5 Models

NASA Technical Reports Server (NTRS)

Jin, Daeho; Oraiopoulos, Lazaros; Lee, Dong Min

2016-01-01

The concept of Cloud Regimes (CRs) is used to develop a framework for evaluating the cloudiness of 12 fifth Coupled Model Intercomparison Project (CMIP5) models. Reference CRs come from existing global International Satellite Cloud Climatology Project (ISCCP) weather states. The evaluation is made possible by the implementation in several CMIP5 models of the ISCCP simulator generating for each gridcell daily joint histograms of cloud optical thickness and cloud top pressure. Model performance is assessed with several metrics such as CR global cloud fraction (CF), CR relative frequency of occurrence (RFO), their product (long-term average total cloud amount [TCA]), cross-correlations of CR RFO maps, and a metric of resemblance between model and ISCCP CRs. In terms of CR global RFO, arguably the most fundamental metric, the models perform unsatisfactorily overall, except for CRs representing thick storm clouds. Because model CR CF is internally constrained by our method, RFO discrepancies yield also substantial TCA errors. Our findings support previous studies showing that CMIP5 models underestimate cloudiness. The multi-model mean performs well in matching observed RFO maps for many CRs, but is not the best for this or other metrics. When overall performance across all CRs is assessed, some models, despite their shortcomings, apparently outperform Moderate Resolution Imaging Spectroradiometer (MODIS) cloud observations evaluated against ISCCP as if they were another model output. Lastly, cloud simulation performance is contrasted with each model's equilibrium climate sensitivity (ECS) in order to gain insight on whether good cloud simulation pairs with particular values of this parameter.

Adaptation Method for Overall and Local Performances of Gas Turbine Engine Model

NASA Astrophysics Data System (ADS)

Kim, Sangjo; Kim, Kuisoon; Son, Changmin

2018-04-01

An adaptation method was proposed to improve the modeling accuracy of overall and local performances of gas turbine engine. The adaptation method was divided into two steps. First, the overall performance parameters such as engine thrust, thermal efficiency, and pressure ratio were adapted by calibrating compressor maps, and second, the local performance parameters such as temperature of component intersection and shaft speed were adjusted by additional adaptation factors. An optimization technique was used to find the correlation equation of adaptation factors for compressor performance maps. The multi-island genetic algorithm (MIGA) was employed in the present optimization. The correlations of local adaptation factors were generated based on the difference between the first adapted engine model and performance test data. The proposed adaptation method applied to a low-bypass ratio turbofan engine of 12,000 lb thrust. The gas turbine engine model was generated and validated based on the performance test data in the sea-level static condition. In flight condition at 20,000 ft and 0.9 Mach number, the result of adapted engine model showed improved prediction in engine thrust (overall performance parameter) by reducing the difference from 14.5 to 3.3%. Moreover, there was further improvement in the comparison of low-pressure turbine exit temperature (local performance parameter) as the difference is reduced from 3.2 to 0.4%.

ASSESSMENT OF TWO PHYSICALLY BASED WATERSHED MODELS BASED ON THEIR PERFORMANCES OF SIMULATING SEDIMENT MOVEMENT OVER SMALL WATERSHEDS

EPA Science Inventory

Abstract: Two physically based and deterministic models, CASC2-D and KINEROS are evaluated and compared for their performances on modeling sediment movement on a small agricultural watershed over several events. Each model has different conceptualization of a watershed. CASC...

47 CFR 73.151 - Field strength measurements to establish performance of directional antennas.

Code of Federal Regulations, 2010 CFR

2010-10-01

... verified either by field strength measurement or by computer modeling and sampling system verification. (a... specifically identified by the Commission. (c) Computer modeling and sample system verification of modeled... performance verified by computer modeling and sample system verification. (1) A matrix of impedance...

ASSESSMENT OF TWO PHYSICALLY-BASED WATERSHED MODELS BASED ON THEIR PERFORMANCES OF SIMULATING WATER AND SEDIMENT MOVEMENT

EPA Science Inventory

Two physically based watershed models, GSSHA and KINEROS-2 are evaluated and compared for their performances on modeling flow and sediment movement. Each model has a different watershed conceptualization. GSSHA divides the watershed into cells, and flow and sediments are routed t...

Business Models for Training and Performance Improvement Departments

ERIC Educational Resources Information Center

Carliner, Saul

2004-01-01

Although typically applied to entire enterprises, the concept of business models applies to training and performance improvement groups. Business models are "the method by which firm[s] build and use [their] resources to offer.. value." Business models affect the types of projects, services offered, skills required, business processes, and type of…

Conceptual Modeling Framework for E-Area PA HELP Infiltration Model Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyer, J. A.

A conceptual modeling framework based on the proposed E-Area Low-Level Waste Facility (LLWF) closure cap design is presented for conducting Hydrologic Evaluation of Landfill Performance (HELP) model simulations of intact and subsided cap infiltration scenarios for the next E-Area Performance Assessment (PA).

The Effect of Covert Modeling on Communication Apprehension, Communication Confidence, and Performance.

ERIC Educational Resources Information Center

Nimocks, Mittie J.; Bromley, Patricia L.; Parsons, Theron E.; Enright, Corinne S.; Gates, Elizabeth A.

This study examined the effect of covert modeling on communication apprehension, public speaking anxiety, and communication competence. Students identified as highly communication apprehensive received covert modeling, a technique in which one first observes a model doing a behavior, then visualizes oneself performing the behavior and obtaining a…

Performance of Renormalization Group Algebraic Turbulence Model on Boundary Layer Transition Simulation

NASA Technical Reports Server (NTRS)

Ahn, Kyung H.

1994-01-01

The RNG-based algebraic turbulence model, with a new method of solving the cubic equation and applying new length scales, is introduced. An analysis is made of the RNG length scale which was previously reported and the resulting eddy viscosity is compared with those from other algebraic turbulence models. Subsequently, a new length scale is introduced which actually uses the two previous RNG length scales in a systematic way to improve the model performance. The performance of the present RNG model is demonstrated by simulating the boundary layer flow over a flat plate and the flow over an airfoil.

Loss model for off-design performance analysis of radial turbines with pivoting-vane, variable-area stators

NASA Technical Reports Server (NTRS)

Meitner, P. L.; Glassman, A. J.

1980-01-01

An off-design performance loss model is developed for variable-area (pivoted vane) radial turbines. The variation in stator loss with stator area is determined by a viscous loss model while the variation in rotor loss due to stator area variation (for no stator end-clearance gap) is determined through analytical matching of experimental data. An incidence loss model is also based on matching of the experimental data. A stator vane end-clearance leakage model is developed and sample calculations are made to show the predicted effects of stator vane end-clearance leakage on performance.

A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules

PubMed Central

Ramakrishnan, Sridhar; Wesensten, Nancy J.; Balkin, Thomas J.; Reifman, Jaques

2016-01-01

Study Objectives: Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss—from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges—and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. Methods: We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. Results: The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. Conclusions: The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss. Citation: Ramakrishnan S, Wesensten NJ, Balkin TJ, Reifman J. A unified model of performance: validation of its predictions across different sleep/wake schedules. SLEEP 2016;39(1):249–262. PMID:26518594

Business School's Performance Management System Standards Design

ERIC Educational Resources Information Center

Azis, Anton Mulyono; Simatupang, Togar M.; Wibisono, Dermawan; Basri, Mursyid Hasan

2014-01-01

This paper aims to compare various Performance Management Systems (PMS) for business school in order to find the strengths of each standard as inputs to design new model of PMS. There are many critical aspects and gaps notified for new model to improve performance and even recognized that self evaluation performance management is not well…

Rotorcraft performance data for AEDT : Methods of using the NASA Design and Analysis of Rotorcraft tool for developing data for AEDT's Rotorcraft Performance Model

DOT National Transportation Integrated Search

2016-09-01

This report documents use of the NASA Design and Analysis of Rotorcraft (NDARC) helicopter performance software tool in developing data for the FAAs Aviation Environmental Design Tool (AEDT). These data support the Rotorcraft Performance Model (RP...

Exploring Pre-Service Training and School Counselor Interns Use of the ASCA Model Tasks

ERIC Educational Resources Information Center

Oberman, Aaron; Studer, Jeannine

2016-01-01

Activities performed by school counselor interns perform that are related to the American School Counselor Association (ASCA) National Model and Performance Standards were explored in this study. Interns were more likely to perform tasks that included individual and small group counseling, monitoring student progress, and conducting individual…

Geospace environment modeling 2008--2009 challenge: Dst index

USGS Publications Warehouse

Rastätter, L.; Kuznetsova, M.M.; Glocer, A.; Welling, D.; Meng, X.; Raeder, J.; Wittberger, M.; Jordanova, V.K.; Yu, Y.; Zaharia, S.; Weigel, R.S.; Sazykin, S.; Boynton, R.; Wei, H.; Eccles, V.; Horton, W.; Mays, M.L.; Gannon, J.

2013-01-01

This paper reports the metrics-based results of the Dst index part of the 2008–2009 GEM Metrics Challenge. The 2008–2009 GEM Metrics Challenge asked modelers to submit results for four geomagnetic storm events and five different types of observations that can be modeled by statistical, climatological or physics-based models of the magnetosphere-ionosphere system. We present the results of 30 model settings that were run at the Community Coordinated Modeling Center and at the institutions of various modelers for these events. To measure the performance of each of the models against the observations, we use comparisons of 1 hour averaged model data with the Dst index issued by the World Data Center for Geomagnetism, Kyoto, Japan, and direct comparison of 1 minute model data with the 1 minute Dst index calculated by the United States Geological Survey. The latter index can be used to calculate spectral variability of model outputs in comparison to the index. We find that model rankings vary widely by skill score used. None of the models consistently perform best for all events. We find that empirical models perform well in general. Magnetohydrodynamics-based models of the global magnetosphere with inner magnetosphere physics (ring current model) included and stand-alone ring current models with properly defined boundary conditions perform well and are able to match or surpass results from empirical models. Unlike in similar studies, the statistical models used in this study found their challenge in the weakest events rather than the strongest events.

Working parameters affecting earth-air heat exchanger (EAHE) system performance for passive cooling: A review

NASA Astrophysics Data System (ADS)

Darius, D.; Misaran, M. S.; Rahman, Md. M.; Ismail, M. A.; Amaludin, A.

2017-07-01

The study on the effect of the working parameters such as pipe material, pipe length, pipe diameter, depth of burial of the pipe, air flow rate and different types of soils on the thermal performance of earth-air heat exchanger (EAHE) systems is very crucial to ensure that thermal comfort can be achieved. In the past decade, researchers have performed studies to develop numerical models for analysis of EAHE systems. Until recently, two-dimensional models replaced the numerical models in the 1990s and in recent times, more advanced analysis using three-dimensional models, specifically the Computational Fluid Dynamics (CFD) simulation in the analysis of EAHE system. This paper reviews previous models used to analyse the EAHE system and working parameters that affects the earth-air heat exchanger (EAHE) thermal performance as of February 2017. Recent findings on the parameters affecting EAHE performance are also presented and discussed. As a conclusion, with the advent of CFD methods, investigational work have geared up to modelling and simulation work as it saves time and cost. Comprehension of the EAHE working parameters and its effect on system performance is largely established. However, the study on type of soil and its characteristics on the performance of EAHEs systems are surprisingly barren. Therefore, future studies should focus on the effect of soil characteristics such as moisture content, density of soil, and type of soil on the thermal performance of EAHEs system.

Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong

2011-12-01

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

Integrated Modeling Activities for the James Webb Space Telescope: Structural-Thermal-Optical Analysis

NASA Technical Reports Server (NTRS)

Johnston, John D.; Howard, Joseph M.; Mosier, Gary E.; Parrish, Keith A.; McGinnis, Mark A.; Bluth, Marcel; Kim, Kevin; Ha, Kong Q.

2004-01-01

The James Web Space Telescope (JWST) is a large, infrared-optimized space telescope scheduled for launch in 2011. This is a continuation of a series of papers on modeling activities for JWST. The structural-thermal-optical, often referred to as STOP, analysis process is used to predict the effect of thermal distortion on optical performance. The benchmark STOP analysis for JWST assesses the effect of an observatory slew on wavefront error. Temperatures predicted using geometric and thermal math models are mapped to a structural finite element model in order to predict thermally induced deformations. Motions and deformations at optical surfaces are then input to optical models, and optical performance is predicted using either an optical ray trace or a linear optical analysis tool. In addition to baseline performance predictions, a process for performing sensitivity studies to assess modeling uncertainties is described.

Learning Instance-Specific Predictive Models

PubMed Central

Visweswaran, Shyam; Cooper, Gregory F.

2013-01-01

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable models to average over. We call this method the instance-specific Markov blanket (ISMB) algorithm. The ISMB algorithm was evaluated on 21 UCI data sets using five different performance measures and its performance was compared to that of several commonly used predictive algorithms, including nave Bayes, C4.5 decision tree, logistic regression, neural networks, k-Nearest Neighbor, Lazy Bayesian Rules, and AdaBoost. Over all the data sets, the ISMB algorithm performed better on average on all performance measures against all the comparison algorithms. PMID:25045325

Use of single-well simulators and economic performance criteria to optimize fracturing treatment design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, R.W.; Phillips, A.M.

1990-02-01

Low-permeability reservoirs are currently being propped with sand, resin-coated sand, intermediate-density proppants, and bauxite. This wide range of proppant cost and performance has resulted in the proliferation of proppant selection models. Initially, a rather vague relationship between well depth and proppant strength dictated the choice of proppant. More recently, computerized models of varying complexity that use net-present-value (NPV) calculations have become available. The input is based on the operator's performance goals for each well and specific reservoir properties. Simpler, noncomputerized approaches include cost/performance comparisons and nomographs. Each type of model, including several of the computerized models, is examined here. Bymore » use of these models and NPV calculations, optimum fracturing treatment designs have been developed for such low-permeability reservoirs as the Prue in Oklahoma. Typical well conditions are used in each of the selection models, and the results are compared.« less

Performance analysis of OOK-based FSO systems in Gamma-Gamma turbulence with imprecise channel models

NASA Astrophysics Data System (ADS)

Feng, Jianfeng; Zhao, Xiaohui

2017-11-01

For an FSO communication system with imprecise channel model, we investigate its system performance based on outage probability, average BEP and ergodic capacity. The exact FSO links are modeled as Gamma-Gamma fading channel in consideration of both atmospheric turbulence and pointing errors, and the imprecise channel model is treated as the superposition of exact channel gain and a Gaussian random variable. After we derive the PDF, CDF and nth moment of the imprecise channel gain, and based on these statistics the expressions for the outage probability, the average BEP and the ergodic capacity in terms of the Meijer's G functions are obtained. Both numerical and analytical results are presented. The simulation results show that the communication performance deteriorates in the imprecise channel model, and approaches to the exact performance curves as the channel model becomes accurate.

Technical Manual for the SAM Physical Trough Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wagner, M. J.; Gilman, P.

2011-06-01

NREL, in conjunction with Sandia National Lab and the U.S Department of Energy, developed the System Advisor Model (SAM) analysis tool for renewable energy system performance and economic analysis. This paper documents the technical background and engineering formulation for one of SAM's two parabolic trough system models in SAM. The Physical Trough model calculates performance relationships based on physical first principles where possible, allowing the modeler to predict electricity production for a wider range of component geometries than is possible in the Empirical Trough model. This document describes the major parabolic trough plant subsystems in detail including the solar field,more » power block, thermal storage, piping, auxiliary heating, and control systems. This model makes use of both existing subsystem performance modeling approaches, and new approaches developed specifically for SAM.« less

Modelling of diesel engine fuelled with biodiesel using engine simulation software

NASA Astrophysics Data System (ADS)

Said, Mohd Farid Muhamad; Said, Mazlan; Aziz, Azhar Abdul

2012-06-01

This paper is about modelling of a diesel engine that operates using biodiesel fuels. The model is used to simulate or predict the performance and combustion of the engine by simplified the geometry of engine component in the software. The model is produced using one-dimensional (1D) engine simulation software called GT-Power. The fuel properties library in the software is expanded to include palm oil based biodiesel fuels. Experimental works are performed to investigate the effect of biodiesel fuels on the heat release profiles and the engine performance curves. The model is validated with experimental data and good agreement is observed. The simulation results show that combustion characteristics and engine performances differ when biodiesel fuels are used instead of no. 2 diesel fuel.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rainer, Leo I.; Hoeschele, Marc A.; Apte, Michael G.

This report addresses the results of detailed monitoring completed under Program Element 6 of Lawrence Berkeley National Laboratory's High Performance Commercial Building Systems (HPCBS) PIER program. The purpose of the Energy Simulations and Projected State-Wide Energy Savings project is to develop reasonable energy performance and cost models for high performance relocatable classrooms (RCs) across California climates. A key objective of the energy monitoring was to validate DOE2 simulations for comparison to initial DOE2 performance projections. The validated DOE2 model was then used to develop statewide savings projections by modeling base case and high performance RC operation in the 16 Californiamore » climate zones. The primary objective of this phase of work was to utilize detailed field monitoring data to modify DOE2 inputs and generate performance projections based on a validated simulation model. Additional objectives include the following: (1) Obtain comparative performance data on base case and high performance HVAC systems to determine how they are operated, how they perform, and how the occupants respond to the advanced systems. This was accomplished by installing both HVAC systems side-by-side (i.e., one per module of a standard two module, 24 ft by 40 ft RC) on the study RCs and switching HVAC operating modes on a weekly basis. (2) Develop projected statewide energy and demand impacts based on the validated DOE2 model. (3) Develop cost effectiveness projections for the high performance HVAC system in the 16 California climate zones.« less

ILS Glide Slope Performance Prediction. Volume B

DTIC Science & Technology

1974-09-01

figures are identical in both volumes. 󈧔. Abottec A mathematical model for predicting the performance of ILS glide slope arrays in the presence of...irregularities on the performance of ILS Glide Slope antenna systems, a mathematical -electromagnetic scattering computer model has been developed. This work was...Antenna ........... 4-4 9. Test Case Results ..................................... r-3 ix PART I. IEO -j 1.INTRODUCTION IA mathematical model has been

Visual performance modeling in the human operator simulator

NASA Technical Reports Server (NTRS)

Strieb, M. I.

1979-01-01

A brief description of the history of the development of the human operator simulator (HOS) model is presented. Features of the HOS micromodels that impact on the obtainment of visual performance data are discussed along with preliminary details on a HOS pilot model designed to predict the results of visual performance workload data obtained through oculometer studies on pilots in real and simulated approaches and landings.

Formal implementation of a performance evaluation model for the face recognition system.

PubMed

Shin, Yong-Nyuo; Kim, Jason; Lee, Yong-Jun; Shin, Woochang; Choi, Jin-Young

2008-01-01

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

Performance of hybrid programming models for multiscale cardiac simulations: preparing for petascale computation.

PubMed

Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias

2011-10-01

Future multiscale and multiphysics models that support research into human disease, translational medical science, and treatment can utilize the power of high-performance computing (HPC) systems. We anticipate that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message-passing processes [e.g., the message-passing interface (MPI)] with multithreading (e.g., OpenMP, Pthreads). The objective of this study is to compare the performance of such hybrid programming models when applied to the simulation of a realistic physiological multiscale model of the heart. Our results show that the hybrid models perform favorably when compared to an implementation using only the MPI and, furthermore, that OpenMP in combination with the MPI provides a satisfactory compromise between performance and code complexity. Having the ability to use threads within MPI processes enables the sophisticated use of all processor cores for both computation and communication phases. Considering that HPC systems in 2012 will have two orders of magnitude more cores than what was used in this study, we believe that faster than real-time multiscale cardiac simulations can be achieved on these systems.

Applications of psychophysical models to the study of auditory development

NASA Astrophysics Data System (ADS)

Werner, Lynne

2003-04-01

Psychophysical models of listening, such as the energy detector model, have provided a framework from which to characterize the function of the mature auditory system and to explore how mature listeners make use of auditory information in sound identification. The application of such models to the study of auditory development has similarly provided insight into the characteristics of infant hearing and listening. Infants intensity, frequency, temporal and spatial resolution have been described at least grossly and some contributions of immature listening strategies to infant hearing have been identified. Infants psychoacoustic performance is typically poorer than adults under identical stimulus conditions. However, the infant's performance typically varies with stimulus condition in a way that is qualitatively similar to the adult's performance. In some cases, though, infants perform in a qualitatively different way from adults in psychoacoustic experiments. Further, recent psychoacoustic studies of children suggest that the classic models of listening may be inadequate to describe the children's performance. The characteristics of a model that might be appropriate for the immature listener will be outlined and the implications for models of mature listening will be discussed. [Work supported by NIH grants DC00396 and by DC04661.

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

NASA Astrophysics Data System (ADS)

Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

2009-08-01

SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

An alternative method for centrifugal compressor loading factor modelling

NASA Astrophysics Data System (ADS)

Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

2017-08-01

The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

Development and validation of chemistry agnostic flow battery cost performance model and application to nonaqueous electrolyte systems: Chemistry agnostic flow battery cost performance model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crawford, Alasdair; Thomsen, Edwin; Reed, David

2016-04-20

A chemistry agnostic cost performance model is described for a nonaqueous flow battery. The model predicts flow battery performance by estimating the active reaction zone thickness at each electrode as a function of current density, state of charge, and flow rate using measured data for electrode kinetics, electrolyte conductivity, and electrode-specific surface area. Validation of the model is conducted using a 4kW stack data at various current densities and flow rates. This model is used to estimate the performance of a nonaqueous flow battery with electrode and electrolyte properties used from the literature. The optimized cost for this system ismore » estimated for various power and energy levels using component costs provided by vendors. The model allows optimization of design parameters such as electrode thickness, area, flow path design, and operating parameters such as power density, flow rate, and operating SOC range for various application duty cycles. A parametric analysis is done to identify components and electrode/electrolyte properties with the highest impact on system cost for various application durations. A pathway to 100$kWh -1 for the storage system is identified.« less

Validation of the PVSyst Performance Model for the Concentrix CPV Technology

NASA Astrophysics Data System (ADS)

Gerstmaier, Tobias; Gomez, María; Gombert, Andreas; Mermoud, André; Lejeune, Thibault

2011-12-01

The accuracy of the two-stage PVSyst model for the Concentrix CPV Technology is determined by comparing modeled to measured values. For both stages, i) the module model and ii) the power plant model, the underlying approaches are explained and methods for obtaining the model parameters are presented. The performance of both models is quantified using 19 months of outdoor measurements for the module model and 9 months of measurements at four different sites for the power plant model. Results are presented by giving statistical quantities for the model accuracy.

Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

PubMed

Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

2017-07-03

Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

A holistic approach to movement education in sport and fitness: a systems based model.

PubMed

Polsgrove, Myles Jay

2012-01-01

The typical model used by movement professionals to enhance performance relies on the notion that a linear increase in load results in steady and progressive gains, whereby, the greater the effort, the greater the gains in performance. Traditional approaches to movement progression typically rely on the proper sequencing of extrinsically based activities to facilitate the individual in reaching performance objectives. However, physical rehabilitation or physical performance rarely progresses in such a linear fashion; instead they tend to evolve non-linearly and rather unpredictably. A dynamic system can be described as an entity that self-organizes into increasingly complex forms. Applying this view to the human body, practitioners could facilitate non-linear performance gains through a systems based programming approach. Utilizing a dynamic systems view, the Holistic Approach to Movement Education (HADME) is a model designed to optimize performance by accounting for non-linear and self-organizing traits associated with human movement. In this model, gains in performance occur through advancing individual perspectives and through optimizing sub-system performance. This inward shift of the focus of performance creates a sharper self-awareness and may lead to more optimal movements. Copyright © 2011 Elsevier Ltd. All rights reserved.

Ecosystem performance monitoring of rangelands by integrating modeling and remote sensing

USGS Publications Warehouse

Wylie, Bruce K.; Boyte, Stephen P.; Major, Donald J.

2012-01-01

Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and management decisions, making interannual time-series analysis more consistent and interpretable. We compared the actual ecosystem performance (AEP) of five rangeland vegetation types in the Owyhee Uplands for 9 yr to their expected ecosystem performance (EEP). Integrated growing season Normalized Difference Vegetation Index data for each of the nine growing seasons served as a proxy for annual AEP. Regression-tree models used long-term site potential, seasonal weather, and land cover data sets to generate annual EEP, an estimate of ecosystem performance incorporating annual weather variations. The difference between AEP and EEP provided a performance measure for each pixel in the study area. Ecosystem performance anomalies occurred when the ecosystem performed significantly better or worse than the model predicted. About 14% of the Owyhee Uplands showed a trend of significant underperformance or overperformance (P<0.10). Land managers can use results from weather-based rangeland ecosystem performance models to help support adaptive management strategies.

The Impact of Varied Discrimination Parameters on Mixed-Format Item Response Theory Model Selection

ERIC Educational Resources Information Center

Whittaker, Tiffany A.; Chang, Wanchen; Dodd, Barbara G.

2013-01-01

Whittaker, Chang, and Dodd compared the performance of model selection criteria when selecting among mixed-format IRT models and found that the criteria did not perform adequately when selecting the more parameterized models. It was suggested by M. S. Johnson that the problems when selecting the more parameterized models may be because of the low…

Predicting performance: relative importance of students' background and past performance.

PubMed

Stegers-Jager, Karen M; Themmen, Axel P N; Cohen-Schotanus, Janke; Steyerberg, Ewout W

2015-09-01

Despite evidence for the predictive value of both pre-admission characteristics and past performance at medical school, their relative contribution to predicting medical school performance has not been thoroughly investigated. This study was designed to determine the relative importance of pre-admission characteristics and past performance in medical school in predicting student performance in pre-clinical and clinical training. This longitudinal prospective study followed six cohorts of students admitted to a Dutch, 6-year, undergraduate medical course during 2002-2007 (n = 2357). Four prediction models were developed using multivariate logistic regression analysis. Main outcome measures were 'Year 1 course completion within 1 year' (models 1a, 1b), 'Pre-clinical course completion within 4 years' (model 2) and 'Achievement of at least three of five clerkship grades of ≥ 8.0' (model 3). Pre-admission characteristics (models 1a, 1b, 2, 3) and past performance at medical school (models 1b, 2, 3) were included as predictor variables. In model 1a - including pre-admission characteristics only - the strongest predictor for Year 1 course completion was pre-university grade point average (GPA). Success factors were 'selected by admission testing' and 'age > 21 years'; risk factors were 'Surinamese/Antillean background', 'foreign pre-university degree', 'doctor parent' and male gender. In model 1b, number of attempts and GPA at 4 months were the strongest predictors for Year 1 course completion, and male gender remained a risk factor. Year 1 GPA was the strongest predictor for pre-clinical course completion, whereas being male or aged 19-21 years were risk factors. Pre-clinical course GPA positively predicted clinical performance, whereas being non-Dutch or a first-generation university student were important risk factors for lower clinical grades. Nagelkerke's R(2) ranged from 0.16 to 0.62. This study not only confirms the importance of past performance as a predictor of future performance in pre-clinical training, but also reveals the importance of a student's background as a predictor in clinical training. These findings have important practical implications for selection and support during medical school. © 2015 John Wiley & Sons Ltd.

Coupled modeling of the competitive gettering of transition metals and impact on performance of lifetime sensitive devices

NASA Astrophysics Data System (ADS)

Yazdani, Armin; Chen, Renyu; Dunham, Scott T.

2017-03-01

This work models competitive gettering of metals (Cu, Ni, Fe, Mo, and W) by boron, phosphorus, and dislocation loops, and connects those results directly to device performance. Density functional theory calculations were first performed to determine the binding energies of metals to the gettering sites, and based on that, continuum models were developed to model the redistribution and trapping of the metals. Our models found that Fe is most strongly trapped by the dislocation loops while Cu and Ni are most strongly trapped by the P4V clusters formed in high phosphorus concentrations. In addition, it is found that none of the mentioned gettering sites are effective in gettering Mo and W. The calculated metal redistribution along with the associated capture cross sections and trap energy levels are passed to device simulation via the recombination models to calculate carrier lifetime and the resulting device performance. Thereby, a comprehensive and predictive TCAD framework is developed to optimize the processing conditions to maximize performance of lifetime sensitive devices.

Experimental investigation of nozzle/plume aerodynamics at hypersonic speeds

NASA Technical Reports Server (NTRS)

Bogdanoff, David W.; Cambier, Jean-Luc; Papadopoulos, Perikles

1994-01-01

Much of the work involved the Ames 16-Inch Shock Tunnel facility. The facility was reactivated and upgraded, a data acquisition system was configured and upgraded several times, several facility calibrations were performed and test entries with a wedge model with hydrogen injection and a full scramjet combustor model, with hydrogen injection, were performed. Extensive CFD modeling of the flow in the facility was done. This includes modeling of the unsteady flow in the driver and driven tubes and steady flow modeling of the nozzle flow. Other modeling efforts include simulations of non-equilibrium flows and turbulence, plasmas, light gas guns and the use of non-ideal gas equations of state. New experimental techniques to improve the performance of gas guns, shock tubes and tunnels and scramjet combustors were conceived and studied computationally. Ways to improve scramjet engine performance using steady and pulsed detonation waves were also studied computationally. A number of studies were performed on the operation of the ram accelerator, including investigations of in-tube gasdynamic heating and the use of high explosives to raise the velocity capability of the device.

Effects of thermal blooming on systems comprised of tiled subapertures

NASA Astrophysics Data System (ADS)

Leakeas, Charles L.; Bartell, Richard J.; Krizo, Matthew J.; Fiorino, Steven T.; Cusumano, Salvatore J.; Whiteley, Matthew R.

2010-04-01

Laser weapon systems comprise of tiled subapertures are rapidly emerging in the directed energy community. The Air Force Institute of Technology Center for Directed Energy (AFIT/CDE), under sponsorship of the HEL Joint Technology Office has developed performance models of such laser weapon system configurations consisting of tiled arrays of both slab and fiber subapertures. These performance models are based on results of detailed waveoptics analyses conducted using WaveTrain. Previous performance model versions developed in this effort represent system characteristics such as subaperture shape, aperture fill factor, subaperture intensity profile, subaperture placement in the primary aperture, subaperture mutual coherence (piston), subaperture differential jitter (tilt), and beam quality wave-front error associated with each subaperture. The current work is a prerequisite for the development of robust performance models for turbulence and thermal blooming effects for tiled systems. Emphasis is placed on low altitude tactical scenarios. The enhanced performance model developed will be added to AFIT/CDE's HELEEOS parametric one-on-one engagement level model via the Scaling for High Energy Laser and Relay Engagement (SHaRE) toolbox.

Performance evaluation of an agent-based occupancy simulation model

DOE PAGES

Luo, Xuan; Lam, Khee Poh; Chen, Yixing; ...

2017-01-17

Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types weremore » first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.« less

Performance evaluation of an agent-based occupancy simulation model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Xuan; Lam, Khee Poh; Chen, Yixing

Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types weremore » first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.« less

Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM). I: Model intercomparison with current land use

USGS Publications Warehouse

Breuer, L.; Huisman, J.A.; Willems, P.; Bormann, H.; Bronstert, A.; Croke, B.F.W.; Frede, H.-G.; Graff, T.; Hubrechts, L.; Jakeman, A.J.; Kite, G.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Viney, N.R.

2009-01-01

This paper introduces the project on 'Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM)' that aims at investigating the envelope of predictions on changes in hydrological fluxes due to land use change. As part of a series of four papers, this paper outlines the motivation and setup of LUCHEM, and presents a model intercomparison for the present-day simulation results. Such an intercomparison provides a valuable basis to investigate the effects of different model structures on model predictions and paves the ground for the analysis of the performance of multi-model ensembles and the reliability of the scenario predictions in companion papers. In this study, we applied a set of 10 lumped, semi-lumped and fully distributed hydrological models that have been previously used in land use change studies to the low mountainous Dill catchment, Germany. Substantial differences in model performance were observed with Nash-Sutcliffe efficiencies ranging from 0.53 to 0.92. Differences in model performance were attributed to (1) model input data, (2) model calibration and (3) the physical basis of the models. The models were applied with two sets of input data: an original and a homogenized data set. This homogenization of precipitation, temperature and leaf area index was performed to reduce the variation between the models. Homogenization improved the comparability of model simulations and resulted in a reduced average bias, although some variation in model data input remained. The effect of the physical differences between models on the long-term water balance was mainly attributed to differences in how models represent evapotranspiration. Semi-lumped and lumped conceptual models slightly outperformed the fully distributed and physically based models. This was attributed to the automatic model calibration typically used for this type of models. Overall, however, we conclude that there was no superior model if several measures of model performance are considered and that all models are suitable to participate in further multi-model ensemble set-ups and land use change scenario investigations. ?? 2008 Elsevier Ltd. All rights reserved.

How motivation affects academic performance: a structural equation modelling analysis.

PubMed

Kusurkar, R A; Ten Cate, Th J; Vos, C M P; Westers, P; Croiset, G

2013-03-01

Few studies in medical education have studied effect of quality of motivation on performance. Self-Determination Theory based on quality of motivation differentiates between Autonomous Motivation (AM) that originates within an individual and Controlled Motivation (CM) that originates from external sources. To determine whether Relative Autonomous Motivation (RAM, a measure of the balance between AM and CM) affects academic performance through good study strategy and higher study effort and compare this model between subgroups: males and females; students selected via two different systems namely qualitative and weighted lottery selection. Data on motivation, study strategy and effort was collected from 383 medical students of VU University Medical Center Amsterdam and their academic performance results were obtained from the student administration. Structural Equation Modelling analysis technique was used to test a hypothesized model in which high RAM would positively affect Good Study Strategy (GSS) and study effort, which in turn would positively affect academic performance in the form of grade point averages. This model fit well with the data, Chi square = 1.095, df = 3, p = 0.778, RMSEA model fit = 0.000. This model also fitted well for all tested subgroups of students. Differences were found in the strength of relationships between the variables for the different subgroups as expected. In conclusion, RAM positively correlated with academic performance through deep strategy towards study and higher study effort. This model seems valid in medical education in subgroups such as males, females, students selected by qualitative and weighted lottery selection.

Performance assessment of geospatial simulation models of land-use change--a landscape metric-based approach.

PubMed

Sakieh, Yousef; Salmanmahiny, Abdolrassoul

2016-03-01

Performance evaluation is a critical step when developing land-use and cover change (LUCC) models. The present study proposes a spatially explicit model performance evaluation method, adopting a landscape metric-based approach. To quantify GEOMOD model performance, a set of composition- and configuration-based landscape metrics including number of patches, edge density, mean Euclidean nearest neighbor distance, largest patch index, class area, landscape shape index, and splitting index were employed. The model takes advantage of three decision rules including neighborhood effect, persistence of change direction, and urbanization suitability values. According to the results, while class area, largest patch index, and splitting indices demonstrated insignificant differences between spatial pattern of ground truth and simulated layers, there was a considerable inconsistency between simulation results and real dataset in terms of the remaining metrics. Specifically, simulation outputs were simplistic and the model tended to underestimate number of developed patches by producing a more compact landscape. Landscape-metric-based performance evaluation produces more detailed information (compared to conventional indices such as the Kappa index and overall accuracy) on the model's behavior in replicating spatial heterogeneity features of a landscape such as frequency, fragmentation, isolation, and density. Finally, as the main characteristic of the proposed method, landscape metrics employ the maximum potential of observed and simulated layers for a performance evaluation procedure, provide a basis for more robust interpretation of a calibration process, and also deepen modeler insight into the main strengths and pitfalls of a specific land-use change model when simulating a spatiotemporal phenomenon.

V and V Efforts of Auroral Precipitation Models: Preliminary Results

NASA Technical Reports Server (NTRS)

Zheng, Yihua; Kuznetsova, Masha; Rastaetter, Lutz; Hesse, Michael

2011-01-01

Auroral precipitation models have been valuable both in terms of space weather applications and space science research. Yet very limited testing has been performed regarding model performance. A variety of auroral models are available, including empirical models that are parameterized by geomagnetic indices or upstream solar wind conditions, now casting models that are based on satellite observations, or those derived from physics-based, coupled global models. In this presentation, we will show our preliminary results regarding V&V efforts of some of the models.

A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution.

PubMed

Harrison, Xavier A

2015-01-01

Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels), I investigate the performance of both model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed poorly when models contained <5 levels of the random intercept term, especially for estimating variance components, and this effect appeared independent of total sample size. These results suggest that OLRE are a useful tool for modelling overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.

Understanding performance properties of chemical engines under a trade-off optimization: Low-dissipation versus endoreversible model

NASA Astrophysics Data System (ADS)

Tang, F. R.; Zhang, Rong; Li, Huichao; Li, C. N.; Liu, Wei; Bai, Long

2018-05-01

The trade-off criterion is used to systemically investigate the performance features of two chemical engine models (the low-dissipation model and the endoreversible model). The optimal efficiencies, the dissipation ratios, and the corresponding ratios of the dissipation rates for two models are analytically determined. Furthermore, the performance properties of two kinds of chemical engines are precisely compared and analyzed, and some interesting physics is revealed. Our investigations show that the certain universal equivalence between two models is within the framework of the linear irreversible thermodynamics, and their differences are rooted in the different physical contexts. Our results can contribute to a precise understanding of the general features of chemical engines.

Real-Time Robust Adaptive Modeling and Scheduling for an Electronic Commerce Server

NASA Astrophysics Data System (ADS)

Du, Bing; Ruan, Chun

With the increasing importance and pervasiveness of Internet services, it is becoming a challenge for the proliferation of electronic commerce services to provide performance guarantees under extreme overload. This paper describes a real-time optimization modeling and scheduling approach for performance guarantee of electronic commerce servers. We show that an electronic commerce server may be simulated as a multi-tank system. A robust adaptive server model is subject to unknown additive load disturbances and uncertain model matching. Overload control techniques are based on adaptive admission control to achieve timing guarantees. We evaluate the performance of the model using a complex simulation that is subjected to varying model parameters and massive overload.

Theoretical performance of hydrogen-bromine rechargeable SPE fuel cell. [Solid Polymer Electrolyte

NASA Technical Reports Server (NTRS)

Savinell, R. F.; Fritts, S. D.

1988-01-01

A mathematical model was formulated to describe the performance of a hydrogen-bromine fuel cell. Porous electrode theory was applied to the carbon felt flow-by electrode and was coupled to theory describing the solid polymer electrolyte (SPE) system. Parametric studies using the numerical solution to this model were performed to determine the effect of kinetic, mass transfer, and design parameters on the performance of the fuel cell. The results indicate that the cell performance is most sensitive to the transport properties of the SPE membrane. The model was also shown to be a useful tool for scale-up studies.

Performance analysis of LAN bridges and routers

NASA Technical Reports Server (NTRS)

Hajare, Ankur R.

1991-01-01

Bridges and routers are used to interconnect Local Area Networks (LANs). The performance of these devices is important since they can become bottlenecks in large multi-segment networks. Performance metrics and test methodology for bridges and routers were not standardized. Performance data reported by vendors is not applicable to the actual scenarios encountered in an operational network. However, vendor-provided data can be used to calibrate models of bridges and routers that, along with other models, yield performance data for a network. Several tools are available for modeling bridges and routers - Network II.5 was used. The results of the analysis of some bridges and routers are presented.

40 CFR 60.2695 - How are the performance test data used?

Code of Federal Regulations, 2013 CFR

2013-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Performance Testing.... [76 FR 15773, Mar. 21, 2011] Model Rule—Initial Compliance Requirements ...

40 CFR 60.2695 - How are the performance test data used?

Code of Federal Regulations, 2014 CFR

2014-07-01

... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and Compliance Times for Commercial and Industrial Solid Waste Incineration Units Model Rule-Performance Testing.... [76 FR 15773, Mar. 21, 2011] Model Rule—Initial Compliance Requirements ...

Critical research issues in development of biomathematical models of fatigue and performance.

PubMed

Dinges, David F

2004-03-01

This article reviews the scientific research needed to ensure the continued development, validation, and operational transition of biomathematical models of fatigue and performance. These models originated from the need to ascertain the formal underlying relationships among sleep and circadian dynamics in the control of alertness and neurobehavioral performance capability. Priority should be given to research that further establishes their basic validity, including the accuracy of the core mathematical formulae and parameters that instantiate the interactions of sleep/wake and circadian processes. Since individuals can differ markedly and reliably in their responses to sleep loss and to countermeasures for it, models must incorporate estimates of these inter-individual differences, and research should identify predictors of them. To ensure models accurately predict recovery of function with sleep of varying durations, dose-response curves for recovery of performance as a function of prior sleep homeostatic load and the number of days of recovery are needed. It is also necessary to establish whether the accuracy of models is affected by using work/rest schedules as surrogates for sleep/wake inputs to models. Given the importance of light as both a circadian entraining agent and an alerting agent, research should determine the extent to which light input could incrementally improve model predictions of performance, especially in persons exposed to night work, jet lag, and prolonged work. Models seek to estimate behavioral capability and/or the relative risk of adverse events in a fatigued state. Research is needed on how best to scale and interpret metrics of behavioral capability, and incorporate factors that amplify or diminish the relationship between model predictions of performance and risk outcomes.

Linking asphalt binder fatigue to asphalt mixture fatigue performance using viscoelastic continuum damage modeling

NASA Astrophysics Data System (ADS)

Safaei, Farinaz; Castorena, Cassie; Kim, Y. Richard

2016-08-01

Fatigue cracking is a major form of distress in asphalt pavements. Asphalt binder is the weakest asphalt concrete constituent and, thus, plays a critical role in determining the fatigue resistance of pavements. Therefore, the ability to characterize and model the inherent fatigue performance of an asphalt binder is a necessary first step to design mixtures and pavements that are not susceptible to premature fatigue failure. The simplified viscoelastic continuum damage (S-VECD) model has been used successfully by researchers to predict the damage evolution in asphalt mixtures for various traffic and climatic conditions using limited uniaxial test data. In this study, the S-VECD model, developed for asphalt mixtures, is adapted for asphalt binders tested under cyclic torsion in a dynamic shear rheometer. Derivation of the model framework is presented. The model is verified by producing damage characteristic curves that are both temperature- and loading history-independent based on time sweep tests, given that the effects of plasticity and adhesion loss on the material behavior are minimal. The applicability of the S-VECD model to the accelerated loading that is inherent of the linear amplitude sweep test is demonstrated, which reveals reasonable performance predictions, but with some loss in accuracy compared to time sweep tests due to the confounding effects of nonlinearity imposed by the high strain amplitudes included in the test. The asphalt binder S-VECD model is validated through comparisons to asphalt mixture S-VECD model results derived from cyclic direct tension tests and Accelerated Loading Facility performance tests. The results demonstrate good agreement between the asphalt binder and mixture test results and pavement performance, indicating that the developed model framework is able to capture the asphalt binder's contribution to mixture fatigue and pavement fatigue cracking performance.

Modeling task-specific neuronal ensembles improves decoding of grasp

NASA Astrophysics Data System (ADS)

Smith, Ryan J.; Soares, Alcimar B.; Rouse, Adam G.; Schieber, Marc H.; Thakor, Nitish V.

2018-06-01

Objective. Dexterous movement involves the activation and coordination of networks of neuronal populations across multiple cortical regions. Attempts to model firing of individual neurons commonly treat the firing rate as directly modulating with motor behavior. However, motor behavior may additionally be associated with modulations in the activity and functional connectivity of neurons in a broader ensemble. Accounting for variations in neural ensemble connectivity may provide additional information about the behavior being performed. Approach. In this study, we examined neural ensemble activity in primary motor cortex (M1) and premotor cortex (PM) of two male rhesus monkeys during performance of a center-out reach, grasp and manipulate task. We constructed point process encoding models of neuronal firing that incorporated task-specific variations in the baseline firing rate as well as variations in functional connectivity with the neural ensemble. Models were evaluated both in terms of their encoding capabilities and their ability to properly classify the grasp being performed. Main results. Task-specific ensemble models correctly predicted the performed grasp with over 95% accuracy and were shown to outperform models of neuronal activity that assume only a variable baseline firing rate. Task-specific ensemble models exhibited superior decoding performance in 82% of units in both monkeys (p  <  0.01). Inclusion of ensemble activity also broadly improved the ability of models to describe observed spiking. Encoding performance of task-specific ensemble models, measured by spike timing predictability, improved upon baseline models in 62% of units. Significance. These results suggest that additional discriminative information about motor behavior found in the variations in functional connectivity of neuronal ensembles located in motor-related cortical regions is relevant to decode complex tasks such as grasping objects, and may serve the basis for more reliable and accurate neural prosthesis.

Assessment of human epidermal model LabCyte EPI-MODEL for in vitro skin irritation testing according to European Centre for the Validation of Alternative Methods (ECVAM)-validated protocol.

PubMed

Katoh, Masakazu; Hamajima, Fumiyasu; Ogasawara, Takahiro; Hata, Ken-Ichiro

2009-06-01

A validation study of an in vitro skin irritation testing method using a reconstructed human skin model has been conducted by the European Centre for the Validation of Alternative Methods (ECVAM), and a protocol using EpiSkin (SkinEthic, France) has been approved. The structural and performance criteria of skin models for testing are defined in the ECVAM Performance Standards announced along with the approval. We have performed several evaluations of the new reconstructed human epidermal model LabCyte EPI-MODEL, and confirmed that it is applicable to skin irritation testing as defined in the ECVAM Performance Standards. We selected 19 materials (nine irritants and ten non-irritants) available in Japan as test chemicals among the 20 reference chemicals described in the ECVAM Performance Standard. A test chemical was applied to the surface of the LabCyte EPI-MODEL for 15 min, after which it was completely removed and the model then post-incubated for 42 hr. Cell v iability was measured by MTT assay and skin irritancy of the test chemical evaluated. In addition, interleukin-1 alpha (IL-1alpha) concentration in the culture supernatant after post-incubation was measured to provide a complementary evaluation of skin irritation. Evaluation of the 19 test chemicals resulted in 79% accuracy, 78% sensitivity and 80% specificity, confirming that the in vitro skin irritancy of the LabCyte EPI-MODEL correlates highly with in vivo skin irritation. These results suggest that LabCyte EPI-MODEL is applicable to the skin irritation testing protocol set out in the ECVAM Performance Standards.

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.

PubMed

Benndorf, Matthias; Kotter, Elmar; Langer, Mathias; Herda, Christoph; Wu, Yirong; Burnside, Elizabeth S

2015-06-01

To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.

A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics.

PubMed

Devriendt, Floris; Moldovan, Darie; Verbeke, Wouter

2018-03-01

Prescriptive analytics extends on predictive analytics by allowing to estimate an outcome in function of control variables, allowing as such to establish the required level of control variables for realizing a desired outcome. Uplift modeling is at the heart of prescriptive analytics and aims at estimating the net difference in an outcome resulting from a specific action or treatment that is applied. In this article, a structured and detailed literature survey on uplift modeling is provided by identifying and contrasting various groups of approaches. In addition, evaluation metrics for assessing the performance of uplift models are reviewed. An experimental evaluation on four real-world data sets provides further insight into their use. Uplift random forests are found to be consistently among the best performing techniques in terms of the Qini and Gini measures, although considerable variability in performance across the various data sets of the experiments is observed. In addition, uplift models are frequently observed to be unstable and display a strong variability in terms of performance across different folds in the cross-validation experimental setup. This potentially threatens their actual use for business applications. Moreover, it is found that the available evaluation metrics do not provide an intuitively understandable indication of the actual use and performance of a model. Specifically, existing evaluation metrics do not facilitate a comparison of uplift models and predictive models and evaluate performance either at an arbitrary cutoff or over the full spectrum of potential cutoffs. In conclusion, we highlight the instability of uplift models and the need for an application-oriented approach to assess uplift models as prime topics for further research.

Modeling and analysis to quantify MSE wall behavior and performance.

DOT National Transportation Integrated Search

2009-08-01

To better understand potential sources of adverse performance of mechanically stabilized earth (MSE) walls, a suite of analytical models was studied using the computer program FLAC, a numerical modeling computer program widely used in geotechnical en...

Performance-based maintenance of gas turbines for reliable control of degraded power systems

NASA Astrophysics Data System (ADS)

Mo, Huadong; Sansavini, Giovanni; Xie, Min

2018-03-01

Maintenance actions are necessary for ensuring proper operations of control systems under component degradation. However, current condition-based maintenance (CBM) models based on component health indices are not suitable for degraded control systems. Indeed, failures of control systems are only determined by the controller outputs, and the feedback mechanism compensates the control performance loss caused by the component deterioration. Thus, control systems may still operate normally even if the component health indices exceed failure thresholds. This work investigates the CBM model of control systems and employs the reduced control performance as a direct degradation measure for deciding maintenance activities. The reduced control performance depends on the underlying component degradation modelled as a Wiener process and the feedback mechanism. To this aim, the controller features are quantified by developing a dynamic and stochastic control block diagram-based simulation model, consisting of the degraded components and the control mechanism. At each inspection, the system receives a maintenance action if the control performance deterioration exceeds its preventive-maintenance or failure thresholds. Inspired by realistic cases, the component degradation model considers random start time and unit-to-unit variability. The cost analysis of maintenance model is conducted via Monte Carlo simulation. Optimal maintenance strategies are investigated to minimize the expected maintenance costs, which is a direct consequence of the control performance. The proposed framework is able to design preventive maintenance actions on a gas power plant, to ensuring required load frequency control performance against a sudden load increase. The optimization results identify the trade-off between system downtime and maintenance costs as a function of preventive maintenance thresholds and inspection frequency. Finally, the control performance-based maintenance model can reduce maintenance costs as compared to CBM and pre-scheduled maintenance.

Fatigue models for applied research in warfighting.

PubMed

Hursh, Steven R; Redmond, Daniel P; Johnson, Michael L; Thorne, David R; Belenky, Gregory; Balkin, Thomas J; Storm, William F; Miller, James C; Eddy, Douglas R

2004-03-01

The U.S. Department of Defense (DOD) has long pursued applied research concerning fatigue in sustained and continuous military operations. In 1996, Hursh developed a simple homeostatic fatigue model and programmed the model into an actigraph to give a continuous indication of performance. Based on this initial work, the Army conducted a study of 1 wk of restricted sleep in 66 subjects with multiple measures of performance, termed the Sleep Dose-Response Study (SDR). This study provided numerical estimation of parameters for the Walter Reed Army Institute of Research Sleep Performance Model (SPM) and elucidated the relationships among several sleep-related performance measures. Concurrently, Hursh extended the original actigraph modeling structure and software expressions for use in other practical applications. The model became known as the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model, and Hursh has applied it in the construction of a Fatigue Avoidance Scheduling Tool. This software is designed to help optimize the operational management of aviation ground and flight crews, but is not limited to that application. This paper describes the working fatigue model as it is being developed by the DOD laboratories, using the conceptual framework, vernacular, and notation of the SAFTE Model. At specific points where the SPM may differ from SAFTE, this is discussed. Extensions of the SAFTE Model to incorporate dynamic phase adjustment for both transmeridian relocation and shift work are described. The unexpected persistence of performance effects following chronic sleep restriction found in the SDR study necessitated some revisions of the SAFTE Model that are also described. The paper concludes with a discussion of several important modeling issues that remain to be addressed.

Predicting BRCA1 and BRCA2 gene mutation carriers: comparison of LAMBDA, BRCAPRO, Myriad II, and modified Couch models.

PubMed

Lindor, Noralane M; Lindor, Rachel A; Apicella, Carmel; Dowty, James G; Ashley, Amanda; Hunt, Katherine; Mincey, Betty A; Wilson, Marcia; Smith, M Cathie; Hopper, John L

2007-01-01

Models have been developed to predict the probability that a person carries a detectable germline mutation in the BRCA1 or BRCA2 genes. Their relative performance in a clinical setting is unclear. To compare the performance characteristics of four BRCA1/BRCA2 gene mutation prediction models: LAMBDA, based on a checklist and scores developed from data on Ashkenazi Jewish (AJ) women; BRCAPRO, a Bayesian computer program; modified Couch tables based on regression analyses; and Myriad II tables collated by Myriad Genetics Laboratories. Family cancer history data were analyzed from 200 probands from the Mayo Clinic Familial Cancer Program, in a multispecialty tertiary care group practice. All probands had clinical testing for BRCA1 and BRCA2 mutations conducted in a single laboratory. For each model, performance was assessed by the area under the receiver operator characteristic curve (ROC) and by tests of accuracy and dispersion. Cases "missed" by one or more models (model predicted less than 10% probability of mutation when a mutation was actually found) were compared across models. All models gave similar areas under the ROC curve of 0.71 to 0.76. All models except LAMBDA substantially under-predicted the numbers of carriers. All models were too dispersed. In terms of ranking, all prediction models performed reasonably well with similar performance characteristics. Model predictions were widely discrepant for some families. Review of cancer family histories by an experienced clinician continues to be vital to ensure that critical elements are not missed and that the most appropriate risk prediction figures are provided.

Integrated Modeling Activities for the James Webb Space Telescope (JWST): Structural-Thermal-Optical Analysis

NASA Technical Reports Server (NTRS)

Johnston, John D.; Parrish, Keith; Howard, Joseph M.; Mosier, Gary E.; McGinnis, Mark; Bluth, Marcel; Kim, Kevin; Ha, Hong Q.

2004-01-01

This is a continuation of a series of papers on modeling activities for JWST. The structural-thermal- optical, often referred to as "STOP", analysis process is used to predict the effect of thermal distortion on optical performance. The benchmark STOP analysis for JWST assesses the effect of an observatory slew on wavefront error. The paper begins an overview of multi-disciplinary engineering analysis, or integrated modeling, which is a critical element of the JWST mission. The STOP analysis process is then described. This process consists of the following steps: thermal analysis, structural analysis, and optical analysis. Temperatures predicted using geometric and thermal math models are mapped to the structural finite element model in order to predict thermally-induced deformations. Motions and deformations at optical surfaces are input to optical models and optical performance is predicted using either an optical ray trace or WFE estimation techniques based on prior ray traces or first order optics. Following the discussion of the analysis process, results based on models representing the design at the time of the System Requirements Review. In addition to baseline performance predictions, sensitivity studies are performed to assess modeling uncertainties. Of particular interest is the sensitivity of optical performance to uncertainties in temperature predictions and variations in metal properties. The paper concludes with a discussion of modeling uncertainty as it pertains to STOP analysis.

Implementation of an Integrated On-Board Aircraft Engine Diagnostic Architecture

NASA Technical Reports Server (NTRS)

Armstrong, Jeffrey B.; Simon, Donald L.

2012-01-01

An on-board diagnostic architecture for aircraft turbofan engine performance trending, parameter estimation, and gas-path fault detection and isolation has been developed and evaluated in a simulation environment. The architecture incorporates two independent models: a realtime self-tuning performance model providing parameter estimates and a performance baseline model for diagnostic purposes reflecting long-term engine degradation trends. This architecture was evaluated using flight profiles generated from a nonlinear model with realistic fleet engine health degradation distributions and sensor noise. The architecture was found to produce acceptable estimates of engine health and unmeasured parameters, and the integrated diagnostic algorithms were able to perform correct fault isolation in approximately 70 percent of the tested cases

A generic simulation model to assess the performance of sterilization services in health establishments.

PubMed

Di Mascolo, Maria; Gouin, Alexia

2013-03-01

The work presented here is with a view to improving performance of sterilization services in hospitals. We carried out a survey in a large number of health establishments in the Rhône-Alpes region in France. Based on the results of this survey and a detailed study of a specific service, we have built a generic model. The generic nature of the model relies on a common structure with a high level of detail. This model can be used to improve the performance of a specific sterilization service and/or to dimension its resources. It can also serve for quantitative comparison of performance indicators of various sterilization services.

Aerodynamic comparison of a butterfly-like flapping wing-body model and a revolving-wing model

NASA Astrophysics Data System (ADS)

Suzuki, Kosuke; Yoshino, Masato

2017-06-01

The aerodynamic performance of flapping- and revolving-wing models is investigated by numerical simulations based on an immersed boundary-lattice Boltzmann method. As wing models, we use (i) a butterfly-like model with a body and flapping-rectangular wings and (ii) a revolving-wing model with the same wings as the flapping case. Firstly, we calculate aerodynamic performance factors such as the lift force, the power, and the power loading of the two models for Reynolds numbers in the range of 50-1000. For the flapping-wing model, the power loading is maximal for the maximum angle of attack of 90°, a flapping amplitude of roughly 45°, and a phase shift between the flapping angle and the angle of attack of roughly 90°. For the revolving-wing model, the power loading peaks for an angle of attack of roughly 45°. In addition, we examine the ground effect on the aerodynamic performance of the revolving-wing model. Secondly, we compare the aerodynamic performance of the flapping- and revolving-wing models at their respective maximal power loadings. It is found that the revolving-wing model is more efficient than the flapping-wing model both when the body of the latter is fixed and where it can move freely. Finally, we discuss the relative agilities of the flapping- and revolving-wing models.

A comparison of hydrologic models for ecological flows and water availability

USGS Publications Warehouse

Caldwell, Peter V; Kennen, Jonathan G.; Sun, Ge; Kiang, Julie E.; Butcher, John B; Eddy, Michelle C; Hay, Lauren E.; LaFontaine, Jacob H.; Hain, Ernie F.; Nelson, Stacy C; McNulty, Steve G

2015-01-01

Robust hydrologic models are needed to help manage water resources for healthy aquatic ecosystems and reliable water supplies for people, but there is a lack of comprehensive model comparison studies that quantify differences in streamflow predictions among model applications developed to answer management questions. We assessed differences in daily streamflow predictions by four fine-scale models and two regional-scale monthly time step models by comparing model fit statistics and bias in ecologically relevant flow statistics (ERFSs) at five sites in the Southeastern USA. Models were calibrated to different extents, including uncalibrated (level A), calibrated to a downstream site (level B), calibrated specifically for the site (level C) and calibrated for the site with adjusted precipitation and temperature inputs (level D). All models generally captured the magnitude and variability of observed streamflows at the five study sites, and increasing level of model calibration generally improved performance. All models had at least 1 of 14 ERFSs falling outside a +/−30% range of hydrologic uncertainty at every site, and ERFSs related to low flows were frequently over-predicted. Our results do not indicate that any specific hydrologic model is superior to the others evaluated at all sites and for all measures of model performance. Instead, we provide evidence that (1) model performance is as likely to be related to calibration strategy as it is to model structure and (2) simple, regional-scale models have comparable performance to the more complex, fine-scale models at a monthly time step.

A computational model of prefrontal control in free recall: strategic memory use in the California Verbal Learning Task.

PubMed

Becker, Suzanna; Lim, Jean

2003-08-15

Several decades of research into the function of the frontal lobes in brain-damaged patients, and more recently in intact individuals using function brain imaging, has delineated the complex executive functions of the frontal cortex. And yet, the mechanisms by which the brain achieves these functions remain poorly understood. Here, we present a computational model of the role of the prefrontal cortex (PFC) in controlled memory use that may help to shed light on the mechanisms underlying one aspect of frontal control: the development and deployment of recall strategies. The model accounts for interactions between the PFC and medial temporal lobe in strategic memory use. The PFC self-organizes its own mnemonic codes using internally derived performance measures. These mnemonic codes serve as retrieval cues by biasing retrieval in the medial temporal lobe memory system. We present data from three simulation experiments that demonstrate strategic encoding and retrieval in the free recall of categorized lists of words. Experiment 1 compares the performance of the model with two control networks to evaluate the contribution of various components of the model. Experiment 2 compares the performance of normal and frontally lesioned models to data from several studies using frontally intact and frontally lesioned individuals, as well as normal, healthy individuals under conditions of divided attention. Experiment 3 compares the model's performance on the recall of blocked and unblocked categorized lists of words to data from Stuss et al. (1994) for individuals with control and frontal lobe lesions. Overall, our model captures a number of aspects of human performance on free recall tasks: an increase in total words recalled and in semantic clustering scores across trials, superiority on blocked lists of related items compared to unblocked lists of related items, and similar patterns of performance across trials in the normal and frontally lesioned models, with poorer overall performance of the lesioned models on all measures. The model also has a number of shortcomings, in light of which we suggest extensions to the model that would enable more sophisticated forms of strategic control.

Firm performance model in small and medium enterprises (SMEs) based on learning orientation and innovation

NASA Astrophysics Data System (ADS)

Lestari, E. R.; Ardianti, F. L.; Rachmawati, L.

2018-03-01

This study investigated the relationship between learning orientation, innovation, and firm performance. A conceptual model and hypothesis were empirically examined using structural equation modelling. The study involved a questionnaire-based survey of owners of small and medium enterprises (SMEs) operating in Batu City, Indonesia. The results showed that both variables of learning orientation and innovation effect positively on firm performance. Additionally, learning orientation has positive effect innovation. This study has implication for SMEs aiming at increasing their firm performance based on learning orientation and innovation capability.

Measuring the performance of Internet companies using a two-stage data envelopment analysis model

NASA Astrophysics Data System (ADS)

Cao, Xiongfei; Yang, Feng

2011-05-01

In exploring the business operation of Internet companies, few researchers have used data envelopment analysis (DEA) to evaluate their performance. Since the Internet companies have a two-stage production process: marketability and profitability, this study employs a relational two-stage DEA model to assess the efficiency of the 40 dot com firms. The results show that our model performs better in measuring efficiency, and is able to discriminate the causes of inefficiency, thus helping business management to be more effective through providing more guidance to business performance improvement.

Shuttle passenger couch. [design and performance of engineering model

NASA Technical Reports Server (NTRS)

Rosener, A. A.; Stephenson, M. L.

1974-01-01

Conceptual design and fabrication of a full scale shuttle passenger couch engineering model are reported. The model was utilized to verify anthropometric dimensions, reach dimensions, ingress/egress, couch operation, storage space, restraint locations, and crew acceptability. These data were then incorported in the design of the passenger couch verification model that underwent performance tests.

Performance modeling of automated manufacturing systems

NASA Astrophysics Data System (ADS)

Viswanadham, N.; Narahari, Y.

A unified and systematic treatment is presented of modeling methodologies and analysis techniques for performance evaluation of automated manufacturing systems. The book is the first treatment of the mathematical modeling of manufacturing systems. Automated manufacturing systems are surveyed and three principal analytical modeling paradigms are discussed: Markov chains, queues and queueing networks, and Petri nets.

The Negative Effects of Positive Reinforcement in Teaching Children with Developmental Delay.

ERIC Educational Resources Information Center

Biederman, Gerald B.; And Others

1994-01-01

This study compared the performance of 12 children (ages 4 to 10) with developmental delay, each trained in 2 tasks, one through interactive modeling (with or without verbal reinforcement) and the other through passive modeling. Results showed that passive modeling produced better rated performance than interactive modeling and that verbal…

NEXT Ion Thruster Performance Dispersion Analyses

NASA Technical Reports Server (NTRS)

Soulas, George C.; Patterson, Michael J.

2008-01-01

The NEXT ion thruster is a low specific mass, high performance thruster with a nominal throttling range of 0.5 to 7 kW. Numerous engineering model and one prototype model thrusters have been manufactured and tested. Of significant importance to propulsion system performance is thruster-to-thruster performance dispersions. This type of information can provide a bandwidth of expected performance variations both on a thruster and a component level. Knowledge of these dispersions can be used to more conservatively predict thruster service life capability and thruster performance for mission planning, facilitate future thruster performance comparisons, and verify power processor capabilities are compatible with the thruster design. This study compiles the test results of five engineering model thrusters and one flight-like thruster to determine unit-to-unit dispersions in thruster performance. Component level performance dispersion analyses will include discharge chamber voltages, currents, and losses; accelerator currents, electron backstreaming limits, and perveance limits; and neutralizer keeper and coupling voltages and the spot-to-plume mode transition flow rates. Thruster level performance dispersion analyses will include thrust efficiency.

Study of Adaptive Mathematical Models for Deriving Automated Pilot Performance Measurement Techniques. Volume I. Model Development.

ERIC Educational Resources Information Center

Connelly, Edward A.; And Others

A new approach to deriving human performance measures and criteria for use in automatically evaluating trainee performance is documented in this report. The ultimate application of the research is to provide methods for automatically measuring pilot performance in a flight simulator or from recorded in-flight data. An efficient method of…

Maydays and Murphies: A Study of the Effect of Organizational Design, Task, and Stress on Organizational Performance.

ERIC Educational Resources Information Center

Lin, Zhiang; Carley, Kathleen

How should organizations of intelligent agents be designed so that they exhibit high performance even during periods of stress? A formal model of organizational performance given a distributed decision-making environment in which agents encounter a radar detection task is presented. Using this model the performance of organizations with various…

Review of Literature: Utilizing Film/Videotape through Modeling or Self Examination of Performance To Enhance Performance.

ERIC Educational Resources Information Center

Mills, Brett D.

This paper examines eight studies that utilized film or videotape to enhance motor performance through modeling or self-examination of performance. The studies, dating as far back as 1944, dealt with learning bowling, golf, basketball, throwing, gymnastics, racquetball, and other motor tasks. For each study, the paper outlines the problem, the…

Empirical Performance Model-Driven Data Layout Optimization and Library Call Selection for Tensor Contraction Expressions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Qingda; Gao, Xiaoyang; Krishnamoorthy, Sriram

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empiricallymore » measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to perform data layout optimization together with the selection of library calls and layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.« less

Airloads and Wake Geometry Calculations for an Isolated Tiltrotor Model in a Wind Tunnel

NASA Technical Reports Server (NTRS)

Johnson, Wayne

2001-01-01

Comparisons of measured and calculated aerodynamic behavior of a tiltrotor model are presented. The test of the Tilt Rotor Aeroacoustic Model (TRAM) with a single, 0.25-scale V-22 rotor in the German-Dutch Wind Tunnel (DNW) provides an extensive set of aeroacoustic, performance, and structural loads data. The calculations were performed using the rotorcraft comprehensive analysis CAMRAD II. Presented are comparisons of measured and calculated performance for hover and helicopter mode operation, and airloads for helicopter mode. Calculated induced power, profile power, and wake geometry provide additional information about the aerodynamic behavior. An aerodynamic and wake model and calculation procedure that reflects the unique geometry and phenomena of tiltrotors has been developed. There are major differences between this model and the corresponding aerodynamic and wake model that has been established for helicopter rotors. In general, good correlation between measured and calculated performance and airloads behavior has been shown. Two aspects of the analysis that clearly need improvement are the stall delay model and the trailed vortex formation model.

A model of human event detection in multiple process monitoring situations

NASA Technical Reports Server (NTRS)

Greenstein, J. S.; Rouse, W. B.

1978-01-01

It is proposed that human decision making in many multi-task situations might be modeled in terms of the manner in which the human detects events related to his tasks and the manner in which he allocates his attention among his tasks once he feels events have occurred. A model of human event detection performance in such a situation is presented. An assumption of the model is that, in attempting to detect events, the human generates the probability that events have occurred. Discriminant analysis is used to model the human's generation of these probabilities. An experimental study of human event detection performance in a multiple process monitoring situation is described and the application of the event detection model to this situation is addressed. The experimental study employed a situation in which subjects simulataneously monitored several dynamic processes for the occurrence of events and made yes/no decisions on the presence of events in each process. Input to the event detection model of the information displayed to the experimental subjects allows comparison of the model's performance with the performance of the subjects.

Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis.

PubMed

Xie, Yuanchang; Lord, Dominique; Zhang, Yunlong

2007-09-01

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.

Uncertainty analysis for low-level radioactive waste disposal performance assessment at Oak Ridge National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, D.W.; Yambert, M.W.; Kocher, D.C.

1994-12-31

A performance assessment of the operating Solid Waste Storage Area 6 (SWSA 6) facility for the disposal of low-level radioactive waste at the Oak Ridge National Laboratory has been prepared to provide the technical basis for demonstrating compliance with the performance objectives of DOE Order 5820.2A, Chapter 111.2 An analysis of the uncertainty incorporated into the assessment was performed which addressed the quantitative uncertainty in the data used by the models, the subjective uncertainty associated with the models used for assessing performance of the disposal facility and site, and the uncertainty in the models used for estimating dose and humanmore » exposure. The results of the uncertainty analysis were used to interpret results and to formulate conclusions about the performance assessment. This paper discusses the approach taken in analyzing the uncertainty in the performance assessment and the role of uncertainty in performance assessment.« less

An integrated physiology model to study regional lung damage effects and the physiologic response

PubMed Central

2014-01-01

Background This work expands upon a previously developed exercise dynamic physiology model (DPM) with the addition of an anatomic pulmonary system in order to quantify the impact of lung damage on oxygen transport and physical performance decrement. Methods A pulmonary model is derived with an anatomic structure based on morphometric measurements, accounting for heterogeneous ventilation and perfusion observed experimentally. The model is incorporated into an existing exercise physiology model; the combined system is validated using human exercise data. Pulmonary damage from blast, blunt trauma, and chemical injury is quantified in the model based on lung fluid infiltration (edema) which reduces oxygen delivery to the blood. The pulmonary damage component is derived and calibrated based on published animal experiments; scaling laws are used to predict the human response to lung injury in terms of physical performance decrement. Results The augmented dynamic physiology model (DPM) accurately predicted the human response to hypoxia, altitude, and exercise observed experimentally. The pulmonary damage parameters (shunt and diffusing capacity reduction) were fit to experimental animal data obtained in blast, blunt trauma, and chemical damage studies which link lung damage to lung weight change; the model is able to predict the reduced oxygen delivery in damage conditions. The model accurately estimates physical performance reduction with pulmonary damage. Conclusions We have developed a physiologically-based mathematical model to predict performance decrement endpoints in the presence of thoracic damage; simulations can be extended to estimate human performance and escape in extreme situations. PMID:25044032

Reservoir studies with geostatistics to forecast performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, R.W.; Behrens, R.A.; Emanuel, A.S.

1991-05-01

In this paper example geostatistics and streamtube applications are presented for waterflood and CO{sub 2} flood in two low-permeability sandstone reservoirs. Thy hybrid approach of combining fine vertical resolution in cross-sectional models with streamtubes resulted in models that showed water channeling and provided realistic performance estimates. Results indicate that the combination of detailed geostatistical cross sections and fine-grid streamtube models offers a systematic approach for realistic performance forecasts.

Predictive performance models and multiple task performance

NASA Technical Reports Server (NTRS)

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.

Analysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machine.

PubMed

Silitonga, Arridina Susan; Hassan, Masjuki Haji; Ong, Hwai Chyuan; Kusumo, Fitranto

2017-11-01

The purpose of this study is to investigate the performance, emission and combustion characteristics of a four-cylinder common-rail turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends. A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the input parameter, whereas the performance, exhaust emissions and combustion characteristics were chosen as the output parameters of the KELM model. The performance, emissions and combustion characteristics predicted by the KELM model were validated by comparing the predicted data with the experimental data. The results show that the coefficient of determination of the parameters is within a range of 0.9805-0.9991 for both the KELM model and the experimental data. The mean absolute percentage error is within a range of 0.1259-2.3838. This study shows that KELM modelling is a useful technique in biodiesel production since it facilitates scientists and researchers to predict the performance, exhaust emissions and combustion characteristics of internal combustion engines with high accuracy.

Modeling and Analysis of Actinide Diffusion Behavior in Irradiated Metal Fuel

NASA Astrophysics Data System (ADS)

Edelmann, Paul G.

There have been numerous attempts to model fast reactor fuel behavior in the last 40 years. The US currently does not have a fully reliable tool to simulate the behavior of metal fuels in fast reactors. The experimental database necessary to validate the codes is also very limited. The DOE-sponsored Advanced Fuels Campaign (AFC) has performed various experiments that are ready for analysis. Current metal fuel performance codes are either not available to the AFC or have limitations and deficiencies in predicting AFC fuel performance. A modified version of a new fuel performance code, FEAST-Metal , was employed in this investigation with useful results. This work explores the modeling and analysis of AFC metallic fuels using FEAST-Metal, particularly in the area of constituent actinide diffusion behavior. The FEAST-Metal code calculations for this work were conducted at Los Alamos National Laboratory (LANL) in support of on-going activities related to sensitivity analysis of fuel performance codes. A sensitivity analysis of FEAST-Metal was completed to identify important macroscopic parameters of interest to modeling and simulation of metallic fuel performance. A modification was made to the FEAST-Metal constituent redistribution model to enable accommodation of newer AFC metal fuel compositions with verified results. Applicability of this modified model for sodium fast reactor metal fuel design is demonstrated.

Multiphysics Nuclear Thermal Rocket Thrust Chamber Analysis

NASA Technical Reports Server (NTRS)

Wang, Ten-See

2005-01-01

The objective of this effort is t o develop an efficient and accurate thermo-fluid computational methodology to predict environments for hypothetical thrust chamber design and analysis. The current task scope is to perform multidimensional, multiphysics analysis of thrust performance and heat transfer analysis for a hypothetical solid-core, nuclear thermal engine including thrust chamber and nozzle. The multiphysics aspects of the model include: real fluid dynamics, chemical reactivity, turbulent flow, and conjugate heat transfer. The model will be designed to identify thermal, fluid, and hydrogen environments in all flow paths and materials. This model would then be used to perform non- nuclear reproduction of the flow element failures demonstrated in the Rover/NERVA testing, investigate performance of specific configurations and assess potential issues and enhancements. A two-pronged approach will be employed in this effort: a detailed analysis of a multi-channel, flow-element, and global modeling of the entire thrust chamber assembly with a porosity modeling technique. It is expected that the detailed analysis of a single flow element would provide detailed fluid, thermal, and hydrogen environments for stress analysis, while the global thrust chamber assembly analysis would promote understanding of the effects of hydrogen dissociation and heat transfer on thrust performance. These modeling activities will be validated as much as possible by testing performed by other related efforts.

Update of aircraft profile data for the Integrated Noise Model computer program, vol. 3 : appendix B aircraft performance coefficients

DOT National Transportation Integrated Search

1992-03-01

This report provides aircraft takeoff and landing profiles, : aircraft aerodynamic performance coefficients and engine : performance coefficients for the aircraft data base : (Database 9) in the Integrated Noise Model (INM) computer : program. Flight...

The development of performance prediction models for Virginia's interstate highway system.

DOT National Transportation Integrated Search

1995-01-01

Performance prediction models are a key component of any well-designed pavement management system. In this study, data compiled from the condition surveys conducted annually on Virginia's pavement network were used to develop prediction models for mo...

Updating the Behavior Engineering Model.

ERIC Educational Resources Information Center

Chevalier, Roger

2003-01-01

Considers Thomas Gilbert's Behavior Engineering Model as a tool for systematically identifying barriers to individual and organizational performance. Includes a detailed case study and a performance aid that incorporates gap analysis, cause analysis, and force field analysis to update the original model. (Author/LRW)

The Compass Rose Effectiveness Model

ERIC Educational Resources Information Center

Spiers, Cynthia E.; Kiel, Dorothy; Hohenrink, Brad

2008-01-01

The effectiveness model focuses the institution on mission achievement through assessment and improvement planning. Eleven mission criteria, measured by key performance indicators, are aligned with the accountability interest of internal and external stakeholders. A Web-based performance assessment application supports the model, documenting the…

Modeling and optimum time performance for concurrent processing

NASA Technical Reports Server (NTRS)

Mielke, Roland R.; Stoughton, John W.; Som, Sukhamoy

1988-01-01

The development of a new graph theoretic model for describing the relation between a decomposed algorithm and its execution in a data flow environment is presented. Called ATAMM, the model consists of a set of Petri net marked graphs useful for representing decision-free algorithms having large-grained, computationally complex primitive operations. Performance time measures which determine computing speed and throughput capacity are defined, and the ATAMM model is used to develop lower bounds for these times. A concurrent processing operating strategy for achieving optimum time performance is presented and illustrated by example.

User's Manual for Data for Validating Models for PV Module Performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marion, W.; Anderberg, A.; Deline, C.

2014-04-01

This user's manual describes performance data measured for flat-plate photovoltaic (PV) modules installed in Cocoa, Florida, Eugene, Oregon, and Golden, Colorado. The data include PV module current-voltage curves and associated meteorological data for approximately one-year periods. These publicly available data are intended to facilitate the validation of existing models for predicting the performance of PV modules, and for the development of new and improved models. For comparing different modeling approaches, using these public data will provide transparency and more meaningful comparisons of the relative benefits.

F-14 modeling study

NASA Technical Reports Server (NTRS)

Levison, William H.

1988-01-01

This study explored application of a closed loop pilot/simulator model to the analysis of some simulator fidelity issues. The model was applied to two data bases: (1) a NASA ground based simulation of an air-to-air tracking task in which nonvisual cueing devices were explored, and (2) a ground based and inflight study performed by the Calspan Corporation to explore the effects of simulator delay on attitude tracking performance. The model predicted the major performance trends obtained in both studies. A combined analytical and experimental procedure for exploring simulator fidelity issues is outlined.

Finite element analysis of structural engineering problems using a viscoplastic model incorporating two back stresses

NASA Technical Reports Server (NTRS)

Arya, Vinod K.; Halford, Gary R.

1993-01-01

The feasibility of a viscoplastic model incorporating two back stresses and a drag strength is investigated for performing nonlinear finite element analyses of structural engineering problems. To demonstrate suitability for nonlinear structural analyses, the model is implemented into a finite element program and analyses for several uniaxial and multiaxial problems are performed. Good agreement is shown between the results obtained using the finite element implementation and those obtained experimentally. The advantages of using advanced viscoplastic models for performing nonlinear finite element analyses of structural components are indicated.

Electrochemical kinetic and mass transfer model for direct ethanol alkaline fuel cell (DEAFC)

NASA Astrophysics Data System (ADS)

Abdullah, S.; Kamarudin, S. K.; Hasran, U. A.; Masdar, M. S.; Daud, W. R. W.

2016-07-01

A mathematical model is developed for a liquid-feed DEAFC incorporating an alkaline anion-exchange membrane. The one-dimensional mass transport of chemical species is modelled using isothermal, single-phase and steady-state assumptions. The anode and cathode electrochemical reactions use the Tafel kinetics approach, with two limiting cases, for the reaction order. The model fully accounts for the mixed potential effects of ethanol oxidation at the cathode due to ethanol crossover via an alkaline anion-exchange membrane. In contrast to a polymer electrolyte membrane model, the current model considers the flux of ethanol at the membrane as the difference between diffusive and electroosmotic effects. The model is used to investigate the effects of the ethanol and alkali inlet feed concentrations at the anode. The model predicts that the cell performance is almost identical for different ethanol concentrations at a low current density. Moreover, the model results show that feeding the DEAFC with 5 M NaOH and 3 M ethanol at specific operating conditions yields a better performance at a higher current density. Furthermore, the model indicates that crossover effects on the DEAFC performance are significant. The cell performance decrease from its theoretical value when a parasitic current is enabled in the model.

Multi-Topic Tracking Model for dynamic social network

NASA Astrophysics Data System (ADS)

Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun

2016-07-01

The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.

A neurally plausible parallel distributed processing model of event-related potential word reading data.

PubMed

Laszlo, Sarah; Plaut, David C

2012-03-01

The Parallel Distributed Processing (PDP) framework has significant potential for producing models of cognitive tasks that approximate how the brain performs the same tasks. To date, however, there has been relatively little contact between PDP modeling and data from cognitive neuroscience. In an attempt to advance the relationship between explicit, computational models and physiological data collected during the performance of cognitive tasks, we developed a PDP model of visual word recognition which simulates key results from the ERP reading literature, while simultaneously being able to successfully perform lexical decision-a benchmark task for reading models. Simulations reveal that the model's success depends on the implementation of several neurally plausible features in its architecture which are sufficiently domain-general to be relevant to cognitive modeling more generally. Copyright © 2011 Elsevier Inc. All rights reserved.

Gallium arsenide (GaAs) solar cell modeling studies

NASA Technical Reports Server (NTRS)

Heinbockel, J. H.

1980-01-01

Various models were constructed which will allow for the variation of system components. Computer studies were then performed using the models constructed in order to study the effects of various system changes. In particular, GaAs and Si flat plate solar power arrays were studied and compared. Series and shunt resistance models were constructed. Models for the chemical kinetics of the annealing process were prepared. For all models constructed, various parametric studies were performed.

Comparison of two recent models for estimating actual evapotranspiration using only regularly recorded data

NASA Astrophysics Data System (ADS)

Ali, M. F.; Mawdsley, J. A.

1987-09-01

An advection-aridity model for estimating actual evapotranspiration ET is tested with over 700 days of lysimeter evapotranspiration and meteorological data from barley, turf and rye-grass from three sites in the U.K. The performance of the model is also compared with the API model . It is observed from the test that the advection-aridity model overestimates nonpotential ET and tends to underestimate potential ET, but when tested with potential and nonpotential data together, the tendencies appear to cancel each other. On a daily basis the performance level of this model is found to be of the same order as the API model: correlation coefficients were obtained between the model estimates and lysimeter data of 0.62 and 0.68 respectively. For periods greater than one day, generally the performance of the models are improved. Proposed by Mawdsley and Ali (1979)

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

PubMed

Yu, Kezi; Quirk, J Gerald; Djurić, Petar M

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting.

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme A.; Oliveira, Alexandre C.; Cooper, Gregory F.

2015-01-01

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. PMID:26098570

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models

PubMed Central

Yu, Kezi; Quirk, J. Gerald

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting. PMID:28953927

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

PubMed Central

Choudhry, Shahid A.; Li, Jing; Davis, Darcy; Erdmann, Cole; Sikka, Rishi; Sutariya, Bharat

2013-01-01

Introduction: Preventing the occurrence of hospital readmissions is needed to improve quality of care and foster population health across the care continuum. Hospitals are being held accountable for improving transitions of care to avert unnecessary readmissions. Advocate Health Care in Chicago and Cerner (ACC) collaborated to develop all-cause, 30-day hospital readmission risk prediction models to identify patients that need interventional resources. Ideally, prediction models should encompass several qualities: they should have high predictive ability; use reliable and clinically relevant data; use vigorous performance metrics to assess the models; be validated in populations where they are applied; and be scalable in heterogeneous populations. However, a systematic review of prediction models for hospital readmission risk determined that most performed poorly (average C-statistic of 0.66) and efforts to improve their performance are needed for widespread usage. Methods: The ACC team incorporated electronic health record data, utilized a mixed-method approach to evaluate risk factors, and externally validated their prediction models for generalizability. Inclusion and exclusion criteria were applied on the patient cohort and then split for derivation and internal validation. Stepwise logistic regression was performed to develop two predictive models: one for admission and one for discharge. The prediction models were assessed for discrimination ability, calibration, overall performance, and then externally validated. Results: The ACC Admission and Discharge Models demonstrated modest discrimination ability during derivation, internal and external validation post-recalibration (C-statistic of 0.76 and 0.78, respectively), and reasonable model fit during external validation for utility in heterogeneous populations. Conclusions: The ACC Admission and Discharge Models embody the design qualities of ideal prediction models. The ACC plans to continue its partnership to further improve and develop valuable clinical models. PMID:24224068

Modelling Complex Fenestration Systems using physical and virtual models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thanachareonkit, Anothai; Scartezzini, Jean-Louis

2010-04-15

Physical or virtual models are commonly used to visualize the conceptual ideas of architects, lighting designers and researchers; they are also employed to assess the daylighting performance of buildings, particularly in cases where Complex Fenestration Systems (CFS) are considered. Recent studies have however revealed a general tendency of physical models to over-estimate this performance, compared to those of real buildings; these discrepancies can be attributed to several reasons. In order to identify the main error sources, a series of comparisons in-between a real building (a single office room within a test module) and the corresponding physical and virtual models wasmore » undertaken. The physical model was placed in outdoor conditions, which were strictly identical to those of the real building, as well as underneath a scanning sky simulator. The virtual model simulations were carried out by way of the Radiance program using the GenSky function; an alternative evaluation method, named Partial Daylight Factor method (PDF method), was also employed with the physical model together with sky luminance distributions acquired by a digital sky scanner during the monitoring of the real building. The overall daylighting performance of physical and virtual models were assessed and compared. The causes of discrepancies between the daylighting performance of the real building and the models were analysed. The main identified sources of errors are the reproduction of building details, the CFS modelling and the mocking-up of the geometrical and photometrical properties. To study the impact of these errors on daylighting performance assessment, computer simulation models created using the Radiance program were also used to carry out a sensitivity analysis of modelling errors. The study of the models showed that large discrepancies can occur in daylighting performance assessment. In case of improper mocking-up of the glazing for instance, relative divergences of 25-40% can be found in different room locations, suggesting that more light is entering than actually monitored in the real building. All these discrepancies can however be reduced by making an effort to carefully mock up the geometry and photometry of the real building. A synthesis is presented in this article which can be used as guidelines for daylighting designers to avoid or estimate errors during CFS daylighting performance assessment. (author)« less

Comparison among mathematical models of the photovoltaic cell for computer simulation purposes

NASA Astrophysics Data System (ADS)

Tofoli, Fernando Lessa; Pereira, Denis de Castro; Josias De Paula, Wesley; Moreira Vicente, Eduardo; Vicente, Paula dos Santos; Braga, Henrique Antonio Carvalho

2017-07-01

This paper presents a comparison among mathematical models used in the simulation of solar photovoltaic modules that can be easily integrated with power electronic converters. In order to perform the analysis, three models available in literature and also the physical model of the module in software PSIM® are used. Some results regarding the respective I × V and P × V curves are presented, while some advantages and eventual limitations are discussed. Besides, a DC-DC buck converter performs maximum power point tracking by using perturb and observe method, while the performance of each one of the aforementioned models is investigated.

Selecting among competing models of electro-optic, infrared camera system range performance

USGS Publications Warehouse

Nichols, Jonathan M.; Hines, James E.; Nichols, James D.

2013-01-01

Range performance is often the key requirement around which electro-optical and infrared camera systems are designed. This work presents an objective framework for evaluating competing range performance models. Model selection based on the Akaike’s Information Criterion (AIC) is presented for the type of data collected during a typical human observer and target identification experiment. These methods are then demonstrated on observer responses to both visible and infrared imagery in which one of three maritime targets was placed at various ranges. We compare the performance of a number of different models, including those appearing previously in the literature. We conclude that our model-based approach offers substantial improvements over the traditional approach to inference, including increased precision and the ability to make predictions for some distances other than the specific set for which experimental trials were conducted.

Post2 End-to-End Descent and Landing Simulation for ALHAT Design Analysis Cycle 2

NASA Technical Reports Server (NTRS)

Davis, Jody L.; Striepe, Scott A.; Maddock, Robert W.; Johnson, Andrew E.; Paschall, Stephen C., II

2010-01-01

The ALHAT project is an agency-level program involving NASA centers, academia, and industry, with a primary goal to develop a safe, autonomous, precision-landing system for robotic and crew-piloted lunar and planetary descent vehicles. POST2 is used as the 6DOF descent and landing trajectory simulation for determining integrated system performance of ALHAT landing-system models and lunar environment models. This paper presents updates in the development of the ALHAT POST2 simulation, as well as preliminary system performance analysis for ALDAC-2 used for the testing and assessment of ALHAT system models. The ALDAC-2 POST2 Monte Carlo simulation results have been generated and focus on HRN model performance with the fully integrated system, as well performance improvements of AGNC and TSAR model since the previous design analysis cycle

Charge-coupled-device X-ray detector performance model

NASA Technical Reports Server (NTRS)

Bautz, M. W.; Berman, G. E.; Doty, J. P.; Ricker, G. R.

1987-01-01

A model that predicts the performance characteristics of CCD detectors being developed for use in X-ray imaging is presented. The model accounts for the interactions of both X-rays and charged particles with the CCD and simulates the transport and loss of charge in the detector. Predicted performance parameters include detective and net quantum efficiencies, split-event probability, and a parameter characterizing the effective thickness presented by the detector to cosmic-ray protons. The predicted performance of two CCDs of different epitaxial layer thicknesses is compared. The model predicts that in each device incomplete recovery of the charge liberated by a photon of energy between 0.1 and 10 keV is very likely to be accompanied by charge splitting between adjacent pixels. The implications of the model predictions for CCD data processing algorithms are briefly discussed.

Modeling of Radiotherapy Linac Source Terms Using ARCHER Monte Carlo Code: Performance Comparison for GPU and MIC Parallel Computing Devices

NASA Astrophysics Data System (ADS)

Lin, Hui; Liu, Tianyu; Su, Lin; Bednarz, Bryan; Caracappa, Peter; Xu, X. George

2017-09-01

Monte Carlo (MC) simulation is well recognized as the most accurate method for radiation dose calculations. For radiotherapy applications, accurate modelling of the source term, i.e. the clinical linear accelerator is critical to the simulation. The purpose of this paper is to perform source modelling and examine the accuracy and performance of the models on Intel Many Integrated Core coprocessors (aka Xeon Phi) and Nvidia GPU using ARCHER and explore the potential optimization methods. Phase Space-based source modelling for has been implemented. Good agreements were found in a tomotherapy prostate patient case and a TrueBeam breast case. From the aspect of performance, the whole simulation for prostate plan and breast plan cost about 173s and 73s with 1% statistical error.

Evaluation of Multiclass Model Observers in PET LROC Studies

NASA Astrophysics Data System (ADS)

Gifford, H. C.; Kinahan, P. E.; Lartizien, C.; King, M. A.

2007-02-01

A localization ROC (LROC) study was conducted to evaluate nonprewhitening matched-filter (NPW) and channelized NPW (CNPW) versions of a multiclass model observer as predictors of human tumor-detection performance with PET images. Target localization is explicitly performed by these model observers. Tumors were placed in the liver, lungs, and background soft tissue of a mathematical phantom, and the data simulation modeled a full-3D acquisition mode. Reconstructions were performed with the FORE+AWOSEM algorithm. The LROC study measured observer performance with 2D images consisting of either coronal, sagittal, or transverse views of the same set of cases. Versions of the CNPW observer based on two previously published difference-of-Gaussian channel models demonstrated good quantitative agreement with human observers. One interpretation of these results treats the CNPW observer as a channelized Hotelling observer with implicit internal noise

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

PubMed Central

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

2016-01-01

Study Objectives: Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. Methods: We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). Results: The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. Conclusions: The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance. Citation: Ramakrishnan S, Wesensten NJ, Kamimori GH, Moon JE, Balkin TJ, Reifman J. A unified model of performance for predicting the effects of sleep and caffeine. SLEEP 2016;39(10):1827–1841. PMID:27397562

Using Measured Plane-of-Array Data Directly in Photovoltaic Modeling: Methodology and Validation: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freeman, Janine; Freestate, David; Riley, Cameron

2016-11-01

Measured plane-of-array (POA) irradiance may provide a lower-cost alternative to standard irradiance component data for photovoltaic (PV) system performance modeling without loss of accuracy. Previous work has shown that transposition models typically used by PV models to calculate POA irradiance from horizontal data introduce error into the POA irradiance estimates, and that measured POA data can correlate better to measured performance data. However, popular PV modeling tools historically have not directly used input POA data. This paper introduces a new capability in NREL's System Advisor Model (SAM) to directly use POA data in PV modeling, and compares SAM results frommore » both POA irradiance and irradiance components inputs against measured performance data for eight operating PV systems.« less

Using Measured Plane-of-Array Data Directly in Photovoltaic Modeling: Methodology and Validation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freeman, Janine; Freestate, David; Hobbs, William

2016-11-21

Measured plane-of-array (POA) irradiance may provide a lower-cost alternative to standard irradiance component data for photovoltaic (PV) system performance modeling without loss of accuracy. Previous work has shown that transposition models typically used by PV models to calculate POA irradiance from horizontal data introduce error into the POA irradiance estimates, and that measured POA data can correlate better to measured performance data. However, popular PV modeling tools historically have not directly used input POA data. This paper introduces a new capability in NREL's System Advisor Model (SAM) to directly use POA data in PV modeling, and compares SAM results frommore » both POA irradiance and irradiance components inputs against measured performance data for eight operating PV systems.« less

Using Measured Plane-of-Array Data Directly in Photovoltaic Modeling: Methodology and Validation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Freeman, Janine; Freestate, David; Hobbs, William

2016-06-05

Measured plane-of-array (POA) irradiance may provide a lower-cost alternative to standard irradiance component data for photovoltaic (PV) system performance modeling without loss of accuracy. Previous work has shown that transposition models typically used by PV models to calculate POA irradiance from horizontal data introduce error into the POA irradiance estimates, and that measured POA data can correlate better to measured performance data. However, popular PV modeling tools historically have not directly used input POA data. This paper introduces a new capability in NREL's System Advisor Model (SAM) to directly use POA data in PV modeling, and compares SAM results frommore » both POA irradiance and irradiance components inputs against measured performance data for eight operating PV systems.« less

Error rate information in attention allocation pilot models

NASA Technical Reports Server (NTRS)

Faulkner, W. H.; Onstott, E. D.

1977-01-01

The Northrop urgency decision pilot model was used in a command tracking task to compare the optimized performance of multiaxis attention allocation pilot models whose urgency functions were (1) based on tracking error alone, and (2) based on both tracking error and error rate. A matrix of system dynamics and command inputs was employed, to create both symmetric and asymmetric two axis compensatory tracking tasks. All tasks were single loop on each axis. Analysis showed that a model that allocates control attention through nonlinear urgency functions using only error information could not achieve performance of the full model whose attention shifting algorithm included both error and error rate terms. Subsequent to this analysis, tracking performance predictions for the full model were verified by piloted flight simulation. Complete model and simulation data are presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mou, J.I.; King, C.

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess themore » status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.« less

Performance Analysis of Transposition Models Simulating Solar Radiation on Inclined Surfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Yu; Sengupta, Manajit

2016-06-02

Transposition models have been widely used in the solar energy industry to simulate solar radiation on inclined photovoltaic panels. Following numerous studies comparing the performance of transposition models, this work aims to understand the quantitative uncertainty in state-of-the-art transposition models and the sources leading to the uncertainty. Our results show significant differences between two highly used isotropic transposition models, with one substantially underestimating the diffuse plane-of-array irradiances when diffuse radiation is perfectly isotropic. In the empirical transposition models, the selection of the empirical coefficients and land surface albedo can both result in uncertainty in the output. This study can bemore » used as a guide for the future development of physics-based transposition models and evaluations of system performance.« less

Performance Engineering Research Institute SciDAC-2 Enabling Technologies Institute Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hall, Mary

2014-09-19

Enhancing the performance of SciDAC applications on petascale systems has high priority within DOE SC. As we look to the future, achieving expected levels of performance on high-end com-puting (HEC) systems is growing ever more challenging due to enormous scale, increasing archi-tectural complexity, and increasing application complexity. To address these challenges, PERI has implemented a unified, tripartite research plan encompassing: (1) performance modeling and prediction; (2) automatic performance tuning; and (3) performance engineering of high profile applications. The PERI performance modeling and prediction activity is developing and refining performance models, significantly reducing the cost of collecting the data upon whichmore » the models are based, and increasing model fidelity, speed and generality. Our primary research activity is automatic tuning (autotuning) of scientific software. This activity is spurred by the strong user preference for automatic tools and is based on previous successful activities such as ATLAS, which has automatically tuned components of the LAPACK linear algebra library, and other re-cent work on autotuning domain-specific libraries. Our third major component is application en-gagement, to which we are devoting approximately 30% of our effort to work directly with Sci-DAC-2 applications. This last activity not only helps DOE scientists meet their near-term per-formance goals, but also helps keep PERI research focused on the real challenges facing DOE computational scientists as they enter the Petascale Era.« less

Contribution to the modelling and analysis of logistics system performance by Petri nets and simulation models: Application in a supply chain

NASA Astrophysics Data System (ADS)

Azougagh, Yassine; Benhida, Khalid; Elfezazi, Said

2016-02-01

In this paper, the focus is on studying the performance of complex systems in a supply chain context by developing a structured modelling approach based on the methodology ASDI (Analysis, Specification, Design and Implementation) by combining the modelling by Petri nets and simulation using ARENA. The linear approach typically followed in conducting of this kind of problems has to cope with a difficulty of modelling due to the complexity and the number of parameters of concern. Therefore, the approach used in this work is able to structure modelling a way to cover all aspects of the performance study. The modelling structured approach is first introduced before being applied to the case of an industrial system in the field of phosphate. Results of the performance indicators obtained from the models developed, permitted to test the behaviour and fluctuations of this system and to develop improved models of the current situation. In addition, in this paper, it was shown how Arena software can be adopted to simulate complex systems effectively. The method in this research can be applied to investigate various improvements scenarios and their consequences before implementing them in reality.

Microscopic prediction of speech recognition for listeners with normal hearing in noise using an auditory model.

PubMed

Jürgens, Tim; Brand, Thomas

2009-11-01

This study compares the phoneme recognition performance in speech-shaped noise of a microscopic model for speech recognition with the performance of normal-hearing listeners. "Microscopic" is defined in terms of this model twofold. First, the speech recognition rate is predicted on a phoneme-by-phoneme basis. Second, microscopic modeling means that the signal waveforms to be recognized are processed by mimicking elementary parts of human's auditory processing. The model is based on an approach by Holube and Kollmeier [J. Acoust. Soc. Am. 100, 1703-1716 (1996)] and consists of a psychoacoustically and physiologically motivated preprocessing and a simple dynamic-time-warp speech recognizer. The model is evaluated while presenting nonsense speech in a closed-set paradigm. Averaged phoneme recognition rates, specific phoneme recognition rates, and phoneme confusions are analyzed. The influence of different perceptual distance measures and of the model's a-priori knowledge is investigated. The results show that human performance can be predicted by this model using an optimal detector, i.e., identical speech waveforms for both training of the recognizer and testing. The best model performance is yielded by distance measures which focus mainly on small perceptual distances and neglect outliers.

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models.

PubMed

Šiljić Tomić, Aleksandra N; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

2016-05-01

This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Shengzhi; Ming, Bo; Huang, Qiang

It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecastingmore » models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.« less

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2017-05-01

Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Modelling invasion for a habitat generalist and a specialist plant species

USGS Publications Warehouse

Evangelista, P.H.; Kumar, S.; Stohlgren, T.J.; Jarnevich, C.S.; Crall, A.W.; Norman, J. B.; Barnett, D.T.

2008-01-01

Predicting suitable habitat and the potential distribution of invasive species is a high priority for resource managers and systems ecologists. Most models are designed to identify habitat characteristics that define the ecological niche of a species with little consideration to individual species' traits. We tested five commonly used modelling methods on two invasive plant species, the habitat generalist Bromus tectorum and habitat specialist Tamarix chinensis, to compare model performances, evaluate predictability, and relate results to distribution traits associated with each species. Most of the tested models performed similarly for each species; however, the generalist species proved to be more difficult to predict than the specialist species. The highest area under the receiver-operating characteristic curve values with independent validation data sets of B. tectorum and T. chinensis was 0.503 and 0.885, respectively. Similarly, a confusion matrix for B. tectorum had the highest overall accuracy of 55%, while the overall accuracy for T. chinensis was 85%. Models for the generalist species had varying performances, poor evaluations, and inconsistent results. This may be a result of a generalist's capability to persist in a wide range of environmental conditions that are not easily defined by the data, independent variables or model design. Models for the specialist species had consistently strong performances, high evaluations, and similar results among different model applications. This is likely a consequence of the specialist's requirement for explicit environmental resources and ecological barriers that are easily defined by predictive models. Although defining new invaders as generalist or specialist species can be challenging, model performances and evaluations may provide valuable information on a species' potential invasiveness.

A Multiscale Virtual Fabrication and Lattice Modeling Approach for the Fatigue Performance Prediction of Asphalt Concrete

NASA Astrophysics Data System (ADS)

Dehghan Banadaki, Arash

Predicting the ultimate performance of asphalt concrete under realistic loading conditions is the main key to developing better-performing materials, designing long-lasting pavements, and performing reliable lifecycle analysis for pavements. The fatigue performance of asphalt concrete depends on the mechanical properties of the constituent materials, namely asphalt binder and aggregate. This dependent link between performance and mechanical properties is extremely complex, and experimental techniques often are used to try to characterize the performance of hot mix asphalt. However, given the seemingly uncountable number of mixture designs and loading conditions, it is simply not economical to try to understand and characterize the material behavior solely by experimentation. It is well known that analytical and computational modeling methods can be combined with experimental techniques to reduce the costs associated with understanding and characterizing the mechanical behavior of the constituent materials. This study aims to develop a multiscale micromechanical lattice-based model to predict cracking in asphalt concrete using component material properties. The proposed algorithm, while capturing different phenomena for different scales, also minimizes the need for laboratory experiments. The developed methodology builds on a previously developed lattice model and the viscoelastic continuum damage model to link the component material properties to the mixture fatigue performance. The resulting lattice model is applied to predict the dynamic modulus mastercurves for different scales. A framework for capturing the so-called structuralization effects is introduced that significantly improves the accuracy of the modulus prediction. Furthermore, air voids are added to the model to help capture this important micromechanical feature that affects the fatigue performance of asphalt concrete as well as the modulus value. The effects of rate dependency are captured by implementing the viscoelastic fracture criterion. In the end, an efficient cyclic loading framework is developed to evaluate the damage accumulation in the material that is caused by long-sustained cyclic loads.

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

PubMed

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 (<15 lux) during alertness measurements and controlling for sleep and entrainment before the start of the protocol are selected for modeling. This is done to avoid the direct alerting effects of light and effects of prior sleep debt and circadian misalignment on the data. The results show that linear combination of circadian and homeostatic drives is sufficient to predict dynamics of a variety of sleepiness and 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.

Evolution and revolution: gauging the impact of technological and technical innovation on Olympic performance.

PubMed

Balmer, Nigel; Pleasence, Pascoe; Nevill, Alan

2012-01-01

A number of studies have pointed to a plateauing of athletic performance, with the suggestion that further improvements will need to be driven by revolutions in technology or technique. In the present study, we examine post-war men's Olympic performance in jumping events (pole vault, long jump, high jump, triple jump) to determine whether performance has indeed plateaued and to present techniques, derived from models of human growth, for assessing the impact of technological and technical innovation over time (logistic and double logistic models of growth). Significantly, two of the events involve well-documented changes in technology (pole material in pole vault) or technique (the Fosbury Flop in high jump), while the other two do not. We find that in all four cases, performance appears to have plateaued and that no further "general" improvement should be expected. In the case of high jump, the double logistic model provides a convenient method for modelling and quantifying a performance intervention (in this case the Fosbury Flop). However, some shortcomings are revealed for pole vault, where evolutionary post-war improvements and innovation (fibre glass poles) were concurrent, preventing their separate identification in the model. In all four events, it is argued that further general growth in performance will indeed need to rely predominantly on technological or technical innovation.