performance improvement model: Topics by Science.gov

Sample records for performance improvement model

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.
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.
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)
Performance improvement CME for quality: challenges inherent to the process.

PubMed

Vakani, Farhan Saeed; O'Beirne, Ronan

2015-01-01

The purpose of this paper is to discuss the perspective debates upon the real-time challenges for a three-staged Performance Improvement Continuing Medical Education (PI-CME) model, an innovative and potential approach for future CME, to inform providers to think, prepare and to act proactively. In this discussion, the challenges associated for adopting the American Medical Association's three-staged PI-CME model are reported. Not many institutions in USA are using a three-staged performance improvement model and then customizing it to their own healthcare context for the specific targeted audience. They integrate traditional CME methods with performance and quality initiatives, and linking with CME credits. Overall the US health system is interested in a structured PI-CME model with the potential to improve physicians practicing behaviors. Knowing the dearth of evidence for applying this structured performance improvement methodology into the design of CME activities, and the lack of clarity on challenges inherent to the process that learners and providers encounter. This paper establishes all-important first step to render the set of challenges for a three-staged PI-CME model.
Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance

NASA Astrophysics Data System (ADS)

McCray, Wilmon Wil L., Jr.

The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI RTM) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization model and dashboard that demonstrates the use of statistical methods, statistical process control, sensitivity analysis, quantitative and optimization techniques to establish a baseline and predict future customer satisfaction index scores (outcomes). The American Customer Satisfaction Index (ACSI) model and industry benchmarks were used as a framework for the simulation model.
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…
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…
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.
A Novel Feed-Forward Modeling System Leads to Sustained Improvements in Attention and Academic Performance.

PubMed

McDermott, Ashley F; Rose, Maya; Norris, Troy; Gordon, Eric

2016-01-28

This study tested a novel feed-forward modeling (FFM) system as a nonpharmacological intervention for the treatment of ADHD children and the training of cognitive skills that improve academic performance. This study implemented a randomized, controlled, parallel design comparing this FFM with a nonpharmacological community care intervention. Improvements were measured on parent- and clinician-rated scales of ADHD symptomatology and on academic performance tests completed by the participant. Participants were followed for 3 months after training. Participants in the FFM training group showed significant improvements in ADHD symptomatology and academic performance, while the control group did not. Improvements from FFM were sustained 3 months later. The FFM appeared to be an effective intervention for the treatment of ADHD and improving academic performance. This FFM training intervention shows promise as a first-line treatment for ADHD while improving academic performance. © The Author(s) 2016.
The relationship between quality management practices and organisational performance: A structural equation modelling approach

NASA Astrophysics Data System (ADS)

Jamaluddin, Z.; Razali, A. M.; Mustafa, Z.

2015-02-01

The purpose of this paper is to examine the relationship between the quality management practices (QMPs) and organisational performance for the manufacturing industry in Malaysia. In this study, a QMPs and organisational performance framework is developed according to a comprehensive literature review which cover aspects of hard and soft quality factors in manufacturing process environment. A total of 11 hypotheses have been put forward to test the relationship amongst the six constructs, which are management commitment, training, process management, quality tools, continuous improvement and organisational performance. The model is analysed using Structural Equation Modeling (SEM) with AMOS software version 18.0 using Maximum Likelihood (ML) estimation. A total of 480 questionnaires were distributed, and 210 questionnaires were valid for analysis. The results of the modeling analysis using ML estimation indicate that the fits statistics of QMPs and organisational performance model for manufacturing industry is admissible. From the results, it found that the management commitment have significant impact on the training and process management. Similarly, the training had significant effect to the quality tools, process management and continuous improvement. Furthermore, the quality tools have significant influence on the process management and continuous improvement. Likewise, the process management also has a significant impact to the continuous improvement. In addition the continuous improvement has significant influence the organisational performance. However, the results of the study also found that there is no significant relationship between management commitment and quality tools, and between the management commitment and continuous improvement. The results of the study can be used by managers to prioritize the implementation of QMPs. For instances, those practices that are found to have positive impact on organisational performance can be recommended to managers so that they can allocate resources to improve these practices to get better performance.
Interacting multiple model forward filtering and backward smoothing for maneuvering target tracking

NASA Astrophysics Data System (ADS)

Nandakumaran, N.; Sutharsan, S.; Tharmarasa, R.; Lang, Tom; McDonald, Mike; Kirubarajan, T.

2009-08-01

The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates of target states. Various methods have been proposed for multiple model smoothing in the literature. In this paper, a new smoothing method, which involves forward filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM, is proposed. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother uses standard Kalman smoothing recursion. Resulting algorithm provides improved but delayed estimates of target states. Simulation studies are performed to demonstrate the improved performance with a maneuvering target scenario. The comparison with existing methods confirms the improved smoothing accuracy. This improvement results from avoiding the augmented state vector used by other algorithms. In addition, the new technique to account for model switching in smoothing is a key in improving the performance.
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.
EFFECTS OF VERTICAL-LAYER STRUCTURE AND BOUNDARY CONDITIONS ON CMAQ-V4.5 AND V4.6 MODELS

EPA Science Inventory

This work is aimed at determining whether the increased vertical layers in CMAQ provides substantially improved model performance and assess whether using the spatially and temporally varying boundary conditions from GEOS-CHEM offer improved model performance as compared to the d...
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.
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.
Partnering through Training and Practice to Achieve Performance Improvement

ERIC Educational Resources Information Center

Lyons, Paul R.

2010-01-01

This article presents a partnership effort among managers, trainers, and employees to spring to life performance improvement using the performance templates (P-T) approach. P-T represents a process model as well as a method of training leading to performance improvement. Not only does it add to our repertoire of training and performance management…
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.
The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies.

PubMed

Davis, Michael J; Janke, Robert

2018-01-04

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.
The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies

NASA Astrophysics Data System (ADS)

Davis, Michael J.; Janke, Robert

2018-05-01

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.
Are Low-Performing Schools Adopting Practices Promoted by School Improvement Grants? NCEE Evaluation Brief. NCEE 2015-4001

ERIC Educational Resources Information Center

Herrmann, Mariesa; Dragoset, Lisa; James-Burdumy, Susanne

2014-01-01

The federal School Improvement Grants (SIG) program aims to improve student achievement by promoting the implementation of four school intervention models: transformation, turnaround, restart, and closure. Previous research provides evidence that low-performing schools adopt some practices promoted by the four models, but little is known about how…

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 comparative Thermal Analysis of conventional parabolic receiver tube and Cavity model tube in a Solar Parabolic Concentrator

NASA Astrophysics Data System (ADS)

Arumugam, S.; Ramakrishna, P.; Sangavi, S.

2018-02-01

Improvements in heating technology with solar energy is gaining focus, especially solar parabolic collectors. Solar heating in conventional parabolic collectors is done with the help of radiation concentration on receiver tubes. Conventional receiver tubes are open to atmosphere and loose heat by ambient air currents. In order to reduce the convection losses and also to improve the aperture area, we designed a tube with cavity. This study is a comparative performance behaviour of conventional tube and cavity model tube. The performance formulae were derived for the cavity model based on conventional model. Reduction in overall heat loss coefficient was observed for cavity model, though collector heat removal factor and collector efficiency were nearly same for both models. Improvement in efficiency was also observed in the cavity model’s performance. The approach towards the design of a cavity model tube as the receiver tube in solar parabolic collectors gave improved results and proved as a good consideration.
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...
Improved methods for the measurement and modeling of PV module and system performance for all operating conditions

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

King, D.L.

1995-11-01

The objective of this work was to develop improved performance model for modules and systems for for all operating conditions for use in module specifications, system and BOS component design, and system rating or monitoring. The approach taken was to identify and quantify the influence of dominant factors of solar irradiance, cell temperature, angle-of-incidence; and solar spectrum; use outdoor test procedures to separate the effects of electrical, thermal, and optical performance; use fundamental cell characteristics to improve analysis; and combine factors in simple model using the common variables.
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.
[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.
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
The effect of various parameters of large scale radio propagation models on improving performance mobile communications

NASA Astrophysics Data System (ADS)

Pinem, M.; Fauzi, R.

2018-02-01

One technique for ensuring continuity of wireless communication services and keeping a smooth transition on mobile communication networks is the soft handover technique. In the Soft Handover (SHO) technique the inclusion and reduction of Base Station from the set of active sets is determined by initiation triggers. One of the initiation triggers is based on the strong reception signal. In this paper we observed the influence of parameters of large-scale radio propagation models to improve the performance of mobile communications. The observation parameters for characterizing the performance of the specified mobile system are Drop Call, Radio Link Degradation Rate and Average Size of Active Set (AS). The simulated results show that the increase in altitude of Base Station (BS) Antenna and Mobile Station (MS) Antenna contributes to the improvement of signal power reception level so as to improve Radio Link quality and increase the average size of Active Set and reduce the average Drop Call rate. It was also found that Hata’s propagation model contributed significantly to improvements in system performance parameters compared to Okumura’s propagation model and Lee’s propagation model.
A University Engagement Model for Achieving Technology Adoption and Performance Improvement Impacts in Healthcare, Manufacturing, and Government

ERIC Educational Resources Information Center

McKinnis, David R.; Sloan, Mary Anne; Snow, L. David; Garimella, Suresh V.

2014-01-01

The Purdue Technical Assistance Program (TAP) offers a model of university engagement and service that is achieving technology adoption and performance improvement impacts in healthcare, manufacturing, government, and other sectors. The TAP model focuses on understanding and meeting the changing and challenging needs of those served, always…
Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System

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

Goldhaber, Steve; Holland, Marika

The major goal of this project was to contribute improvements to the infrastructure of an Earth System Model in order to support research in the Multiscale Methods for Accurate, Efficient, and Scale-Aware models of the Earth System project. In support of this, the NCAR team accomplished two main tasks: improving input/output performance of the model and improving atmospheric model simulation quality. Improvement of the performance and scalability of data input and diagnostic output within the model required a new infrastructure which can efficiently handle the unstructured grids common in multiscale simulations. This allows for a more computationally efficient model, enablingmore » more years of Earth System simulation. The quality of the model simulations was improved by reducing grid-point noise in the spectral element version of the Community Atmosphere Model (CAM-SE). This was achieved by running the physics of the model using grid-cell data on a finite-volume grid.« less
Running with horizontal pulling forces: the benefits of towing.

PubMed

Grabowski, Alena M; Kram, Rodger

2008-10-01

Towing, or running with a horizontal pulling force, is a common technique used by adventure racing teams. During an adventure race, the slowest person on a team determines the team's overall performance. To improve overall performance, a faster runner tows a slower runner with an elastic cord attached to their waists. Our purpose was to create and validate a model that predicts the optimal towing force needed by two runners to achieve their best overall performance. We modeled the effects of towing forces between two runners that differ in solo 10-km performance time and/or body mass. We calculated the overall time that could be saved with towing for running distances of 10, 20, and 42.2-km based on equations from previous research. Then, we empirically tested our 10-km model on 15 runners. Towing improved overall running performance considerably and our model accurately predicted this performance improvement. For example, if two runners (a 70 kg runner with a 35 min solo 10-km time and a 70-kg runner with a 50-min solo 10-km time) maintain an optimal towing force throughout a 10-km race, they can improve overall performance by 15%, saving almost 8 min. Ultimately, the race performance time and body mass of each runner determine the optimal towing force.
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.
Testing the Causal Links between School Climate, School Violence, and School Academic Performance: A Cross-Lagged Panel Autoregressive Model

ERIC Educational Resources Information Center

Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.

2016-01-01

The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
Whole School Improvement and Restructuring as Prevention and Promotion: Lessons from STEP and the Project on High Performance Learning Communities.

ERIC Educational Resources Information Center

Felner, Robert D.; Favazza, Antoinette; Shim, Minsuk; Brand, Stephen; Gu, Kenneth; Noonan, Nancy

2001-01-01

Describes the School Transitional Environment Project and its successor, the Project on High Performance Learning Communities, that have contributed to building a model for school improvement called the High Performance Learning Communities. The model seeks to build the principles of prevention into whole school change. Presents findings from…
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.
The Talent Development Middle School Model: Context, Components, and Initial Impacts on Students' Performance and Attendance

ERIC Educational Resources Information Center

Herlihy, Corinne M.; Kemple, James J.

2004-01-01

The Talent Development Middle School model was created to make a difference in struggling urban middle schools. The model is part of a trend in school improvement strategies whereby whole-school reform projects aim to improve performance and attendance outcomes for students through the use of major changes in both the organizational structure and…
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.
Electronic Performance Support Systems: Comparison of Types of Integration Levels on Performance Outcomes

ERIC Educational Resources Information Center

Phillips, Sharon A.

2013-01-01

Selecting appropriate performance improvement interventions is a critical component of a comprehensive model of performance improvement. Intervention selection is an interconnected process involving analysis of an organization's environment, definition of the performance problem, and identification of a performance gap and identification of causal…
The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally 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 predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.
Modeling and Performance Improvement of the Constant Power Regulator Systems in Variable Displacement Axial Piston Pump

PubMed Central

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. PMID:24282389

Constructing an adaptive care model for the management of disease-related symptoms throughout the course of multiple sclerosis--performance improvement CME.

PubMed

Miller, Aaron E; Cohen, Bruce A; Krieger, Stephen C; Markowitz, Clyde E; Mattson, David H; Tselentis, Helen N

2014-01-01

Symptom management remains a challenging clinical aspect of MS. To design a performance improvement continuing medical education (PI CME) activity for better clinical management of multiple sclerosis (MS)-related depression, fatigue, mobility impairment/falls, and spasticity. Ten volunteer MS centers participated in a three-stage PI CME model: A) baseline assessment; B) practice improvement CME intervention; C) reassessment. Expert faculty developed performance measures and activity intervention tools. Designated MS center champions reviewed patient charts and entered data into an online database. Stage C data were collected eight weeks after implementation of the intervention and compared with Stage A baseline data to measure change in performance. Aggregate data from the 10 participating MS centers (405 patient charts) revealed performance improvements in the assessment of all four MS-related symptoms. Statistically significant improvements were found in the documented assessment of mobility impairment/falls (p=0.003) and spasticity (p<0.001). For documentation of care plans, statistically significant improvements were reported for fatigue (p=0.007) and mobility impairment/falls (p=0.040); non-significant changes were noted for depression and spasticity. Our PI CME interventions demonstrated performance improvement in the management of MS-related symptoms. This PI CME model (available at www.achlpicme.org/ms/toolkit) offers a new perspective on enhancing symptom management in patients with MS.
Improvement of sound insulation performance of double-glazed windows by using viscoelastic connectors

NASA Astrophysics Data System (ADS)

Takahashi, D.; Sawaki, S.; Mu, R.-L.

2016-06-01

A new method for improving the sound insulation performance of double-glazed windows is proposed. This technique uses viscoelastic materials as connectors between the two glass panels to ensure that the appropriate spacing is maintained. An analytical model that makes it possible to discuss the effects of spacing, contact area, and viscoelastic properties of the connectors on the performance in terms of sound insulation is developed. The validity of the model is verified by comparing its results with measured data. The numerical experiments using this analytical model showed the importance of the ability of the connectors to achieve the appropriate spacing and their viscoelastic properties, both of which are necessary for improving the sound insulation performance. In addition, it was shown that the most effective factor is damping: the stronger the damping, the more the insulation performance increases.
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…
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.
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.
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.
Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology

NASA Astrophysics Data System (ADS)

Shiri, Jalal

2018-06-01

Among different reference evapotranspiration (ETo) modeling approaches, mass transfer-based methods have been less studied. These approaches utilize temperature and wind speed records. On the other hand, the empirical equations proposed in this context generally produce weak simulations, except when a local calibration is used for improving their performance. This might be a crucial drawback for those equations in case of local data scarcity for calibration procedure. So, application of heuristic methods can be considered as a substitute for improving the performance accuracy of the mass transfer-based approaches. However, given that the wind speed records have usually higher variation magnitudes than the other meteorological parameters, application of a wavelet transform for coupling with heuristic models would be necessary. In the present paper, a coupled wavelet-random forest (WRF) methodology was proposed for the first time to improve the performance accuracy of the mass transfer-based ETo estimation approaches using cross-validation data management scenarios in both local and cross-station scales. The obtained results revealed that the new coupled WRF model (with the minimum scatter index values of 0.150 and 0.192 for local and external applications, respectively) improved the performance accuracy of the single RF models as well as the empirical equations to great extent.
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
Development of Novel PEM Membrane and Multiphase CD Modeling of PEM Fuel Cell

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

K. J. Berry; Susanta Das

2009-12-30

To understand heat and water management phenomena better within an operational proton exchange membrane fuel cell's (PEMFC) conditions, a three-dimensional, two-phase computational fluid dynamic (CFD) flow model has been developed and simulated for a complete PEMFC. Both liquid and gas phases are considered in the model by taking into account the gas flow, diffusion, charge transfer, change of phase, electro-osmosis, and electrochemical reactions to understand the overall dynamic behaviors of species within an operating PEMFC. The CFD model is solved numerically under different parametric conditions in terms of water management issues in order to improve cell performance. The results obtainedmore » from the CFD two-phase flow model simulations show improvement in cell performance as well as water management under PEMFCs operational conditions as compared to the results of a single phase flow model available in the literature. The quantitative information obtained from the two-phase model simulation results helped to develop a CFD control algorithm for low temperature PEM fuel cell stacks which opens up a route in designing improvement of PEMFC for better operational efficiency and performance. To understand heat and water management phenomena better within an operational proton exchange membrane fuel cell's (PEMFC) conditions, a three-dimensional, two-phase computational fluid dynamic (CFD) flow model has been developed and simulated for a complete PEMFC. Both liquid and gas phases are considered in the model by taking into account the gas flow, diffusion, charge transfer, change of phase, electro-osmosis, and electrochemical reactions to understand the overall dynamic behaviors of species within an operating PEMFC. The CFD model is solved numerically under different parametric conditions in terms of water management issues in order to improve cell performance. The results obtained from the CFD two-phase flow model simulations show improvement in cell performance as well as water management under PEMFCs operational conditions as compared to the results of a single phase flow model available in the literature. The quantitative information obtained from the two-phase model simulation results helped to develop a CFD control algorithm for low temperature PEM fuel cell stacks which opens up a route in designing improvement of PEMFC for better operational efficiency and performance.« less
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.
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.
Interpreting incremental value of markers added to risk prediction models.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Pencina, Karol M; Janssens, A Cecile J W; Greenland, Philip

2012-09-15

The discrimination of a risk prediction model measures that model's ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.
The Effect of ISO 9001 and the EFQM Model on Improving Hospital Performance: A Systematic Review.

PubMed

Yousefinezhadi, Taraneh; Mohamadi, Efat; Safari Palangi, Hossein; Akbari Sari, Ali

2015-12-01

This study aimed to explore the effect of the International Organization for Standardization (ISO) ISO 9001 standard and the European foundation for quality management (EFQM) model on improving hospital performance. PubMed, Embase and the Cochrane Library databases were searched. In addition, Elsevier and Springer were searched as main publishers in the field of health sciences. We included empirical studies with any design that had used ISO 9001 or the EFQM model to improve the quality of healthcare. Data were collected and tabulated into a data extraction sheet that was specifically designed for this study. The collected data included authors' names, country, year of publication, intervention, improvement aims, setting, length of program, study design, and outcomes. Seven out of the 121 studies that were retrieved met the inclusion criteria. Three studies assessed the EFQM model and four studies assessed the ISO 9001 standard. Use of the EFQM model increased the degree of patient satisfaction and the number of hospital admissions and reduced the average length of stay, the delay on the surgical waiting list, and the number of emergency re-admissions. ISO 9001 also increased the degree of patient satisfaction and patient safety, increased cost-effectiveness, improved the hospital admissions process, and reduced the percentage of unscheduled returns to the hospital. Generally, there is a lack of robust and high quality empirical evidence regarding the effects of ISO 9001 and the EFQM model on the quality care provided by and the performance of hospitals. However, the limited evidence shows that ISO 9001 and the EFQM model might improve hospital performance.
Flexible Fabrics with High Thermal Conductivity for Advanced Spacesuits

NASA Technical Reports Server (NTRS)

Trevino, Luis A.; Bue, Grant; Orndoff, Evelyne; Kesterson, Matt; Connel, John W.; Smith, Joseph G., Jr.; Southward, Robin E.; Working, Dennis; Watson, Kent A.; Delozier, Donovan M.

2006-01-01

This paper describes the effort and accomplishments for developing flexible fabrics with high thermal conductivity (FFHTC) for spacesuits to improve thermal performance, lower weight and reduce complexity. Commercial and additional space exploration applications that require substantial performance enhancements in removal and transport of heat away from equipment as well as from the human body can benefit from this technology. Improvements in thermal conductivity were achieved through the use of modified polymers containing thermally conductive additives. The objective of the FFHTC effort is to significantly improve the thermal conductivity of the liquid cooled ventilation garment by improving the thermal conductivity of the subcomponents (i.e., fabric and plastic tubes). This paper presents the initial system modeling studies, including a detailed liquid cooling garment model incorporated into the Wissler human thermal regulatory model, to quantify the necessary improvements in thermal conductivity and garment geometries needed to affect system performance. In addition, preliminary results of thermal conductivity improvements of the polymer components of the liquid cooled ventilation garment are presented. By improving thermal garment performance, major technology drivers will be addressed for lightweight, high thermal conductivity, flexible materials for spacesuits that are strategic technical challenges of the Exploration
Limitations of contrast enhancement for infrared target identification

NASA Astrophysics Data System (ADS)

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

2009-05-01

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. Automatic contrast enhancement techniques do not always achieve this improvement. In some cases, the contrast can increase to a level of target saturation. This paper assesses the range-performance effects of contrast enhancement for target identification as a function of image saturation. 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 contrast enhancement processed images at various levels of saturation. Contrast enhancement is modeled in the U.S. 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 specific feature saturation or enhancement. The measured results follow the predicted performance based on the target task difficulty metric used in NVThermIP for the non-saturated cases. The saturated images reduce the information contained in the target and performance suffers. The model treats the contrast of the target as uniform over spatial frequency. As the contrast is enhanced, the model assumes that the contrast is enhanced uniformly over the spatial frequencies. After saturation, the spatial cues that differentiate one tank from another are located in a limited band of spatial frequencies. A frequency dependent treatment of target contrast is needed to predict performance of over-processed images.
Theory of constraints for publicly funded health systems.

PubMed

Sadat, Somayeh; Carter, Michael W; Golden, Brian

2013-03-01

Originally developed in the context of publicly traded for-profit companies, theory of constraints (TOC) improves system performance through leveraging the constraint(s). While the theory seems to be a natural fit for resource-constrained publicly funded health systems, there is a lack of literature addressing the modifications required to adopt TOC and define the goal and performance measures. This paper develops a system dynamics representation of the classical TOC's system-wide goal and performance measures for publicly traded for-profit companies, which forms the basis for developing a similar model for publicly funded health systems. The model is then expanded to include some of the factors that affect system performance, providing a framework to apply TOC's process of ongoing improvement in publicly funded health systems. Future research is required to more accurately define the factors affecting system performance and populate the model with evidence-based estimates for various parameters in order to use the model to guide TOC's process of ongoing improvement.
A framework for multi-criteria assessment of model enhancements

NASA Astrophysics Data System (ADS)

Francke, Till; Foerster, Saskia; Brosinsky, Arlena; Delgado, José; Güntner, Andreas; López-Tarazón, José A.; Bronstert, Axel

2016-04-01

Modellers are often faced with unsatisfactory model performance for a specific setup of a hydrological model. In these cases, the modeller may try to improve the setup by addressing selected causes for the model errors (i.e. data errors, structural errors). This leads to adding certain "model enhancements" (MEs), e.g. climate data based on more monitoring stations, improved calibration data, modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge, guided by some sensitivity analysis at best. When multiple MEs have been implemented, a resulting improvement in model performance is not easily attributed, especially when considering different aspects of this improvement (e.g. better performance dynamics vs. reduced bias). In this study we present an approach for comparing the effect of multiple MEs in the face of multiple improvement aspects. A stepwise selection approach and structured plots help in addressing the multidimensionality of the problem. The approach is applied to a case study, which employs the meso-scale hydrosedimentological model WASA-SED for a sub-humid catchment. The results suggest that the effect of the MEs is quite diverse, with some MEs (e.g. augmented rainfall data) cause improvements for almost all aspects, while the effect of other MEs is restricted to few aspects or even deteriorate some. These specific results may not be generalizable. However, we suggest that based on studies like this, identifying the most promising MEs to implement may be facilitated.
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.
TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

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

Krafft, S; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Briere, T

2015-06-15

Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. Amore » total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted. This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK.« less
Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

NASA Astrophysics Data System (ADS)

Shen, Chengcheng; Shi, Honghua; Liu, Yongzhi; Li, Fen; Ding, Dewen

2016-07-01

Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO), which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.

Recent Progress Towards Predicting Aircraft Ground Handling Performance

NASA Technical Reports Server (NTRS)

Yager, T. J.; White, E. J.

1981-01-01

The significant progress which has been achieved in development of aircraft ground handling simulation capability is reviewed and additional improvements in software modeling identified. The problem associated with providing necessary simulator input data for adequate modeling of aircraft tire/runway friction behavior is discussed and efforts to improve this complex model, and hence simulator fidelity, are described. Aircraft braking performance data obtained on several wet runway surfaces is compared to ground vehicle friction measurements and, by use of empirically derived methods, good agreement between actual and estimated aircraft braking friction from ground vehilce data is shown. The performance of a relatively new friction measuring device, the friction tester, showed great promise in providing data applicable to aircraft friction performance. Additional research efforts to improve methods of predicting tire friction performance are discussed including use of an instrumented tire test vehicle to expand the tire friction data bank and a study of surface texture measurement techniques.
Mechanical-Kinetic Modeling of a Molecular Walker from a Modular Design Principle

NASA Astrophysics Data System (ADS)

Hou, Ruizheng; Loh, Iong Ying; Li, Hongrong; Wang, Zhisong

2017-02-01

Artificial molecular walkers beyond burnt-bridge designs are complex nanomachines that potentially replicate biological walkers in mechanisms and functionalities. Improving the man-made walkers up to performance for widespread applications remains difficult, largely because their biomimetic design principles involve entangled kinetic and mechanical effects to complicate the link between a walker's construction and ultimate performance. Here, a synergic mechanical-kinetic model is developed for a recently reported DNA bipedal walker, which is based on a modular design principle, potentially enabling many directional walkers driven by a length-switching engine. The model reproduces the experimental data of the walker, and identifies its performance-limiting factors. The model also captures features common to the underlying design principle, including counterintuitive performance-construction relations that are explained by detailed balance, entropy production, and bias cancellation. While indicating a low directional fidelity for the present walker, the model suggests the possibility of improving the fidelity above 90% by a more powerful engine, which may be an improved version of the present engine or an entirely new engine motif, thanks to the flexible design principle. The model is readily adaptable to aid these experimental developments towards high-performance molecular walkers.
The development of an integrated Indonesian health care model using Kano's model, quality function deployment and balanced scorecard

NASA Astrophysics Data System (ADS)

Jonny, Zagloed, Teuku Yuri M.

2017-11-01

This paper aims to present an integrated health care model for Indonesian health care industry. Based on previous researches, there are two health care models in the industry such as decease- and patient-centered care models. In their developments, the patient-centered care model is widely applied due to its capability in reducing cost and improving quality simultaneously. However, there is still no comprehensive model resulting in cost reduction, quality improvement, patient satisfaction and hospital profitability simultaneously. Therefore, this research is intended to develop that model. In doing so, first, a conceptual model using Kano's Model, Quality Function Deployment (QFD) and Balanced Scorecard (BSC) is developed to generate several important elements of the model as required by stakeholders. Then, a case study of an Indonesian hospital is presented to evaluate the validity of the model using correlation analysis. As a result, it can be concluded that the model is validated implying several managerial insights among its elements such as l) leadership (r=0.85) and context of the organization (r=0.77) improve operations; 2) planning (r=0.96), support process (r=0.87) and continual improvement (r=0.95) also improve operations; 3) operations improve customer satisfaction (r=0.89) and financial performance (r=0.93) and 4) customer satisfaction improves the financial performance (0.98).
Video Self-Modeling to Improve Academic Performance: A Literature Review

ERIC Educational Resources Information Center

Prater, Mary Anne; Carter, Nari; Hitchcock, Caryl; Dowrick, Peter

2012-01-01

Video self-modeling (VSM) has been used for decades to effectively improve individuals' behaviors and skills. The purpose of this review is to locate and analyze published studies that used VSM for typical school-based academic skills to determine the effect of VSM interventions on students' academic performance. Only eight studies were located…
Show and Tell: Video Modeling and Instruction Without Feedback Improves Performance but Is Not Sufficient for Retention of a Complex Voice Motor Skill.

PubMed

Look, Clarisse; McCabe, Patricia; Heard, Robert; Madill, Catherine J

2018-02-02

Modeling and instruction are frequent components of both traditional and technology-assisted voice therapy. This study investigated the value of video modeling and instruction in the early acquisition and short-term retention of a complex voice task without external feedback. Thirty participants were randomized to two conditions and trained to produce a vocal siren over 40 trials. One group received a model and verbal instructions, the other group received a model only. Sirens were analyzed for phonation time, vocal intensity, cepstral peak prominence, peak-to-peak time, and root-mean-square error at five time points. The model and instruction group showed significant improvement on more outcome measures than the model-only group. There was an interaction effect for vocal intensity, which showed that instructions facilitated greater improvement when they were first introduced. However, neither group reproduced the model's siren performance across all parameters or retained the skill 1 day later. Providing verbal instruction with a model appears more beneficial than providing a model only in the prepractice phase of acquiring a complex voice skill. Improved performance was observed; however, the higher level of performance was not retained after 40 trials in both conditions. Other prepractice variables may need to be considered. Findings have implications for traditional and technology-assisted voice therapy. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling.

PubMed

El-Gabbas, Ahmed; Dormann, Carsten F

2018-02-01

Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence-only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species-specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point-process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor ("prior") to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias-free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data-poor regions.
Leveraging organismal biology to forecast the effects of climate change.

PubMed

Buckley, Lauren B; Cannistra, Anthony F; John, Aji

2018-04-26

Despite the pressing need for accurate forecasts of ecological and evolutionary responses to environmental change, commonly used modelling approaches exhibit mixed performance because they omit many important aspects of how organisms respond to spatially and temporally variable environments. Integrating models based on organismal phenotypes at the physiological, performance and fitness levels can improve model performance. We summarize current limitations of environmental data and models and discuss potential remedies. The paper reviews emerging techniques for sensing environments at fine spatial and temporal scales, accounting for environmental extremes, and capturing how organisms experience the environment. Intertidal mussel data illustrate biologically important aspects of environmental variability. We then discuss key challenges in translating environmental conditions into organismal performance including accounting for the varied timescales of physiological processes, for responses to environmental fluctuations including the onset of stress and other thresholds, and for how environmental sensitivities vary across lifecycles. We call for the creation of phenotypic databases to parameterize forecasting models and advocate for improved sharing of model code and data for model testing. We conclude with challenges in organismal biology that must be solved to improve forecasts over the next decade.acclimation, biophysical models, ecological forecasting, extremes, microclimate, spatial and temporal variability.
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%.
Improving the performance of a filling line based on simulation

NASA Astrophysics Data System (ADS)

Jasiulewicz-Kaczmarek, M.; Bartkowiak, T.

2016-08-01

The paper describes the method of improving performance of a filling line based on simulation. This study concerns a production line that is located in a manufacturing centre of a FMCG company. A discrete event simulation model was built using data provided by maintenance data acquisition system. Two types of failures were identified in the system and were approximated using continuous statistical distributions. The model was validated taking into consideration line performance measures. A brief Pareto analysis of line failures was conducted to identify potential areas of improvement. Two improvements scenarios were proposed and tested via simulation. The outcome of the simulations were the bases of financial analysis. NPV and ROI values were calculated taking into account depreciation, profits, losses, current CIT rate and inflation. A validated simulation model can be a useful tool in maintenance decision-making process.
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.
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 ...
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.
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...
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.
Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

PubMed Central

Chan, Aaron C.; Srinivasan, Vivek J.

2013-01-01

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator’s performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise. PMID:23446044
Augmenting an observation network to facilitate flow and transport model discrimination.

USDA-ARS?s Scientific Manuscript database

Improving understanding of subsurface conditions includes performance comparison for competing models, independently developed or obtained via model abstraction. The model comparison and discrimination can be improved if additional observations will be included. The objective of this work was to i...
Experimental investigations, modeling, and analyses of high-temperature devices for space applications: Part 1. Final report, June 1996--December 1998

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

Tournier, J.; El-Genk, M.S.; Huang, L.

1999-01-01

The Institute of Space and Nuclear Power Studies at the University of New Mexico has developed a computer simulation of cylindrical geometry alkali metal thermal-to-electric converter cells using a standard Fortran 77 computer code. The objective and use of this code was to compare the experimental measurements with computer simulations, upgrade the model as appropriate, and conduct investigations of various methods to improve the design and performance of the devices for improved efficiency, durability, and longer operational lifetime. The Institute of Space and Nuclear Power Studies participated in vacuum testing of PX series alkali metal thermal-to-electric converter cells and developedmore » the alkali metal thermal-to-electric converter Performance Evaluation and Analysis Model. This computer model consisted of a sodium pressure loss model, a cell electrochemical and electric model, and a radiation/conduction heat transfer model. The code closely predicted the operation and performance of a wide variety of PX series cells which led to suggestions for improvements to both lifetime and performance. The code provides valuable insight into the operation of the cell, predicts parameters of components within the cell, and is a useful tool for predicting both the transient and steady state performance of systems of cells.« less
Experimental investigations, modeling, and analyses of high-temperature devices for space applications: Part 2. Final report, June 1996--December 1998

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

Tournier, J.; El-Genk, M.S.; Huang, L.

1999-01-01

The Institute of Space and Nuclear Power Studies at the University of New Mexico has developed a computer simulation of cylindrical geometry alkali metal thermal-to-electric converter cells using a standard Fortran 77 computer code. The objective and use of this code was to compare the experimental measurements with computer simulations, upgrade the model as appropriate, and conduct investigations of various methods to improve the design and performance of the devices for improved efficiency, durability, and longer operational lifetime. The Institute of Space and Nuclear Power Studies participated in vacuum testing of PX series alkali metal thermal-to-electric converter cells and developedmore » the alkali metal thermal-to-electric converter Performance Evaluation and Analysis Model. This computer model consisted of a sodium pressure loss model, a cell electrochemical and electric model, and a radiation/conduction heat transfer model. The code closely predicted the operation and performance of a wide variety of PX series cells which led to suggestions for improvements to both lifetime and performance. The code provides valuable insight into the operation of the cell, predicts parameters of components within the cell, and is a useful tool for predicting both the transient and steady state performance of systems of cells.« less
Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies.

PubMed

Shehata, Ahmed W; Scheme, Erik J; Sensinger, Jonathon W

2018-05-01

On-going developments in myoelectric prosthesis control have provided prosthesis users with an assortment of control strategies that vary in reliability and performance. Many studies have focused on improving performance by providing feedback to the user but have overlooked the effect of this feedback on internal model development, which is key to improve long-term performance. In this paper, the strength of internal models developed for two commonly used myoelectric control strategies: raw control with raw feedback (using a regression-based approach) and filtered control with filtered feedback (using a classifier-based approach), were evaluated using two psychometric measures: trial-by-trial adaptation and just-noticeable difference. The performance of both strategies was also evaluated using Schmidt's style target acquisition task. Results obtained from 24 able-bodied subjects showed that although filtered control with filtered feedback had better short-term performance in path efficiency ( ), raw control with raw feedback resulted in stronger internal model development ( ), which may lead to better long-term performance. Despite inherent noise in the control signals of the regression controller, these findings suggest that rich feedback associated with regression control may be used to improve human understanding of the myoelectric control system.
Improvement of capabilities of the Distributed Electrochemistry Modeling Tool for investigating SOFC long term performance

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

Gonzalez Galdamez, Rinaldo A.; Recknagle, Kurtis P.

2012-04-30

This report provides an overview of the work performed for Solid Oxide Fuel Cell (SOFC) modeling during the 2012 Winter/Spring Science Undergraduate Laboratory Internship at Pacific Northwest National Laboratory (PNNL). A brief introduction on the concept, operation basics and applications of fuel cells is given for the general audience. Further details are given regarding the modifications and improvements of the Distributed Electrochemistry (DEC) Modeling tool developed by PNNL engineers to model SOFC long term performance. Within this analysis, a literature review on anode degradation mechanisms is explained and future plans of implementing these into the DEC modeling tool are alsomore » proposed.« less

The Effect of ISO 9001 and the EFQM Model on Improving Hospital Performance: A Systematic Review

PubMed Central

Yousefinezhadi, Taraneh; Mohamadi, Efat; Safari Palangi, Hossein; Akbari Sari, Ali

2015-01-01

Context: This study aimed to explore the effect of the International Organization for Standardization (ISO) ISO 9001 standard and the European foundation for quality management (EFQM) model on improving hospital performance. Evidence Acquisition: PubMed, Embase and the Cochrane Library databases were searched. In addition, Elsevier and Springer were searched as main publishers in the field of health sciences. We included empirical studies with any design that had used ISO 9001 or the EFQM model to improve the quality of healthcare. Data were collected and tabulated into a data extraction sheet that was specifically designed for this study. The collected data included authors’ names, country, year of publication, intervention, improvement aims, setting, length of program, study design, and outcomes. Results: Seven out of the 121 studies that were retrieved met the inclusion criteria. Three studies assessed the EFQM model and four studies assessed the ISO 9001 standard. Use of the EFQM model increased the degree of patient satisfaction and the number of hospital admissions and reduced the average length of stay, the delay on the surgical waiting list, and the number of emergency re-admissions. ISO 9001 also increased the degree of patient satisfaction and patient safety, increased cost-effectiveness, improved the hospital admissions process, and reduced the percentage of unscheduled returns to the hospital. Conclusions: Generally, there is a lack of robust and high quality empirical evidence regarding the effects of ISO 9001 and the EFQM model on the quality care provided by and the performance of hospitals. However, the limited evidence shows that ISO 9001 and the EFQM model might improve hospital performance. PMID:26756012
Incorporating groundwater flow into the WEPP model

Treesearch

William Elliot; Erin Brooks; Tim Link; Sue Miller

2010-01-01

The water erosion prediction project (WEPP) model is a physically-based hydrology and erosion model. In recent years, the hydrology prediction within the model has been improved for forest watershed modeling by incorporating shallow lateral flow into watershed runoff prediction. This has greatly improved WEPP's hydrologic performance on small watersheds with...
Measuring, managing and maximizing refinery performance

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

Bascur, O.A.; Kennedy, J.P.

1996-01-01

Implementing continuous quality improvement is a confluence of total quality management, people empowerment, performance indicators and information engineering. Supporting information technologies allow a refiner to narrow the gap between management objectives and the process control level. Dynamic performance monitoring benefits come from production cost savings, improved communications and enhanced decision making. A refinery workgroup information flow model helps automate continuous improvement of processes, performance and the organization. The paper discusses the rethinking of refinery operations, dynamic performance monitoring, continuous process improvement, the knowledge coordinator and repository manager, an integrated plant operations workflow, and successful implementation.
Predicting mining activity with parallel genetic algorithms

USGS Publications Warehouse

Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,

2005-01-01

We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.
A Human Resource Development Performance Improvement Model for Workers with Mental Retardation in Supported Employment

ERIC Educational Resources Information Center

Fornes, Sandra; Rosenberg, Howard; Rocco, Tonette S.; Gallagher, Jo

2006-01-01

This literature review discusses the factors for successful job retention of adult workers with mental retardation (MR) including external factors related to work environments and internal issues of the individual worker. Through the synthesis of the literature, a performance improvement model for supported employment (SE) is discussed based on…
A Five-Stage Prediction-Observation-Explanation Inquiry-Based Learning Model to Improve Students' Learning Performance in Science Courses

ERIC Educational Resources Information Center

Hsiao, Hsien-Sheng; Chen, Jyun-Chen; Hong, Jon-Chao; Chen, Po-Hsi; Lu, Chow-Chin; Chen, Sherry Y.

2017-01-01

A five-stage prediction-observation-explanation inquiry-based learning (FPOEIL) model was developed to improve students' scientific learning performance. In order to intensify the science learning effect, the repertory grid technology-assisted learning (RGTL) approach and the collaborative learning (CL) approach were utilized. A quasi-experimental…
Improving Language Learning Strategies and Performance of Pre-Service Language Teachers through a CALLA-TBLT Model

ERIC Educational Resources Information Center

Guapacha Chamorro, Maria Eugenia; Benavidez Paz, Luis Humberto

2017-01-01

This paper reports an action-research study on language learning strategies in tertiary education at a Colombian university. The study aimed at improving the English language performance and language learning strategies use of 33 first-year pre-service language teachers by combining elements from two models: the cognitive academic language…
Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin

NASA Astrophysics Data System (ADS)

da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio

2018-03-01

This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.
Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

NASA Astrophysics Data System (ADS)

Herman, Matthew R.; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Hernandez-Suarez, Juan Sebastian; Daneshvar, Fariborz; Zhang, Zhen; Anderson, Martha C.; Sadeghi, Ali M.; Hain, Christopher R.; Sharifi, Amirreza

2018-01-01

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32-0.52, PBIAS: ±32.73%, and RSR: 0.63-0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59-0.61, PBIAS: ±13.70%, and RSR: 0.63-0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.
Accounting for regional variation in both natural environment and human disturbance to improve performance of multimetric indices of lotic benthic diatoms.

PubMed

Tang, Tao; Stevenson, R Jan; Infante, Dana M

2016-10-15

Regional variation in both natural environment and human disturbance can influence performance of ecological assessments. In this study we calculated 5 types of benthic diatom multimetric indices (MMIs) with 3 different approaches to account for variation in ecological assessments. We used: site groups defined by ecoregions or diatom typologies; the same or different sets of metrics among site groups; and unmodeled or modeled MMIs, where models accounted for natural variation in metrics within site groups by calculating an expected reference condition for each metric and each site. We used data from the USEPA's National Rivers and Streams Assessment to calculate the MMIs and evaluate changes in MMI performance. MMI performance was evaluated with indices of precision, bias, responsiveness, sensitivity and relevancy which were respectively measured as MMI variation among reference sites, effects of natural variables on MMIs, difference between MMIs at reference and highly disturbed sites, percent of highly disturbed sites properly classified, and relation of MMIs to human disturbance and stressors. All 5 types of MMIs showed considerable discrimination ability. Using different metrics among ecoregions sometimes reduced precision, but it consistently increased responsiveness, sensitivity, and relevancy. Site specific metric modeling reduced bias and increased responsiveness. Combined use of different metrics among site groups and site specific modeling significantly improved MMI performance irrespective of site grouping approach. Compared to ecoregion site classification, grouping sites based on diatom typologies improved precision, but did not improve overall performance of MMIs if we accounted for natural variation in metrics with site specific models. We conclude that using different metrics among ecoregions and site specific metric modeling improve MMI performance, particularly when used together. Applications of these MMI approaches in ecological assessments introduced a tradeoff with assessment consistency when metrics differed across site groups, but they justified the convenient and consistent use of ecoregions. Copyright © 2016 Elsevier B.V. All rights reserved.
On improving the performance of nonphotochemical quenching in CP29 light-harvesting antenna complex

NASA Astrophysics Data System (ADS)

Berman, Gennady P.; Nesterov, Alexander I.; Sayre, Richard T.; Still, Susanne

2016-03-01

We model and simulate the performance of charge-transfer in nonphotochemical quenching (NPQ) in the CP29 light-harvesting antenna-complex associated with photosystem II (PSII). The model consists of five discrete excitonic energy states and two sinks, responsible for the potentially damaging processes and charge-transfer channels, respectively. We demonstrate that by varying (i) the parameters of the chlorophyll-based dimer, (ii) the resonant properties of the protein-solvent environment interaction, and (iii) the energy transfer rates to the sinks, one can significantly improve the performance of the NPQ. Our analysis suggests strategies for improving the performance of the NPQ in response to environmental changes, and may stimulate experimental verification.
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.
Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction.

PubMed

Warner, Grace; Hoenig, Helen; Montez, Maria; Wang, Fei; Rosen, Amy

2004-02-01

To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction. Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2. Models were replicated in 3 populations. Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888). Not applicable. Inpatient, outpatient, and total days of care in FY97. The DCG models (R(2) range,.22-.38) performed better than ACG models (R(2) range,.04-.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model (R(2) range for ACG,.14-.34; R(2) range for DCG,.24-.38). Information on self-care function slightly improved performance (R(2) range increased from 0 to.04). The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.
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.
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.
Hysteresis modeling of magnetic shape memory alloy actuator based on Krasnosel'skii-Pokrovskii model.

PubMed

Zhou, Miaolei; Wang, Shoubin; Gao, Wei

2013-01-01

As a new type of intelligent material, magnetically shape memory alloy (MSMA) has a good performance in its applications in the actuator manufacturing. Compared with traditional actuators, MSMA actuator has the advantages as fast response and large deformation; however, the hysteresis nonlinearity of the MSMA actuator restricts its further improving of control precision. In this paper, an improved Krasnosel'skii-Pokrovskii (KP) model is used to establish the hysteresis model of MSMA actuator. To identify the weighting parameters of the KP operators, an improved gradient correction algorithm and a variable step-size recursive least square estimation algorithm are proposed in this paper. In order to demonstrate the validity of the proposed modeling approach, simulation experiments are performed, simulations with improved gradient correction algorithm and variable step-size recursive least square estimation algorithm are studied, respectively. Simulation results of both identification algorithms demonstrate that the proposed modeling approach in this paper can establish an effective and accurate hysteresis model for MSMA actuator, and it provides a foundation for improving the control precision of MSMA actuator.
Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Krasnosel'skii-Pokrovskii Model

PubMed Central

Wang, Shoubin; Gao, Wei

2013-01-01

As a new type of intelligent material, magnetically shape memory alloy (MSMA) has a good performance in its applications in the actuator manufacturing. Compared with traditional actuators, MSMA actuator has the advantages as fast response and large deformation; however, the hysteresis nonlinearity of the MSMA actuator restricts its further improving of control precision. In this paper, an improved Krasnosel'skii-Pokrovskii (KP) model is used to establish the hysteresis model of MSMA actuator. To identify the weighting parameters of the KP operators, an improved gradient correction algorithm and a variable step-size recursive least square estimation algorithm are proposed in this paper. In order to demonstrate the validity of the proposed modeling approach, simulation experiments are performed, simulations with improved gradient correction algorithm and variable step-size recursive least square estimation algorithm are studied, respectively. Simulation results of both identification algorithms demonstrate that the proposed modeling approach in this paper can establish an effective and accurate hysteresis model for MSMA actuator, and it provides a foundation for improving the control precision of MSMA actuator. PMID:23737730
Modelling of different measures for improving removal in a stormwater pond.

PubMed

German, J; Jansons, K; Svensson, G; Karlsson, D; Gustafsson, L G

2005-01-01

The effect of retrofitting an existing pond on removal efficiency and hydraulic performance was modelled using the commercial software Mike21 and compartmental modelling. The Mike21 model had previously been calibrated on the studied pond. Installation of baffles, the addition of culverts under a causeway and removal of an existing island were all studied as possible improvement measures in the pond. The subsequent effect on hydraulic performance and removal of suspended solids was then evaluated. Copper, cadmium, BOD, nitrogen and phosphorus removal were also investigated for that specific improvement measure showing the best results. Outcomes of this study reveal that all measures increase the removal efficiency of suspended solids. The hydraulic efficiency is improved for all cases, except for the case where the island is removed. Compartmental modelling was also used to evaluate hydraulic performance and facilitated a better understanding of the way each of the different measures affected the flow pattern and performance. It was concluded that the installation of baffles is the best of the studied measures resulting in a reduction in the annual load on the receiving lake by approximately 8,000 kg of suspended solids (25% reduction of the annual load), 2 kg of copper (10% reduction of the annual load) and 600 kg of BOD (10% reduction of the annual load).
Development of 3D Oxide Fuel Mechanics Models

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

Spencer, B. W.; Casagranda, A.; Pitts, S. A.

This report documents recent work to improve the accuracy and robustness of the mechanical constitutive models used in the BISON fuel performance code. These developments include migration of the fuel mechanics models to be based on the MOOSE Tensor Mechanics module, improving the robustness of the smeared cracking model, implementing a capability to limit the time step size based on material model response, and improving the robustness of the return mapping iterations used in creep and plasticity models.
Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571

A model of clutter for complex, multivariate geospatial displays.

PubMed

Lohrenz, Maura C; Trafton, J Gregory; Beck, R Melissa; Gendron, Marlin L

2009-02-01

A novel model of measuring clutter in complex geospatial displays was compared with human ratings of subjective clutter as a measure of convergent validity. The new model is called the color-clustering clutter (C3) model. Clutter is a known problem in displays of complex data and has been shown to affect target search performance. Previous clutter models are discussed and compared with the C3 model. Two experiments were performed. In Experiment 1, participants performed subjective clutter ratings on six classes of information visualizations. Empirical results were used to set two free parameters in the model. In Experiment 2, participants performed subjective clutter ratings on aeronautical charts. Both experiments compared and correlated empirical data to model predictions. The first experiment resulted in a .76 correlation between ratings and C3. The second experiment resulted in a .86 correlation, significantly better than results from a model developed by Rosenholtz et al. Outliers to our correlation suggest further improvements to C3. We suggest that (a) the C3 model is a good predictor of subjective impressions of clutter in geospatial displays, (b) geospatial clutter is a function of color density and saliency (primary C3 components), and (c) pattern analysis techniques could further improve C3. The C3 model could be used to improve the design of electronic geospatial displays by suggesting when a display will be too cluttered for its intended audience.
Models for the Effects of G-seat Cuing on Roll-axis Tracking Performance

NASA Technical Reports Server (NTRS)

Levison, W. H.; Mcmillan, G. R.; Martin, E. A.

1984-01-01

Including whole-body motion in a flight simulator improves performance for a variety of tasks requiring a pilot to compensate for the effects of unexpected disturbances. A possible mechanism for this improvement is that whole-body motion provides high derivative vehicle state information whic allows the pilot to generate more lead in responding to the external disturbances. During development of motion simulating algorithms for an advanced g-cuing system it was discovered that an algorithm based on aircraft roll acceleration producted little or no performance improvement. On the other hand, algorithms based on roll position or roll velocity produced performance equivalent to whole-body motion. The analysis and modeling conducted at both the sensory system and manual control performance levels to explain the above results are described.
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.
Exploring the effect of at-risk case management compensation on hospital pay-for-performance outcomes: tools for change.

PubMed

Granata, Randy L; Hamilton, Karen

2015-01-01

Acute care nurse case managers are charged with compliance oversight, managing throughput, and ensuring safe care transitions. Leveraging the roles of nurse case managers and social workers during care transitions translates into improved fiscal performance under the Affordable Care Act. This article aims to equip leaders in the field of case management with tools to facilitate the alignment of case management systems with hospital pay-for-performance measures. A quality improvement project was implemented at a hospital in south Alabama to examine the question: for acute care case managers, what is the effect of key performance indictors using an at-risk compensation model in comparison to past nonincentive models on hospital readmissions, lengths of stay, and patient satisfaction surrounding the discharge process. Inpatient acute care hospital. The implementation of an at-risk compensation model using key performance indicators, Lean Six Sigma methodology, and Creative Health Care Management's Relationship-Based Care framework demonstrated reduced length of stay, hospital readmissions, and improved patient experiences. Regulatory changes and new models of reimbursement in the acute care environment have created the perfect storm for case management leaders. Hospital fiscal performance is dependent on effective case management processes and the ability to optimize scarce resources. The quality improvement project aimed to further align case management systems and structures with hospital pay-for-performance measures. Tools for change were presented to assist leaders with the change acceleration process.
Quality of Care Improves for Patients with Diabetes in Medicare Shared Savings Accountable Care Organizations: Organizational Characteristics Associated with Performance.

PubMed

Fraze, Taressa K; Lewis, Valerie A; Tierney, Emily; Colla, Carrie H

2017-12-06

Accountable care organizations (ACOs), a primary care-centric delivery and payment model, aim to promote integrated population health, which may improve care for those with chronic conditions such as diabetes. Research has shown that, overall, the ACO model is effective at reducing costs, but there is substantial variation in how effective different types of ACOs are at impacting costs and improving care delivery. This study examines how ACO organizational characteristics - such as composition, staffing, care management, and experiences with health reform - were associated with quality of care delivered to patients with diabetes. Secondary data were analyzed retrospectively to examine Medicare Shared Savings Program (MSSP) ACOs' performance on diabetes metrics in the first 2 years of ACO contracts. Ordinary least squares was used to analyze 162 MSSP ACOs with publicly available performance data and the National Survey of ACOs. ACOs improved performance significantly for patients with diabetes between contract years 1 and 2. In year 1, also having a private payer contract and an increased number of services within the ACO were positively associated with performance, while having a community health center or a hospital were negatively associated with performance. Better performance in year 1 was negatively associated with improved performance in year 2. This study found that ACOs substantively improved diabetes management within initial contract years. ACOs may need different types of support throughout their contracts to ensure continued improvements in performance.
Thrust stand evaluation of engine performance improvement algorithms in an F-15 airplane

NASA Technical Reports Server (NTRS)

Conners, Timothy R.

1992-01-01

Results are presented from the evaluation of the performance seeking control (PSC) optimization algorithm developed by Smith et al. (1990) for F-15 aircraft, which optimizes the quasi-steady-state performance of an F100 derivative turbofan engine for several modes of operation. The PSC algorithm uses onboard software engine model that calculates thrust, stall margin, and other unmeasured variables for use in the optimization. Comparisons are presented between the load cell measurements, PSC onboard model thrust calculations, and posttest state variable model computations. Actual performance improvements using the PSC algorithm are presented for its various modes. The results of using PSC algorithm are compared with similar test case results using the HIDEC algorithm.
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
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.
Improved prescribed performance control for air-breathing hypersonic vehicles with unknown deadzone input nonlinearity.

PubMed

Wang, Yingyang; Hu, Jianbo

2018-05-19

An improved prescribed performance controller is proposed for the longitudinal model of an air-breathing hypersonic vehicle (AHV) subject to uncertain dynamics and input nonlinearity. Different from the traditional non-affine model requiring non-affine functions to be differentiable, this paper utilizes a semi-decomposed non-affine model with non-affine functions being locally semi-bounded and possibly in-differentiable. A new error transformation combined with novel prescribed performance functions is proposed to bypass complex deductions caused by conventional error constraint approaches and circumvent high frequency chattering in control inputs. On the basis of backstepping technique, the improved prescribed performance controller with low structural and computational complexity is designed. The methodology guarantees the altitude and velocity tracking error within transient and steady state performance envelopes and presents excellent robustness against uncertain dynamics and deadzone input nonlinearity. Simulation results demonstrate the efficacy of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Improved Learning Outcomes After Flipping a Therapeutics Module: Results of a Controlled Trial.

PubMed

Lockman, Kashelle; Haines, Stuart T; McPherson, Mary Lynn

2017-12-01

To evaluate the impact on learning outcomes of flipping a pain management module in a doctor of pharmacy curriculum. In a required first-professional-year pharmacology and therapeutics course at the University of Maryland School of Pharmacy, the pain therapeutics content of the pain management module was flipped. This redesign transformed the module from a largely lecture-based, instructor-centered model to a learner-centered model that included a variety of preclass activities and in-class active learning exercises. In spring 2015, the module was taught using the traditional model; in spring 2016, it was taught using the flipped model. The same end-of-module objective structured clinical exam (OSCE) and multiple-choice exam were administered in 2015 to the traditional cohort (TC; n = 156) and in 2016 to the flipped cohort (FC; n = 162). Cohort performance was compared. Learning outcomes improved significantly in the FC: The mean OSCE score improved by 12.33/100 points (P < .0001; 95% CI 10.28-14.38; effect size 1.33), and performance on the multiple-choice exam's therapeutics content improved by 5.07 percentage points (P < .0001; 95% CI 2.56-7.59; effect size 0.45). Student performance on exam items assessing higher cognitive levels significantly improved under the flipped model. Grade distribution on both exams shifted, with significantly more FC students earning an A or B and significantly fewer earning a D or F compared with TC students. Student performance on knowledge- and skill-based assessments improved significantly after flipping the therapeutics content of a pain management module.
The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

2017-04-01

Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.
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.
Status of Air Quality in Central California and Needs for Further Study

NASA Astrophysics Data System (ADS)

Tanrikulu, S.; Beaver, S.; Soong, S.; Tran, C.; Jia, Y.; Matsuoka, J.; McNider, R. T.; Biazar, A. P.; Palazoglu, A.; Lee, P.; Wang, J.; Kang, D.; Aneja, V. P.

2012-12-01

Ozone and PM2.5 levels frequently exceed NAAQS in central California (CC). Additional emission reductions are needed to attain and maintain the standards there. Agencies are developing cost-effective emission control strategies along with complementary incentive programs to reduce emissions when exceedances are forecasted. These approaches require accurate modeling and forecasting capabilities. A variety of models have been rigorously applied (MM5, WRF, CMAQ, CAMx) over CC. Despite the vast amount of land-based measurements from special field programs and significant effort, models have historically exhibited marginal performance. Satellite data may improve model performance by: establishing IC/BC over outlying areas of the modeling domain having unknown conditions; enabling FDDA over the Pacific Ocean to characterize important marine inflows and pollutant outflows; and filling in the gaps of the land-based monitoring network. BAAQMD, in collaboration with the NASA AQAST, plans to conduct four studies that include satellite-based data in CC air quality analysis and modeling: The first project enhances and refines weather patterns, especially aloft, impacting summer ozone formation. Surface analyses were unable to characterize the strong attenuating effect of the complex terrain to steer marine winds impinging on the continent. The dense summer clouds and fog over the Pacific Ocean form spatial patterns that can be related to the downstream air flows through polluted areas. The goal of this project is to explore, characterize, and quantify these relationships using cloud cover data. Specifically, cloud agreement statistics will be developed using satellite data and model clouds. Model skin temperature predictions will be compared to both MODIS and GOES skin temperatures. The second project evaluates and improves the initial and simulated fields of meteorological models that provide inputs to air quality models. The study will attempt to determine whether a cloud dynamical adjustment developed by UAHuntsville can improve model performance for maritime stratus and whether a moisture adjustment scheme in the Pleim-Xiu boundary layer scheme can use satellite data in place of coarse surface air temperature measurements. The goal is to improve meteorological model performance that leads to improved air quality model performance. The third project evaluates and improves forecasting skills of the National Air Quality Forecasting Model in CC by using land-based routine measurements as well as satellite data. Local forecasts are mostly based on surface meteorological and air quality measurements and weather charts provided by NWS. The goal is to improve the average accuracy in forecasting exceedances, which is around 60%. The fourth project uses satellite data for monitoring trends in fine particulate matter (PM2.5) in the San Francisco Bay Area. It evaluates the effectiveness of a rule adopted in 2008 that restricts household wood burning on days forecasted to have high PM2.5 levels. The goal is to complement current analyses based on surface data covering the largest sub-regions and population centers. The overall goal is to use satellite data to overcome limitations of land-based measurements. The outcomes will be further conceptual understanding of pollutant formation, improved regulatory model performance, and better optimized forecasting programs.
On improving the performance of nonphotochemical quenching in CP29 light-harvesting antenna complex

DOE PAGES

Berman, Gennady Petrovich; Nesterov, Alexander I.; Sayre, Richard Thomas; ...

2016-02-02

In this study, we model and simulate the performance of charge-transfer in nonphotochemical quenching (NPQ) in the CP29 light-harvesting antenna-complex associated with photosystem II (PSII). The model consists of five discrete excitonic energy states and two sinks, responsible for the potentially damaging processes and charge-transfer channels, respectively. We demonstrate that by varying (i) the parameters of the chlorophyll-based dimer, (ii) the resonant properties of the protein-solvent environment interaction, and (iii) the energy transfer rates to the sinks, one can significantly improve the performance of the NPQ. In conclusion, our analysis suggests strategies for improving the performance of the NPQ inmore » response to environmental changes, and may stimulate experimental verification.« less
Performance improvement: the organization's quest.

PubMed

McKinley, C O; Parmer, D E; Saint-Amand, R A; Harbin, C B; Roulston, J C; Ellis, R A; Buchanan, J R; Leonard, R B

1999-01-01

In today's health care marketplace, quality has become an expectation. Stakeholders are demanding quality clinical outcomes, and accrediting bodies are requiring clinical performance data. The Roosevelt Institute's quest was to define and quantify quality outcomes, develop an organizational culture of performance improvement, and ensure customer satisfaction. Several of the organization's leaders volunteered to work as a team to develop a specific performance improvement approach tailored to the organization. To date, over 200 employees have received an orientation to the model and its philosophy and nine problem action and process improvement teams have been formed.
Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.
Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan

USGS Publications Warehouse

Safaie, Ammar; Wendzel, Aaron; Ge, Zhongfu; Nevers, Meredith; Whitman, Richard L.; Corsi, Steven R.; Phanikumar, Mantha S.

2016-01-01

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.
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…
Improving the Performance of Temperature Index Snowmelt Model of SWAT by Using MODIS Land Surface Temperature Data

PubMed Central

Yang, Yan; Onishi, Takeo; Hiramatsu, Ken

2014-01-01

Simulation results of the widely used temperature index snowmelt model are greatly influenced by input air temperature data. Spatially sparse air temperature data remain the main factor inducing uncertainties and errors in that model, which limits its applications. Thus, to solve this problem, we created new air temperature data using linear regression relationships that can be formulated based on MODIS land surface temperature data. The Soil Water Assessment Tool model, which includes an improved temperature index snowmelt module, was chosen to test the newly created data. By evaluating simulation performance for daily snowmelt in three test basins of the Amur River, performance of the newly created data was assessed. The coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE) were used for evaluation. The results indicate that MODIS land surface temperature data can be used as a new source for air temperature data creation. This will improve snow simulation using the temperature index model in an area with sparse air temperature observations. PMID:25165746

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 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.
Representation of the Community Earth System Model (CESM1) CAM4-chem within the Chemistry-Climate Model Initiative (CCMI)

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

Tilmes, Simone; Lamarque, Jean -Francois; Emmons, Louisa K.

The Community Earth System Model (CESM1) CAM4-chem has been used to perform the Chemistry Climate Model Initiative (CCMI) reference and sensitivity simulations. In this model, the Community Atmospheric Model version 4 (CAM4) is fully coupled to tropospheric and stratospheric chemistry. Details and specifics of each configuration, including new developments and improvements are described. CESM1 CAM4-chem is a low-top model that reaches up to approximately 40 km and uses a horizontal resolution of 1.9° latitude and 2.5° longitude. For the specified dynamics experiments, the model is nudged to Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. We summarize the performance ofmore » the three reference simulations suggested by CCMI, with a focus on the last 15 years of the simulation when most observations are available. Comparisons with selected data sets are employed to demonstrate the general performance of the model. We highlight new data sets that are suited for multi-model evaluation studies. Most important improvements of the model are the treatment of stratospheric aerosols and the corresponding adjustments for radiation and optics, the updated chemistry scheme including improved polar chemistry and stratospheric dynamics and improved dry deposition rates. These updates lead to a very good representation of tropospheric ozone within 20 % of values from available observations for most regions. In particular, the trend and magnitude of surface ozone is much improved compared to earlier versions of the model. Furthermore, stratospheric column ozone of the Southern Hemisphere in winter and spring is reasonably well represented. In conclusion, all experiments still underestimate CO most significantly in Northern Hemisphere spring and show a significant underestimation of hydrocarbons based on surface observations.« less
Representation of the Community Earth System Model (CESM1) CAM4-chem within the Chemistry-Climate Model Initiative (CCMI)

DOE PAGES

Tilmes, Simone; Lamarque, Jean -Francois; Emmons, Louisa K.; ...

2016-05-20

The Community Earth System Model (CESM1) CAM4-chem has been used to perform the Chemistry Climate Model Initiative (CCMI) reference and sensitivity simulations. In this model, the Community Atmospheric Model version 4 (CAM4) is fully coupled to tropospheric and stratospheric chemistry. Details and specifics of each configuration, including new developments and improvements are described. CESM1 CAM4-chem is a low-top model that reaches up to approximately 40 km and uses a horizontal resolution of 1.9° latitude and 2.5° longitude. For the specified dynamics experiments, the model is nudged to Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. We summarize the performance ofmore » the three reference simulations suggested by CCMI, with a focus on the last 15 years of the simulation when most observations are available. Comparisons with selected data sets are employed to demonstrate the general performance of the model. We highlight new data sets that are suited for multi-model evaluation studies. Most important improvements of the model are the treatment of stratospheric aerosols and the corresponding adjustments for radiation and optics, the updated chemistry scheme including improved polar chemistry and stratospheric dynamics and improved dry deposition rates. These updates lead to a very good representation of tropospheric ozone within 20 % of values from available observations for most regions. In particular, the trend and magnitude of surface ozone is much improved compared to earlier versions of the model. Furthermore, stratospheric column ozone of the Southern Hemisphere in winter and spring is reasonably well represented. In conclusion, all experiments still underestimate CO most significantly in Northern Hemisphere spring and show a significant underestimation of hydrocarbons based on surface observations.« less
Recent progress towards predicting aircraft ground handling performance

NASA Technical Reports Server (NTRS)

Yager, T. J.; White, E. J.

1981-01-01

Capability implemented in simulating aircraft ground handling performance is reviewed and areas for further expansion and improvement are identified. Problems associated with providing necessary simulator input data for adequate modeling of aircraft tire/runway friction behavior are discussed and efforts to improve tire/runway friction definition, and simulator fidelity are described. Aircraft braking performance data obtained on several wet runway surfaces are compared to ground vehicle friction measurements. Research to improve methods of predicting tire friction performance are discussed.
Analyzing the Interaction of Performance Appraisal Factors Using Interpretive Structural Modeling

ERIC Educational Resources Information Center

Manoharan, T. R.; Muralidharan, C.; Deshmukh, S. G.

2010-01-01

In today's changed environment where the economy and industry are driven by customers, business is open to worldwide competition. Manufacturing firms have looked at employee performance improvement as a means to succeed. These findings advocate setting up priorities for employee performance improvement. This requires a continuous improvement…
Engine Performance Improvement for the 378-Foot High Endurance Cutter

DOT National Transportation Integrated Search

1978-06-01

Methods for improving the performance of the main diesel engines : of the 378-foot Coast Guard High Endurance Cutter have been investgated. : These engines are models FM3W8-l-/8 rated for 3600hp at : 90QrDM. Present engine performance was evaluated t...
Current target acquisition methodology in force on force simulations

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks. This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors' performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.
The Performance Improvement of the Lagrangian Particle Dispersion Model (LPDM) Using Graphics Processing Unit (GPU) Computing

DTIC Science & Technology

2017-08-01

access to the GPU for general purpose processing .5 CUDA is designed to work easily with multiple programming languages , including Fortran. CUDA is a...Using Graphics Processing Unit (GPU) Computing by Leelinda P Dawson Approved for public release; distribution unlimited...The Performance Improvement of the Lagrangian Particle Dispersion Model (LPDM) Using Graphics Processing Unit (GPU) Computing by Leelinda
National Combustion Code: Parallel Implementation and Performance

NASA Technical Reports Server (NTRS)

Quealy, A.; Ryder, R.; Norris, A.; Liu, N.-S.

2000-01-01

The National Combustion Code (NCC) is being developed by an industry-government team for the design and analysis of combustion systems. CORSAIR-CCD is the current baseline reacting flow solver for NCC. This is a parallel, unstructured grid code which uses a distributed memory, message passing model for its parallel implementation. The focus of the present effort has been to improve the performance of the NCC flow solver to meet combustor designer requirements for model accuracy and analysis turnaround time. Improving the performance of this code contributes significantly to the overall reduction in time and cost of the combustor design cycle. This paper describes the parallel implementation of the NCC flow solver and summarizes its current parallel performance on an SGI Origin 2000. Earlier parallel performance results on an IBM SP-2 are also included. The performance improvements which have enabled a turnaround of less than 15 hours for a 1.3 million element fully reacting combustion simulation are described.
Improving smoothing efficiency of rigid conformal polishing tool using time-dependent smoothing evaluation model

NASA Astrophysics Data System (ADS)

Song, Chi; Zhang, Xuejun; Zhang, Xin; Hu, Haifei; Zeng, Xuefeng

2017-06-01

A rigid conformal (RC) lap can smooth mid-spatial-frequency (MSF) errors, which are naturally smaller than the tool size, while still removing large-scale errors in a short time. However, the RC-lap smoothing efficiency performance is poorer than expected, and existing smoothing models cannot explicitly specify the methods to improve this efficiency. We presented an explicit time-dependent smoothing evaluation model that contained specific smoothing parameters directly derived from the parametric smoothing model and the Preston equation. Based on the time-dependent model, we proposed a strategy to improve the RC-lap smoothing efficiency, which incorporated the theoretical model, tool optimization, and efficiency limit determination. Two sets of smoothing experiments were performed to demonstrate the smoothing efficiency achieved using the time-dependent smoothing model. A high, theory-like tool influence function and a limiting tool speed of 300 RPM were o
Titan I propulsion system modeling and possible performance improvements

NASA Astrophysics Data System (ADS)

Giusti, Oreste

This thesis features the Titan I propulsion systems and offers data-supported suggestions for improvements to increase performance. The original propulsion systems were modeled both graphically in CAD and via equations. Due to the limited availability of published information, it was necessary to create a more detailed, secondary set of models. Various engineering equations---pertinent to rocket engine design---were implemented in order to generate the desired extra detail. This study describes how these new models were then imported into the ESI CFD Suite. Various parameters are applied to these imported models as inputs that include, for example, bi-propellant combinations, pressure, temperatures, and mass flow rates. The results were then processed with ESI VIEW, which is visualization software. The output files were analyzed for forces in the nozzle, and various results were generated, including sea level thrust and ISP. Experimental data are provided to compare the original engine configuration models to the derivative suggested improvement models.
Proates a computer modelling system for power plant: Its description and application to heatrate improvement within PowerGen

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

Green, C.H.; Ready, A.B.; Rea, J.

1995-06-01

Versions of the computer program PROATES (PROcess Analysis for Thermal Energy Systems) have been used since 1979 to analyse plant performance improvement proposals relating to existing plant and also to evaluate new plant designs. Several plant modifications have been made to improve performance based on the model predictions and the predicted performance has been realised in practice. The program was born out of a need to model the overall steady state performance of complex plant to enable proposals to change plant component items or operating strategy to be evaluated. To do this with confidence it is necessary to model themore » multiple thermodynamic interactions between the plant components. The modelling system is modular in concept allowing the configuration of individual plant components to represent any particular power plant design. A library exists of physics based modules which have been extensively validated and which provide representations of a wide range of boiler, turbine and CW system components. Changes to model data and construction is achieved via a user friendly graphical model editing/analysis front-end with results being presented via the computer screen or hard copy. The paper describes briefly the modelling system but concentrates mainly on the application of the modelling system to assess design re-optimisation, firing with different fuels and the re-powering of an existing plant.« less
Improved prediction models for PCC pavement performance-related specifications

DOT National Transportation Integrated Search

2000-01-01

Performance-related specifications (PRS) for the acceptance of newly constructed jointed plain concrete pavements (JPCP) have been developed over the past decade. The main objectives of this study were to improve the distress and smoothness predictio...
A novel double loop control model design for chemical unstable processes.

PubMed

Cong, Er-Ding; Hu, Ming-Hui; Tu, Shan-Tung; Xuan, Fu-Zhen; Shao, Hui-He

2014-03-01

In this manuscript, based on Smith predictor control scheme for unstable process in industry, an improved double loop control model is proposed for chemical unstable processes. Inner loop is to stabilize integrating the unstable process and transform the original process to first-order plus pure dead-time dynamic stable process. Outer loop is to enhance the performance of set point response. Disturbance controller is designed to enhance the performance of disturbance response. The improved control system is simple with exact physical meaning. The characteristic equation is easy to realize stabilization. Three controllers are separately design in the improved scheme. It is easy to design each controller and good control performance for the respective closed-loop transfer function separately. The robust stability of the proposed control scheme is analyzed. Finally, case studies illustrate that the improved method can give better system performance than existing design methods. © 2013 ISA Published by ISA All rights reserved.
Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.
Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707
Microburst vertical wind estimation from horizontal wind measurements

NASA Technical Reports Server (NTRS)

Vicroy, Dan D.

1994-01-01

The vertical wind or downdraft component of a microburst-generated wind shear can significantly degrade airplane performance. Doppler radar and lidar are two sensor technologies being tested to provide flight crews with early warning of the presence of hazardous wind shear. An inherent limitation of Doppler-based sensors is the inability to measure velocities perpendicular to the line of sight, which results in an underestimate of the total wind shear hazard. One solution to the line-of-sight limitation is to use a vertical wind model to estimate the vertical component from the horizontal wind measurement. The objective of this study was to assess the ability of simple vertical wind models to improve the hazard prediction capability of an airborne Doppler sensor in a realistic microburst environment. Both simulation and flight test measurements were used to test the vertical wind models. The results indicate that in the altitude region of interest (at or below 300 m), the simple vertical wind models improved the hazard estimate. The radar simulation study showed that the magnitude of the performance improvement was altitude dependent. The altitude of maximum performance improvement occurred at about 300 m.
Assessment and Improvement of GOCE based Global Geopotential Models Using Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Erol, Serdar; Erol, Bihter; Serkan Isik, Mustafa

2016-07-01

The contribution of recent Earth gravity field satellite missions, specifically GOCE mission, leads significant improvement in quality of gravity field models in both accuracy and resolution manners. However the performance and quality of each released model vary not only depending on the spatial location of the Earth but also the different bands of the spectral expansion. Therefore the assessment of the global model performances with validations using in situ-data in varying territories on the Earth is essential for clarifying their exact performances in local. Beside of this, their spectral evaluation and quality assessment of the signal in each part of the spherical harmonic expansion spectrum is essential to have a clear decision for the commission error content of the model and determining its optimal degree, revealed the best results, as well. The later analyses provide also a perspective and comparison on the global behavior of the models and opportunity to report the sequential improvement of the models depending on the mission developments and hence the contribution of the new data of missions. In this study a review on spectral assessment results of the recently released GOCE based global geopotential models DIR-R5, TIM-R5 with the enhancement using EGM2008, as reference model, in Turkey, versus the terrestrial data is provided. Beside of reporting the GOCE mission contribution to the models in Turkish territory, the possible improvement in the spectral quality of these models, via decomposition that are highly contaminated by noise, is purposed. In the analyses the motivation is on achieving an optimal amount of improvement that rely on conserving the useful component of the GOCE signal as much as possible, while fusing the filtered GOCE based models with EGM2008 in the appropriate spectral bands. The investigation also contain the assessment of the coherence and the correlation between the Earth gravity field parameters (free-air gravity anomalies and geoid undulations), derived from the validated geopotential models and terrestrial data (GPS/leveling, terrestrial gravity observations, DTM etc.), as well as the WGM2012 products. In the conclusion, with the numerical results, the performance of the assessed models are clarified in Turkish territory and the potential of the Wavelet decomposition in the improvement of the geopotential models is verified.
PV System 'Availability' as a Reliability Metric -- Improving Standards, Contract Language and Performance Models

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

Klise, Geoffrey T.; Hill, Roger; Walker, Andy

The use of the term 'availability' to describe a photovoltaic (PV) system and power plant has been fraught with confusion for many years. A term that is meant to describe equipment operational status is often omitted, misapplied or inaccurately combined with PV performance metrics due to attempts to measure performance and reliability through the lens of traditional power plant language. This paper discusses three areas where current research in standards, contract language and performance modeling is improving the way availability is used with regards to photovoltaic systems and power plants.

Data Envelopment Analysis (DEA) Model in Operation Management

NASA Astrophysics Data System (ADS)

Malik, Meilisa; Efendi, Syahril; Zarlis, Muhammad

2018-01-01

Quality management is an effective system in operation management to develops, maintains, and improves quality from groups of companies that allow marketing, production, and service at the most economycal level as well as ensuring customer satisfication. Many companies are practicing quality management to improve their bussiness performance. One of performance measurement is through measurement of efficiency. One of the tools can be used to assess efficiency of companies performance is Data Envelopment Analysis (DEA). The aim of this paper is using Data Envelopment Analysis (DEA) model to assess efficiency of quality management. In this paper will be explained CCR, BCC, and SBM models to assess efficiency of quality management.
High-precision radiometric tracking for planetary approach and encounter in the inner solar system

NASA Technical Reports Server (NTRS)

Christensen, C. S.; Thurman, S. W.; Davidson, J. M.; Finger, M. H.; Folkner, W. M.

1989-01-01

The benefits of improved radiometric tracking data have been studied for planetary approach within the inner Solar System using the Mars Rover Sample Return trajectory as a model. It was found that the benefit of improved data to approach and encounter navigation was highly dependent on the a priori uncertainties assumed for several non-estimated parameters, including those for frame-tie, Earth orientation, troposphere delay, and station locations. With these errors at their current levels, navigational performance was found to be insensitive to enhancements in data accuracy. However, when expected improvements in these errors are modeled, performance with current-accuracy data significantly improves, with substantial further improvements possible with enhancements in data accuracy.
Using modeling to understand how athletes in different disciplines solve the same problem: swimming versus running versus speed skating.

PubMed

de Koning, Jos J; Foster, Carl; Lucia, Alejandro; Bobbert, Maarten F; Hettinga, Florentina J; Porcari, John P

2011-06-01

Every new competitive season offers excellent examples of human locomotor abilities, regardless of the sport. As a natural consequence of competitions, world records are broken every now and then. World record races not only offer spectators the pleasure of watching very talented and highly trained athletes performing muscular tasks with remarkable skill, but also represent natural models of the ultimate expression of human integrated muscle biology, through strength, speed, or endurance performances. Given that humans may be approaching our species limit for muscular power output, interest in how athletes improve on world records has led to interest in the strategy of how limited energetic resources are best expended over a race. World record performances may also shed light on how athletes in different events solve exactly the same problem-minimizing the time required to reach the finish line. We have previously applied mathematical modeling to the understanding of world record performances in terms of improvements in facilities/equipment and improvements in the athletes' physical capacities. In this commentary, we attempt to demonstrate that differences in world record performances in various sports can be explained using a very simple modeling process.
Rotary engine performance limits predicted by a zero-dimensional model

NASA Technical Reports Server (NTRS)

Bartrand, Timothy A.; Willis, Edward A.

1992-01-01

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

PubMed

Pierce, Richard; Fox, Jeremy

2012-12-12

To implement a "flipped classroom" model for a renal pharmacotherapy topic module and assess the impact on pharmacy students' performance and attitudes. Students viewed vodcasts (video podcasts) of lectures prior to the scheduled class and then discussed interactive cases of patients with end-stage renal disease in class. A process-oriented guided inquiry learning (POGIL) activity was developed and implemented that complemented, summarized, and allowed for application of the material contained in the previously viewed lectures. Students' performance on the final examination significantly improved compared to performance of students the previous year who completed the same module in a traditional classroom setting. Students' opinions of the POGIL activity and the flipped classroom instructional model were mostly positive. Implementing a flipped classroom model to teach a renal pharmacotherapy module resulted in improved student performance and favorable student perceptions about the instructional approach. Some of the factors that may have contributed to students' improved scores included: student mediated contact with the course material prior to classes, benchmark and formative assessments administered during the module, and the interactive class activities.
An evaluation of parent-produced video self-modeling to improve independence in an adolescent with intellectual developmental disorder and an autism spectrum disorder: a controlled case study.

PubMed

Allen, Keith D; Vatland, Christopher; Bowen, Scott L; Burke, Raymond V

2015-07-01

We evaluated a parent-created video self-modeling (VSM) intervention to improve independence in an adolescent diagnosed with Intellectual Developmental Disorder (IDD) and Autism Spectrum Disorder (ASD). In a multiple baseline design across routines, a parent and her 17-year-old daughter created self-modeling videos of three targeted routines needed for independence in the community. The parent used a tablet device with a mobile app called "VideoTote" to produce videos of the daughter performing the targeted routines. The mobile app includes a 30-s tutorial about making modeling videos. The parent and daughter produced and watched a VSM scene prior to performing each of the three routines in an analogue community setting. The adolescent showed marked, immediate, and sustained improvements in performing each routine following the production and implementation of the VSM. Performance was found to generalize to the natural community setting. Results suggest that parents can use available technology to promote community independence for transition age individuals. © The Author(s) 2015.
Improvement of Latvian Geoid Model Using GNSS/Levelling, GOCE Data and Vertical Deflection Measurements

NASA Astrophysics Data System (ADS)

Janpaule, Inese; Haritonova, Diana; Balodis, Janis; Zarins, Ansis; Silabriedis, Gunars; Kaminskis, Janis

2015-03-01

Development of a digital zenith telescope prototype, improved zenith camera construction and analysis of experimental vertical deflection measurements for the improvement of the Latvian geoid model has been performed at the Institute of Geodesy and Geoinformatics (GGI), University of Latvia. GOCE satellite data was used to compute geoid model for the Riga region, and European gravimetric geoid model EGG97 and 102 data points of GNSS/levelling were used as input data in the calculations of Latvian geoid model.
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…
Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model

NASA Astrophysics Data System (ADS)

Sharma, Kuldeep; Ashrit, Raghavendra; Bhatla, R.; Mitra, A. K.; Iyengar, G. R.; Rajagopal, E. N.

2017-11-01

The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains.
An improved Burgers cellular automaton model for bicycle flow

NASA Astrophysics Data System (ADS)

Xue, Shuqi; Jia, Bin; Jiang, Rui; Li, Xingang; Shan, Jingjing

2017-12-01

As an energy-efficient and healthy transport mode, bicycling has recently attracted the attention of governments, transport planners, and researchers. The dynamic characteristics of the bicycle flow must be investigated to improve the facility design and traffic operation of bicycling. We model the bicycle flow by using an improved Burgers cellular automaton model. Through a following move mechanism, the modified model enables bicycles to move smoothly and increase the critical density to a more rational level than the original model. The model is calibrated and validated by using experimental data and field data. The results show that the improved model can effectively simulate the bicycle flow. The performance of the model under different parameters is investigated and discussed. Strengths and limitations of the improved model are suggested for future work.
Development of a Higher Fidelity Model for the Cascade Distillation Subsystem (CDS)

NASA Technical Reports Server (NTRS)

Perry, Bruce; Anderson, Molly

2014-01-01

Significant improvements have been made to the ACM model of the CDS, enabling accurate predictions of dynamic operations with fewer assumptions. The model has been utilized to predict how CDS performance would be impacted by changing operating parameters, revealing performance trade-offs and possibilities for improvement. CDS efficiency is driven by the THP coefficient of performance, which in turn is dependent on heat transfer within the system. Based on the remaining limitations of the simulation, priorities for further model development include: center dot Relaxing the assumption of total condensation center dot Incorporating dynamic simulation capability for the buildup of dissolved inert gasses in condensers center dot Examining CDS operation with more complex feeds center dot Extending heat transfer analysis to all surfaces
Performance Analysis of Several GPS/Galileo Precise Point Positioning Models

PubMed Central

Afifi, Akram; El-Rabbany, Ahmed

2015-01-01

This paper examines the performance of several precise point positioning (PPP) models, which combine dual-frequency GPS/Galileo observations in the un-differenced and between-satellite single-difference (BSSD) modes. These include the traditional un-differenced model, the decoupled clock model, the semi-decoupled clock model, and the between-satellite single-difference model. We take advantage of the IGS-MGEX network products to correct for the satellite differential code biases and the orbital and satellite clock errors. Natural Resources Canada’s GPSPace PPP software is modified to handle the various GPS/Galileo PPP models. A total of six data sets of GPS and Galileo observations at six IGS stations are processed to examine the performance of the various PPP models. It is shown that the traditional un-differenced GPS/Galileo PPP model, the GPS decoupled clock model, and the semi-decoupled clock GPS/Galileo PPP model improve the convergence time by about 25% in comparison with the un-differenced GPS-only model. In addition, the semi-decoupled GPS/Galileo PPP model improves the solution precision by about 25% compared to the traditional un-differenced GPS/Galileo PPP model. Moreover, the BSSD GPS/Galileo PPP model improves the solution convergence time by about 50%, in comparison with the un-differenced GPS PPP model, regardless of the type of BSSD combination used. As well, the BSSD model improves the precision of the estimated parameters by about 50% and 25% when the loose and the tight combinations are used, respectively, in comparison with the un-differenced GPS-only model. Comparable results are obtained through the tight combination when either a GPS or a Galileo satellite is selected as a reference. PMID:26102495
Performance Analysis of Several GPS/Galileo Precise Point Positioning Models.

PubMed

Afifi, Akram; El-Rabbany, Ahmed

2015-06-19

This paper examines the performance of several precise point positioning (PPP) models, which combine dual-frequency GPS/Galileo observations in the un-differenced and between-satellite single-difference (BSSD) modes. These include the traditional un-differenced model, the decoupled clock model, the semi-decoupled clock model, and the between-satellite single-difference model. We take advantage of the IGS-MGEX network products to correct for the satellite differential code biases and the orbital and satellite clock errors. Natural Resources Canada's GPSPace PPP software is modified to handle the various GPS/Galileo PPP models. A total of six data sets of GPS and Galileo observations at six IGS stations are processed to examine the performance of the various PPP models. It is shown that the traditional un-differenced GPS/Galileo PPP model, the GPS decoupled clock model, and the semi-decoupled clock GPS/Galileo PPP model improve the convergence time by about 25% in comparison with the un-differenced GPS-only model. In addition, the semi-decoupled GPS/Galileo PPP model improves the solution precision by about 25% compared to the traditional un-differenced GPS/Galileo PPP model. Moreover, the BSSD GPS/Galileo PPP model improves the solution convergence time by about 50%, in comparison with the un-differenced GPS PPP model, regardless of the type of BSSD combination used. As well, the BSSD model improves the precision of the estimated parameters by about 50% and 25% when the loose and the tight combinations are used, respectively, in comparison with the un-differenced GPS-only model. Comparable results are obtained through the tight combination when either a GPS or a Galileo satellite is selected as a reference.
Improving emissions inventories in Mexico through systematic analysis of model performance along C-130 and DC-8 flight tracks during MILAGRO

NASA Astrophysics Data System (ADS)

Mena-Carrasco, M.; Carmichael, G. R.; Campbell, J. E.; Tang, Y.; Chai, T.

2007-05-01

During the MILAGRO campaign in March 2006 the University of Iowa provided regional air quality forecasting for scientific flight planning for the C-130 and DC-8. Model performance showed positive bias of ozone prediction (~15ppbv), associated to overpredictions in precursor concentrations (~2.15 ppbv NOy and ~1ppmv ARO1). Model bias showed a distinct geographical pattern in which the higher values were in and near Mexico City. Newer runs in which NOx and VOC emissions were decreased improved ozone prediction, decreasing bias and increasing model correlation, at the same time reducing regional bias over Mexico. This work will evaluate model performance using the newly published Mexico National Emissions Inventory, and the introduction of data assimilation to recover emissions scaling factors to optimize model performance. Finally the results of sensitivity runs showing the regional impact of Mexico City emissions on ozone concentrations will be shown, along with the influence of Mexico City aerosol concentrations on regional photochemistry.
Minimizing Actuator-Induced Residual Error in Active Space Telescope Primary Mirrors

DTIC Science & Technology

2010-09-01

actuator geometry, and rib-to-facesheet intersection geometry are exploited to achieve improved performance in silicon carbide ( SiC ) mirrors . A...are exploited to achieve improved performance in silicon carbide ( SiC ) mirrors . A parametric finite element model is used to explore the trade space...MOST) finite element model. The move to lightweight actively-controlled silicon carbide ( SiC ) mirrors is traced back to previous generations of space
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.
A performance improvement case study in aircraft maintenance and its implications for hazard identification.

PubMed

Ward, Marie; McDonald, Nick; Morrison, Rabea; Gaynor, Des; Nugent, Tony

2010-02-01

Aircraft maintenance is a highly regulated, safety critical, complex and competitive industry. There is a need to develop innovative solutions to address process efficiency without compromising safety and quality. This paper presents the case that in order to improve a highly complex system such as aircraft maintenance, it is necessary to develop a comprehensive and ecologically valid model of the operational system, which represents not just what is meant to happen, but what normally happens. This model then provides the backdrop against which to change or improve the system. A performance report, the Blocker Report, specific to aircraft maintenance and related to the model was developed gathering data on anything that 'blocks' task or check performance. A Blocker Resolution Process was designed to resolve blockers and improve the current check system. Significant results were obtained for the company in the first trial and implications for safety management systems and hazard identification are discussed. Statement of Relevance: Aircraft maintenance is a safety critical, complex, competitive industry with a need to develop innovative solutions to address process and safety efficiency. This research addresses this through the development of a comprehensive and ecologically valid model of the system linked with a performance reporting and resolution system.
Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1

NASA Astrophysics Data System (ADS)

Langenbrunner, B.; Neelin, J. D.

2017-09-01

Global climate models (GCMs) are examples of high-dimensional input-output systems, where model output is a function of many variables, and an update in model physics commonly improves performance in one objective function (i.e., measure of model performance) at the expense of degrading another. Here concepts from multiobjective optimization in the engineering literature are used to investigate parameter sensitivity and optimization in the face of such trade-offs. A metamodeling technique called cut high-dimensional model representation (cut-HDMR) is leveraged in the context of multiobjective optimization to improve GCM simulation of the tropical Pacific climate, focusing on seasonal precipitation, column water vapor, and skin temperature. An evolutionary algorithm is used to solve for Pareto fronts, which are surfaces in objective function space along which trade-offs in GCM performance occur. This approach allows the modeler to visualize trade-offs quickly and identify the physics at play. In some cases, Pareto fronts are small, implying that trade-offs are minimal, optimal parameter value choices are more straightforward, and the GCM is well-functioning. In all cases considered here, the control run was found not to be Pareto-optimal (i.e., not on the front), highlighting an opportunity for model improvement through objectively informed parameter selection. Taylor diagrams illustrate that these improvements occur primarily in field magnitude, not spatial correlation, and they show that specific parameter updates can improve fields fundamental to tropical moist processes—namely precipitation and skin temperature—without significantly impacting others. These results provide an example of how basic elements of multiobjective optimization can facilitate pragmatic GCM tuning processes.
Improved Fuzzy Modelling to Predict the Academic Performance of Distance Education Students

ERIC Educational Resources Information Center

Yildiz, Osman; Bal, Abdullah; Gulsecen, Sevinc

2013-01-01

It is essential to predict distance education students' year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the…

Operational seasonal forecasting of crop performance.

PubMed

Stone, Roger C; Meinke, Holger

2005-11-29

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.
Operational seasonal forecasting of crop performance

PubMed Central

Stone, Roger C; Meinke, Holger

2005-01-01

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097
Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA

USGS Publications Warehouse

Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.

2016-01-01

Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.
Unexpected Dual Task Benefits on Cycling in Parkinson Disease and Healthy Adults: A Neuro-Behavioral Model

PubMed Central

Altmann, Lori J. P.; Stegemöller, Elizabeth; Hazamy, Audrey A.; Wilson, Jonathan P.; Okun, Michael S.; McFarland, Nikolaus R.; Shukla, Aparna Wagle; Hass, Chris J.

2015-01-01

Background When performing two tasks at once, a dual task, performance on one or both tasks typically suffers. People with Parkinson’s disease (PD) usually experience larger dual task decrements on motor tasks than healthy older adults (HOA). Our objective was to investigate the decrements in cycling caused by performing cognitive tasks with a range of difficulty in people with PD and HOAs. Methods Twenty-eight participants with Parkinson’s disease and 20 healthy older adults completed a baseline cycling task with no secondary tasks and then completed dual task cycling while performing 12 tasks from six cognitive domains representing a wide range of difficulty. Results Cycling was faster during dual task conditions than at baseline, and was significantly faster for six tasks (all p<.02) across both groups. Cycling speed improved the most during the easiest cognitive tasks, and cognitive performance was largely unaffected. Cycling improvement was predicted by task difficulty (p<.001). People with Parkinson’s disease cycled slower (p<.03) and showed reduced dual task benefits (p<.01) than healthy older adults. Conclusions Unexpectedly, participants’ motor performance improved during cognitive dual tasks, which cannot be explained in current models of dual task performance. To account for these findings, we propose a model integrating dual task and acute exercise approaches which posits that cognitive arousal during dual tasks increases resources to facilitate motor and cognitive performance, which is subsequently modulated by motor and cognitive task difficulty. This model can explain both the improvement observed on dual tasks in the current study and more typical dual task findings in other studies. PMID:25970607
Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed

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

Cai, Xitian; Yang, Zong-Liang; Xia, Youlong

2014-12-27

This study assesses the hydrologic performance of four land surface models (LSMs) for the conterminous United States using the North American Land Data Assimilation System (NLDAS) test bed. The four LSMs are the baseline community Noah LSM (Noah, version 2.8), the Variable Infiltration Capacity (VIC, version 4.0.5) model, the substantially augmented Noah LSM with multiparameterization options (hence Noah-MP), and the Community Land Model version 4 (CLM4). All four models are driven by the same NLDAS-2 atmospheric forcing. Modeled terrestrial water storage (TWS), streamflow, evapotranspiration (ET), and soil moisture are compared with each other and evaluated against the identical observations. Relativemore » to Noah, the other three models offer significant improvements in simulating TWS and streamflow and moderate improvements in simulating ET and soil moisture. Noah-MP provides the best performance in simulating soil moisture and is among the best in simulating TWS, CLM4 shows the best performance in simulating ET, and VIC ranks the highest in performing the streamflow simulations. Despite these improvements, CLM4, Noah-MP, and VIC exhibit deficiencies, such as the low variability of soil moisture in CLM4, the fast growth of spring ET in Noah-MP, and the constant overestimation of ET in VIC.« less
Iowa calibration of MEPDG performance prediction models.

DOT National Transportation Integrated Search

2013-06-01

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

NASA Astrophysics Data System (ADS)

Byerly, Diane Leslie De Caix

The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing injury.
Transonic wing DFVLR-F4 as European test model

NASA Technical Reports Server (NTRS)

Redeker, G.; Schmidt, N.

1980-01-01

A transonic wing, the DFVLR-F4 was designed and tested as a model in European transonic wind tunnels and was found to give performance improvements over conventional wings. One reason for the improvement was the reduction of compression shocks in the transonic region as the result of improved wing design.
Performance Analysis of Receive Diversity in Wireless Sensor Networks over GBSBE Models

PubMed Central

Goel, Shivali; Abawajy, Jemal H.; Kim, Tai-hoon

2010-01-01

Wireless sensor networks have attracted a lot of attention recently. In this paper, we develop a channel model based on the elliptical model for multipath components involving randomly placed scatterers in the scattering region with sensors deployed on a field. We verify that in a sensor network, the use of receive diversity techniques improves the performance of the system. Extensive performance analysis of the system is carried out for both single and multiple antennas with the applied receive diversity techniques. Performance analyses based on variations in receiver height, maximum multipath delay and transmit power have been performed considering different numbers of antenna elements present in the receiver array, Our results show that increasing the number of antenna elements for a wireless sensor network does indeed improve the BER rates that can be obtained. PMID:22163510
Integration of multi-objective structural optimization into cementless hip prosthesis design: Improved Austin-Moore model.

PubMed

Kharmanda, G

2016-11-01

A new strategy of multi-objective structural optimization is integrated into Austin-Moore prosthesis in order to improve its performance. The new resulting model is so-called Improved Austin-Moore. The topology optimization is considered as a conceptual design stage to sketch several kinds of hollow stems according to the daily loading cases. The shape optimization presents the detailed design stage considering several objectives. Here, A new multiplicative formulation is proposed as a performance scale in order to define the best compromise between several requirements. Numerical applications on 2D and 3D problems are carried out to show the advantages of the proposed model.
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…
Does box model training improve surgical dexterity and economy of movement during virtual reality laparoscopy? A randomised trial.

PubMed

Clevin, Lotte; Grantcharov, Teodor P

2008-01-01

Laparoscopic box model trainers have been used in training curricula for a long time, however data on their impact on skills acquisition is still limited. Our aim was to validate a low cost box model trainer as a tool for the training of skills relevant to laparoscopic surgery. Randomised, controlled trial (Canadian Task Force Classification I). University Hospital. Sixteen gynaecologic residents with limited laparoscopic experience were randomised to a group that received a structured box model training curriculum, and a control group. Performance before and after the training was assessed in a virtual reality laparoscopic trainer (LapSim and was based on objective parameters, registered by the computer system (time, error, and economy of motion scores). Group A showed significantly greater improvement in all performance parameters compared with the control group: economy of movement (p=0.001), time (p=0.001) and tissue damage (p=0.036), confirming the positive impact of box-trainer curriculum on laparoscopic skills acquisition. Structured laparoscopic skill training on a low cost box model trainer improves performance as assessed using the VR system. Trainees who used the box model trainer showed significant improvement compared to the control group. Box model trainers are valid tools for laparoscopic skills training and should be implemented in the comprehensive training curricula in gynaecology.
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
Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

PubMed

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Improving cardiovascular care through outpatient cardiac rehabilitation: an analysis of payment models that would improve quality and promote use.

PubMed

Mead, Holly; Grantham, Sarah; Siegel, Bruce

2014-01-01

Much attention has been paid to improving the care of patients with cardiovascular disease by focusing attention on delivery system redesign and payment reforms that encompass the healthcare spectrum, from an acute episode to maintenance of care. However, 1 area of cardiovascular disease care that has received little attention in the advancement of quality is cardiac rehabilitation (CR), a comprehensive secondary prevention program that is significantly underused despite evidence-based guidelines that recommending its use. The purpose of this article was to analyze the applicability of 2 payment and reimbursement models-pay-for-performance and bundled payments for episodes of care--that can promote the use of CR. We conclude that a payment model combining elements of both pay-for-performance and episodes of care would increase the use of CR, which would both improve quality and increase efficiency in cardiac care. Specific elements would need to be clearly defined, however, including: (a) how an episode is defined, (b) how to hold providers accountable for the care they provider, (c) how to encourage participation among CR providers, and (d) how to determine an equitable distribution of payment. Demonstrations testing new payment models must be implemented to generate empirical evidence that a melded pay-for-performance and episode-based care payment model will improve quality and efficiency.
Performance Optimization of Marine Science and Numerical Modeling on HPC Cluster

PubMed Central

Yang, Dongdong; Yang, Hailong; Wang, Luming; Zhou, Yucong; Zhang, Zhiyuan; Wang, Rui; Liu, Yi

2017-01-01

Marine science and numerical modeling (MASNUM) is widely used in forecasting ocean wave movement, through simulating the variation tendency of the ocean wave. Although efforts have been devoted to improve the performance of MASNUM from various aspects by existing work, there is still large space unexplored for further performance improvement. In this paper, we aim at improving the performance of propagation solver and data access during the simulation, in addition to the efficiency of output I/O and load balance. Our optimizations include several effective techniques such as the algorithm redesign, load distribution optimization, parallel I/O and data access optimization. The experimental results demonstrate that our approach achieves higher performance compared to the state-of-the-art work, about 3.5x speedup without degrading the prediction accuracy. In addition, the parameter sensitivity analysis shows our optimizations are effective under various topography resolutions and output frequencies. PMID:28045972
Utilizing soil polypedons to improve model performance for digital soil mapping

USDA-ARS?s Scientific Manuscript database

Most digital soil mapping approaches that use point data to develop relationships with covariate data intersect sample locations with one raster pixel regardless of pixel size. Resulting models are subject to spurious values in covariate data which may limit model performance. An alternative approac...
Effects of Video Modeling on Treatment Integrity of Behavioral Interventions

ERIC Educational Resources Information Center

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…
Apollo oxygen tank stratification analysis, volume 2

NASA Technical Reports Server (NTRS)

Barton, J. E.; Patterson, H. W.

1972-01-01

An analysis of flight performance of the Apollo 15 cryogenic oxygen tanks was conducted with the variable grid stratification math model developed earlier in the program. Flight conditions investigated were the CMP-EVA and one passive thermal control period which exhibited heater temperature characteristics not previously observed. Heater temperatures for these periods were simulated with the math model using flight acceleration data. Simulation results (heater temperature and tank pressure) compared favorably with the Apollo 15 flight data, and it was concluded that tank performance was nominal. Math model modifications were also made to improve the simulation accuracy. The modifications included the addition of the effects of the tank wall thermal mass and an improved system flow distribution model. The modifications improved the accuracy of simulated pressure response based on comparisons with flight data.

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.
A comparison of WEC control strategies

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

Wilson, David G.; Bacelli, Giorgio; Coe, Ryan Geoffrey

2016-04-01

The operation of Wave Energy Converter (WEC) devices can pose many challenging problems to the Water Power Community. A key research question is how to significantly improve the performance of these WEC devices through improving the control system design. This report summarizes an effort to analyze and improve the performance of WEC through the design and implementation of control systems. Controllers were selected to span the WEC control design space with the aim of building a more comprehensive understanding of different controller capabilities and requirements. To design and evaluate these control strategies, a model scale test-bed WEC was designed formore » both numerical and experimental testing (see Section 1.1). Seven control strategies have been developed and applied on a numerical model of the selected WEC. This model is capable of performing at a range of levels, spanning from a fully-linear realization to varying levels of nonlinearity. The details of this model and its ongoing development are described in Section 1.2.« less
Management of undescended testis may be improved with educational updates and new transferring model.

PubMed

Yi, Wei; Sheng-de, Wu; Lian-Ju, Shen; Tao, Lin; Da-Wei, He; Guang-Hui, Wei

2018-05-24

To investigate whether management of undescended testis (UDT) may be improved with educational updates and new transferring model among referring providers (RPs). The age of orchidopexies performed in Children's Hospital of Chongqing Medical University were reviewed. We then proposed educational updates and new transferring model among RPs. The age of orchidopexies performed after our intervention were collected. Data were represented graphically and statistical analysis Chi-square for trend were used. A total of 1543 orchidopexies were performed. The median age of orchidopexy did not matched the target age of 6-12 months in any subsequent year. Survey of the RPs showed that 48.85% of their recommended age was below 12 months. However, only 25.50% of them would directly make a surgical referral to pediatric surgery specifically at this point. After we proposed educational updates, tracking the age of orchidopexy revealed a statistically significant trend downward. The management of undescended testis may be improved with educational updates and new transferring model among primary healthcare practitioners.
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.
Structural Design Strategies for Improved Small Overlap Crashworthiness Performance.

PubMed

Mueller, Becky C; Brethwaite, Andrew S; Zuby, David S; Nolan, Joseph M

2014-11-01

In 2012, the Insurance Institute for Highway Safety (IIHS) began a 64 km/h small overlap frontal crash test consumer information test program. Thirteen automakers already have redesigned models to improve test performance. One or more distinct strategies are evident in these redesigns: reinforcement of the occupant compartment, use of energy-absorbing fender structures, and the addition of engagement structures to induce vehicle lateral translation. Each strategy influences vehicle kinematics, posing additional challenges for the restraint systems. The objective of this two-part study was to examine how vehicles were modified to improve small overlap test performance and then to examine how these modifications affect dummy response and restraint system performance. Among eight models tested before and after design changes, occupant compartment intrusion reductions ranged from 6 cm to 45 cm, with the highest reductions observed in models with the largest number of modifications. All redesigns included additional occupant compartment reinforcement, one-third added structures to engage the barrier, and two modified a shotgun load path. Designs with engagement structures produced greater glance-off from the barrier and exhibited lower delta Vs but experienced more lateral outboard motion of the dummy. Designs with heavy reinforcement of the occupant compartment had higher vehicle accelerations and delta V. In three cases, these apparent trade-offs were not well addressed by concurrent changes in restraint systems and resulted in increased injury risk compared with the original tests. Among the 36 models tested after design changes, the extent of design changes correlated to structural performance. Half of the vehicles with the lowest intrusion levels incorporated aspects of all three design strategies. Vehicle kinematics and dummy and restraint system characteristics were similar to those observed in the before/after pairs. Different combinations of structural improvement strategies for improving small overlap test performance were found to be effective in reducing occupant compartment intrusion and improving dummy kinematics in the IIHS small overlap test with modest weight increase.
CFD Code Development for Combustor Flows

NASA Technical Reports Server (NTRS)

Norris, Andrew

2003-01-01

During the lifetime of this grant, work has been performed in the areas of model development, code development, code validation and code application. For model development, this has included the PDF combustion module, chemical kinetics based on thermodynamics, neural network storage of chemical kinetics, ILDM chemical kinetics and assumed PDF work. Many of these models were then implemented in the code, and in addition many improvements were made to the code, including the addition of new chemistry integrators, property evaluation schemes, new chemistry models and turbulence-chemistry interaction methodology. Validation of all new models and code improvements were also performed, while application of the code to the ZCET program and also the NPSS GEW combustor program were also performed. Several important items remain under development, including the NOx post processing, assumed PDF model development and chemical kinetic development. It is expected that this work will continue under the new grant.
Risk-adjusted hospital outcomes for children's surgery.

PubMed

Saito, Jacqueline M; Chen, Li Ern; Hall, Bruce L; Kraemer, Kari; Barnhart, Douglas C; Byrd, Claudia; Cohen, Mark E; Fei, Chunyuan; Heiss, Kurt F; Huffman, Kristopher; Ko, Clifford Y; Latus, Melissa; Meara, John G; Oldham, Keith T; Raval, Mehul V; Richards, Karen E; Shah, Rahul K; Sutton, Laura C; Vinocur, Charles D; Moss, R Lawrence

2013-09-01

BACKGROUND The American College of Surgeons National Surgical Quality Improvement Program-Pediatric was initiated in 2008 to drive quality improvement in children's surgery. Low mortality and morbidity in previous analyses limited differentiation of hospital performance. Participating institutions included children's units within general hospitals and free-standing children's hospitals. Cases selected by Current Procedural Terminology codes encompassed procedures within pediatric general, otolaryngologic, orthopedic, urologic, plastic, neurologic, thoracic, and gynecologic surgery. Trained personnel abstracted demographic, surgical profile, preoperative, intraoperative, and postoperative variables. Incorporating procedure-specific risk, hierarchical models for 30-day mortality and morbidities were developed with significant predictors identified by stepwise logistic regression. Reliability was estimated to assess the balance of information versus error within models. In 2011, 46 281 patients from 43 hospitals were accrued; 1467 codes were aggregated into 226 groupings. Overall mortality was 0.3%, composite morbidity 5.8%, and surgical site infection (SSI) 1.8%. Hierarchical models revealed outlier hospitals with above or below expected performance for composite morbidity in the entire cohort, pediatric abdominal subgroup, and spine subgroup; SSI in the entire cohort and pediatric abdominal subgroup; and urinary tract infection in the entire cohort. Based on reliability estimates, mortality discriminates performance poorly due to very low event rate; however, reliable model construction for composite morbidity and SSI that differentiate institutions is feasible. The National Surgical Quality Improvement Program-Pediatric expansion has yielded risk-adjusted models to differentiate hospital performance in composite and specific morbidities. However, mortality has low utility as a children's surgery performance indicator. Programmatic improvements have resulted in actionable data.
Assessment of the suitability of GOCE-based geoid models for the unification of the North American vertical datums

NASA Astrophysics Data System (ADS)

Amjadiparvar, Babak; Sideris, Michael

2015-04-01

Precise gravimetric geoid heights are required when the unification of vertical datums is performed using the Geodetic Boundary Value Problem (GBVP) approach. Five generations of Global Geopotential Models (GGMs) derived from Gravity field and steady-state Ocean Circulation Explorer (GOCE) observations have been computed and released so far (available via IAG's International Centre for Global Earth Models, ICGEM, http://icgem.gfz-potsdam.de/ICGEM/). The performance of many of these models with respect to geoid determination has been studied in order to select the best performing model to be used in height datum unification in North America. More specifically, Release-3, 4 and 5 of the GOCE-based global geopotential models have been evaluated using GNSS-levelling data as independent control values. Comparisons against EGM2008 show that each successive release improves upon the previous one, with Release-5 models showing an improvement over EGM2008 in Canada and CONUS between spherical harmonic degrees 100 and 210. In Alaska and Mexico, a considerable improvement over EGM2008 was brought by the Release-5 models when used up to spherical harmonic degrees of 250 and 280, respectively. The positive impact of the Release-5 models was also felt when a gravimetric geoid was computed using the GOCE-based GGMs together with gravity and topography data in Canada. This geoid model, with appropriately modified Stokes kernel between spherical harmonic degrees 190 and 260, performed better than the official Canadian gravimetric geoid model CGG2013, thus illustrating the advantages of using the latest release GOCE-based models for vertical datum unification in North America.
Performance Improvement [in HRD].

ERIC Educational Resources Information Center

1995

These four papers are from a symposium that was facilitated by Richard J. Torraco at the 1995 conference of the Academy of Human Resource Development (HRD). "Performance Technology--Isn't It Time We Found Some New Models?" (William J. Rothwell) reviews briefly two classic models, describes criteria for the high performance workplace…
A Composite Model for Employees' Performance Appraisal and Improvement

ERIC Educational Resources Information Center

Manoharan, T. R.; Muralidharan, C.; Deshmukh, S. G.

2012-01-01

Purpose: The purpose of this paper is to develop an innovative method of performance appraisal that will be useful for designing a structured training programme. Design/methodology/approach: Employees' performance appraisals are conducted using new approaches, namely data envelopment analysis and an integrated fuzzy model. Interpretive structural…
Study of G-S/D underlap for enhanced analog performance and RF/circuit analysis of UTB InAs-OI-Si MOSFET using NQS small signal model

NASA Astrophysics Data System (ADS)

Maity, Subir Kumar; Pandit, Soumya

2017-01-01

InGaAs (and its variant) appears to be a promising channel material for high-performance, low-power scaled CMOS applications due to its excellent carrier transport properties. However, MOS transistors made of this suffer from poor electrostatic integrity. In this work, we consider an underlap ultra thin body (UTB) InAs-on-Insulator n-channel MOS transistor, and study the effect of varying the gate-source/drain (G-S/D) underlap length on the analog performance of the device with the help of technology computer-aided design (TCAD) simulation, calibrated with Schrodinger-Poisson solver and experimental results. The underlap technique improves the gate electrostatic integrity which in turn improves the analog performance. We develop a non-quasi-static (NQS) small signal equivalent circuit model of the device which is used for study of the RF performance. With increase of the underlap length, the unity gain cut-off frequency degrades and the maximum oscillation frequency improves beyond a certain value of the underlap length. We further study the gain-frequency response of a common source amplifier using the NQS model, through SPICE simulation and observe that the voltage gain and the gain bandwidth improves.
Using the weighted area under the net benefit curve for decision curve analysis.

PubMed

Talluri, Rajesh; Shete, Sanjay

2016-07-18

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.
Optimal reconstruction for closed-loop ground-layer adaptive optics with elongated spots.

PubMed

Béchet, Clémentine; Tallon, Michel; Tallon-Bosc, Isabelle; Thiébaut, Éric; Le Louarn, Miska; Clare, Richard M

2010-11-01

The design of the laser-guide-star-based adaptive optics (AO) systems for the Extremely Large Telescopes requires careful study of the issue of elongated spots produced on Shack-Hartmann wavefront sensors. The importance of a correct modeling of the nonuniformity and correlations of the noise induced by this elongation has already been demonstrated for wavefront reconstruction. We report here on the first (to our knowledge) end-to-end simulations of closed-loop ground-layer AO with laser guide stars with such an improved noise model. The results are compared with the level of performance predicted by a classical noise model for the reconstruction. The performance is studied in terms of ensquared energy and confirms that, thanks to the improved noise model, central or side launching of the lasers does not affect the performance with respect to the laser guide stars' flux. These two launching schemes also perform similarly whatever the atmospheric turbulence strength.
A business case for quality improvement in addiction treatment: evidence from the NIATx collaborative.

PubMed

Quanbeck, Andrew R; Madden, Lynn; Edmundson, Eldon; Ford, James H; McConnell, K John; McCarty, Dennis; Gustafson, David H

2012-01-01

The Network for the Improvement of Addiction Treatment (NIATx) promotes treatment access and retention through a customer-focused quality improvement model. This paper explores the issue of the "business case" for quality improvement in addiction treatment from the provider's perspective. The business case model developed in this paper is based on case examples of early NIATx participants coupled with a review of the literature. Process inefficiencies indicated by long waiting times, high no-show rates, and low continuation rates cause underutilization of capacity and prevent optimal financial performance. By adopting customer-focused practices aimed at removing barriers to treatment access and retention, providers may be able to improve financial performance, increase staff retention, and gain long-term strategic advantage.
Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

PubMed Central

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian

2015-01-01

Background Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public’s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics. PMID:26307512
Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

PubMed

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

2015-08-25

Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.
Uncertain behaviours of integrated circuits improve computational performance.

PubMed

Yoshimura, Chihiro; Yamaoka, Masanao; Hayashi, Masato; Okuyama, Takuya; Aoki, Hidetaka; Kawarabayashi, Ken-ichi; Mizuno, Hiroyuki

2015-11-20

Improvements to the performance of conventional computers have mainly been achieved through semiconductor scaling; however, scaling is reaching its limitations. Natural phenomena, such as quantum superposition and stochastic resonance, have been introduced into new computing paradigms to improve performance beyond these limitations. Here, we explain that the uncertain behaviours of devices due to semiconductor scaling can improve the performance of computers. We prototyped an integrated circuit by performing a ground-state search of the Ising model. The bit errors of memory cell devices holding the current state of search occur probabilistically by inserting fluctuations into dynamic device characteristics, which will be actualised in the future to the chip. As a result, we observed more improvements in solution accuracy than that without fluctuations. Although the uncertain behaviours of devices had been intended to be eliminated in conventional devices, we demonstrate that uncertain behaviours has become the key to improving computational performance.
Flow Channel Influence of a Collision-Based Piezoelectric Jetting Dispenser on Jet Performance

PubMed Central

Deng, Guiling; Li, Junhui; Duan, Ji’an

2018-01-01

To improve the jet performance of a bi-piezoelectric jet dispenser, mathematical and simulation models were established according to the operating principle. In order to improve the accuracy and reliability of the simulation calculation, a viscosity model of the fluid was fitted to a fifth-order function with shear rate based on rheological test data, and the needle displacement model was fitted to a nine-order function with time based on real-time displacement test data. The results show that jet performance is related to the diameter of the nozzle outlet and the cone angle of the nozzle, and the impacts of the flow channel structure were confirmed. The approach of numerical simulation is confirmed by the testing results of droplet volume. It will provide a reliable simulation platform for mechanical collision-based jet dispensing and a theoretical basis for micro jet valve design and improvement. PMID:29677140
Visual Perceptual Learning and Models.

PubMed

Dosher, Barbara; Lu, Zhong-Lin

2017-09-15

Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.
How social information can improve estimation accuracy in human groups.

PubMed

Jayles, Bertrand; Kim, Hye-Rin; Escobedo, Ramón; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Theraulaz, Guy

2017-11-21

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects' sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance. Copyright © 2017 the Author(s). Published by PNAS.

How social information can improve estimation accuracy in human groups

PubMed Central

Jayles, Bertrand; Kim, Hye-rin; Cezera, Stéphane; Blanchet, Adrien; Kameda, Tatsuya; Sire, Clément; Theraulaz, Guy

2017-01-01

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects’ sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance. PMID:29118142
Project CARE: reducing wet weather overflows to improve beach water quality. Council Action in Respect of the Environment.

PubMed

Heijs, J; Wilkinson, D; Couriel, E

2002-01-01

The people who live in North Shore City (New Zealand) consider the beaches as their greatest asset. Following public outcry on frequent beach pollution caused by wet weather sewer overflows, Project CARE commenced in 1998 to plan the improvements to the city's separated wastewater and stormwater systems to protect the streams and beaches, particularly from a public health perspective. The investigation included building hydrological and hydraulic models to represent the wastewater and stormwater systems and a receiving waters model to simulate the impacts on the beaches. These models were later used to explore options for improvement. It was found that North Shore City has a very leaky wastewater system that is under capacity. The resulting wet weather overflows (12 per year on average) are the most important contributor to the problem although stormwater pollution alone is big enough to cause problems (at a smaller magnitude). A cost optimisation model (iterative process using performance/cost relationships) was then used to assist in identifying the optimal set of improvement works (storage, repair and increased capacity, wastewater treatment plant) to meet different performance targets and to cater for growth up to the year 2050. Cost Benefit analyses, looking at improvements in system performance and water quality, show diminishing returns for performance levels better than 2 overflows per year. The total costs that meet this target are estimated at almost NZ$300M (US$135M).
Experimental and analytical investigations to improve low-speed performance and stability and control characteristics of supersonic cruise fighter vehicles

NASA Technical Reports Server (NTRS)

Graham, A. B.

1977-01-01

Small- and large-scale models of supersonic cruise fighter vehicles were used to determine the effectiveness of airframe/propulsion integration concepts for improved low-speed performance and stability and control characteristics. Computer programs were used for engine/airframe sizing studies to yield optimum vehicle performance.
Variable selection with random forest: Balancing stability, performance, and interpretation in ecological and environmental modeling

EPA Science Inventory

Random forest (RF) is popular in ecological and environmental modeling, in part, because of its insensitivity to correlated predictors and resistance to overfitting. Although variable selection has been proposed to improve both performance and interpretation of RF models, it is u...
Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.
Inclusion of Highest Glasgow Coma Scale Motor Component Score in Mortality Risk Adjustment for Benchmarking of Trauma Center Performance.

PubMed

Gomez, David; Byrne, James P; Alali, Aziz S; Xiong, Wei; Hoeft, Chris; Neal, Melanie; Subacius, Harris; Nathens, Avery B

2017-12-01

The Glasgow Coma Scale (GCS) is the most widely used measure of traumatic brain injury (TBI) severity. Currently, the arrival GCS motor component (mGCS) score is used in risk-adjustment models for external benchmarking of mortality. However, there is evidence that the highest mGCS score in the first 24 hours after injury might be a better predictor of death. Our objective was to evaluate the impact of including the highest mGCS score on the performance of risk-adjustment models and subsequent external benchmarking results. Data were derived from the Trauma Quality Improvement Program analytic dataset (January 2014 through March 2015) and were limited to the severe TBI cohort (16 years or older, isolated head injury, GCS ≤8). Risk-adjustment models were created that varied in the mGCS covariates only (initial score, highest score, or both initial and highest mGCS scores). Model performance and fit, as well as external benchmarking results, were compared. There were 6,553 patients with severe TBI across 231 trauma centers included. Initial and highest mGCS scores were different in 47% of patients (n = 3,097). Model performance and fit improved when both initial and highest mGCS scores were included, as evidenced by improved C-statistic, Akaike Information Criterion, and adjusted R-squared values. Three-quarters of centers changed their adjusted odds ratio decile, 2.6% of centers changed outlier status, and 45% of centers exhibited a ≥0.5-SD change in the odds ratio of death after including highest mGCS score in the model. This study supports the concept that additional clinical information has the potential to not only improve the performance of current risk-adjustment models, but can also have a meaningful impact on external benchmarking strategies. Highest mGCS score is a good potential candidate for inclusion in additional models. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.
Design of a Kaplan turbine for a wide range of operating head -Curved draft tube design and model test verification-

NASA Astrophysics Data System (ADS)

KO, Pohan; MATSUMOTO, Kiyoshi; OHTAKE, Norio; DING, Hua

2016-11-01

As for turbomachine off-design performance improvement is challenging but critical for maximising the performing area. In this paper, a curved draft tube for a medium head Kaplan type hydro turbine is introduced and discussed for its significant effect on expanding operating head range. Without adding any extra structure and working fluid for swirl destruction and damping, a carefully designed outline shape of draft tube with the selected placement of center-piers successfully supresses the growth of turbulence eddy and the transport of the swirl to the outlet. Also, more kinetic energy is recovered and the head lost is improved. Finally, the model test results are also presented. The obvious performance improvement was found in the lower net head area, where the maximum efficiency improvement was measured up to 20% without compromising the best efficiency point. Additionally, this design results in a new draft tube more compact in size and so leads to better construction and manufacturing cost performance for prototype. The draft tube geometry parameter designing process was concerning the best efficiency point together with the off-design points covering various water net heads and discharges. The hydraulic performance and flow behavior was numerically previewed and visualized by solving Reynolds-Averaged Navier-Stokes equations with Shear Stress Transport turbulence model. The simulation was under the assumption of steady-state incompressible turbulence flow inside the flow passage, and the inlet boundary condition was the carefully simulated flow pattern from the runner outlet. For confirmation, the corresponding turbine efficiency performance of the entire operating area was verified by model test.
Improving Hydrological Simulations by Incorporating GRACE Data for Parameter Calibration

NASA Astrophysics Data System (ADS)

Bai, P.

2017-12-01

Hydrological model parameters are commonly calibrated by observed streamflow data. This calibration strategy is questioned when the modeled hydrological variables of interest are not limited to streamflow. Well-performed streamflow simulations do not guarantee the reliable reproduction of other hydrological variables. One of the reasons is that hydrological model parameters are not reasonably identified. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage change (TWSC) data provide an opportunity to constrain hydrological model parameterizations in combination with streamflow observations. We constructed a multi-objective calibration scheme based on GRACE-derived TWSC and streamflow observations, with the aim of improving the parameterizations of hydrological models. The multi-objective calibration scheme was compared with the traditional single-objective calibration scheme, which is based only on streamflow observations. Two monthly hydrological models were employed on 22 Chinese catchments with different hydroclimatic conditions. The model evaluation was performed using observed streamflows, GRACE-derived TWSC, and evapotranspiraiton (ET) estimates from flux towers and from the water balance approach. Results showed that the multi-objective calibration provided more reliable TWSC and ET simulations without significant deterioration in the accuracy of streamflow simulations than the single-objective calibration. In addition, the improvements of TWSC and ET simulations were more significant in relatively dry catchments than in relatively wet catchments. This study highlights the importance of including additional constraints besides streamflow observations in the parameter estimation to improve the performances of hydrological models.
The price of performance: a cost and performance analysis of the implementation of cell-free fetal DNA testing for Down syndrome in Ontario, Canada.

PubMed

Okun, N; Teitelbaum, M; Huang, T; Dewa, C S; Hoch, J S

2014-04-01

To examine the cost and performance implications of introducing cell-free fetal DNA (cffDNA) testing within modeled scenarios in a publicly funded Canadian provincial Down syndrome (DS) prenatal screening program. Two clinical algorithms were created: the first to represent the current screening program and the second to represent one that incorporates cffDNA testing. From these algorithms, eight distinct scenarios were modeled to examine: (1) the current program (no cffDNA), (2) the current program with first trimester screening (FTS) as the nuchal translucency-based primary screen (no cffDNA), (3) a program substituting current screening with primary cffDNA, (4) contingent cffDNA with current FTS performance, (5) contingent cffDNA at a fixed price to result in overall cost neutrality,(6) contingent cffDNA with an improved detection rate (DR) of FTS, (7) contingent cffDNA with higher uptake of FTS, and (8) contingent cffDNA with optimized FTS (higher uptake and improved DR). This modeling study demonstrates that introducing contingent cffDNA testing improves performance by increasing the number of cases of DS detected prenatally, and reducing the number of amniocenteses performed and concomitant iatrogenic pregnancy loss of pregnancies not affected by DS. Costs are modestly increased, although the cost per case of DS detected is decreased with contingent cffDNA testing. Contingent models of cffDNA testing can improve overall screening performance while maintaining the provision of an 11- to 13-week scan. Costs are modestly increased, but cost per prenatally detected case of DS is decreased. © 2013 John Wiley & Sons, Ltd.
Supplementation with macular carotenoids improves visual performance of transgenic mice.

PubMed

Li, Binxing; Rognon, Gregory T; Mattinson, Ty; Vachali, Preejith P; Gorusupudi, Aruna; Chang, Fu-Yen; Ranganathan, Arunkumar; Nelson, Kelly; George, Evan W; Frederick, Jeanne M; Bernstein, Paul S

2018-07-01

Carotenoid supplementation can improve human visual performance, but there is still no validated rodent model to test their effects on visual function in laboratory animals. We recently showed that mice deficient in β-carotene oxygenase 2 (BCO2) and/or β-carotene oxygenase 1 (BCO1) enzymes can accumulate carotenoids in their retinas, allowing us to investigate the effects of carotenoids on the visual performance of mice. Using OptoMotry, a device to measure visual function in rodents, we examined the effect of zeaxanthin, lutein, and β-carotene on visual performance of various BCO knockout mice. We then transgenically expressed the human zeaxanthin-binding protein GSTP1 (hGSTP1) in the rods of bco2 -/- mice to examine if delivering more zeaxanthin to retina will improve their visual function further. The visual performance of bco2 -/- mice fed with zeaxanthin or lutein was significantly improved relative to control mice fed with placebo beadlets. β-Carotene had no significant effect in bco2 -/- mice but modestly improved cone visual function of bco1 -/- mice. Expression of hGSTP1 in the rods of bco2 -/- mice resulted in a 40% increase of retinal zeaxanthin and further improvement of visual performance. This work demonstrates that these "macular pigment mice" may serve as animal models to study carotenoid function in the retina. Copyright © 2018 Elsevier Inc. All rights reserved.
Preconditioned augmented Lagrangian formulation for nearly incompressible cardiac mechanics.

PubMed

Campos, Joventino Oliveira; Dos Santos, Rodrigo Weber; Sundnes, Joakim; Rocha, Bernardo Martins

2018-04-01

Computational modeling of the heart is a subject of substantial medical and scientific interest, which may contribute to increase the understanding of several phenomena associated with cardiac physiological and pathological states. Modeling the mechanics of the heart have led to considerable insights, but it still represents a complex and a demanding computational problem, especially in a strongly coupled electromechanical setting. Passive cardiac tissue is commonly modeled as hyperelastic and is characterized by quasi-incompressible, orthotropic, and nonlinear material behavior. These factors are known to be very challenging for the numerical solution of the model. The near-incompressibility is known to cause numerical issues such as the well-known locking phenomenon and ill-conditioning of the stiffness matrix. In this work, the augmented Lagrangian method is used to handle the nearly incompressible condition. This approach can potentially improve computational performance by reducing the condition number of the stiffness matrix and thereby improving the convergence of iterative solvers. We also improve the performance of iterative solvers by the use of an algebraic multigrid preconditioner. Numerical results of the augmented Lagrangian method combined with a preconditioned iterative solver for a cardiac mechanics benchmark suite are presented to show its improved performance. Copyright © 2017 John Wiley & Sons, Ltd.
National Combustion Code Parallel Performance Enhancements

NASA Technical Reports Server (NTRS)

Quealy, Angela; Benyo, Theresa (Technical Monitor)

2002-01-01

The National Combustion Code (NCC) is being developed by an industry-government team for the design and analysis of combustion systems. The unstructured grid, reacting flow code uses a distributed memory, message passing model for its parallel implementation. The focus of the present effort has been to improve the performance of the NCC code to meet combustor designer requirements for model accuracy and analysis turnaround time. Improving the performance of this code contributes significantly to the overall reduction in time and cost of the combustor design cycle. This report describes recent parallel processing modifications to NCC that have improved the parallel scalability of the code, enabling a two hour turnaround for a 1.3 million element fully reacting combustion simulation on an SGI Origin 2000.
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.
Using technology-enhanced, cooperative, group-project learning for student comprehension and academic performance

NASA Astrophysics Data System (ADS)

Tlhoaele, Malefyane; Suhre, Cor; Hofman, Adriaan

2016-05-01

Cooperative learning may improve students' motivation, understanding of course concepts, and academic performance. This study therefore enhanced a cooperative, group-project learning technique with technology resources to determine whether doing so improved students' deep learning and performance. A sample of 118 engineering students, randomly divided into two groups, participated in this study and provided data through questionnaires issued before and after the experiment. The results, obtained through analyses of variance and structural equation modelling, reveal that technology-enhanced, cooperative, group-project learning improves students' comprehension and academic performance.
Physician groups' use of data from patient experience surveys.

PubMed

Friedberg, Mark W; SteelFisher, Gillian K; Karp, Melinda; Schneider, Eric C

2011-05-01

In Massachusetts, physician groups' performance on validated surveys of patient experience has been publicly reported since 2006. Groups also receive detailed reports of their own performance, but little is known about how physician groups have responded to these reports. To examine whether and how physician groups are using patient experience data to improve patient care. During 2008, we conducted semi-structured interviews with the leaders of 72 participating physician groups (out of 117 groups receiving patient experience reports). Based on leaders' responses, we identified three levels of engagement with patient experience reporting: no efforts to improve (level 1), efforts to improve only the performance of low-scoring physicians or practice sites (level 2), and efforts to improve group-wide performance (level 3). Groups' level of engagement and specific efforts to improve patient care. Forty-four group leaders (61%) reported group-wide improvement efforts (level 3), 16 (22%) reported efforts to improve only the performance of low-scoring physicians or practice sites (level 2), and 12 (17%) reported no performance improvement efforts (level 1). Level 3 groups were more likely than others to have an integrated medical group organizational model (84% vs. 31% at level 2 and 33% at level 1; P < 0.005) and to employ the majority of their physicians (69% vs. 25% and 20%; P < 0.05). Among level 3 groups, the most common targets for improvement were access, communication with patients, and customer service. The most commonly reported improvement initiatives were changing office workflow, providing additional training for nonclinical staff, and adopting or enhancing an electronic health record. Despite statewide public reporting, physician groups' use of patient experience data varied widely. Integrated organizational models were associated with greater engagement, and efforts to enhance clinicians' interpersonal skills were uncommon, with groups predominantly focusing on office workflow and support staff.
Spatially distributed modeling of soil organic carbon across China with improved accuracy

NASA Astrophysics Data System (ADS)

Li, Qi-quan; Zhang, Hao; Jiang, Xin-ye; Luo, Youlin; Wang, Chang-quan; Yue, Tian-xiang; Li, Bing; Gao, Xue-song

2017-06-01

There is a need for more detailed spatial information on soil organic carbon (SOC) for the accurate estimation of SOC stock and earth system models. As it is effective to use environmental factors as auxiliary variables to improve the prediction accuracy of spatially distributed modeling, a combined method (HASM_EF) was developed to predict the spatial pattern of SOC across China using high accuracy surface modeling (HASM), artificial neural network (ANN), and principal component analysis (PCA) to introduce land uses, soil types, climatic factors, topographic attributes, and vegetation cover as predictors. The performance of HASM_EF was compared with ordinary kriging (OK), OK, and HASM combined, respectively, with land uses and soil types (OK_LS and HASM_LS), and regression kriging combined with land uses and soil types (RK_LS). Results showed that HASM_EF obtained the lowest prediction errors and the ratio of performance to deviation (RPD) presented the relative improvements of 89.91%, 63.77%, 55.86%, and 42.14%, respectively, compared to the other four methods. Furthermore, HASM_EF generated more details and more realistic spatial information on SOC. The improved performance of HASM_EF can be attributed to the introduction of more environmental factors, to explicit consideration of the multicollinearity of selected factors and the spatial nonstationarity and nonlinearity of relationships between SOC and selected factors, and to the performance of HASM and ANN. This method may play a useful tool in providing more precise spatial information on soil parameters for global modeling across large areas.
Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.
Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate.

PubMed

Shimadera, Hikari; Hayami, Hiroshi; Chatani, Satoru; Morino, Yu; Mori, Yasuaki; Morikawa, Tazuko; Yamaji, Kazuyo; Ohara, Toshimasa

2014-04-01

Improvement of air quality models is required so that they can be utilized to design effective control strategies for fine particulate matter (PM2.5). The Community Multiscale Air Quality modeling system was applied to the Greater Tokyo Area of Japan in winter 2010 and summer 2011. The model results were compared with observed concentrations of PM2.5 sulfate (SO4(2-)), nitrate (NO3(-)) and ammonium, and gaseous nitric acid (HNO3) and ammonia (NH3). The model approximately reproduced PM2.5 SO4(2-) concentration, but clearly overestimated PM2.5 NO3(-) concentration, which was attributed to overestimation of production of ammonium nitrate (NH4NO3). This study conducted sensitivity analyses of factors associated with the model performance for PM2.5 NO3(-) concentration, including temperature and relative humidity, emission of nitrogen oxides, seasonal variation of NH3 emission, HNO3 and NH3 dry deposition velocities, and heterogeneous reaction probability of dinitrogen pentoxide. Change in NH3 emission directly affected NH3 concentration, and substantially affected NH4NO3 concentration. Higher dry deposition velocities of HNO3 and NH3 led to substantial reductions of concentrations of the gaseous species and NH4NO3. Because uncertainties in NH3 emission and dry deposition processes are probably large, these processes may be key factors for improvement of the model performance for PM2.5 NO3(-). The Community Multiscale Air Quality modeling system clearly overestimated the concentration of fine particulate nitrate in the Greater Tokyo Area of Japan, which was attributed to overestimation of production of ammonium nitrate. Sensitivity analyses were conducted for factors associated with the model performance for nitrate. Ammonia emission and dry deposition of nitric acid and ammonia may be key factors for improvement of the model performance.
Does adding clinical data to administrative data improve agreement among hospital quality measures?

PubMed

Hanchate, Amresh D; Stolzmann, Kelly L; Rosen, Amy K; Fink, Aaron S; Shwartz, Michael; Ash, Arlene S; Abdulkerim, Hassen; Pugh, Mary Jo V; Shokeen, Priti; Borzecki, Ann

2017-09-01

Hospital performance measures based on patient mortality and readmission have indicated modest rates of agreement. We examined if combining clinical data on laboratory tests and vital signs with administrative data leads to improved agreement with each other, and with other measures of hospital performance in the nation's largest integrated health care system. We used patient-level administrative and clinical data, and hospital-level data on quality indicators, for 2007-2010 from the Veterans Health Administration (VA). For patients admitted for acute myocardial infarction (AMI), heart failure (HF) and pneumonia we examined changes in hospital performance on 30-d mortality and 30-d readmission rates as a result of adding clinical data to administrative data. We evaluated whether this enhancement yielded improved measures of hospital quality, based on concordance with other hospital quality indicators. For 30-d mortality, data enhancement improved model performance, and significantly changed hospital performance profiles; for 30-d readmission, the impact was modest. Concordance between enhanced measures of both outcomes, and with other hospital quality measures - including Joint Commission process measures, VA Surgical Quality Improvement Program (VASQIP) mortality and morbidity, and case volume - remained poor. Adding laboratory tests and vital signs to measure hospital performance on mortality and readmission did not improve the poor rates of agreement across hospital quality indicators in the VA. Efforts to improve risk adjustment models should continue; however, evidence of validation should precede their use as reliable measures of quality. Published by Elsevier Inc.
Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Performance model-directed data sieving for high-performance I/O

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

Chen, Yong; Lu, Yin; Amritkar, Prathamesh

2014-09-10

Many scientific computing applications and engineering simulations exhibit noncontiguous I/O access patterns. Data sieving is an important technique to improve the performance of noncontiguous I/O accesses by combining small and noncontiguous requests into a large and contiguous request. It has been proven effective even though more data are potentially accessed than demanded. In this study, we propose a new data sieving approach namely performance model-directed data sieving, or PMD data sieving in short. It improves the existing data sieving approach from two aspects: (1) dynamically determines when it is beneficial to perform data sieving; and (2) dynamically determines how tomore » perform data sieving if beneficial. It improves the performance of the existing data sieving approach considerably and reduces the memory consumption as verified by both theoretical analysis and experimental results. Given the importance of supporting noncontiguous accesses effectively and reducing the memory pressure in a large-scale system, the proposed PMD data sieving approach in this research holds a great promise and will have an impact on high-performance I/O systems.« 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.
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
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.
Sootblowing optimization for improved boiler performance

DOEpatents

James, John Robert; McDermott, John; Piche, Stephen; Pickard, Fred; Parikh, Neel J.

2012-12-25

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.
Sootblowing optimization for improved boiler performance

DOEpatents

James, John Robert; McDermott, John; Piche, Stephen; Pickard, Fred; Parikh, Neel J

2013-07-30

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.
The impact of a freshman academy on science performance of first-time ninth-grade students at one Georgia high school

NASA Astrophysics Data System (ADS)

Daniel, Vivian Summerour

The purpose of this within-group experimental study was to find out to what extent ninth-grade students improved their science performance beyond their middle school science performance at one Georgia high school utilizing a freshman academy model. Freshman academies have been recognized as a useful tool for increasing academic performance among ninth-grade students because they address a range of academic support initiatives tailored to improve academic performance among ninth-grade students. The talent development model developed by Legters, Balfanz, Jordan, and McPartland (2002) has served as a foundational standard for many ninth grade academy programs. A cornerstone feature of this model is the creation of small learning communities used to increase ninth-grade student performance. Another recommendation was to offer credit recovery opportunities for ninth graders along with creating parent and community involvement activities to increase academic success among ninth-grade students. While the site's program included some of the initiatives outlined by the talent development model, it did not utilize all of them. The study concluded that the academy did not show a definitive increase in academic performance among ninth-grade students since most students stayed within their original performance category.
Exemplary Care and Learning Sites: A Model for Achieving Continual Improvement in Care and Learning in the Clinical Setting

PubMed Central

Ogrinc, Greg; Hoffman, Kimberly G.; Stevenson, Katherine M.; Shalaby, Marc; Beard, Albertine S.; Thörne, Karin E.; Coleman, Mary T.; Baum, Karyn D.

2016-01-01

Problem Current models of health care quality improvement do not explicitly describe the role of health professions education. The authors propose the Exemplary Care and Learning Site (ECLS) model as an approach to achieving continual improvement in care and learning in the clinical setting. Approach From 2008–2012, an iterative, interactive process was used to develop the ECLS model and its core elements—patients and families informing process changes; trainees engaging both in care and the improvement of care; leaders knowing, valuing, and practicing improvement; data transforming into useful information; and health professionals competently engaging both in care improvement and teaching about care improvement. In 2012–2013, a three-part feasibility test of the model, including a site self-assessment, an independent review of each site’s ratings, and implementation case stories, was conducted at six clinical teaching sites (in the United States and Sweden). Outcomes Site leaders reported the ECLS model provided a systematic approach toward improving patient (and population) outcomes, system performance, and professional development. Most sites found it challenging to incorporate the patients and families element. The trainee element was strong at four sites. The leadership and data elements were self-assessed as the most fully developed. The health professionals element exhibited the greatest variability across sites. Next Steps The next test of the model should be prospective, linked to clinical and educa tional outcomes, to evaluate whether it helps care delivery teams, educators, and patients and families take action to achieve better patient (and population) outcomes, system performance, and professional development. PMID:26760058
OAO battery data analysis

NASA Technical Reports Server (NTRS)

Gaston, S.; Wertheim, M.; Orourke, J. A.

1973-01-01

Summary, consolidation and analysis of specifications, manufacturing process and test controls, and performance results for OAO-2 and OAO-3 lot 20 Amp-Hr sealed nickel cadmium cells and batteries are reported. Correlation of improvements in control requirements with performance is a key feature. Updates for a cell/battery computer model to improve performance prediction capability are included. Applicability of regression analysis computer techniques to relate process controls to performance is checked.
Vodcasts and Active-Learning Exercises in a “Flipped Classroom” Model of a Renal Pharmacotherapy Module

PubMed Central

Fox, Jeremy

2012-01-01

Objective. To implement a “flipped classroom” model for a renal pharmacotherapy topic module and assess the impact on pharmacy students’ performance and attitudes. Design. Students viewed vodcasts (video podcasts) of lectures prior to the scheduled class and then discussed interactive cases of patients with end-stage renal disease in class. A process-oriented guided inquiry learning (POGIL) activity was developed and implemented that complemented, summarized, and allowed for application of the material contained in the previously viewed lectures. Assessment. Students’ performance on the final examination significantly improved compared to performance of students the previous year who completed the same module in a traditional classroom setting. Students’ opinions of the POGIL activity and the flipped classroom instructional model were mostly positive. Conclusion. Implementing a flipped classroom model to teach a renal pharmacotherapy module resulted in improved student performance and favorable student perceptions about the instructional approach. Some of the factors that may have contributed to students’ improved scores included: student mediated contact with the course material prior to classes, benchmark and formative assessments administered during the module, and the interactive class activities. PMID:23275661
The Five-Factor Model Personality Assessment for Improved Student Design Team Performance

ERIC Educational Resources Information Center

Ogot, Madara; Okudan, Gul E.

2006-01-01

Researchers have long noted the correlation of various personality traits and team performance. Studies relating aggregate team personality traits to team performance are scattered in the literature and may not always be relevant to engineering design teams. This paper synthesizes the results from applicable Five-Factor Model (FFM)-based…
Effects of Prompting Multiple Solutions for Modelling Problems on Students' Performance

ERIC Educational Resources Information Center

Schukajlow, Stanislaw; Krug, André; Rakoczy, Katrin

2015-01-01

Prompting students to construct multiple solutions for modelling problems with vague conditions has been found to be an effective way to improve students' performance on interest-oriented measures. In the current study, we investigated the influence of this teaching element on students' performance. To assess the impact of prompting multiple…
MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA

USGS Publications Warehouse

Lestina, Jordan; Cook, Maxwell; Kumar, Sunil; Morisette, Jeffrey T.; Ode, Paul J.; Peirs, Frank

2016-01-01

Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0–5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.
Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.
TORNADO-WARNING PERFORMANCE IN THE PAST AND FUTURE: A Perspective from Signal Detection Theory.

NASA Astrophysics Data System (ADS)

Brooks, Harold E.

2004-06-01

Changes over the years in tornado-warning performance in the United States can be modeled from the perspective of signal detection theory. From this view, it can be seen that there have been distinct periods of change in performance, most likely associated with deployment of radars, and changes in scientific understanding and training. The model also makes it clear that improvements in the false alarm ratio can only occur at the cost of large decreases in the probability of detection, or with large improvements in the overall quality of the warning system.
Detection of no-model input-output pairs in closed-loop systems.

PubMed

Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio

2017-11-01

The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Improving sixth year medical students' performance in knee arthrocentesis using a synthetic knee model.

PubMed

Chiowchanwisawakit, Praveena; Ratanarat, Ranistha; Srinonprasert, Varalak

2015-09-01

A knee arthrocentesis (KA) workshop using synthetic knee model was arranged for all sixth-year medical students (MS) in our institute to ensure equity in receiving training. We evaluated confidence level and knowledge of KA and synovial fluid analysis testing pre- and post-workshop for MS. The workshop was divided into two parts. The first part was to provide knowledge in arthrocentesis and synovial fluid interpretation and the second was a practice session on the synthetic model under supervision. This is a report of pre-and post-workshop self-evaluation about the confidence in performing KA (0-10 scales), improvement of knowledge in KA, and synovial fluid analysis earned from attending the workshop. Pearson χ(2) test or Fisher's exact test was used to compare categorical variables, where appropriate. There were 247 MS attended and 228 (92.3%) evaluated the workshops. Ninety-six (42.1%) MS had experience in KA prior to this workshop. The mean (SD) levels of confidence in performing the procedure before and after the workshop were 3.6 (2.5) and 7.5 (1.7), respectively, P < 0.001. Improvement was shown regardless of previous exposure to KA. Knowledge of appropriate testing for synovial fluid was significantly improved in all items explored after the workshop and extended to the better scores earned from a competency examination. A hands-on structured workshop using a synthetic knee model for KA is a successful model for improving medical students' confidence in performing the procedure with evidence of sustaining knowledge in short-term follow-up. © 2015 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd.
Improving Learner Handovers in Medical Education.

PubMed

Warm, Eric J; Englander, Robert; Pereira, Anne; Barach, Paul

2017-07-01

Multiple studies have demonstrated that the information included in the Medical Student Performance Evaluation fails to reliably predict medical students' future performance. This faulty transfer of information can lead to harm when poorly prepared students fail out of residency or, worse, are shuttled through the medical education system without an honest accounting of their performance. Such poor learner handovers likely arise from two root causes: (1) the absence of agreed-on outcomes of training and/or accepted assessments of those outcomes, and (2) the lack of standardized ways to communicate the results of those assessments. To improve the current learner handover situation, an authentic, shared mental model of competency is needed; high-quality tools to assess that competency must be developed and tested; and transparent, reliable, and safe ways to communicate this information must be created.To achieve these goals, the authors propose using a learner handover process modeled after a patient handover process. The CLASS model includes a description of the learner's Competency attainment, a summary of the Learner's performance, an Action list and statement of Situational awareness, and Synthesis by the receiving program. This model also includes coaching oriented towards improvement along the continuum of education and care. Just as studies have evaluated patient handover models using metrics that matter most to patients, studies must evaluate this learner handover model using metrics that matter most to providers, patients, and learners.
An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-05-01

Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
Making a Difference.

ERIC Educational Resources Information Center

Panza, Carol M.

2001-01-01

Suggests that human performance technologists need to have an analysis approach to support the development of an appropriate set of improvement recommendations for clients and then move to an action plan to help them see results. Presents a performance improvement model and a systematic approach that considers organizational context, ownership,…

Data envelopment analysis in service quality evaluation: an empirical study

NASA Astrophysics Data System (ADS)

Najafi, Seyedvahid; Saati, Saber; Tavana, Madjid

2015-09-01

Service quality is often conceptualized as the comparison between service expectations and the actual performance perceptions. It enhances customer satisfaction, decreases customer defection, and promotes customer loyalty. Substantial literature has examined the concept of service quality, its dimensions, and measurement methods. We introduce the perceived service quality index (PSQI) as a single measure for evaluating the multiple-item service quality construct based on the SERVQUAL model. A slack-based measure (SBM) of efficiency with constant inputs is used to calculate the PSQI. In addition, a non-linear programming model based on the SBM is proposed to delineate an improvement guideline and improve service quality. An empirical study is conducted to assess the applicability of the method proposed in this study. A large number of studies have used DEA as a benchmarking tool to measure service quality. These models do not propose a coherent performance evaluation construct and consequently fail to deliver improvement guidelines for improving service quality. The DEA models proposed in this study are designed to evaluate and improve service quality within a comprehensive framework and without any dependency on external data.
Evaluating the Performance Improvement Preferences of Disability Service Managers: An Exploratory Study Using Gilbert's Behavior Engineering Model

ERIC Educational Resources Information Center

Wooderson, John R.; Cuskelly, Monica; Meyer, Kim A.

2017-01-01

Background: Front-line managers play an important role in managing the performance of staff working in services for people with intellectual disability, but little is known about the practices they prefer to use to improve staff performance and whether these align with what research has shown to be effective. Method: This study comprised two…
A Complete Procedure for Predicting and Improving the Performance of HAWT's

NASA Astrophysics Data System (ADS)

Al-Abadi, Ali; Ertunç, Özgür; Sittig, Florian; Delgado, Antonio

2014-06-01

A complete procedure for predicting and improving the performance of the horizontal axis wind turbine (HAWT) has been developed. The first process is predicting the power extracted by the turbine and the derived rotor torque, which should be identical to that of the drive unit. The BEM method and a developed post-stall treatment for resolving stall-regulated HAWT is incorporated in the prediction. For that, a modified stall-regulated prediction model, which can predict the HAWT performance over the operating range of oncoming wind velocity, is derived from existing models. The model involves radius and chord, which has made it more general in applications for predicting the performance of different scales and rotor shapes of HAWTs. The second process is modifying the rotor shape by an optimization process, which can be applied to any existing HAWT, to improve its performance. A gradient- based optimization is used for adjusting the chord and twist angle distribution of the rotor blade to increase the extraction of the power while keeping the drive torque constant, thus the same drive unit can be kept. The final process is testing the modified turbine to predict its enhanced performance. The procedure is applied to NREL phase-VI 10kW as a baseline turbine. The study has proven the applicability of the developed model in predicting the performance of the baseline as well as the optimized turbine. In addition, the optimization method has shown that the power coefficient can be increased while keeping same design rotational speed.
Theoretical vibro-acoustic modeling of acoustic noise transmission through aircraft windows

NASA Astrophysics Data System (ADS)

Aloufi, Badr; Behdinan, Kamran; Zu, Jean

2016-06-01

In this paper, a fully vibro-acoustic model for sound transmission across a multi-pane aircraft window is developed. The proposed model is efficiently applied for a set of window models to perform extensive theoretical parametric studies. The studied window configurations generally simulate the passenger window designs of modern aircraft classes which have an exterior multi-Plexiglas pane, an interior single acrylic glass pane and a dimmable glass ("smart" glass), all separated by thin air cavities. The sound transmission loss (STL) characteristics of three different models, triple-, quadruple- and quintuple-paned windows identical in size and surface density, are analyzed for improving the acoustic insulation performances. Typical results describing the influence of several system parameters, such as the thicknesses, number and spacing of the window panes, on the transmission loss are then investigated. In addition, a comparison study is carried out to evaluate the acoustic reduction capability of each window model. The STL results show that the higher frequencies sound transmission loss performance can be improved by increasing the number of window panels, however, the low frequency performance is decreased, particularly at the mass-spring resonances.
Realization of process improvement at a diagnostic radiology department with aid of simulation modeling.

PubMed

Oh, Hong-Choon; Toh, Hong-Guan; Giap Cheong, Eddy Seng

2011-11-01

Using the classical process improvement framework of Plan-Do-Study-Act (PDSA), the diagnostic radiology department of a tertiary hospital identified several patient cycle time reduction strategies. Experimentation of these strategies (which included procurement of new machines, hiring of new staff, redesign of queue system, etc.) through pilot scale implementation was impractical because it might incur substantial expenditure or be operationally disruptive. With this in mind, simulation modeling was used to test these strategies via performance of "what if" analyses. Using the output generated by the simulation model, the team was able to identify a cost-free cycle time reduction strategy, which subsequently led to a reduction of patient cycle time and achievement of a management-defined performance target. As healthcare professionals work continually to improve healthcare operational efficiency in response to rising healthcare costs and patient expectation, simulation modeling offers an effective scientific framework that can complement established process improvement framework like PDSA to realize healthcare process enhancement. © 2011 National Association for Healthcare Quality.
Improvements in the Scalability of the NASA Goddard Multiscale Modeling Framework for Hurricane Climate Studies

NASA Technical Reports Server (NTRS)

Shen, Bo-Wen; Tao, Wei-Kuo; Chern, Jiun-Dar

2007-01-01

Improving our understanding of hurricane inter-annual variability and the impact of climate change (e.g., doubling CO2 and/or global warming) on hurricanes brings both scientific and computational challenges to researchers. As hurricane dynamics involves multiscale interactions among synoptic-scale flows, mesoscale vortices, and small-scale cloud motions, an ideal numerical model suitable for hurricane studies should demonstrate its capabilities in simulating these interactions. The newly-developed multiscale modeling framework (MMF, Tao et al., 2007) and the substantial computing power by the NASA Columbia supercomputer show promise in pursuing the related studies, as the MMF inherits the advantages of two NASA state-of-the-art modeling components: the GEOS4/fvGCM and 2D GCEs. This article focuses on the computational issues and proposes a revised methodology to improve the MMF's performance and scalability. It is shown that this prototype implementation enables 12-fold performance improvements with 364 CPUs, thereby making it more feasible to study hurricane climate.
Are well functioning civil registration and vital statistics systems associated with better health outcomes?

PubMed

Phillips, David E; AbouZahr, Carla; Lopez, Alan D; Mikkelsen, Lene; de Savigny, Don; Lozano, Rafael; Wilmoth, John; Setel, Philip W

2015-10-03

In this Series paper, we examine whether well functioning civil registration and vital statistics (CRVS) systems are associated with improved population health outcomes. We present a conceptual model connecting CRVS to wellbeing, and describe an ecological association between CRVS and health outcomes. The conceptual model posits that the legal identity that civil registration provides to individuals is key to access entitlements and services. Vital statistics produced by CRVS systems provide essential information for public health policy and prevention. These outcomes benefit individuals and societies, including improved health. We use marginal linear models and lag-lead analysis to measure ecological associations between a composite metric of CRVS performance and three health outcomes. Results are consistent with the conceptual model: improved CRVS performance coincides with improved health outcomes worldwide in a temporally consistent manner. Investment to strengthen CRVS systems is not only an important goal for individuals and societies, but also a development imperative that is good for health. Copyright © 2015 Elsevier Ltd. All rights reserved.
Atomic scale simulations for improved CRUD and fuel performance modeling

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

Andersson, Anders David Ragnar; Cooper, Michael William Donald

2017-01-06

A more mechanistic description of fuel performance codes can be achieved by deriving models and parameters from atomistic scale simulations rather than fitting models empirically to experimental data. The same argument applies to modeling deposition of corrosion products on fuel rods (CRUD). Here are some results from publications in 2016 carried out using the CASL allocation at LANL.
An exponential filter model predicts lightness illusions

PubMed Central

Zeman, Astrid; Brooks, Kevin R.; Ghebreab, Sennay

2015-01-01

Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects. PMID:26157381
Integrated water system simulation by considering hydrological and biogeochemical processes: model development, with parameter sensitivity and autocalibration

NASA Astrophysics Data System (ADS)

Zhang, Y. Y.; Shao, Q. X.; Ye, A. Z.; Xing, H. T.; Xia, J.

2016-02-01

Integrated water system modeling is a feasible approach to understanding severe water crises in the world and promoting the implementation of integrated river basin management. In this study, a classic hydrological model (the time variant gain model: TVGM) was extended to an integrated water system model by coupling multiple water-related processes in hydrology, biogeochemistry, water quality, and ecology, and considering the interference of human activities. A parameter analysis tool, which included sensitivity analysis, autocalibration and model performance evaluation, was developed to improve modeling efficiency. To demonstrate the model performances, the Shaying River catchment, which is the largest highly regulated and heavily polluted tributary of the Huai River basin in China, was selected as the case study area. The model performances were evaluated on the key water-related components including runoff, water quality, diffuse pollution load (or nonpoint sources) and crop yield. Results showed that our proposed model simulated most components reasonably well. The simulated daily runoff at most regulated and less-regulated stations matched well with the observations. The average correlation coefficient and Nash-Sutcliffe efficiency were 0.85 and 0.70, respectively. Both the simulated low and high flows at most stations were improved when the dam regulation was considered. The daily ammonium-nitrogen (NH4-N) concentration was also well captured with the average correlation coefficient of 0.67. Furthermore, the diffuse source load of NH4-N and the corn yield were reasonably simulated at the administrative region scale. This integrated water system model is expected to improve the simulation performances with extension to more model functionalities, and to provide a scientific basis for the implementation in integrated river basin managements.
Staffs’ and managers’ perceptions of how and when discrete event simulation modelling can be used as a decision support in quality improvement: a focus group discussion study at two hospital settings in Sweden

PubMed Central

Hvitfeldt-Forsberg, Helena; Mazzocato, Pamela; Glaser, Daniel; Keller, Christina; Unbeck, Maria

2017-01-01

Objective To explore healthcare staffs’ and managers’ perceptions of how and when discrete event simulation modelling can be used as a decision support in improvement efforts. Design Two focus group discussions were performed. Setting Two settings were included: a rheumatology department and an orthopaedic section both situated in Sweden. Participants Healthcare staff and managers (n=13) from the two settings. Interventions Two workshops were performed, one at each setting. Workshops were initiated by a short introduction to simulation modelling. Results from the respective simulation model were then presented and discussed in the following focus group discussion. Results Categories from the content analysis are presented according to the following research questions: how and when simulation modelling can assist healthcare improvement? Regarding how, the participants mentioned that simulation modelling could act as a tool for support and a way to visualise problems, potential solutions and their effects. Regarding when, simulation modelling could be used both locally and by management, as well as a pedagogical tool to develop and test innovative ideas and to involve everyone in the improvement work. Conclusions Its potential as an information and communication tool and as an instrument for pedagogic work within healthcare improvement render a broader application and value of simulation modelling than previously reported. PMID:28588107
Development and Validity of a Silicone Renal Tumor Model for Robotic Partial Nephrectomy Training.

PubMed

Monda, Steven M; Weese, Jonathan R; Anderson, Barrett G; Vetter, Joel M; Venkatesh, Ramakrishna; Du, Kefu; Andriole, Gerald L; Figenshau, Robert S

2018-04-01

To provide a training tool to address the technical challenges of robot-assisted laparoscopic partial nephrectomy, we created silicone renal tumor models using 3-dimensional printed molds of a patient's kidney with a mass. In this study, we assessed the face, content, and construct validity of these models. Surgeons of different training levels completed 4 simulations on silicone renal tumor models. Participants were surveyed on the usefulness and realism of the model as a training tool. Performance was measured using operation-specific metrics, self-reported operative demands (NASA Task Load Index [NASA TLX]), and blinded expert assessment (Global Evaluative Assessment of Robotic Surgeons [GEARS]). Twenty-four participants included attending urologists, endourology fellows, urology residents, and medical students. Post-training surveys of expert participants yielded mean results of 79.2 on the realism of the model's overall feel and 90.2 on the model's overall usefulness for training. Renal artery clamp times and GEARS scores were significantly better in surgeons further in training (P ≤.005 and P ≤.025). Renal artery clamp times, preserved renal parenchyma, positive margins, NASA TLX, and GEARS scores were all found to improve across trials (P <.001, P = .025, P = .024, P ≤.020, and P ≤.006, respectively). Face, content, and construct validity were demonstrated in the use of a silicone renal tumor model in a cohort of surgeons of different training levels. Expert participants deemed the model useful and realistic. Surgeons of higher training levels performed better than less experienced surgeons in various study metrics, and improvements within individuals were observed over sequential trials. Future studies should aim to assess model predictive validity, namely, the association between model performance improvements and improvements in live surgery. Copyright © 2018 Elsevier Inc. All rights reserved.
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.
A Mixed-Methods Study of the Transformation Model for Rapid Improvement of Low Achieving Rural Schools

ERIC Educational Resources Information Center

Atkinson Duina, Angela

2013-01-01

New regulations attached to ARRA funding of federal School Improvement Fund grants aimed at producing rapid turnaround of low performing schools were highly criticized as unsuitable for rural schools. This mixed-methods study looked at the implementation of the School Improvement Fund Transformation Model in two rural Maine high schools during the…
High-performance heat pipes for heat recovery applications

NASA Technical Reports Server (NTRS)

Saaski, E. W.; Hartl, J. H.

1980-01-01

Methods to improve the performance of reflux heat pipes for heat recovery applications were examined both analytically and experimentally. Various models for the estimation of reflux heat pipe transport capacity were surveyed in the literature and compared with experimental data. A high transport capacity reflux heat pipe was developed that provides up to a factor of 10 capacity improvement over conventional open tube designs; analytical models were developed for this device and incorporated into a computer program HPIPE. Good agreement of the model predictions with data for R-11 and benzene reflux heat pipes was obtained.
Finite element modelling of radial lentotomy cuts to improve the accommodation performance of the human lens.

PubMed

Burd, H J; Wilde, G S

2016-04-01

The use of a femtosecond laser to form planes of cavitation bubbles within the ocular lens has been proposed as a potential treatment for presbyopia. The intended purpose of these planes of cavitation bubbles (referred to in this paper as 'cutting planes') is to increase the compliance of the lens, with a consequential increase in the amplitude of accommodation. The current paper describes a computational modelling study, based on three-dimensional finite element analysis, to investigate the relationship between the geometric arrangement of the cutting planes and the resulting improvement in lens accommodation performance. The study is limited to radial cutting planes. The effectiveness of a variety of cutting plane geometries was investigated by means of modelling studies conducted on a 45-year human lens. The results obtained from the analyses depend on the particular modelling procedures that are employed. When the lens substance is modelled as an incompressible material, radial cutting planes are found to be ineffective. However, when a poroelastic model is employed for the lens substance, radial cuts are shown to cause an increase in the computed accommodation performance of the lens. In this case, radial cuts made in the peripheral regions of the lens have a relatively small influence on the accommodation performance of the lens; the lentotomy process is seen to be more effective when cuts are made near to the polar axis. When the lens substance is modelled as a poroelastic material, the computational results suggest that useful improvements in lens accommodation performance can be achieved, provided that the radial cuts are extended to the polar axis. Radial cuts are ineffective when the lens substance is modelled as an incompressible material. Significant challenges remain in developing a safe and effective surgical procedure based on this lentotomy technique.
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.
Improved Range Estimation Model for Three-Dimensional (3D) Range Gated Reconstruction

PubMed Central

Chua, Sing Yee; Guo, Ningqun; Tan, Ching Seong; Wang, Xin

2017-01-01

Accuracy is an important measure of system performance and remains a challenge in 3D range gated reconstruction despite the advancement in laser and sensor technology. The weighted average model that is commonly used for range estimation is heavily influenced by the intensity variation due to various factors. Accuracy improvement in term of range estimation is therefore important to fully optimise the system performance. In this paper, a 3D range gated reconstruction model is derived based on the operating principles of range gated imaging and time slicing reconstruction, fundamental of radiant energy, Laser Detection And Ranging (LADAR), and Bidirectional Reflection Distribution Function (BRDF). Accordingly, a new range estimation model is proposed to alleviate the effects induced by distance, target reflection, and range distortion. From the experimental results, the proposed model outperforms the conventional weighted average model to improve the range estimation for better 3D reconstruction. The outcome demonstrated is of interest to various laser ranging applications and can be a reference for future works. PMID:28872589
Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

NASA Astrophysics Data System (ADS)

Aksoy, Hafzullah; Dahamsheh, Ahmad

2018-07-01

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.
Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.

PubMed

Temple, Michael W; Lehmann, Christoph U; Fabbri, Daniel

2016-01-01

Discharging patients from the Neonatal Intensive Care Unit (NICU) can be delayed for non-medical reasons including the procurement of home medical equipment, parental education, and the need for children's services. We previously created a model to identify patients that will be medically ready for discharge in the subsequent 2-10 days. In this study we use Natural Language Processing to improve upon that model and discern why the model performed poorly on certain patients. We retrospectively examined the text of the Assessment and Plan section from daily progress notes of 4,693 patients (103,206 patient-days) from the NICU of a large, academic children's hospital. A matrix was constructed using words from NICU notes (single words and bigrams) to train a supervised machine learning algorithm to determine the most important words differentiating poorly performing patients compared to well performing patients in our original discharge prediction model. NLP using a bag of words (BOW) analysis revealed several cohorts that performed poorly in our original model. These included patients with surgical diagnoses, pulmonary hypertension, retinopathy of prematurity, and psychosocial issues. The BOW approach aided in cohort discovery and will allow further refinement of our original discharge model prediction. Adequately identifying patients discharged home on g-tube feeds alone could improve the AUC of our original model by 0.02. Additionally, this approach identified social issues as a major cause for delayed discharge. A BOW analysis provides a method to improve and refine our NICU discharge prediction model and could potentially avoid over 900 (0.9%) hospital days.

Achieving continuous improvement in laboratory organization through performance measurements: a seven-year experience.

PubMed

Salinas, Maria; López-Garrigós, Maite; Gutiérrez, Mercedes; Lugo, Javier; Sirvent, Jose Vicente; Uris, Joaquin

2010-01-01

Laboratory performance can be measured using a set of model key performance indicators (KPIs). The design and implementation of KPIs are important issues. KPI results from 7 years are reported and their implementation, monitoring, objectives, interventions, result reporting and delivery are analyzed. The KPIs of the entire laboratory process were obtained using Laboratory Information System (LIS) registers. These were collected automatically using a data warehouse application, spreadsheets and external quality program reports. Customer satisfaction was assessed using surveys. Nine model laboratory KPIs were proposed and measured. The results of some examples of KPIs used in our laboratory are reported. Their corrective measurements or the implementation of objectives led to improvement in the associated KPIs results. Measurement of laboratory performance using KPIs and a data warehouse application that continuously collects registers and calculates KPIs confirmed the reliability of indicators, indicator acceptability and usability for users, and continuous process improvement.
Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling

NASA Technical Reports Server (NTRS)

Gupta, Hoshin V.; Kling, Harald; Yilmaz, Koray K.; Martinez-Baquero, Guillermo F.

2009-01-01

The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification.
42 CFR § 414.1360 - Data submission criteria for the improvement activities performance category.

Code of Federal Regulations, 2010 CFR

2017-10-01

... & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICARE PROGRAM (CONTINUED) PAYMENT FOR PART B MEDICAL AND OTHER HEALTH SERVICES Merit-Based Incentive Payment System and Alternative Payment Model Incentive § 414.1360 Data submission criteria for the improvement activities 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.
Tool Efficiency Analysis model research in SEMI industry

NASA Astrophysics Data System (ADS)

Lei, Ma; Nana, Zhang; Zhongqiu, Zhang

2018-06-01

One of the key goals in SEMI industry is to improve equipment through put and ensure equipment production efficiency maximization. This paper is based on SEMI standards in semiconductor equipment control, defines the transaction rules between different tool states, and presents a TEA system model which is to analysis tool performance automatically based on finite state machine. The system was applied to fab tools and verified its effectiveness successfully, and obtained the parameter values used to measure the equipment performance, also including the advices of improvement.
Research on Zheng Classification Fusing Pulse Parameters in Coronary Heart Disease

PubMed Central

Guo, Rui; Wang, Yi-Qin; Xu, Jin; Yan, Hai-Xia; Yan, Jian-Jun; Li, Fu-Feng; Xu, Zhao-Xia; Xu, Wen-Jie

2013-01-01

This study was conducted to illustrate that nonlinear dynamic variables of Traditional Chinese Medicine (TCM) pulse can improve the performances of TCM Zheng classification models. Pulse recordings of 334 coronary heart disease (CHD) patients and 117 normal subjects were collected in this study. Recurrence quantification analysis (RQA) was employed to acquire nonlinear dynamic variables of pulse. TCM Zheng models in CHD were constructed, and predictions using a novel multilabel learning algorithm based on different datasets were carried out. Datasets were designed as follows: dataset1, TCM inquiry information including inspection information; dataset2, time-domain variables of pulse and dataset1; dataset3, RQA variables of pulse and dataset1; and dataset4, major principal components of RQA variables and dataset1. The performances of the different models for Zheng differentiation were compared. The model for Zheng differentiation based on RQA variables integrated with inquiry information had the best performance, whereas that based only on inquiry had the worst performance. Meanwhile, the model based on time-domain variables of pulse integrated with inquiry fell between the above two. This result showed that RQA variables of pulse can be used to construct models of TCM Zheng and improve the performance of Zheng differentiation models. PMID:23737839
A conceptual model to empower software requirements conflict detection and resolution with rule-based reasoning

NASA Astrophysics Data System (ADS)

Ahmad, Sabrina; Jalil, Intan Ermahani A.; Ahmad, Sharifah Sakinah Syed

2016-08-01

It is seldom technical issues which impede the process of eliciting software requirements. The involvement of multiple stakeholders usually leads to conflicts and therefore the need of conflict detection and resolution effort is crucial. This paper presents a conceptual model to further improve current efforts. Hence, this paper forwards an improved conceptual model to assist the conflict detection and resolution effort which extends the model ability and improves overall performance. The significant of the new model is to empower the automation of conflicts detection and its severity level with rule-based reasoning.
A Comparison of Service Delivery Models for Special Education Middle School Students Receiving Moderate Intervention Services

ERIC Educational Resources Information Center

Jones-Mason, Keely S.

2012-01-01

In an effort to improve academic performance for students receiving special education services, a large urban school district in Tennessee has implemented Integrated Service Delivery Model. The purpose of this study was to compare the performance of students receiving instruction in self-contained classrooms to the performance of students…
Improving CNN Performance Accuracies With Min-Max Objective.

PubMed

Shi, Weiwei; Gong, Yihong; Tao, Xiaoyu; Wang, Jinjun; Zheng, Nanning

2017-06-09

We propose a novel method for improving performance accuracies of convolutional neural network (CNN) without the need to increase the network complexity. We accomplish the goal by applying the proposed Min-Max objective to a layer below the output layer of a CNN model in the course of training. The Min-Max objective explicitly ensures that the feature maps learned by a CNN model have the minimum within-manifold distance for each object manifold and the maximum between-manifold distances among different object manifolds. The Min-Max objective is general and able to be applied to different CNNs with insignificant increases in computation cost. Moreover, an incremental minibatch training procedure is also proposed in conjunction with the Min-Max objective to enable the handling of large-scale training data. Comprehensive experimental evaluations on several benchmark data sets with both the image classification and face verification tasks reveal that employing the proposed Min-Max objective in the training process can remarkably improve performance accuracies of a CNN model in comparison with the same model trained without using this objective.
EnergyPlus Run Time Analysis

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

Hong, Tianzhen; Buhl, Fred; Haves, Philip

2008-09-20

EnergyPlus is a new generation building performance simulation program offering many new modeling capabilities and more accurate performance calculations integrating building components in sub-hourly time steps. However, EnergyPlus runs much slower than the current generation simulation programs. This has become a major barrier to its widespread adoption by the industry. This paper analyzed EnergyPlus run time from comprehensive perspectives to identify key issues and challenges of speeding up EnergyPlus: studying the historical trends of EnergyPlus run time based on the advancement of computers and code improvements to EnergyPlus, comparing EnergyPlus with DOE-2 to understand and quantify the run time differences,more » identifying key simulation settings and model features that have significant impacts on run time, and performing code profiling to identify which EnergyPlus subroutines consume the most amount of run time. This paper provides recommendations to improve EnergyPlus run time from the modeler?s perspective and adequate computing platforms. Suggestions of software code and architecture changes to improve EnergyPlus run time based on the code profiling results are also discussed.« less
Charge to Road Map Development Sessions

NASA Technical Reports Server (NTRS)

Barth, Janet

2004-01-01

Develop a road map for new standard Model applications radiation belt models. Model applications: Spacecraft and instruments. Reduce risk. Reduce cost. Improve performance. Increase system lifetime. Reduce risk to astronauts.
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.
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.
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.
Temporal and geographical external validation study and extension of the Mayo Clinic prediction model to predict eGFR in the younger population of Swiss ADPKD patients.

PubMed

Girardat-Rotar, Laura; Braun, Julia; Puhan, Milo A; Abraham, Alison G; Serra, Andreas L

2017-07-17

Prediction models in autosomal dominant polycystic kidney disease (ADPKD) are useful in clinical settings to identify patients with greater risk of a rapid disease progression in whom a treatment may have more benefits than harms. Mayo Clinic investigators developed a risk prediction tool for ADPKD patients using a single kidney value. Our aim was to perform an independent geographical and temporal external validation as well as evaluate the potential for improving the predictive performance by including additional information on total kidney volume. We used data from the on-going Swiss ADPKD study from 2006 to 2016. The main analysis included a sample size of 214 patients with Typical ADPKD (Class 1). We evaluated the Mayo Clinic model performance calibration and discrimination in our external sample and assessed whether predictive performance could be improved through the addition of subsequent kidney volume measurements beyond the baseline assessment. The calibration of both versions of the Mayo Clinic prediction model using continuous Height adjusted total kidney volume (HtTKV) and using risk subclasses was good, with R 2 of 78% and 70%, respectively. Accuracy was also good with 91.5% and 88.7% of the predicted within 30% of the observed, respectively. Additional information regarding kidney volume did not substantially improve the model performance. The Mayo Clinic prediction models are generalizable to other clinical settings and provide an accurate tool based on available predictors to identify patients at high risk for rapid disease progression.
Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

PubMed Central

Liu, Hua; Wu, Wen

2017-01-01

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF). PMID:28608843
Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

PubMed

Liu, Hua; Wu, Wen

2017-06-13

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).
Drug drug interaction extraction from the literature using a recursive neural network

PubMed Central

Lim, Sangrak; Lee, Kyubum

2018-01-01

Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI information from the literature is a laborious task. However, text mining can be used to find DDIs in the biomedical literature. Among the recently developed neural networks, we use a Recursive Neural Network to improve the performance of DDI extraction. Our recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction. Compared with the state-of-the-art models, the DDI detection and type classifiers of our model performed 4.4% and 2.8% better, respectively, on the DDIExtraction Challenge’13 test data. We also validated our model on the PK DDI corpus that consists of two types of DDIs data: in vivo DDI and in vitro DDI. Compared with the existing model, our detection classifier performed 2.3% and 6.7% better on in vivo and in vitro data respectively. The results of our validation demonstrate that our model can automatically extract DDIs better than existing models. PMID:29373599
Noise-robust speech triage.

PubMed

Bartos, Anthony L; Cipr, Tomas; Nelson, Douglas J; Schwarz, Petr; Banowetz, John; Jerabek, Ladislav

2018-04-01

A method is presented in which conventional speech algorithms are applied, with no modifications, to improve their performance in extremely noisy environments. It has been demonstrated that, for eigen-channel algorithms, pre-training multiple speaker identification (SID) models at a lattice of signal-to-noise-ratio (SNR) levels and then performing SID using the appropriate SNR dependent model was successful in mitigating noise at all SNR levels. In those tests, it was found that SID performance was optimized when the SNR of the testing and training data were close or identical. In this current effort multiple i-vector algorithms were used, greatly improving both processing throughput and equal error rate classification accuracy. Using identical approaches in the same noisy environment, performance of SID, language identification, gender identification, and diarization were significantly improved. A critical factor in this improvement is speech activity detection (SAD) that performs reliably in extremely noisy environments, where the speech itself is barely audible. To optimize SAD operation at all SNR levels, two algorithms were employed. The first maximized detection probability at low levels (-10 dB ≤ SNR < +10 dB) using just the voiced speech envelope, and the second exploited features extracted from the original speech to improve overall accuracy at higher quality levels (SNR ≥ +10 dB).
Quality of asthma care under different primary care models in Canada: a population-based study.

PubMed

To, Teresa; Guan, Jun; Zhu, Jingqin; Lougheed, M Diane; Kaplan, Alan; Tamari, Itamar; Stanbrook, Matthew B; Simatovic, Jacqueline; Feldman, Laura; Gershon, Andrea S

2015-02-14

Previous research has shown variations in quality of care and patient outcomes under different primary care models. The objective of this study was to use previously validated, evidence-based performance indicators to measure quality of asthma care over time and to compare quality of care between different primary care models. Data were obtained for years 2006 to 2010 from the Ontario Asthma Surveillance Information System, which uses health administrative databases to track individuals with asthma living in the province of Ontario, Canada. Individuals with asthma (n=1,813,922) were divided into groups based on the practice model of their primary care provider (i.e., fee-for-service, blended fee-for-service, blended capitation). Quality of asthma care was measured using six validated, evidence-based asthma care performance indicators. All of the asthma performance indicators improved over time within each of the primary care models. Compared to the traditional fee-for-service model, the blended fee-for-service and blended capitation models had higher use of spirometry for asthma diagnosis and monitoring, higher rates of inhaled corticosteroid prescription, and lower outpatient claims. Emergency department visits were lowest in the blended fee-for-service group. Quality of asthma care improved over time within each of the primary care models. However, the amount by which they improved differed between the models. The newer primary care models (i.e., blended fee-for-service, blended capitation) appear to provide better quality of asthma care compared to the traditional fee-for-service model.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

USGS Publications Warehouse

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
Autonomous learning in humanoid robotics through mental imagery.

PubMed

Di Nuovo, Alessandro G; Marocco, Davide; Di Nuovo, Santo; Cangelosi, Angelo

2013-05-01

In this paper we focus on modeling autonomous learning to improve performance of a humanoid robot through a modular artificial neural networks architecture. A model of a neural controller is presented, which allows a humanoid robot iCub to autonomously improve its sensorimotor skills. This is achieved by endowing the neural controller with a secondary neural system that, by exploiting the sensorimotor skills already acquired by the robot, is able to generate additional imaginary examples that can be used by the controller itself to improve the performance through a simulated mental training. Results and analysis presented in the paper provide evidence of the viability of the approach proposed and help to clarify the rational behind the chosen model and its implementation. Copyright © 2012 Elsevier Ltd. All rights reserved.
Anatomically constrained neural network models for the categorization of facial expression

NASA Astrophysics Data System (ADS)

McMenamin, Brenton W.; Assadi, Amir H.

2004-12-01

The ability to recognize facial expression in humans is performed with the amygdala which uses parallel processing streams to identify the expressions quickly and accurately. Additionally, it is possible that a feedback mechanism may play a role in this process as well. Implementing a model with similar parallel structure and feedback mechanisms could be used to improve current facial recognition algorithms for which varied expressions are a source for error. An anatomically constrained artificial neural-network model was created that uses this parallel processing architecture and feedback to categorize facial expressions. The presence of a feedback mechanism was not found to significantly improve performance for models with parallel architecture. However the use of parallel processing streams significantly improved accuracy over a similar network that did not have parallel architecture. Further investigation is necessary to determine the benefits of using parallel streams and feedback mechanisms in more advanced object recognition tasks.
Anatomically constrained neural network models for the categorization of facial expression

NASA Astrophysics Data System (ADS)

McMenamin, Brenton W.; Assadi, Amir H.

2005-01-01

The ability to recognize facial expression in humans is performed with the amygdala which uses parallel processing streams to identify the expressions quickly and accurately. Additionally, it is possible that a feedback mechanism may play a role in this process as well. Implementing a model with similar parallel structure and feedback mechanisms could be used to improve current facial recognition algorithms for which varied expressions are a source for error. An anatomically constrained artificial neural-network model was created that uses this parallel processing architecture and feedback to categorize facial expressions. The presence of a feedback mechanism was not found to significantly improve performance for models with parallel architecture. However the use of parallel processing streams significantly improved accuracy over a similar network that did not have parallel architecture. Further investigation is necessary to determine the benefits of using parallel streams and feedback mechanisms in more advanced object recognition tasks.
Numerical modeling of cold room's hinged door opening and closing processes

NASA Astrophysics Data System (ADS)

Carneiro, R.; Gaspar, P. D.; Silva, P. D.; Domingues, L. C.

2016-06-01

The need of rationalize energy consumption in agrifood industry has fasten the development of methodologies to improve the thermal and energy performances of cold rooms. This paper presents a three-dimensional (3D) transient Computational Fluid Dynamics (CFD) modelling of a cold room to evaluate the air infiltration rate through hinged doors. A species transport model is used for modelling the tracer gas concentration decay technique. Numerical predictions indicate that air temperature difference between spaces affects the air infiltration. For this case study, the infiltration rate increases 0.016 m3 s-1 per K of air temperature difference. The knowledge about the evolution of air infiltration during door opening/closing times allows to draw some conclusions about its influence on the air conditions inside the cold room, as well as to suggest best practices and simple technical improvements that can minimize air infiltration, and consequently improve thermal performance and energy consumption rationalization.
Analysis of a virtual memory model for maintaining database views

NASA Technical Reports Server (NTRS)

Kinsley, Kathryn C.; Hughes, Charles E.

1992-01-01

This paper presents an analytical model for predicting the performance of a new support strategy for database views. This strategy, called the virtual method, is compared with traditional methods for supporting views. The analytical model's predictions of improved performance by the virtual method are then validated by comparing these results with those achieved in an experimental implementation.
Development Of A Parallel Performance Model For The THOR Neutral Particle Transport Code

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

Yessayan, Raffi; Azmy, Yousry; Schunert, Sebastian

The THOR neutral particle transport code enables simulation of complex geometries for various problems from reactor simulations to nuclear non-proliferation. It is undergoing a thorough V&V requiring computational efficiency. This has motivated various improvements including angular parallelization, outer iteration acceleration, and development of peripheral tools. For guiding future improvements to the code’s efficiency, better characterization of its parallel performance is useful. A parallel performance model (PPM) can be used to evaluate the benefits of modifications and to identify performance bottlenecks. Using INL’s Falcon HPC, the PPM development incorporates an evaluation of network communication behavior over heterogeneous links and a functionalmore » characterization of the per-cell/angle/group runtime of each major code component. After evaluating several possible sources of variability, this resulted in a communication model and a parallel portion model. The former’s accuracy is bounded by the variability of communication on Falcon while the latter has an error on the order of 1%.« less
Polishing, coating and integration of SiC mirrors for space telescopes

NASA Astrophysics Data System (ADS)

Rodolfo, Jacques

2017-11-01

In the last years, the technology of SiC mirrors took an increasingly significant part in the field of space telescopes. Sagem is involved in the JWST program to manufacture and test the optical components of the NIRSpec instrument. The instrument is made of 3 TMAs and 4 plane mirrors made of SiC. Sagem is in charge of the CVD cladding, the polishing, the coating of the mirrors and the integration and testing of the TMAs. The qualification of the process has been performed through the manufacturing and testing of the qualification model of the FOR TMA. This TMA has shown very good performances both at ambient and during the cryo test. The polishing process has been improved for the manufacturing of the flight model. This improvement has been driven by the BRDF performance of the mirror. This parameter has been deeply analysed and a model has been built to predict the performance of the mirrors. The existing Dittman model have been analysed and found to be optimistic.
AQUATOX Fact Sheet

EPA Pesticide Factsheets

AQUATOX Release 3.1 includes numerous enhancements designed to improve model performance, more closely match data requirements with generally available data, improve data manipulation and analysis, and increase user friendliness.
A Caveat Note on Tuning in the Development of Coupled Climate Models

NASA Astrophysics Data System (ADS)

Dommenget, Dietmar; Rezny, Michael

2018-01-01

State-of-the-art coupled general circulation models (CGCMs) have substantial errors in their simulations of climate. In particular, these errors can lead to large uncertainties in the simulated climate response (both globally and regionally) to a doubling of CO2. Currently, tuning of the parameterization schemes in CGCMs is a significant part of the developed. It is not clear whether such tuning actually improves models. The tuning process is (in general) neither documented, nor reproducible. Alternative methods such as flux correcting are not used nor is it clear if such methods would perform better. In this study, ensembles of perturbed physics experiments are performed with the Globally Resolved Energy Balance (GREB) model to test the impact of tuning. The work illustrates that tuning has, in average, limited skill given the complexity of the system, the limited computing resources, and the limited observations to optimize parameters. While tuning may improve model performance (such as reproducing observed past climate), it will not get closer to the "true" physics nor will it significantly improve future climate change projections. Tuning will introduce artificial compensating error interactions between submodels that will hamper further model development. In turn, flux corrections do perform well in most, but not all aspects. A main advantage of flux correction is that it is much cheaper, simpler, more transparent, and it does not introduce artificial error interactions between submodels. These GREB model experiments should be considered as a pilot study to motivate further CGCM studies that address the issues of model tuning.
Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Lin, Jin; Deng, Wenbing; Cheng, Weiguo

2017-08-01

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.
Combining surface reanalysis and remote sensing data for monitoring evapotranspiration

USGS Publications Warehouse

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, Pat; Williams, C.; Ardö, J.; Marie, B.; Cappelaere, B.; Grandcourt, A.; Nickless, A.; Noubellon, Y.; Scholes, R.; Kutsch, W.

2012-01-01

Climate change is expected to have the greatest impact on the world's poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled actual evapotranspiration (AET), a key input in continental-scale hydrologic models. In this study, a model driven by dynamic canopy AET was combined with the Global Land Data Assimilation System realization of the NOAH Land Surface Model (GNOAH) wet canopy and soil AET for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against AET from the GNOAH model and dynamic model using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance are at humid sites with dense vegetation, while performance at semi-arid sites is poor, but better than individual models. The reduction in errors using the hybrid model can be attributed to the integration of a dynamic vegetation component with land surface model estimates, improved model parameterization, and reduction of multiplicative effects of uncertain data.
The European Academy laparoscopic “Suturing Training and Testing’’ (SUTT) significantly improves surgeons’ performance

PubMed Central

Sleiman, Z.; Tanos, V.; Van Belle, Y.; Carvalho, J.L.; Campo, R.

2015-01-01

The efficiency of suturing training and testing (SUTT) model by laparoscopy was evaluated, measuring the suturingskill acquisition of trainee gynecologists at the beginning and at the end of a teaching course. During a workshop organized by the European Academy of Gynecological Surgery (EAGS), 25 participants with three different experience levels in laparoscopy (minor, intermediate and major) performed the 4 exercises of the SUTT model (Ex 1: both hands stitching and continuous suturing, Ex 2: right hand stitching and intracorporeal knotting, Ex 3: left hand stitching and intracorporeal knotting, Ex 4: dominant hand stitching, tissue approximation and intracorporeal knotting). The time needed to perform the exercises is recorded for each trainee and group and statistical analysis used to note the differences. Overall, all trainees achieved significant improvement in suturing time (p < 0.005) as measured before and after completion of the training. Similar significantly improved suturing time differences (p < 0.005) were noted among the groups of trainees with different laparoscopic experience. In conclusion a short well-guided training course, using the SUTT model, improves significantly surgeon’s laparoscopic suturing ability, independently of the level of experience in laparoscopic surgery. Key words: Endoscopy, laparoscopic suturing, psychomotor skills, surgery, teaching, training suturing model. PMID:26977264
Evaluation of an instructional model to teach clinically relevant medicinal chemistry in a campus and a distance pathway.

PubMed

Alsharif, Naser Z; Galt, Kimberly A

2008-04-15

To evaluate an instructional model for teaching clinically relevant medicinal chemistry. An instructional model that uses Bloom's cognitive and Krathwohl's affective taxonomy, published and tested concepts in teaching medicinal chemistry, and active learning strategies, was introduced in the medicinal chemistry courses for second-professional year (P2) doctor of pharmacy (PharmD) students (campus and distance) in the 2005-2006 academic year. Student learning and the overall effectiveness of the instructional model were assessed. Student performance after introducing the instructional model was compared to that in prior years. Student performance on course examinations improved compared to previous years. Students expressed overall enthusiasm about the course and better understood the value of medicinal chemistry to clinical practice. The explicit integration of the cognitive and affective learning objectives improved student performance, student ability to apply medicinal chemistry to clinical practice, and student attitude towards the discipline. Testing this instructional model provided validation to this theoretical framework. The model is effective for both our campus and distance-students. This instructional model may also have broad-based applications to other science courses.
A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2014-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.
A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan Walker

2015-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.
Model-data integration to improve the LPJmL dynamic global vegetation model

NASA Astrophysics Data System (ADS)

Forkel, Matthias; Thonicke, Kirsten; Schaphoff, Sibyll; Thurner, Martin; von Bloh, Werner; Dorigo, Wouter; Carvalhais, Nuno

2017-04-01

Dynamic global vegetation models show large uncertainties regarding the development of the land carbon balance under future climate change conditions. This uncertainty is partly caused by differences in how vegetation carbon turnover is represented in global vegetation models. Model-data integration approaches might help to systematically assess and improve model performances and thus to potentially reduce the uncertainty in terrestrial vegetation responses under future climate change. Here we present several applications of model-data integration with the LPJmL (Lund-Potsdam-Jena managed Lands) dynamic global vegetation model to systematically improve the representation of processes or to estimate model parameters. In a first application, we used global satellite-derived datasets of FAPAR (fraction of absorbed photosynthetic activity), albedo and gross primary production to estimate phenology- and productivity-related model parameters using a genetic optimization algorithm. Thereby we identified major limitations of the phenology module and implemented an alternative empirical phenology model. The new phenology module and optimized model parameters resulted in a better performance of LPJmL in representing global spatial patterns of biomass, tree cover, and the temporal dynamic of atmospheric CO2. Therefore, we used in a second application additionally global datasets of biomass and land cover to estimate model parameters that control vegetation establishment and mortality. The results demonstrate the ability to improve simulations of vegetation dynamics but also highlight the need to improve the representation of mortality processes in dynamic global vegetation models. In a third application, we used multiple site-level observations of ecosystem carbon and water exchange, biomass and soil organic carbon to jointly estimate various model parameters that control ecosystem dynamics. This exercise demonstrates the strong role of individual data streams on the simulated ecosystem dynamics which consequently changed the development of ecosystem carbon stocks and fluxes under future climate and CO2 change. In summary, our results demonstrate challenges and the potential of using model-data integration approaches to improve a dynamic global vegetation model.
Can Video Self-Modeling Improve Affected Limb Reach and Grasp Ability in Stroke Patients?

PubMed

Steel, Kylie Ann; Mudie, Kurt; Sandoval, Remi; Anderson, David; Dogramaci, Sera; Rehmanjan, Mohammad; Birznieks, Ingvars

2018-01-01

The authors examined whether feedforward video self-modeling (FF VSM) would improve control over the affected limb, movement self-confidence, movement self-consciousness, and well-being in 18 stroke survivors. Participants completed a cup transport task and 2 questionnaires related to psychological processes pre- and postintervention. Pretest video footage of the unaffected limb performing the task was edited to create a best-of or mirror-reversed training DVD, creating the illusion that patients were performing proficiently with the affected limb. The training yielded significant improvements for the forward movement of the affected limb compared to the unaffected limb. Significant improvements were also seen in movement self-confidence, movement self-consciousness, and well-being. FF VSM appears to be a viable way to improve motor ability in populations with movement disorders.
Piezoresistive Cantilever Performance—Part II: Optimization

PubMed Central

Park, Sung-Jin; Doll, Joseph C.; Rastegar, Ali J.; Pruitt, Beth L.

2010-01-01

Piezoresistive silicon cantilevers fabricated by ion implantation are frequently used for force, displacement, and chemical sensors due to their low cost and electronic readout. However, the design of piezoresistive cantilevers is not a straightforward problem due to coupling between the design parameters, constraints, process conditions, and performance. We systematically analyzed the effect of design and process parameters on force resolution and then developed an optimization approach to improve force resolution while satisfying various design constraints using simulation results. The combined simulation and optimization approach is extensible to other doping methods beyond ion implantation in principle. The optimization results were validated by fabricating cantilevers with the optimized conditions and characterizing their performance. The measurement results demonstrate that the analytical model accurately predicts force and displacement resolution, and sensitivity and noise tradeoff in optimal cantilever performance. We also performed a comparison between our optimization technique and existing models and demonstrated eight times improvement in force resolution over simplified models. PMID:20333323

CF6 jet engine performance improvement program. Short core exhaust nozzle performance improvement concept. [specific fuel consumption reduction

NASA Technical Reports Server (NTRS)

Fasching, W. A.

1979-01-01

The short core exhaust nozzle was evaluated in CF6-50 engine ground tests including performance, acoustic, and endurance tests. The test results verified the performance predictions from scale model tests. The short core exhaust nozzle provides an internal cruise sfc reduction of 0.9 percent without an increase in engine noise. The nozzle hardware successfully completed 1000 flight cycles of endurance testing without any signs of distress.
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.
MOGO: Model-Oriented Global Optimization of Petascale Applications

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

Malony, Allen D.; Shende, Sameer S.

The MOGO project was initiated under in 2008 under the DOE Program Announcement for Software Development Tools for Improved Ease-of-Use on Petascale systems (LAB 08-19). The MOGO team consisted of Oak Ridge National Lab, Argonne National Lab, and the University of Oregon. The overall goal of MOGO was to attack petascale performance analysis by developing a general framework where empirical performance data could be efficiently and accurately compared with performance expectations at various levels of abstraction. This information could then be used to automatically identify and remediate performance problems. MOGO was be based on performance models derived from application knowledge,more » performance experiments, and symbolic analysis. MOGO was able to make reasonable impact on existing DOE applications and systems. New tools and techniques were developed, which, in turn, were used on important DOE applications on DOE LCF systems to show significant performance improvements.« less
Performance monitoring can boost turboexpander efficiency

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

McIntire, R.

1982-07-05

This paper discusses ways of improving the productivity of the turboexpander/refrigeration system's radial expander and radial compressor through systematic review of component performance. It reviews several techniques to determine the performance of an expander and compressor. It suggests that any performance improvement program requires quantifying the performance of separate components over a range of operating conditions; estimating the increase in performance associated with any hardware change; and developing an analytical (computer) model of the entire system by using the performance curve of individual components. The model is used to quantify the economic benefits of any change in the system, eithermore » a change in operating procedures or a hardware modification. Topics include proper ways of using antisurge control valves and modifying flow rate/shaft speed (Q/N). It is noted that compressor efficiency depends on the incidence angle of blade at the rotor leading edge and the angle of the incoming gas stream.« less
Methylphenidate improves performance on the radial arm maze in periadolescent rats

PubMed Central

Dow-Edwards, Diana L.; Weedon, Jeremy C.; Hellmann, Esther

2008-01-01

Methylphenidate (Ritalin; MPD) is one of the most commonly prescribed drugs in childhood and adolescence and many clinical studies have documented its efficacy. Due to the limitations of conducting invasive research in humans, animal models can be beneficial for studying drug effects. However, few animal studies have demonstrated the effects of methylphenidate on cognitive processes. The objective of this study was to find a dose of methylphenidate that was effective in improving performance on a spatial working memory cognitive task when administered orally to periadolescent rats. Therefore, we dosed subjects with methylphenidate at 1 or 3 mg/kg/day via gastric intubation from postnatal day 22 to 59 and assessed the effects of the drug on performance on the radial arm maze each day. To enhance performance overall, a second experiment was conducted where the subjects were moderately food restricted (to 90% of the free-feeding weight). Results of Experiment 1 show that during the first week of testing only the 3mg/kg MPD-treated males showed improved performance (entries prior to repeated entry) when ad-lib fed and housed in pairs while the same dose significantly improved performance in both males and females under conditions of food-restriction and individual housing in Experiment 2. MPD also produced a pattern of increased errors and arms entered during the first week, especially in Experiment 2. MPD increased locomotor activity when tested at postnatal day 60 in both experiments. The data suggest that 3mg/kg oral methylphenidate improves performance on a spatial cognitive task only early in treatment in the rat. While males show improvement under conditions of both high and low motivation, females only show MPD effects when highly motivated. Hypothetically, methylphenidate may improve radial arm maze performance through increased attention and improved spatial working memory and/or alterations in locomotion, reactivity to novelty or anxiety. Regardless, the study supports the utility of the rat as a suitable model to examine the effects of low dose oral MPD. PMID:18538539
Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer.

PubMed

Coates, James; Jeyaseelan, Asha K; Ybarra, Norma; David, Marc; Faria, Sergio; Souhami, Luis; Cury, Fabio; Duclos, Marie; El Naqa, Issam

2015-04-01

We explore analytical and data-driven approaches to investigate the integration of genetic variations (single nucleotide polymorphisms [SNPs] and copy number variations [CNVs]) with dosimetric and clinical variables in modeling radiation-induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients. Sixty-two patients who underwent curative hypofractionated radiotherapy (66 Gy in 22 fractions) between 2002 and 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Fifty-four patients had full dosimetric profiles. Two parallel modeling approaches were compared to assess the risk of severe RB (Grade⩾3) and ED (Grade⩾1); Maximum likelihood estimated generalized Lyman-Kutcher-Burman (LKB) and logistic regression. Statistical resampling based on cross-validation was used to evaluate model predictive power and generalizability to unseen data. Integration of biological variables xrcc1 CNV and SNP improved the fit of the RB and ED analytical and data-driven models. Cross-validation of the generalized LKB models yielded increases in classification performance of 27.4% for RB and 14.6% for ED when xrcc1 CNV and SNP were included, respectively. Biological variables added to logistic regression modeling improved classification performance over standard dosimetric models by 33.5% for RB and 21.2% for ED models. As a proof-of-concept, we demonstrated that the combination of genetic and dosimetric variables can provide significant improvement in NTCP prediction using analytical and data-driven approaches. The improvement in prediction performance was more pronounced in the data driven approaches. Moreover, we have shown that CNVs, in addition to SNPs, may be useful structural genetic variants in predicting radiation toxicities. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Evaluating mepindolol in a test model of examination anxiety in students.

PubMed

Krope, P; Kohrs, A; Ott, H; Wagner, W; Fichte, K

1982-03-01

The effect of a single dose of beta-blocker (5 or 10 mg mepindolol) during a written examination was investigated in two double-blind studies (N : 49 and 55 students, respectively). The question was whether the beta-blocker would in comparison to placebo diminish examination anxiety and improve the performance of highly complex tasks, while leaving the performance of less complex tasks unchanged. A reduction in examination anxiety after beta-blocker intake could not be demonstrated with a multi-level test model (which included the parameters self-rated anxiety, motor behaviour, task performance and physiology), although pulse rates were lowered significantly. An improvement in performance could not be observed, while - by the same token - the performance was not impaired by the beta-blocker. A hypothesis according to which a beta-blocker has an anxiolytic effect and improves performance, dependent on the level of habitual examination anxiety, was tested post hoc, but could not be confirmed. Ten of the subjects treated with 10 mg mepindolol, complained of different side effects, including dizziness, fatigue and headache.
Opportunities of probabilistic flood loss models

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Lüdtke, Stefan; Vogel, Kristin; Merz, Bruno

2016-04-01

Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. However, reliable flood damage models are a prerequisite for the practical usefulness of the model results. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks and traditional stage damage functions. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005, 2006 and 2013 in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of sharpness of the predictions the reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The comparison of the uni-variable Stage damage function and the multivariable model approach emphasises the importance to quantify predictive uncertainty. With each explanatory variable, the multi-variable model reveals an additional source of uncertainty. However, the predictive performance in terms of precision (mbe), accuracy (mae) and reliability (HR) is clearly improved in comparison to uni-variable Stage damage function. Overall, Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.
Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

2013-03-01

Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.
Enhancing Self-Efficacy and Performance: An Experimental Comparison of Psychological Techniques.

PubMed

Wright, Bradley James; O'Halloran, Paul Daniel; Stukas, Arthur Anthony

2016-01-01

We assessed how 6 psychological performance enhancement techniques (PETs) differentially improved self-efficacy (SE) and skill performance. We also assessed whether vicarious experiences and verbal persuasion as posited sources of SE (Bandura, 1982 ) were supported and, further, if the effects of the 6 PETs remained after controlling for achievement motivation traits and self-esteem. A within-subject design assessed each individual across 2 trials for 3 disparate PETs. A between-groups design assessed differences between PETs paired against each other for 3 similar novel tasks. Participants (N = 96) performed 2 trials of 10 attempts at each of the tasks (kick, throw, golf putt) in a counterbalanced sequence using their nondominant limb. Participants completed the Sport Orientation Questionnaire, Rosenberg Self-Esteem Scale, and General Self-Efficacy Scale and were randomly allocated to either the modeling or imagery, goal-setting or instructional self-statement, or knowledge-of-results or motivational feedback conditions aligned with each task. An instructional self-statement improved performance better than imagery, modeling, goal setting, and motivational and knowledge-of-results augmented feedback. Motivational auditory feedback most improved SE. Increased SE change scores were related to increased performance difference scores on all tasks after controlling for age, sex, achievement motivation, and self-esteem. Some sources of SE may be more influential than others on both SE and performance improvements. We provide partial support for the sources of SE proposed by Bandura's social-cognitive theory with verbal persuasion but not vicarious experiences improving SE.
State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

NASA Astrophysics Data System (ADS)

Gibbs, Matthew S.; McInerney, David; Humphrey, Greer; Thyer, Mark A.; Maier, Holger R.; Dandy, Graeme C.; Kavetski, Dmitri

2018-02-01

Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.
Driving Performance Improvements by Integrating Competencies with Human Resource Practices

ERIC Educational Resources Information Center

Lee, Jin Gu; Park, Yongho; Yang, Gi Hun

2010-01-01

This study explores the issues in the development and application of a competency model and provides implications for more precise integration of competencies into human resource (HR) functions driving performance improvement. This research is based on a case study from a Korean consumer corporation. This study employed document reviews,…
Improving Problem-Solving Performance of Students with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Yakubova, Gulnoza; Taber-Doughty, Teresa

2017-01-01

The effectiveness of a multicomponent intervention to improve the problem-solving performance of students with autism spectrum disorders (ASD) during vocational tasks was examined. A multiple-probe across-students design was used to illustrate the effectiveness of point-of-view video modeling paired with practice sessions and a self-operated cue…
User Documentation; POTW EXPERT v1.1; An Advisory System for Improving the Performance of Wastewater Treatment Facilities

EPA Science Inventory

POTW Expert is a PCX-based software program modeled after EPA/s Handbook Retrofitting POTWs (EPA-625/6-89/020) (formerly, Handbook for Improving POTW Performance Using the Composite Correction Program Approach). POTW Expert assists POTW owners and operators, state and local regu...
Field Tests of In-Service Modifications to Improve Performance of An Icebreaker Main Diesel Engine

DOT National Transportation Integrated Search

1977-08-01

Field tests of in-service modifications to improve engine efficiency and lower the emissions were performed on the no. 3 main diesel engine of the USCGC Mackinaw (WAGB-83). This engine is a model 38D8-1/8 manufactured by Colt Industries, Fairbanks Mo...
Systems Engineering | Wind | NREL

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platform to leverage its research capabilities toward integrating wind energy engineering and cost models achieve a better understanding of how to improve system-level performance and achieve system-level cost research capabilities to: Integrate wind plant engineering performance and cost software modeling to enable
The Pennsylvania Trauma Outcomes Study Risk-Adjusted Mortality Model: Results of a Statewide Benchmarking Program

PubMed Central

WIEBE, DOUGLAS J.; HOLENA, DANIEL N.; DELGADO, M. KIT; McWILLIAMS, NATHAN; ALTENBURG, JULIET; CARR, BRENDAN G.

2018-01-01

Trauma centers need objective feedback on performance to inform quality improvement efforts. The Trauma Quality Improvement Program recently published recommended methodology for case mix adjustment and benchmarking performance. We tested the feasibility of applying this methodology to develop risk-adjusted mortality models for a statewide trauma system. We performed a retrospective cohort study of patients ≥16 years old at Pennsylvania trauma centers from 2011 to 2013 (n = 100,278). Our main outcome measure was observed-to-expected mortality ratios (overall and within blunt, penetrating, multisystem, isolated head, and geriatric subgroups). Patient demographic variables, physiology, mechanism of injury, transfer status, injury severity, and pre-existing conditions were included as predictor variables. The statistical model had excellent discrimination (area under the curve = 0.94). Funnel plots of observed-to-expected identified five centers with lower than expected mortality and two centers with higher than expected mortality. No centers were outliers for management of penetrating trauma, but five centers had lower and three had higher than expected mortality for blunt trauma. It is feasible to use Trauma Quality Improvement Program methodology to develop risk-adjusted models for statewide trauma systems. Even with smaller numbers of trauma centers that are available in national datasets, it is possible to identify high and low outliers in performance. PMID:28541852
The Pennsylvania Trauma Outcomes Study Risk-Adjusted Mortality Model: Results of a Statewide Benchmarking Program.

PubMed

Wiebe, Douglas J; Holena, Daniel N; Delgado, M Kit; McWilliams, Nathan; Altenburg, Juliet; Carr, Brendan G

2017-05-01

Trauma centers need objective feedback on performance to inform quality improvement efforts. The Trauma Quality Improvement Program recently published recommended methodology for case mix adjustment and benchmarking performance. We tested the feasibility of applying this methodology to develop risk-adjusted mortality models for a statewide trauma system. We performed a retrospective cohort study of patients ≥16 years old at Pennsylvania trauma centers from 2011 to 2013 (n = 100,278). Our main outcome measure was observed-to-expected mortality ratios (overall and within blunt, penetrating, multisystem, isolated head, and geriatric subgroups). Patient demographic variables, physiology, mechanism of injury, transfer status, injury severity, and pre-existing conditions were included as predictor variables. The statistical model had excellent discrimination (area under the curve = 0.94). Funnel plots of observed-to-expected identified five centers with lower than expected mortality and two centers with higher than expected mortality. No centers were outliers for management of penetrating trauma, but five centers had lower and three had higher than expected mortality for blunt trauma. It is feasible to use Trauma Quality Improvement Program methodology to develop risk-adjusted models for statewide trauma systems. Even with smaller numbers of trauma centers that are available in national datasets, it is possible to identify high and low outliers in performance.
Time to Loosen the Apron Strings: Cohort-based Evaluation of a Learner-driven Remediation Model at One Medical School.

PubMed

Bierer, S Beth; Dannefer, Elaine F; Tetzlaff, John E

2015-09-01

Remediation in the era of competency-based assessment demands a model that empowers students to improve performance. To examine a remediation model where students, rather than faculty, develop remedial plans to improve performance. Private medical school, 177 medical students. A promotion committee uses student-generated portfolios and faculty referrals to identify struggling students, and has them develop formal remediation plans with personal reflections, improvement strategies, and performance evidence. Students submit reports to document progress until formally released from remediation by the promotion committee. Participants included 177 students from six classes (2009-2014). Twenty-six were placed in remediation, with more referrals occurring during Years 1 or 2 (n = 20, 76 %). Unprofessional behavior represented the most common reason for referral in Years 3-5. Remedial students did not differ from classmates (n = 151) on baseline characteristics (Age, Gender, US citizenship, MCAT) or willingness to recommend their medical school to future students (p < 0.05). Two remedial students did not graduate and three did not pass USLME licensure exams on first attempt. Most remedial students (92 %) generated appropriate plans to address performance deficits. Students can successfully design remedial interventions. This learner-driven remediation model promotes greater autonomy and reinforces self-regulated learning.
Health-Related Quality of Life in a Predictive Model for Mortality in Older Breast Cancer Survivors.

PubMed

DuMontier, Clark; Clough-Gorr, Kerri M; Silliman, Rebecca A; Stuck, Andreas E; Moser, André

2018-03-13

To develop a predictive model and risk score for 10-year mortality using health-related quality of life (HRQOL) in a cohort of older women with early-stage breast cancer. Prospective cohort. Community. U.S. women aged 65 and older diagnosed with Stage I to IIIA primary breast cancer (N=660). We used medical variables (age, comorbidity), HRQOL measures (10-item Physical Function Index and 5-item Mental Health Index from the Medical Outcomes Study (MOS) 36-item Short-Form Survey; 8-item Modified MOS Social Support Survey), and breast cancer variables (stage, surgery, chemotherapy, endocrine therapy) to develop a 10-year mortality risk score using penalized logistic regression models. We assessed model discriminative performance using the area under the receiver operating characteristic curve (AUC), calibration performance using the Hosmer-Lemeshow test, and overall model performance using Nagelkerke R 2 (NR). Compared to a model including only age, comorbidity, and cancer stage and treatment variables, adding HRQOL variables improved discrimination (AUC 0.742 from 0.715) and overall performance (NR 0.221 from 0.190) with good calibration (p=0.96 from HL test). In a cohort of older women with early-stage breast cancer, HRQOL measures predict 10-year mortality independently of traditional breast cancer prognostic variables. These findings suggest that interventions aimed at improving physical function, mental health, and social support might improve both HRQOL and survival. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

PubMed

Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko

2018-05-04

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population. Copyright © 2018 Elsevier Ltd. All rights reserved.
Improving parallel I/O autotuning with performance modeling

DOE PAGES

Behzad, Babak; Byna, Surendra; Wild, Stefan M.; ...

2014-01-01

Various layers of the parallel I/O subsystem offer tunable parameters for improving I/O performance on large-scale computers. However, searching through a large parameter space is challenging. We are working towards an autotuning framework for determining the parallel I/O parameters that can achieve good I/O performance for different data write patterns. In this paper, we characterize parallel I/O and discuss the development of predictive models for use in effectively reducing the parameter space. Furthermore, applying our technique on tuning an I/O kernel derived from a large-scale simulation code shows that the search time can be reduced from 12 hours to 2more » hours, while achieving 54X I/O performance speedup.« less
Performance Analysis and Optimization on the UCLA Parallel Atmospheric General Circulation Model Code

NASA Technical Reports Server (NTRS)

Lou, John; Ferraro, Robert; Farrara, John; Mechoso, Carlos

1996-01-01

An analysis is presented of several factors influencing the performance of a parallel implementation of the UCLA atmospheric general circulation model (AGCM) on massively parallel computer systems. Several modificaitons to the original parallel AGCM code aimed at improving its numerical efficiency, interprocessor communication cost, load-balance and issues affecting single-node code performance are discussed.
Leakage flow simulation in a specific pump model

NASA Astrophysics Data System (ADS)

Dupont, P.; Bayeul-Lainé, A. C.; Dazin, A.; Bois, G.; Roussette, O.; Si, Q.

2014-03-01

This paper deals with the influence of leakage flow existing in SHF pump model on the analysis of internal flow behaviour inside the vane diffuser of the pump model performance using both experiments and calculations. PIV measurements have been performed at different hub to shroud planes inside one diffuser channel passage for a given speed of rotation and various flow rates. For each operating condition, the PIV measurements have been trigged with different angular impeller positions. The performances and the static pressure rise of the diffuser were also measured using a three-hole probe. The numerical simulations were carried out with Star CCM+ 8.06 code (RANS frozen and unsteady calculations). Comparisons between numerical and experimental results are presented and discussed for three flow rates. The performances of the diffuser obtained by numerical simulation results are compared to the performances obtained by three-hole probe indications. The comparisons show few influence of fluid leakage on global performances but a real improvement concerning the efficiency of the impeller, the pump and the velocity distributions. These results show that leakage is an important parameter that has to be taken into account in order to make improved comparisons between numerical approaches and experiments in such a specific model set up.
Effect of roughness formulation on the performance of a coupled wave, hydrodynamic, and sediment transport model

USGS Publications Warehouse

Ganju, Neil K.; Sherwood, Christopher R.

2010-01-01

A variety of algorithms are available for parameterizing the hydrodynamic bottom roughness associated with grain size, saltation, bedforms, and wave–current interaction in coastal ocean models. These parameterizations give rise to spatially and temporally variable bottom-drag coefficients that ostensibly provide better representations of physical processes than uniform and constant coefficients. However, few studies have been performed to determine whether improved representation of these variable bottom roughness components translates into measurable improvements in model skill. We test the hypothesis that improved representation of variable bottom roughness improves performance with respect to near-bed circulation, bottom stresses, or turbulence dissipation. The inner shelf south of Martha’s Vineyard, Massachusetts, is the site of sorted grain-size features which exhibit sharp alongshore variations in grain size and ripple geometry over gentle bathymetric relief; this area provides a suitable testing ground for roughness parameterizations. We first establish the skill of a nested regional model for currents, waves, stresses, and turbulent quantities using a uniform and constant roughness; we then gauge model skill with various parameterization of roughness, which account for the influence of the wave-boundary layer, grain size, saltation, and rippled bedforms. We find that commonly used representations of ripple-induced roughness, when combined with a wave–current interaction routine, do not significantly improve skill for circulation, and significantly decrease skill with respect to stresses and turbulence dissipation. Ripple orientation with respect to dominant currents and ripple shape may be responsible for complicating a straightforward estimate of the roughness contribution from ripples. In addition, sediment-induced stratification may be responsible for lower stresses than predicted by the wave–current interaction model.
Phase 2 and 3 wind tunnel tests of the J-97 powered, external augmentor V/STOL model. [conducted in Ames 40- by 80-foot wind tunnel

NASA Technical Reports Server (NTRS)

Garland, D. B.

1980-01-01

Modifications were made to the model to improve longitudinal acceleration capability during transition from hovering to wing borne flight. A rearward deflection of the fuselage augmentor thrust vector is shown to be beneficial in this regard. Other agmentor modifications were tested, notably the removal of both endplates, which improved acceleration performance at the higher transition speeds. The model tests again demonstrated minimal interference of the fuselage augmentor on aerodynamic lift. A flapped canard surface also shows negligible influence on the performance of the wing and of the fuselage augmentor.
An Integrated Miniature Pulse Tube Cryocooler at 80K

NASA Astrophysics Data System (ADS)

Chen, H. L.; Yang, L. W.; Cai, J. H.; Liang, J. T.; Zhang, L.; Zhou, Y.

2008-03-01

Two integrated models of coaxial miniature pulse tube coolers based on an experimental model are manufactured. Performance of the integrated models is compared to that of the experimental model. Reliability and stability of an integrated model are tested and improved.
Multiple model analysis with discriminatory data collection (MMA-DDC): A new method for improving measurement selection

NASA Astrophysics Data System (ADS)

Kikuchi, C.; Ferre, P. A.; Vrugt, J. A.

2011-12-01

Hydrologic models are developed, tested, and refined based on the ability of those models to explain available hydrologic data. The optimization of model performance based upon mismatch between model outputs and real world observations has been extensively studied. However, identification of plausible models is sensitive not only to the models themselves - including model structure and model parameters - but also to the location, timing, type, and number of observations used in model calibration. Therefore, careful selection of hydrologic observations has the potential to significantly improve the performance of hydrologic models. In this research, we seek to reduce prediction uncertainty through optimization of the data collection process. A new tool - multiple model analysis with discriminatory data collection (MMA-DDC) - was developed to address this challenge. In this approach, multiple hydrologic models are developed and treated as competing hypotheses. Potential new data are then evaluated on their ability to discriminate between competing hypotheses. MMA-DDC is well-suited for use in recursive mode, in which new observations are continuously used in the optimization of subsequent observations. This new approach was applied to a synthetic solute transport experiment, in which ranges of parameter values constitute the multiple hydrologic models, and model predictions are calculated using likelihood-weighted model averaging. MMA-DDC was used to determine the optimal location, timing, number, and type of new observations. From comparison with an exhaustive search of all possible observation sequences, we find that MMA-DDC consistently selects observations which lead to the highest reduction in model prediction uncertainty. We conclude that using MMA-DDC to evaluate potential observations may significantly improve the performance of hydrologic models while reducing the cost associated with collecting new data.
Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Jamaluddin; Siringoringo, Rimbun

2017-12-01

Fuzzy k-Nearest Neighbor (FkNN) is one of the most powerful classification methods. The presence of fuzzy concepts in this method successfully improves its performance on almost all classification issues. The main drawbackof FKNN is that it is difficult to determine the parameters. These parameters are the number of neighbors (k) and fuzzy strength (m). Both parameters are very sensitive. This makes it difficult to determine the values of ‘m’ and ‘k’, thus making FKNN difficult to control because no theories or guides can deduce how proper ‘m’ and ‘k’ should be. This study uses Modified Particle Swarm Optimization (MPSO) to determine the best value of ‘k’ and ‘m’. MPSO is focused on the Constriction Factor Method. Constriction Factor Method is an improvement of PSO in order to avoid local circumstances optima. The model proposed in this study was tested on the German Credit Dataset. The test of the data/The data test has been standardized by UCI Machine Learning Repository which is widely applied to classification problems. The application of MPSO to the determination of FKNN parameters is expected to increase the value of classification performance. Based on the experiments that have been done indicating that the model offered in this research results in a better classification performance compared to the Fk-NN model only. The model offered in this study has an accuracy rate of 81%, while. With using Fk-NN model, it has the accuracy of 70%. At the end is done comparison of research model superiority with 2 other classification models;such as Naive Bayes and Decision Tree. This research model has a better performance level, where Naive Bayes has accuracy 75%, and the decision tree model has 70%
Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest Intracranial Tumor Volume for Stereotactic Radiosurgery-Treated Brain Metastasis Patients.

PubMed

Hirshman, Brian R; Wilson, Bayard; Ali, Mir Amaan; Proudfoot, James A; Koiso, Takao; Nagano, Osamu; Carter, Bob S; Serizawa, Toru; Yamamoto, Masaaki; Chen, Clark C

2018-04-01

Two intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV). To determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components-LITV-with CITV. We compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients. In both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model. After accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients.
Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees.

PubMed

Price, B; Gomez, A; Mathys, L; Gardi, O; Schellenberger, A; Ginzler, C; Thürig, E

2017-03-01

Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates.
Modelled female sale options demonstrate improved profitability in northern beef herds.

PubMed

Niethe, G E; Holmes, W E

2008-12-01

To examine the impact of improving the average value of cows sold, the risk of decreasing the number weaned, and total sales on the profitability of northern Australian cattle breeding properties. Gather, model and interpret breeder herd performances and production parameters on properties from six beef-producing regions in northern Australia. Production parameters, prices, costs and herd structure were entered into a herd simulation model for six northern Australian breeding properties that spay females to enhance their marketing options. After the data were validated by management, alternative management strategies were modelled using current market prices and most likely herd outcomes. The model predicted a close relationship between the average sale value of cows, the total herd sales and the gross margin/adult equivalent. Keeping breeders out of the herd to fatten generally improves their sale value, and this can be cost-effective, despite the lower number of progeny produced and the subsequent reduction in total herd sales. Furthermore, if the price of culled cows exceeds the price of culled heifers, provided there are sufficient replacement pregnant heifers available to maintain the breeder herd nucleus, substantial gains in profitability can be obtained by decreasing the age at which cows are culled from the herd. Generalised recommendations on improving reproductive performance are not necessarily the most cost-effective strategy to improve breeder herd profitability. Judicious use of simulation models is essential to help develop the best turnoff strategies for females and to improve station profitability.
Team Knowledge Sharing Intervention Effects on Team Shared Mental Models and Student Performance in an Undergraduate Science Course

ERIC Educational Resources Information Center

Sikorski, Eric G.; Johnson, Tristan E.; Ruscher, Paul H.

2012-01-01

The purpose of this study was to examine the effects of a shared mental model (SMM) based intervention on student team mental model similarity and ultimately team performance in an undergraduate meteorology course. The team knowledge sharing (TKS) intervention was designed to promote team reflection, communication, and improvement planning.…
NEIGHBORHOOD SCALE AIR QUALITY MODELING IN HOUSTON USING URBAN CANOPY PARAMETERS IN MM5 AND CMAQ WITH IMPROVED CHARACTERIZATION OF MESOSCALE LAKE-LAND BREEZE CIRCULATION

EPA Science Inventory

Advanced capability of air quality simulation models towards accurate performance at finer scales will be needed for such models to serve as tools for performing exposure and risk assessments in urban areas. It is recognized that the impact of urban features such as street and t...
Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

PubMed

Hunter, A; Kennedy, L; Henry, J; Ferguson, I

2000-05-01

The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.
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.
Hypoglycemia alarm enhancement using data fusion.

PubMed

Skladnev, Victor N; Tarnavskii, Stanislav; McGregor, Thomas; Ghevondian, Nejhdeh; Gourlay, Steve; Jones, Timothy W

2010-01-01

The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features. Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions. The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels. Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms. 2010 Diabetes Technology Society.
Improving the realism of hydrologic model through multivariate parameter estimation

NASA Astrophysics Data System (ADS)

Rakovec, Oldrich; Kumar, Rohini; Attinger, Sabine; Samaniego, Luis

2017-04-01

Increased availability and quality of near real-time observations should improve understanding of predictive skills of hydrological models. Recent studies have shown the limited capability of river discharge data alone to adequately constrain different components of distributed model parameterizations. In this study, the GRACE satellite-based total water storage (TWS) anomaly is used to complement the discharge data with an aim to improve the fidelity of mesoscale hydrologic model (mHM) through multivariate parameter estimation. The study is conducted in 83 European basins covering a wide range of hydro-climatic regimes. The model parameterization complemented with the TWS anomalies leads to statistically significant improvements in (1) discharge simulations during low-flow period, and (2) evapotranspiration estimates which are evaluated against independent (FLUXNET) data. Overall, there is no significant deterioration in model performance for the discharge simulations when complemented by information from the TWS anomalies. However, considerable changes in the partitioning of precipitation into runoff components are noticed by in-/exclusion of TWS during the parameter estimation. A cross-validation test carried out to assess the transferability and robustness of the calibrated parameters to other locations further confirms the benefit of complementary TWS data. In particular, the evapotranspiration estimates show more robust performance when TWS data are incorporated during the parameter estimation, in comparison with the benchmark model constrained against discharge only. This study highlights the value for incorporating multiple data sources during parameter estimation to improve the overall realism of hydrologic model and its applications over large domains. Rakovec, O., Kumar, R., Attinger, S. and Samaniego, L. (2016): Improving the realism of hydrologic model functioning through multivariate parameter estimation. Water Resour. Res., 52, http://dx.doi.org/10.1002/2016WR019430
Improving spatio-temporal model estimation of satellite-derived PM2.5 concentrations: Implications for public health

NASA Astrophysics Data System (ADS)

Barik, M. G.; Al-Hamdan, M. Z.; Crosson, W. L.; Yang, C. A.; Coffield, S. R.

2017-12-01

Satellite-derived environmental data, available in a range of spatio-temporal scales, are contributing to the growing use of health impact assessments of air pollution in the public health sector. Models developed using correlation of Moderate Resolution Imaging Spectrometer (MODIS) Aerosol Optical Depth (AOD) with ground measurements of fine particulate matter less than 2.5 microns (PM2.5) are widely applied to measure PM2.5 spatial and temporal variability. In the public health sector, associations of PM2.5 with respiratory and cardiovascular diseases are often investigated to quantify air quality impacts on these health concerns. In order to improve predictability of PM2.5 estimation using correlation models, we have included meteorological variables, higher-resolution AOD products and instantaneous PM2.5 observations into statistical estimation models. Our results showed that incorporation of high-resolution (1-km) Multi-Angle Implementation of Atmospheric Correction (MAIAC)-generated MODIS AOD, meteorological variables and instantaneous PM2.5 observations improved model performance in various parts of California (CA), USA, where single variable AOD-based models showed relatively weak performance. In this study, we further asked whether these improved models actually would be more successful for exploring associations of public health outcomes with estimated PM2.5. To answer this question, we geospatially investigated model-estimated PM2.5's relationship with respiratory and cardiovascular diseases such as asthma, high blood pressure, coronary heart disease, heart attack and stroke in CA using health data from the Centers for Disease Control and Prevention (CDC)'s Wide-ranging Online Data for Epidemiologic Research (WONDER) and the Behavioral Risk Factor Surveillance System (BRFSS). PM2.5 estimation from these improved models have the potential to improve our understanding of associations between public health concerns and air quality.
A Positive Deviance Approach to Understanding Key Features to Improving Diabetes Care in the Medical Home

PubMed Central

Gabbay, Robert A.; Friedberg, Mark W.; Miller-Day, Michelle; Cronholm, Peter F.; Adelman, Alan; Schneider, Eric C.

2013-01-01

PURPOSE The medical home has gained national attention as a model to reorganize primary care to improve health outcomes. Pennsylvania has undertaken one of the largest state-based, multipayer medical home pilot projects. We used a positive deviance approach to identify and compare factors driving the care models of practices showing the greatest and least improvement in diabetes care in a sample of 25 primary care practices in southeast Pennsylvania. METHODS We ranked practices into improvement quintiles on the basis of the average absolute percentage point improvement from baseline to 18 months in 3 registry-based measures of performance related to diabetes care: glycated hemoglobin concentration, blood pressure, and low-density lipoprotein cholesterol level. We then conducted surveys and key informant interviews with leaders and staff in the 5 most and least improved practices, and compared their responses. RESULTS The most improved/higher-performing practices tended to have greater structural capabilities (eg, electronic health records) than the least improved/lower-performing practices at baseline. Interviews revealed striking differences between the groups in terms of leadership styles and shared vision; sense, use, and development of teams; processes for monitoring progress and obtaining feedback; and presence of technologic and financial distractions. CONCLUSIONS Positive deviance analysis suggests that primary care practices’ baseline structural capabilities and abilities to buffer the stresses of change may be key facilitators of performance improvement in medical home transformations. Attention to the practices’ structural capabilities and factors shaping successful change, especially early in the process, will be necessary to improve the likelihood of successful medical home transformation and better care. PMID:23690393

Intercomparison of the community multiscale air quality model and CALGRID using process analysis.

PubMed

O'Neill, Susan M; Lamb, Brian K

2005-08-01

This study was designed to examine the similarities and differences between two advanced photochemical air quality modeling systems: EPA Models-3/CMAQ and CALGRID/CALMET. Both modeling systems were applied to an ozone episode that occurred along the I-5 urban corridor in western Washington and Oregon during July 11-14, 1996. Both models employed the same modeling domain and used the same detailed gridded emission inventory. The CMAQ model was run using both the CB-IV and RADM2 chemical mechanisms, while CALGRID was used with the SAPRC-97 chemical mechanism. Outputfrom the Mesoscale Meteorological Model (MM5) employed with observational nudging was used in both models. The two modeling systems, representing three chemical mechanisms and two sets of meteorological inputs, were evaluated in terms of statistical performance measures for both 1- and 8-h average observed ozone concentrations. The results showed that the different versions of the systems were more similar than different, and all versions performed well in the Portland region and downwind of Seattle but performed poorly in the more rural region north of Seattle. Improving the meteorological input into the CALGRID/CALMET system with planetary boundary layer (PBL) parameters from the Models-3/CMAQ meteorology preprocessor (MCIP) improved the performance of the CALGRID/CALMET system. The 8-h ensemble case was often the best performer of all the cases indicating that the models perform better over longer analysis periods. The 1-h ensemble case, derived from all runs, was not necessarily an improvement over the five individual cases, but the standard deviation about the mean provided a measure of overall modeling uncertainty. Process analysis was applied to examine the contribution of the individual processes to the species conservation equation. The process analysis results indicated that the two modeling systems arrive at similar solutions by very different means. Transport rates are faster and exhibit greater fluctuations in the CMAQ cases than in the CALGRID cases, which lead to different placement of the urban ozone plumes. The CALGRID cases, which rely on the SAPRC97 chemical mechanism, exhibited a greater diurnal production/loss cycle of ozone concentrations per hour compared to either the RADM2 or CBIV chemical mechanisms in the CMAQ cases. These results demonstrate the need for specialized process field measurements to confirm whether we are modeling ozone with valid processes.
The role of interior watershed processes in improving parameter estimation and performance of watershed models

USDA-ARS?s Scientific Manuscript database

Watershed models typically are evaluated solely through comparison of in-stream water and nutrient fluxes with measured data using established performance criteria, whereas processes and responses within the interior of the watershed that govern these global fluxes often are neglected. Due to the l...
Rationale and Resources for Teaching the Mathematical Modeling of Athletic Training and Performance

ERIC Educational Resources Information Center

Clarke, David C.; Skiba, Philip F.

2013-01-01

A number of professions rely on exercise prescription to improve health or athletic performance, including coaching, fitness/personal training, rehabilitation, and exercise physiology. It is therefore advisable that the professionals involved learn the various tools available for designing effective training programs. Mathematical modeling of…
Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.
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.
Microphysics, Radiation and Surface Processes in the Goddard Cumulus Ensemble (GCE) Model

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2002-01-01

In this talk, five specific major GCE improvements: (1) ice microphysics, (2) longwave and shortwave radiative transfer processes, (3) land surface processes, (4) ocean surface fluxes and (5) ocean mixed layer processes are presented. The performance of these new GCE improvements will be examined. Observations are used for model validation.
Performance improvement with patient service partners.

PubMed

Burns, J P

1998-01-01

Once the decision is made to use a patient-focused care delivery system, a variety of methods can be used to successfully design the model. The author describes the process used by a multilevel, multidisciplinary team at a community hospital to design and implement a Service Partner role that would meet and exceed customer expectations. Demonstrated performance improvements included increased patient satisfaction, productive labor dollar savings, and improvements in the work environment for staff members.
Investigation into Improvement for Anti-Rollover Propensity of SUV

NASA Astrophysics Data System (ADS)

Xiong, Fei; Lan, Fengchong; Chen, Jiqing; Yang, Yuedong

2017-05-01

Currently, many research from domestic and foreign on improving anti-rollover performance of vehicle mainly focus on the electronic control of auxiliary equipment, do not make full use of suspension layout to optimize anti-rollover performance of vehicle. This investigation into anti-rollover propensity improvement concentrates on the vehicle parameters greatly influencing on anti-rollover propensity of vehicle. A simulation based on fishhook procedure is used to perform design trials and evaluations aimed at ensuring an optimal balance between vehicle's design parameters and various engineering capacities, the anti-rollover propensity is optimized at the detailed design stage of a new SUV model. Firstly a four-DOF theoretical kinematic model is established, then a complete multi-body dynamics model built in ADAMS/car based on the whole vehicle parameters is correlated to the objective handing and stability test results of a mule car. Secondly, in fishhook test simulations, the Design of Experiments method is used to quantify the effect of the vehicle parameters on the anti-rollover performance. By means of the simulation, the roll center height of front suspension should be more than 30 mm, that of rear suspension less than 150 mm, and the HCG less than 620 mm for the SUV. The ratio of front to rear suspension roll stiffness should be ranged from 1.4 to 1.6 for the SUV. As a result, at the detailed design stage of product, the anti-rollover performance of vehicle can be improved by optimizing chassis and integrated vehicle parameters.
Shuttle TPS thermal performance and analysis methodology

NASA Technical Reports Server (NTRS)

Neuenschwander, W. E.; Mcbride, D. U.; Armour, G. A.

1983-01-01

Thermal performance of the thermal protection system was approximately as predicted. The only extensive anomalies were filler bar scorching and over-predictions in the high Delta p gap heating regions of the orbiter. A technique to predict filler bar scorching has been developed that can aid in defining a solution. Improvement in high Delta p gap heating methodology is still under study. Minor anomalies were also examined for improvements in modeling techniques and prediction capabilities. These include improved definition of low Delta p gap heating, an analytical model for inner mode line convection heat transfer, better modeling of structure, and inclusion of sneak heating. The limited number of problems related to penetration items that presented themselves during orbital flight tests were resolved expeditiously, and designs were changed and proved successful within the time frame of that program.
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.
Enhancing performance of P300-Speller under mental workload by incorporating dual-task data during classifier training.

PubMed

Chen, Yuqian; Ke, Yufeng; Meng, Guifang; Jiang, Jin; Qi, Hongzhi; Jiao, Xuejun; Xu, Minpeng; Zhou, Peng; He, Feng; Ming, Dong

2017-12-01

As one of the most important brain-computer interface (BCI) paradigms, P300-Speller was shown to be significantly impaired once applied in practical situations due to effects of mental workload. This study aims to provide a new method of building training models to enhance performance of P300-Speller under mental workload. Three experiment conditions based on row-column P300-Speller paradigm were performed including speller-only, 3-back-speller and mental-arithmetic-speller. Data under dual-task conditions were introduced to speller-only data respectively to build new training models. Then performance of classifiers with different models was compared under the same testing condition. The results showed that when tasks of imported training data and testing data were the same, character recognition accuracies and round accuracies of P300-Speller with mixed-data training models significantly improved (FDR, p < 0.005). When they were different, performance significantly improved when tested on mental-arithmetic-speller (FDR, p < 0.05) while the improvement was modest when tested on n-back-speller (FDR, p < 0.1). The analysis of ERPs revealed that ERP difference between training data and testing data was significantly diminished when the dual-task data was introduced to training data (FDR, p < 0.05). The new method of training classifier on mixed data proved to be effective in enhancing performance of P300-Speller under mental workload, confirmed the feasibility to build a universal training model and overcome the effects of mental workload in its practical applications. Copyright © 2017 Elsevier B.V. All rights reserved.
Effects of intra-aortic balloon pump counterpulsation on left ventricular mechanoenergetics in a porcine model of acute ischemic heart failure.

PubMed

Malliaras, Konstantinos; Charitos, Efstratios; Diakos, Nikolaos; Pozios, Iraklis; Papalois, Apostolos; Terrovitis, John; Nanas, John

2014-12-01

We investigated the effects of intra-aortic balloon pump (IABP) counterpulsation on left ventricular (LV) contractility, relaxation, and energy consumption and probed the underlying physiologic mechanisms in 12 farm pigs, using an ischemia-reperfusion model of acute heart failure. During both ischemia and reperfusion, IABP support unloaded the LV, decreased LV energy consumption (pressure-volume area, stroke work), and concurrently improved LV mechanical performance (ejection fraction, stroke volume, cardiac output). During reperfusion exclusively, IABP also improved LV relaxation (tau) and contractility (Emax, PRSW). The beneficial effects of IABP support on LV relaxation and contractility correlated with IABP-induced augmentation of coronary blood flow. In conclusion, we find that during both ischemia and reperfusion, IABP support optimizes LV energetic performance (decreases energy consumption and concurrently improves mechanical performance) by LV unloading. During reperfusion exclusively, IABP support also improves LV contractility and active relaxation, possibly due to a synergistic effect of unloading and augmentation of coronary blood flow.
IMPROVED ALGORITHMS FOR RADAR-BASED RECONSTRUCTION OF ASTEROID SHAPES

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

Greenberg, Adam H.; Margot, Jean-Luc

We describe our implementation of a global-parameter optimizer and Square Root Information Filter into the asteroid-modeling software shape. We compare the performance of our new optimizer with that of the existing sequential optimizer when operating on various forms of simulated data and actual asteroid radar data. In all cases, the new implementation performs substantially better than its predecessor: it converges faster, produces shape models that are more accurate, and solves for spin axis orientations more reliably. We discuss potential future changes to improve shape's fitting speed and accuracy.
Simulating the role of visual selective attention during the development of perceptual completion

PubMed Central

Schlesinger, Matthew; Amso, Dima; Johnson, Scott P.

2014-01-01

We recently proposed a multi-channel, image-filtering model for simulating the development of visual selective attention in young infants (Schlesinger, Amso & Johnson, 2007). The model not only captures the performance of 3-month-olds on a visual search task, but also implicates two cortical regions that may play a role in the development of visual selective attention. In the current simulation study, we used the same model to simulate 3-month-olds’ performance on a second measure, the perceptual unity task. Two parameters in the model – corresponding to areas in the occipital and parietal cortices – were systematically varied while the gaze patterns produced by the model were recorded and subsequently analyzed. Three key findings emerged from the simulation study. First, the model successfully replicated the performance of 3-month-olds on the unity perception task. Second, the model also helps to explain the improved performance of 2-month-olds when the size of the occluder in the unity perception task is reduced. Third, in contrast to our previous simulation results, variation in only one of the two cortical regions simulated (i.e. recurrent activity in posterior parietal cortex) resulted in a performance pattern that matched 3-month-olds. These findings provide additional support for our hypothesis that the development of perceptual completion in early infancy is promoted by progressive improvements in visual selective attention and oculomotor skill. PMID:23106728
Simulating the role of visual selective attention during the development of perceptual completion.

PubMed

Schlesinger, Matthew; Amso, Dima; Johnson, Scott P

2012-11-01

We recently proposed a multi-channel, image-filtering model for simulating the development of visual selective attention in young infants (Schlesinger, Amso & Johnson, 2007). The model not only captures the performance of 3-month-olds on a visual search task, but also implicates two cortical regions that may play a role in the development of visual selective attention. In the current simulation study, we used the same model to simulate 3-month-olds' performance on a second measure, the perceptual unity task. Two parameters in the model - corresponding to areas in the occipital and parietal cortices - were systematically varied while the gaze patterns produced by the model were recorded and subsequently analyzed. Three key findings emerged from the simulation study. First, the model successfully replicated the performance of 3-month-olds on the unity perception task. Second, the model also helps to explain the improved performance of 2-month-olds when the size of the occluder in the unity perception task is reduced. Third, in contrast to our previous simulation results, variation in only one of the two cortical regions simulated (i.e. recurrent activity in posterior parietal cortex) resulted in a performance pattern that matched 3-month-olds. These findings provide additional support for our hypothesis that the development of perceptual completion in early infancy is promoted by progressive improvements in visual selective attention and oculomotor skill. © 2012 Blackwell Publishing Ltd.
Projecting technology change to improve space technology planning and systems management

NASA Astrophysics Data System (ADS)

Walk, Steven Robert

2011-04-01

Projecting technology performance evolution has been improving over the years. Reliable quantitative forecasting methods have been developed that project the growth, diffusion, and performance of technology in time, including projecting technology substitutions, saturation levels, and performance improvements. These forecasts can be applied at the early stages of space technology planning to better predict available future technology performance, assure the successful selection of technology, and improve technology systems management strategy. Often what is published as a technology forecast is simply scenario planning, usually made by extrapolating current trends into the future, with perhaps some subjective insight added. Typically, the accuracy of such predictions falls rapidly with distance in time. Quantitative technology forecasting (QTF), on the other hand, includes the study of historic data to identify one of or a combination of several recognized universal technology diffusion or substitution patterns. In the same manner that quantitative models of physical phenomena provide excellent predictions of system behavior, so do QTF models provide reliable technological performance trajectories. In practice, a quantitative technology forecast is completed to ascertain with confidence when the projected performance of a technology or system of technologies will occur. Such projections provide reliable time-referenced information when considering cost and performance trade-offs in maintaining, replacing, or migrating a technology, component, or system. This paper introduces various quantitative technology forecasting techniques and illustrates their practical application in space technology and technology systems management.
Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

NASA Astrophysics Data System (ADS)

Ma, Chaoqun; Wang, Tijian; Zang, Zengliang; Li, Zhijin

2018-07-01

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation (DA) and model output statistics (MOS). However, the relative importance and combined effects of the two techniques have not been clarified. Here, a one-month air quality forecast with the Weather Research and Forecasting-Chemistry (WRF-Chem) model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational (3DVar) DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3DVar DA in improving the operational forecasting ability of WRF-Chem.
Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.
Design, Modeling and Performance Optimization of a Novel Rotary Piezoelectric Motor

NASA Technical Reports Server (NTRS)

Duong, Khanh A.; Garcia, Ephrahim

1997-01-01

This work has demonstrated a proof of concept for a torsional inchworm type motor. The prototype motor has shown that piezoelectric stack actuators can be used for rotary inchworm motor. The discrete linear motion of piezoelectric stacks can be converted into rotary stepping motion. The stacks with its high force and displacement output are suitable actuators for use in piezoelectric motor. The designed motor is capable of delivering high torque and speed. Critical issues involving the design and operation of piezoelectric motors were studied. The tolerance between the contact shoes and the rotor has proved to be very critical to the performance of the motor. Based on the prototype motor, a waveform optimization scheme was proposed and implemented to improve the performance of the motor. The motor was successfully modeled in MATLAB. The model closely represents the behavior of the prototype motor. Using the motor model, the input waveforms were successfully optimized to improve the performance of the motor in term of speed, torque, power and precision. These optimized waveforms drastically improve the speed of the motor at different frequencies and loading conditions experimentally. The optimized waveforms also increase the level of precision of the motor. The use of the optimized waveform is a break-away from the traditional use of sinusoidal and square waves as the driving signals. This waveform optimization scheme can be applied to any inchworm motors to improve their performance. The prototype motor in this dissertation as a proof of concept was designed to be robust and large. Future motor can be designed much smaller and more efficient with lessons learned from the prototype motor.
Slushy weightings for the optimal pilot model. [considering visual tracking task

NASA Technical Reports Server (NTRS)

Dillow, J. D.; Picha, D. G.; Anderson, R. O.

1975-01-01

A pilot model is described which accounts for the effect of motion cues in a well defined visual tracking task. The effect of visual and motion cues are accounted for in the model in two ways. First, the observation matrix in the pilot model is structured to account for the visual and motion inputs presented to the pilot. Secondly, the weightings in the quadratic cost function associated with the pilot model are modified to account for the pilot's perception of the variables he considers important in the task. Analytic results obtained using the pilot model are compared to experimental results and in general good agreement is demonstrated. The analytic model yields small improvements in tracking performance with the addition of motion cues for easily controlled task dynamics and large improvements in tracking performance with the addition of motion cues for difficult task dynamics.

Polar versus Cartesian velocity models for maneuvering target tracking with IMM

NASA Astrophysics Data System (ADS)

Laneuville, Dann

This paper compares various model sets in different IMM filters for the maneuvering target tracking problem. The aim is to see whether we can improve the tracking performance of what is certainly the most widely used model set in the literature for the maneuvering target tracking problem: a Nearly Constant Velocity model and a Nearly Coordinated Turn model. Our new challenger set consists of a mixed Cartesian position and polar velocity state vector to describe the uniform motion segments and is augmented with the turn rate to obtain the second model for the maneuvering segments. This paper also gives a general procedure to discretize up to second order any non-linear continuous time model with linear diffusion. Comparative simulations on an air defence scenario with a 2D radar, show that this new approach improves significantly the tracking performance in this case.
Roll and pitch independently tuned interconnected suspension: modelling and dynamic analysis

NASA Astrophysics Data System (ADS)

Xu, Guangzhong; Zhang, Nong; Roser, Holger M.

2015-12-01

In this paper, a roll and pitch independently tuned hydraulically interconnected passive suspension is presented. Due to decoupling of vibration modes and the improved lateral and longitudinal stability, the stiffness of individual suspension spring can be reduced for improving ride comfort and road grip. A generalised 14 degree-of-freedom nonlinear vehicle model with anti-roll bars is established to investigate the vehicle ride and handling dynamic responses. The nonlinear fluidic model of the hydraulically interconnected suspension is developed and integrated with the full vehicle model to investigate the anti-roll and anti-pitch characteristics. Time domain analysis of the vehicle model with the proposed suspension is conducted under different road excitations and steering/braking manoeuvres. The dynamic responses are compared with conventional suspensions to demonstrate the potential of enhanced ride and handling performance. The results illustrate the model-decoupling property of the hydraulically interconnected system. The anti-roll and anti-pitch performance could be tuned independently by the interconnected systems. With the improved anti-roll and anti-pitch characteristics, the bounce stiffness and ride damping can be optimised for better ride comfort and tyre grip.
Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent LER Calculations

NASA Astrophysics Data System (ADS)

Fasnacht, Z.; Qin, W.; Haffner, D. P.; Loyola, D. G.; Joiner, J.; Krotkov, N. A.; Vasilkov, A. P.; Spurr, R. J. D.

2017-12-01

In order to estimate surface reflectance used in trace gas retrieval algorithms, radiative transfer models (RTM) such as the Vector Linearized Discrete Ordinate Radiative Transfer Model (VLIDORT) can be used to simulate the top of the atmosphere (TOA) radiances with advanced models of surface properties. With large volumes of satellite data, these model simulations can become computationally expensive. Look up table interpolation can improve the computational cost of the calculations, but the non-linear nature of the radiances requires a dense node structure if interpolation errors are to be minimized. In order to reduce our computational effort and improve the performance of look-up tables, neural networks can be trained to predict these radiances. We investigate the impact of using look-up table interpolation versus a neural network trained using the smart sampling technique, and show that neural networks can speed up calculations and reduce errors while using significantly less memory and RTM calls. In future work we will implement a neural network in operational processing to meet growing demands for reflectance modeling in support of high spatial resolution satellite missions.
RTC simulations on large branched sewer systems with SmaRTControl.

PubMed

de Korte, Kees; van Beest, Dick; van der Plaat, Marcel; de Graaf, Erno; Schaart, Niels

2009-01-01

In The Netherlands many large branched sewer systems exist. RTC can improve the performance of these systems. The objective of the universal algorithm of SmaRTControl is to improve the performance of the sewer system and the WWTP. The effect of RTC under rain weather flow conditions is simulated using a hydrological model with 19 drainage districts. The system related inefficiency coefficient (SIC) is introduced for assessment of the performance of sewer systems. The performance can be improved by RTC in combination with increased pumping capacities in the drainage districts, but without increasing the flow to the WWTP. Under dry weather flow conditions the flow to the WWTP can be equalized by storage of wastewater in the sewer system. It is concluded that SmaRTControl can improve the performance, that simulations are necessary and that SIC is an excellent parameter for assessment of the performance.
Discretization in time gives rise to noise-induced improvement of the signal-to-noise ratio in static nonlinearities.

PubMed

Davidović, A; Huntington, E H; Frater, M R

2009-07-01

For some nonlinear systems the performance can improve with an increasing noise level. Such noise-induced improvement in static nonlinearities is of great interest for practical applications since many systems can be modeled in that way (e.g., sensors, quantizers, limiters, etc.). We present experimental evidence that noise-induced performance improvement occurs in those systems as a consequence of discretization in time with the achievable signal-to-noise ratio (SNR) gain increasing with decreasing ratio of input noise bandwidth and total measurement bandwidth. By modifying the input noise bandwidth, noise-induced improvement with SNR gain larger than unity is demonstrated in a system where it was not previously thought possible. Our experimental results bring closer two different theoretical models for the same class of nonlinearities and shed light on the behavior of static nonlinear discrete-time systems.
Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).

PubMed

Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Sadeghfam, Sina; Moghaddam, Asghar Asghari

2017-01-01

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters. Copyright © 2016 Elsevier B.V. All rights reserved.
Blended Learning Improves Science Education.

PubMed

Stockwell, Brent R; Stockwell, Melissa S; Cennamo, Michael; Jiang, Elise

2015-08-27

Blended learning is an emerging paradigm for science education but has not been rigorously assessed. We performed a randomized controlled trial of blended learning. We found that in-class problem solving improved exam performance, and video assignments increased attendance and satisfaction. This validates a new model for science communication and education. Copyright © 2015 Elsevier Inc. All rights reserved.
The effects of speech production and vocabulary training on different components of spoken language performance.

PubMed

Paatsch, Louise E; Blamey, Peter J; Sarant, Julia Z; Bow, Catherine P

2006-01-01

A group of 21 hard-of-hearing and deaf children attending primary school were trained by their teachers on the production of selected consonants and on the meanings of selected words. Speech production, vocabulary knowledge, reading aloud, and speech perception measures were obtained before and after each type of training. The speech production training produced a small but significant improvement in the percentage of consonants correctly produced in words. The vocabulary training improved knowledge of word meanings substantially. Performance on speech perception and reading aloud were significantly improved by both types of training. These results were in accord with the predictions of a mathematical model put forward to describe the relationships between speech perception, speech production, and language measures in children (Paatsch, Blamey, Sarant, Martin, & Bow, 2004). These training data demonstrate that the relationships between the measures are causal. In other words, improvements in speech production and vocabulary performance produced by training will carry over into predictable improvements in speech perception and reading scores. Furthermore, the model will help educators identify the most effective methods of improving receptive and expressive spoken language for individual children who are deaf or hard of hearing.
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.
Modifying the Toyota Production System for continuous performance improvement in an academic children's hospital.

PubMed

Stapleton, F Bruder; Hendricks, James; Hagan, Patrick; DelBeccaro, Mark

2009-08-01

The Toyota Production System (TPS) has become a successful model for improving efficiency and eliminating errors in manufacturing processes. In an effort to provide patients and families with the highest quality clinical care, our academic children's hospital has modified the techniques of the TPS for a program in continuous performance improvement (CPI) and has expanded its application to educational and research programs. Over a period of years, physicians, nurses, residents, administrators, and hospital staff have become actively engaged in a culture of continuous performance improvement. This article provides background into the methods of CPI and describes examples of how we have applied these methods for improvement in clinical care, resident teaching, and research administration.
Improved MIMO radar GMTI via cyclic-shift transmission of orthogonal frequency division signals

NASA Astrophysics Data System (ADS)

Li, Fuyou; He, Feng; Dong, Zhen; Wu, Manqing

2018-05-01

Minimum detectable velocity (MDV) and maximum detectable velocity are both important in ground moving target indication (GMTI) systems. Smaller MDV can be achieved by longer baseline via multiple-input multiple-output (MIMO) radar. Maximum detectable velocity is decided by blind velocities associated with carrier frequencies, and blind velocities can be mitigated by orthogonal frequency division signals. However, the scattering echoes from different carrier frequencies are independent, which is not good for improving MDV performance. An improved cyclic-shift transmission is applied in MIMO GMTI system in this paper. MDV performance is improved due to the longer baseline, and maximum detectable velocity performance is improved due to the mitigation of blind velocities via multiple carrier frequencies. The signal model for this mode is established, the principle of mitigating blind velocities with orthogonal frequency division signals is presented; the performance of different MIMO GMTI waveforms is analysed; and the performance of different array configurations is analysed. Simulation results by space-time-frequency adaptive processing proves that our proposed method is a valid way to improve GMTI performance.
[The methods of assessment of health risk from exposure to radon and radon daughters].

PubMed

Demin, V F; Zhukovskiy, M V; Kiselev, S M

2014-01-01

The critical analysis of existing models of the relationship dose-effect (RDE) for radon exposure on human health has been performed. Conclusion about the necessity and possibility of improving these models has been made. A new improved version ofthe RDE has been developed. A technique for assessing the human health risk of exposure to radon, including the method for estimating of exposure doses of radon, an improved model of RDE, proper methodology risk assessment has been described. Methodology is proposed for the use in the territory of Russia.
Considerations on the Assessment and Use of Cycling Performance Metrics and their Integration in the Athlete's Biological Passport.

PubMed

Menaspà, Paolo; Abbiss, Chris R

2017-01-01

Over the past few decades the possibility to capture real-time data from road cyclists has drastically improved. Given the increasing pressure for improved transparency and openness, there has been an increase in publication of cyclists' physiological and performance data. Recently, it has been suggested that the use of such performance biometrics may be used to strengthen the sensitivity and applicability of the Athlete Biological Passport (ABP) and aid in the fight against doping. This is an interesting concept which has merit, although there are several important factors that need to be considered. These factors include accuracy of the data collected and validity (and reliability) of the subsequent performance modeling. In order to guarantee high quality standards, the implementation of well-structured Quality-Systems within sporting organizations should be considered, and external certifications may be required. Various modeling techniques have been developed, many of which are based on fundamental intensity/time relationships. These models have increased our understanding of performance but are currently limited in their application, for example due to the largely unaccounted effects of environmental factors such as, heat and altitude. In conclusion, in order to use power data as a performance biometric to be integrated in the biological passport, a number of actions must be taken to ensure accuracy of the data and better understand road cycling performance in the field. This article aims to outline considerations in the quantification of cycling performance, also presenting an alternative method (i.e., monitoring race results) to allow for determination of unusual performance improvements.
Considerations on the Assessment and Use of Cycling Performance Metrics and their Integration in the Athlete's Biological Passport

PubMed Central

Menaspà, Paolo; Abbiss, Chris R.

2017-01-01

Over the past few decades the possibility to capture real-time data from road cyclists has drastically improved. Given the increasing pressure for improved transparency and openness, there has been an increase in publication of cyclists' physiological and performance data. Recently, it has been suggested that the use of such performance biometrics may be used to strengthen the sensitivity and applicability of the Athlete Biological Passport (ABP) and aid in the fight against doping. This is an interesting concept which has merit, although there are several important factors that need to be considered. These factors include accuracy of the data collected and validity (and reliability) of the subsequent performance modeling. In order to guarantee high quality standards, the implementation of well-structured Quality-Systems within sporting organizations should be considered, and external certifications may be required. Various modeling techniques have been developed, many of which are based on fundamental intensity/time relationships. These models have increased our understanding of performance but are currently limited in their application, for example due to the largely unaccounted effects of environmental factors such as, heat and altitude. In conclusion, in order to use power data as a performance biometric to be integrated in the biological passport, a number of actions must be taken to ensure accuracy of the data and better understand road cycling performance in the field. This article aims to outline considerations in the quantification of cycling performance, also presenting an alternative method (i.e., monitoring race results) to allow for determination of unusual performance improvements. PMID:29163232
The development of furrower model blade to paddlewheel aerator for improving aeration efficiency

NASA Astrophysics Data System (ADS)

Bahri, Samsul; Praeko Agus Setiawan, Radite; Hermawan, Wawan; Zairin Junior, Muhammad

2018-05-01

The successful of intensive aquaculture is strongly influenced by the ability of the farmers to overcome the deterioration of water quality. The problem is low dissolved oxygen through aeration process. The aerator device which widely used in pond farming is paddle wheel aerator because it is the best aerator in aeration mechanism and usable driven power. However, this aerator still has a low performance of aeration, so that the cost of aerator operational for aquaculture is still high. Up to now, the effort to improve the performance of aeration was made by two-dimensional blade design. Obviously, it does not provide the optimum result due to the power requirements for aeration is directly proportional to the increase of aeration rate. The aim of this research is to develop three-dimensional model furrowed blades. Design of Furrower model blades was 1.6 cm diameter hole, 45º of vertical angle blade position and 30º of the horizontal position. The optimum performance furrowed model blades operated on the submerged blade 9 cm with 567.54 Watt of electrical power consumption and 4.322 m3 of splash coverage volume. The standard efficiency aeration is 2.72 kg O2 kWh-1. The furrowed model blades can improve the aeration efficiency of paddlewheel aerator.
Spectral cumulus parameterization based on cloud-resolving model

NASA Astrophysics Data System (ADS)

Baba, Yuya

2018-02-01

We have developed a spectral cumulus parameterization using a cloud-resolving model. This includes a new parameterization of the entrainment rate which was derived from analysis of the cloud properties obtained from the cloud-resolving model simulation and was valid for both shallow and deep convection. The new scheme was examined in a single-column model experiment and compared with the existing parameterization of Gregory (2001, Q J R Meteorol Soc 127:53-72) (GR scheme). The results showed that the GR scheme simulated more shallow and diluted convection than the new scheme. To further validate the physical performance of the parameterizations, Atmospheric Model Intercomparison Project (AMIP) experiments were performed, and the results were compared with reanalysis data. The new scheme performed better than the GR scheme in terms of mean state and variability of atmospheric circulation, i.e., the new scheme improved positive bias of precipitation in western Pacific region, and improved positive bias of outgoing shortwave radiation over the ocean. The new scheme also simulated better features of convectively coupled equatorial waves and Madden-Julian oscillation. These improvements were found to be derived from the modification of parameterization for the entrainment rate, i.e., the proposed parameterization suppressed excessive increase of entrainment, thus suppressing excessive increase of low-level clouds.
Surgical Anatomy of Vaginal Hysterectomy-Impact of a Resident-Constructed Simulation Model.

PubMed

Anand, Mallika; Duffy, Conor P; Vragovic, Olivera; Abbasi, Wafaa; Bell, Shannon L

Obstetrics and gynecology residents are less prepared to perform vaginal hysterectomy (VH), despite its advantages over other hysterectomy routes. The American Congress of Obstetricians and Gynecologists and Council on Resident Education in Obstetrics and Gynecology have prioritized simulation training in VH. Our objective was to improve residents' understanding of surgical anatomy of VH using a resident-constructed, low-cost, low-fidelity model. A single simulation session was held in November 2016. Residents constructed a pelvic model, guided by 2 surgeons. A pretest and a posttest were administered. Experienced-based responses were tabulated for frequencies and contents. Improvement on knowledge-based questions was assessed using McNemar's test. Of 20 residents, 16 completed the pretest and 14 (70%) completed pretests and posttests. One hundred percent of postgraduate year (PGY)-4 had performed greater than 10 VH (11-21) and 75% of PGY-3 had performed 5 to 12 VH. Although 75% of PGY-3 and 100% of PGY-4 felt comfortable performing VH, baseline knowledge of essential surgical anatomy of VH was low (65.8%). The PGY-3 and -4 group (n=8) experienced a mean improvement of 24.4% (mean pretest score 65.8% vs mean posttest score 90%; 95% confidence interval, +14.1% to +33.3%, P=0.0005). The PGY-1 and -2 groups (n=6) experienced a mean improvement of 43.3% (mean pretest score, 41.7% vs mean posttest score, 85%; 95% confidence interval, +26.7% to +59.2%, P=0.001). After the session, all residents reported improved understanding surgical anatomy of VH and "more hands-on sessions" was the most frequently requested teaching aid. Residents desire additional model-based simulation training in VH, and such structured, model-based simulations can identify and address gaps in resident knowledge of surgical anatomy of this important operation.
Mediation analysis of severity of needs, service performance and outcomes for patients with mental disorders.

PubMed

Roux, Paul; Passerieux, Christine; Fleury, Marie-Josée

2016-12-01

Needs and service performance assessment are key components in improving recovery among individuals with mental disorders. To test the role of service performance as a mediating factor between severity of patients' needs and outcomes. A total of 339 adults with mental disorders were interviewed. A mediation analysis between severity of needs, service performance (adequacy of help, continuity of care and recovery orientation of services) and outcomes (personal recovery and quality of life) was carried out using structural equation modelling. The structural equation model provided a good fit with the data. An increase in needs was associated with lower service performance and worse outcomes, whereas higher service performance was associated with better outcomes. Service performance partially mediated the effect of patient needs on outcomes. Poorer service performance has a negative impact on outcomes for patients with the highest needs. Ensuring more efficient services for patients with high needs may help improve their recovery and quality of life. © The Royal College of Psychiatrists 2016.
Learning representations for the early detection of sepsis with deep neural networks.

PubMed

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.
An Improved K-Epsilon Model for Near-Wall Turbulence and Comparison with Direct Numerical Simulation

NASA Technical Reports Server (NTRS)

Shih, T. H.

1990-01-01

An improved k-epsilon model for low Reynolds number turbulence near a wall is presented. The near-wall asymptotic behavior of the eddy viscosity and the pressure transport term in the turbulent kinetic energy equation is analyzed. Based on this analysis, a modified eddy viscosity model, having correct near-wall behavior, is suggested, and a model for the pressure transport term in the k-equation is proposed. In addition, a modeled dissipation rate equation is reformulated. Fully developed channel flows were used for model testing. The calculations using various k-epsilon models are compared with direct numerical simulations. The results show that the present k-epsilon model performs well in predicting the behavior of near-wall turbulence. Significant improvement over previous k-epsilon models is obtained.

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480
Energy-efficient container handling using hybrid model predictive control

NASA Astrophysics Data System (ADS)

Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel

2015-11-01

The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.
Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.
Performance Analysis of Transposition Models Simulating Solar Radiation on Inclined Surfaces: Preprint

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

Xie, Yu; Sengupta, Manajit

2016-06-01

Transposition models are widely used in the solar energy industry to simulate solar radiation on inclined photovoltaic (PV) panels. These transposition models have been developed using various assumptions about the distribution of the diffuse radiation, and most of the parameterizations in these models have been developed using hourly ground data sets. Numerous studies have compared the performance of transposition models, but this paper aims to understand the quantitative uncertainty in the state-of-the-art transposition models and the sources leading to the uncertainty using high-resolution ground measurements in the plane of array. Our results suggest that the amount of aerosol optical depthmore » can affect the accuracy of isotropic models. The choice of empirical coefficients and the use of decomposition models can both result in uncertainty in the output from the transposition models. It is expected that the results of this study will ultimately lead to improvements of the parameterizations as well as the development of improved physical models.« less
Aqua/Aura Updated Inclination Adjust Maneuver Performance Prediction Model

NASA Technical Reports Server (NTRS)

Boone, Spencer

2017-01-01

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

NASA Astrophysics Data System (ADS)

Giuliano, C.; Schumacher, P.; Matson, C.; Chun, F.; Duncan, B.; Borelli, K.; Desonia, R.; Gusciora, G.; Roe, K.

The High Performance Computing Software Applications Institute for Space Situational Awareness (HSAI-SSA) has completed its first full year of applications development. The emphasis of our work in this first year was in improving space surveillance sensor models and image enhancement software. These applications are the Space Surveillance Network Analysis Model (SSNAM), the Air Force Space Fence simulation (SimFence), and physically constrained iterative de-convolution (PCID) image enhancement software tool. Specifically, we have demonstrated order of magnitude speed-up in those codes running on the latest Cray XD-1 Linux supercomputer (Hoku) at the Maui High Performance Computing Center. The software applications improvements that HSAI-SSA has made, has had significant impact to the warfighter and has fundamentally changed the role of high performance computing in SSA.
Numerical Study of the Propulsive Performance of the Hollow Rotating Detonation Engine with a Laval Nozzle

NASA Astrophysics Data System (ADS)

Yao, Songbai; Tang, Xinmeng; Wang, Jianping

2017-04-01

The aim of the present paper is to investigate the propulsive performance of the hollow rotating detonation engine (RDE) with a Laval nozzle. Three-dimensional simulations are carried out with a one-step Arrhenius chemistry model. The Laval nozzle is found to improve the propulsive performance of hollow RDE in all respects. The thrust and fuel-based specific impulse are increased up to 12.60 kN and 7484.40 s, respectively, from 6.46 kN and 6720.48 s. Meanwhile, the total mass flow rate increases from 3.63 kg/s to 6.68 kg/s. Overall, the Laval nozzle significantly improves the propulsive performance of the hollow RDE and makes it a promising model among detonation engines.
Improving riverine constituent concentration and flux estimation by accounting for antecedent discharge conditions

NASA Astrophysics Data System (ADS)

Zhang, Qian; Ball, William P.

2017-04-01

Regression-based approaches are often employed to estimate riverine constituent concentrations and fluxes based on typically sparse concentration observations. One such approach is the recently developed WRTDS ("Weighted Regressions on Time, Discharge, and Season") method, which has been shown to provide more accurate estimates than prior approaches in a wide range of applications. Centered on WRTDS, this work was aimed at developing improved models for constituent concentration and flux estimation by accounting for antecedent discharge conditions. Twelve modified models were developed and tested, each of which contains one additional flow variable to represent antecedent conditions and which can be directly derived from the daily discharge record. High-resolution (∼daily) data at nine diverse monitoring sites were used to evaluate the relative merits of the models for estimation of six constituents - chloride (Cl), nitrate-plus-nitrite (NOx), total Kjeldahl nitrogen (TKN), total phosphorus (TP), soluble reactive phosphorus (SRP), and suspended sediment (SS). For each site-constituent combination, 30 concentration subsets were generated from the original data through Monte Carlo subsampling and then used to evaluate model performance. For the subsampling, three sampling strategies were adopted: (A) 1 random sample each month (12/year), (B) 12 random monthly samples plus additional 8 random samples per year (20/year), and (C) flow-stratified sampling with 12 regular (non-storm) and 8 storm samples per year (20/year). Results reveal that estimation performance varies with both model choice and sampling strategy. In terms of model choice, the modified models show general improvement over the original model under all three sampling strategies. Major improvements were achieved for NOx by the long-term flow-anomaly model and for Cl by the ADF (average discounted flow) model and the short-term flow-anomaly model. Moderate improvements were achieved for SS, TP, and TKN by the ADF model. By contrast, no such achievement was achieved for SRP by any proposed model. In terms of sampling strategy, performance of all models (including the original) was generally best using strategy C and worst using strategy A, and especially so for SS, TP, and SRP, confirming the value of routinely collecting stormflow samples. Overall, this work provides a comprehensive set of statistical evidence for supporting the incorporation of antecedent discharge conditions into the WRTDS model for estimation of constituent concentration and flux, thereby combining the advantages of two recent developments in water quality modeling.
Evaluating the Performance of the ff99SB Force Field Based on NMR Scalar Coupling Data

PubMed Central

Wickstrom, Lauren; Okur, Asim; Simmerling, Carlos

2009-01-01

Abstract Force-field validation is essential for the identification of weaknesses in current models and the development of more accurate models of biomolecules. NMR coupling and relaxation methods have been used to effectively diagnose the strengths and weaknesses of many existing force fields. Studies using the ff99SB force field have shown excellent agreement between experimental and calculated order parameters and residual dipolar calculations. However, recent studies have suggested that ff99SB demonstrates poor agreement with J-coupling constants for short polyalanines. We performed extensive replica-exchange molecular-dynamics simulations on Ala3 and Ala5 in TIP3P and TIP4P-Ew solvent models. Our results suggest that the performance of ff99SB is among the best of currently available models. In addition, scalar coupling constants derived from simulations in the TIP4P-Ew model show a slight improvement over those obtained using the TIP3P model. Despite the overall excellent agreement, the data suggest areas for possible improvement. PMID:19651043
An Improved BeiDou-2 Satellite-Induced Code Bias Estimation Method.

PubMed

Fu, Jingyang; Li, Guangyun; Wang, Li

2018-04-27

Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively) and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP). Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL) ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL) time to first fix (TTFF) in PPP ambiguity resolution (AR) as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have a better performance in these aspects compared with the model which does not account for the CMB correction.
An Improved BeiDou-2 Satellite-Induced Code Bias Estimation Method

PubMed Central

Fu, Jingyang; Li, Guangyun; Wang, Li

2018-01-01

Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively) and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP). Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL) ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL) time to first fix (TTFF) in PPP ambiguity resolution (AR) as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have a better performance in these aspects compared with the model which does not account for the CMB correction. PMID:29702559
Development of an Improved Simulator for Chemical and Microbial EOR Methods

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

Pope, Gary A.; Sepehrnoori, Kamy; Delshad, Mojdeh

2000-09-11

The objective of this research was to extend the capability of an existing simulator (UTCHEM) to improved oil recovery methods that use surfactants, polymers, gels, alkaline chemicals, microorganisms and foam as well as various combinations of these in both conventional and naturally fractured oil reservoirs. Task 1 is the addition of a dual-porosity model for chemical improved of recovery processes in naturally fractured oil reservoirs. Task 2 is the addition of a foam model. Task 3 addresses several numerical and coding enhancements that will greatly improve the versatility and performance of UTCHEM. Task 4 is the enhancements of physical propertymore » models.« less
Updating the Synchrotron Radiation Monitor at TLS

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

Kuo, C. H.; Hsu, S. Y.; Wang, C. J.

2007-01-19

The synchrotron radiation monitor provides useful information to support routine operation and physics experiments using the beam. Precisely knowing the profile of the beam helps to improve machine performance. The synchrotron radiation monitor at the Taiwan Light Source (TLS) was recently upgraded. The optics and modeling were improved to increase the accuracy of measurement in the small beam size. A high-performance IEEE-1394 digital CCD camera was used to improve the quality of images and extend the dynamic range of measurement. The image analysis is also improved. This report summarizes status and results.
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.
Optimizing efficiency and operations at a California safety-net endoscopy center: a modeling and simulation approach.

PubMed

Day, Lukejohn W; Belson, David; Dessouky, Maged; Hawkins, Caitlin; Hogan, Michael

2014-11-01

Improvements in endoscopy center efficiency are needed, but scant data are available. To identify opportunities to improve patient throughput while balancing resource use and patient wait times in a safety-net endoscopy center. Safety-net endoscopy center. Outpatients undergoing endoscopy. A time and motion study was performed and a discrete event simulation model constructed to evaluate multiple scenarios aimed at improving endoscopy center efficiency. Procedure volume and patient wait time. Data were collected on 278 patients. Time and motion study revealed that 53.8 procedures were performed per week, with patients spending 2.3 hours at the endoscopy center. By using discrete event simulation modeling, a number of proposed changes to the endoscopy center were assessed. Decreasing scheduled endoscopy appointment times from 60 to 45 minutes led to a 26.4% increase in the number of procedures performed per week, but also increased patient wait time. Increasing the number of endoscopists by 1 each half day resulted in increased procedure volume, but there was a concomitant increase in patient wait time and nurse utilization exceeding capacity. By combining several proposed scenarios together in the simulation model, the greatest improvement in performance metrics was created by moving patient endoscopy appointments from the afternoon to the morning. In this simulation at 45- and 40-minute appointment times, procedure volume increased by 30.5% and 52.0% and patient time spent in the endoscopy center decreased by 17.4% and 13.0%, respectively. The predictions of the simulation model were found to be accurate when compared with actual changes implemented in the endoscopy center. Findings may not be generalizable to non-safety-net endoscopy centers. The combination of minor, cost-effective changes such as reducing appointment times, minimizing and standardizing recovery time, and making small increases in preprocedure ancillary staff maximized endoscopy center efficiency across a number of performance metrics. Copyright © 2014 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.
Study of skin model and geometry effects on thermal performance of thermal protective fabrics

NASA Astrophysics Data System (ADS)

Zhu, Fanglong; Ma, Suqin; Zhang, Weiyuan

2008-05-01

Thermal protective clothing has steadily improved over the years as new materials and improved designs have reached the market. A significant method that has brought these improvements to the fire service is the NFPA 1971 standard on structural fire fighters’ protective clothing. However, this testing often neglects the effects of cylindrical geometry on heat transmission in flame resistant fabrics. This paper deals with methods to develop cylindrical geometry testing apparatus incorporating novel skin bioheat transfer model to test flame resistant fabrics used in firefighting. Results show that fabrics which shrink during the test can have reduced thermal protective performance compared with the qualities measured with a planar geometry tester. Results of temperature differences between skin simulant sensors of planar and cylindrical tester are also compared. This test method provides a new technique to accurately and precisely characterize the thermal performance of thermal protective fabrics.
A systematic review of evidence for education and training interventions in microsurgery.

PubMed

Ghanem, Ali M; Hachach-Haram, Nadine; Leung, Clement Chi Ming; Myers, Simon Richard

2013-07-01

Over the past decade, driven by advances in educational theory and pressures for efficiency in the clinical environment, there has been a shift in surgical education and training towards enhanced simulation training. Microsurgery is a technical skill with a steep competency learning curve on which the clinical outcome greatly depends. This paper investigates the evidence for educational and training interventions of traditional microsurgical skills courses in order to establish the best evidence practice in education and training and curriculum design. A systematic review of MEDLINE, EMBASE, and PubMed databases was performed to identify randomized control trials looking at educational and training interventions that objectively improved microsurgical skill acquisition, and these were critically appraised using the BestBETs group methodology. The databases search yielded 1,148, 1,460, and 2,277 citations respectively. These were then further limited to randomized controlled trials from which abstract reviews reduced the number to 5 relevant randomised controlled clinical trials. The best evidence supported a laboratory based low fidelity model microsurgical skills curriculum. There was strong evidence that technical skills acquired on low fidelity models transfers to improved performance on higher fidelity human cadaver models and that self directed practice leads to improved technical performance. Although there is significant paucity in the literature to support current microsurgical education and training practices, simulated training on low fidelity models in microsurgery is an effective intervention that leads to acquisition of transferable skills and improved technical performance. Further research to identify educational interventions associated with accelerated skill acquisition is required.
Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736
Fish Assemblage Indicators for the National Rivers and Streams Assessment: Performance of model-based vs. traditionally constructed multimetric indices

EPA Science Inventory

The development of multimetric indices (MMIs) for use in assessing the ecological condition of rivers and streams has advanced in recent years with the use of various types of modeling approaches to factor out the influence of natural variability and improve the performance. Ass...

Statistical prediction with Kanerva's sparse distributed memory

NASA Technical Reports Server (NTRS)

Rogers, David

1989-01-01

A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.
Aerodynamics of High-Lift Configuration Civil Aircraft Model in JAXA

NASA Astrophysics Data System (ADS)

Yokokawa, Yuzuru; Murayama, Mitsuhiro; Ito, Takeshi; Yamamoto, Kazuomi

This paper presents basic aerodynamics and stall characteristics of the high-lift configuration aircraft model JSM (JAXA Standard Model). During research process of developing high-lift system design method, wind tunnel testing at JAXA 6.5m by 5.5m low-speed wind tunnel and Navier-Stokes computation on unstructured hybrid mesh were performed for a realistic configuration aircraft model equipped with high-lift devices, fuselage, nacelle-pylon, slat tracks and Flap Track Fairings (FTF), which was assumed 100 passenger class modern commercial transport aircraft. The testing and the computation aimed to understand flow physics and then to obtain some guidelines for designing a high performance high-lift system. As a result of the testing, Reynolds number effects within linear region and stall region were observed. Analysis of static pressure distribution and flow visualization gave the knowledge to understand the aerodynamic performance. CFD could capture the whole characteristics of basic aerodynamics and clarify flow mechanism which governs stall characteristics even for complicated geometry and its flow field. This collaborative work between wind tunnel testing and CFD is advantageous for improving or has improved the aerodynamic performance.
Cognitive Performance in Older Adults with Stable Heart Failure: Longitudinal Evidence for Stability and Improvement

PubMed Central

Alosco, Michael L.; Garcia, Sarah; Spitznagel, Mary Beth; van Dulmen, Manfred; Cohen, Ronald; Sweet, Lawrence H.; Josephson, Richard; Hughes, Joel; Rosneck, Jim; Gunstad, John

2013-01-01

Cognitive impairment is prevalent in heart failure (HF), though substantial variability in the pattern of cognitive impairment is found across studies. To clarify the nature of cognitive impairment in HF, we examined longitudinal trajectories across multiple domains of cognition in HF patients using latent growth class modeling. 115 HF patients completed a neuropsychological battery at baseline, 3-months and 12-months. Participants also completed the Beck Depression Inventory-II (BDI-II). Latent class growth analyses revealed a three-class model for attention/executive function, four-class model for memory, and a three-class model for language. The slope for attention/executive function and language remained stable, while improvements were noted in memory performance. Education and BDI-II significantly predicted the intercept for attention/executive function and language abilities. The BDI-II also predicted baseline memory. The current findings suggest that multiple performance-based classes of neuropsychological test performance exist within cognitive domains, though case-controlled prospective studies with extended follow-ups are needed to fully elucidate changes and predictors of cognitive function in HF. PMID:23906182
Evaluation of an Instructional Model to Teach Clinically Relevant Medicinal Chemistry in a Campus and a Distance Pathway

PubMed Central

Galt, Kimberly A.

2008-01-01

Objectives To evaluate an instructional model for teaching clinically relevant medicinal chemistry. Methods An instructional model that uses Bloom's cognitive and Krathwohl's affective taxonomy, published and tested concepts in teaching medicinal chemistry, and active learning strategies, was introduced in the medicinal chemistry courses for second-professional year (P2) doctor of pharmacy (PharmD) students (campus and distance) in the 2005-2006 academic year. Student learning and the overall effectiveness of the instructional model were assessed. Student performance after introducing the instructional model was compared to that in prior years. Results Student performance on course examinations improved compared to previous years. Students expressed overall enthusiasm about the course and better understood the value of medicinal chemistry to clinical practice. Conclusion The explicit integration of the cognitive and affective learning objectives improved student performance, student ability to apply medicinal chemistry to clinical practice, and student attitude towards the discipline. Testing this instructional model provided validation to this theoretical framework. The model is effective for both our campus and distance-students. This instructional model may also have broad-based applications to other science courses. PMID:18483599
DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Kandler A; Santhanagopalan, Shriram; Yang, Chuanbo

Computer models are helping to accelerate the design and validation of next generation batteries and provide valuable insights not possible through experimental testing alone. Validated 3-D physics-based models exist for predicting electrochemical performance, thermal and mechanical response of cells and packs under normal and abuse scenarios. The talk describes present efforts to make the models better suited for engineering design, including improving their computation speed, developing faster processes for model parameter identification including under aging, and predicting the performance of a proposed electrode material recipe a priori using microstructure models.
Applying knowledge compilation techniques to model-based reasoning

NASA Technical Reports Server (NTRS)

Keller, Richard M.

1991-01-01

Researchers in the area of knowledge compilation are developing general purpose techniques for improving the efficiency of knowledge-based systems. In this article, an attempt is made to define knowledge compilation, to characterize several classes of knowledge compilation techniques, and to illustrate how some of these techniques can be applied to improve the performance of model-based reasoning systems.
Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System

NASA Technical Reports Server (NTRS)

Li, Hui; Faruque, Fazlay; Williams, Worth; Al-Hamdan, Mohammad; Luvall, Jeffrey C.; Crosson, William; Rickman, Douglas; Limaye, Ashutosh

2009-01-01

Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.
Smart caching based on mobile agent of power WebGIS platform.

PubMed

Wang, Xiaohui; Wu, Kehe; Chen, Fei

2013-01-01

Power information construction is developing towards intensive, platform, distributed direction with the expansion of power grid and improvement of information technology. In order to meet the trend, power WebGIS was designed and developed. In this paper, we first discuss the architecture and functionality of power WebGIS, and then we study caching technology in detail, which contains dynamic display cache model, caching structure based on mobile agent, and cache data model. We have designed experiments of different data capacity to contrast performance between WebGIS with the proposed caching model and traditional WebGIS. The experimental results showed that, with the same hardware environment, the response time of WebGIS with and without caching model increased as data capacity growing, while the larger the data was, the higher the performance of WebGIS with proposed caching model improved.
REVIEW OF MECHANISTIC UNDERSTANDING AND MODELING AND UNCERTAINTY ANALYSIS METHODS FOR PREDICTING CEMENTITIOUS BARRIER PERFORMANCE

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

Langton, C.; Kosson, D.

2009-11-30

Cementitious barriers for nuclear applications are one of the primary controls for preventing or limiting radionuclide release into the environment. At the present time, performance and risk assessments do not fully incorporate the effectiveness of engineered barriers because the processes that influence performance are coupled and complicated. Better understanding the behavior of cementitious barriers is necessary to evaluate and improve the design of materials and structures used for radioactive waste containment, life extension of current nuclear facilities, and design of future nuclear facilities, including those needed for nuclear fuel storage and processing, nuclear power production and waste management. The focusmore » of the Cementitious Barriers Partnership (CBP) literature review is to document the current level of knowledge with respect to: (1) mechanisms and processes that directly influence the performance of cementitious materials (2) methodologies for modeling the performance of these mechanisms and processes and (3) approaches to addressing and quantifying uncertainties associated with performance predictions. This will serve as an important reference document for the professional community responsible for the design and performance assessment of cementitious materials in nuclear applications. This review also provides a multi-disciplinary foundation for identification, research, development and demonstration of improvements in conceptual understanding, measurements and performance modeling that would be lead to significant reductions in the uncertainties and improved confidence in the estimating the long-term performance of cementitious materials in nuclear applications. This report identifies: (1) technology gaps that may be filled by the CBP project and also (2) information and computational methods that are in currently being applied in related fields but have not yet been incorporated into performance assessments of cementitious barriers. The various chapters contain both a description of the mechanism or and a discussion of the current approaches to modeling the phenomena.« less
Can Quality Improvement System Improve Childcare Site Performance in School Readiness?

ERIC Educational Resources Information Center

Ma, Xin; Shen, Jianping; Lu, Xuejin; Brandi, Karen; Goodman, Jeff; Watson, Grace

2013-01-01

The authors evaluated the effectiveness of the Quality Improvement System (QIS) developed and implemented by Children's Services Council of Palm Beach County (Florida) as a voluntary initiative to improve the quality of childcare and education. They adopted a growth model approach to investigate whether childcare sites that participated in QIS…
Modeling and analysis of Galfenol cantilever vibration energy harvester with nonlinear magnetic force

NASA Astrophysics Data System (ADS)

Cao, Shuying; Sun, Shuaishuai; Zheng, Jiaju; Wang, Bowen; Wan, Lili; Pan, Ruzheng; Zhao, Ran; Zhang, Changgeng

2018-05-01

Galfenol traditional cantilever energy harvesters (TCEHs) have bigger electrical output only at resonance and exhibit nonlinear mechanical-magnetic-electric coupled (NMMEC) behaviors. To increase low-frequency broadband performances of a TCEH, an improved CEH (ICEH) with magnetic repulsive force is studied. Based on the magnetic dipole model, the nonlinear model of material, the Faraday law and the dynamic principle, a lumped parameter NMMEC model of the devices is established. Comparisons between the calculated and measured results show that the proposed model can provide reasonable data trends of TCEH under acceleration, bias field and different loads. Simulated results show that ICEH exhibits low-frequency resonant, hard spring and bistable behaviors, thus can harvest more low-frequency broadband vibration energy than TCEH, and can elicit snap-through and generate higher voltage even under weak noise. The proposed structure and model are useful for improving performances of the devices.
Primary care access improvement: an empowerment-interaction model.

PubMed

Ledlow, G R; Bradshaw, D M; Shockley, C

2000-05-01

Improving community primary care access is a difficult and dynamic undertaking. Realizing a need to improve appointment availability, a systematic approach based on measurement, empowerment, and interaction was developed. The model fostered exchange of information and problem solving between interdependent staff sections within a managed care system. Measuring appointments demanded but not available proved to be a credible customer-focused approach to benchmark against set goals. Changing the organizational culture to become more sensitive to changing beneficiary needs was a paramount consideration. Dependent-group t tests were performed to compare the pretreatment and posttreatment effect. The empowerment-interaction model significantly improved the availability of routine and wellness-type appointments. The availability of urgent appointments improved but not significantly; a better prospective model needs to be developed. In aggregate, appointments demanded but not available (empowerment-interaction model) were more than 10% before the treatment and less than 3% with the treatment.
Staffs' and managers' perceptions of how and when discrete event simulation modelling can be used as a decision support in quality improvement: a focus group discussion study at two hospital settings in Sweden.

PubMed

Hvitfeldt-Forsberg, Helena; Mazzocato, Pamela; Glaser, Daniel; Keller, Christina; Unbeck, Maria

2017-06-06

To explore healthcare staffs' and managers' perceptions of how and when discrete event simulation modelling can be used as a decision support in improvement efforts. Two focus group discussions were performed. Two settings were included: a rheumatology department and an orthopaedic section both situated in Sweden. Healthcare staff and managers (n=13) from the two settings. Two workshops were performed, one at each setting. Workshops were initiated by a short introduction to simulation modelling. Results from the respective simulation model were then presented and discussed in the following focus group discussion. Categories from the content analysis are presented according to the following research questions: how and when simulation modelling can assist healthcare improvement? Regarding how, the participants mentioned that simulation modelling could act as a tool for support and a way to visualise problems, potential solutions and their effects. Regarding when, simulation modelling could be used both locally and by management, as well as a pedagogical tool to develop and test innovative ideas and to involve everyone in the improvement work. Its potential as an information and communication tool and as an instrument for pedagogic work within healthcare improvement render a broader application and value of simulation modelling than previously reported. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Tenotomy procedure alleviates the "slow to see" phenomenon in infantile nystagmus syndrome: model prediction and patient data.

PubMed

Wang, Z I; Dell'Osso, L F

2008-06-01

Our purpose was to perform a systematic study of the post-four-muscle-tenotomy procedure changes in target acquisition time by comparing predictions from the behavioral ocular motor system (OMS) model and data from infantile nystagmus syndrome (INS) patients. We studied five INS patients who underwent only tenotomy at the enthesis and reattachment at the original insertion of each (previously unoperated) horizontal rectus muscle for their INS treatment. We measured their pre- and post-tenotomy target acquisition changes using data from infrared reflection and high-speed digital video. Three key aspects were calculated and analyzed: the saccadic latency (Ls), the time to target acquisition after the target jump (Lt) and the normalized stimulus time within the cycle. Analyses were performed in MATLAB environment (The MathWorks, Natick, MA) using OMLAB software (OMtools, available from http://www.omlab.org). Model simulations were performed in MATLAB Simulink environment. The model simulation suggested an Lt reduction due to an overall foveation-quality improvement. Consistent with that prediction, improvement in Lt, ranging from approximately 200 ms to approximately 500 ms (average approximately 280 ms), was documented in all five patients post-tenotomy. The Lt improvement was not a result of a reduced Ls. INS patients acquired step-target stimuli faster post-tenotomy. This target acquisition improvement may be due to the elevated foveation quality resulting in less inherent variation in the input to the OMS. A refined behavioral OMS model, with "fast" and "slow" motor neuron pathways and a more physiological plant, successfully predicted this improved visual behavior and again demonstrated its utility in guiding ocular motor research.
Improving emissions inventories in North America through systematic analysis of model performance during ICARTT and MILAGRO

NASA Astrophysics Data System (ADS)

Mena, Marcelo Andres

During 2004 and 2006 the University of Iowa provided air quality forecast support for flight planning of the ICARTT and MILAGRO field campaigns. A method for improvement of model performance in comparison to observations is showed. The method allows identifying sources of model error from boundary conditions and emissions inventories. Simultaneous analysis of horizontal interpolation of model error and error covariance showed that error in ozone modeling is highly correlated to the error of its precursors, and that there is geographical correlation also. During ICARTT ozone modeling error was improved by updating from the National Emissions Inventory from 1999 and 2001, and furthermore by updating large point source emissions from continuous monitoring data. Further improvements were achieved by reducing area emissions of NOx y 60% for states in the Southeast United States. Ozone error was highly correlated to NOy error during this campaign. Also ozone production in the United States was most sensitive to NOx emissions. During MILAGRO model performance in terms of correlation coefficients was higher, but model error in ozone modeling was high due overestimation of NOx and VOC emissions in Mexico City during forecasting. Large model improvements were shown by decreasing NOx emissions in Mexico City by 50% and VOC by 60%. Recurring ozone error is spatially correlated to CO and NOy error. Sensitivity studies show that Mexico City aerosol can reduce regional photolysis rates by 40% and ozone formation by 5-10%. Mexico City emissions can enhance NOy and O3 concentrations over the Gulf of Mexico in up to 10-20%. Mexico City emissions can convert regional ozone production regimes from VOC to NOx limited. A method of interpolation of observations along flight tracks is shown, which can be used to infer on the direction of outflow plumes. The use of ratios such as O3/NOy and NOx/NOy can be used to provide information on chemical characteristics of the plume, such as age, and ozone production regime. Interpolated MTBE observations can be used as a tracer of urban mobile source emissions. Finally procedures for estimating and gridding emissions inventories in Brazil and Mexico are presented.
Development of speed models for improving travel forecasting and highway performance evaluation : [technical summary].

DOT National Transportation Integrated Search

2013-12-01

Travel forecasting models predict travel demand based on the present transportation system and its use. Transportation modelers must develop, validate, and calibrate models to ensure that predicted travel demand is as close to reality as possible. Mo...
Modeling Enclosure Design in Above-Grade Walls

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

Lstiburek, J.; Ueno, K.; Musunuru, S.

2016-03-01

This report describes the modeling of typical wall assemblies that have performed well historically in various climate zones. The WUFI (Warme und Feuchte instationar) software (Version 5.3) model was used. A library of input data and results are provided. The provided information can be generalized for application to a broad population of houses, within the limits of existing experience. The WUFI software model was calibrated or tuned using wall assemblies with historically successful performance. The primary performance criteria or failure criteria establishing historic performance was moisture content of the exterior sheathing. The primary tuning parameters (simulation inputs) were airflow andmore » specifying appropriate material properties. Rational hygric loads were established based on experience - specifically rain wetting and interior moisture (RH levels). The tuning parameters were limited or bounded by published data or experience. The WUFI templates provided with this report supply useful information resources to new or less-experienced users. The files present various custom settings that will help avoid results that will require overly conservative enclosure assemblies. Overall, better material data, consistent initial assumptions, and consistent inputs among practitioners will improve the quality of WUFI modeling, and improve the level of sophistication in the field.« less
Towards an Improved Represenation of Reservoirs and Water Management in a Land Surface-Hydrology Model

NASA Astrophysics Data System (ADS)

Yassin, F.; Anis, M. R.; Razavi, S.; Wheater, H. S.

2017-12-01

Water management through reservoirs, diversions, and irrigation have significantly changed river flow regimes and basin-wide energy and water balance cycles. Failure to represent these effects limits the performance of land surface-hydrology models not only for streamflow prediction but also for the estimation of soil moisture, evapotranspiration, and feedbacks to the atmosphere. Despite recent research to improve the representation of water management in land surface models, there remains a need to develop improved modeling approaches that work in complex and highly regulated basins such as the 406,000 km2 Saskatchewan River Basin (SaskRB). A particular challenge for regional and global application is a lack of local information on reservoir operational management. To this end, we implemented a reservoir operation, water abstraction, and irrigation algorithm in the MESH land surface-hydrology model and tested it over the SaskRB. MESH is Environment Canada's Land Surface-hydrology modeling system that couples Canadian Land Surface Scheme (CLASS) with hydrological routing model. The implemented reservoir algorithm uses an inflow-outflow relationship that accounts for the physical characteristics of reservoirs (e.g., storage-area-elevation relationships) and includes simplified operational characteristics based on local information (e.g., monthly target volume and release under limited, normal, and flood storage zone). The irrigation algorithm uses the difference between actual and potential evapotranspiration to estimate irrigation water demand. This irrigation demand is supplied from the neighboring reservoirs/diversion in the river system. We calibrated the model enabled with the new reservoir and irrigation modules in a multi-objective optimization setting. Results showed that the reservoir and irrigation modules significantly improved the MESH model performance in generating streamflow and evapotranspiration across the SaskRB and that this our approach provides a basis for improved large scale hydrological modelling.
Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732
Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Hot kinetic model as a guide to improve organic photovoltaic materials.

PubMed

Sosorev, Andrey Yu; Godovsky, Dmitry Yu; Paraschuk, Dmitry Yu

2018-01-31

The modeling of organic solar cells (OSCs) can provide a roadmap for their further improvement. Many OSC models have been proposed in recent years; however, the impact of the key intermediates from photons to electricity-hot charge-transfer (CT) states-on the OSC efficiency is highly ambiguous. In this study, we suggest an analytical kinetic model for OSC that considers a two-step charge generation via hot CT states. This hot kinetic model allowed us to evaluate the impact of different material parameters on the OSC performance: the driving force for charge separation, optical bandgap, charge mobility, geminate recombination rate, thermalization rate, average electron-hole separation distance in the CT state, dielectric permittivity, reorganization energy and charge delocalization. In contrast to a widespread trend of lowering the material bandgap, the model predicts that this approach is only efficient along with improvement of the other material properties. The most promising ways to increase the OSC performance are decreasing the reorganization energy, i.e., an energy change accompanying CT from the donor molecule to the acceptor, increasing the dielectric permittivity and charge delocalization. The model suggests that there are no fundamental limitations that can prevent achieving the OSC efficiency above 20%.
A Robust Sound Source Localization Approach for Microphone Array with Model Errors

NASA Astrophysics Data System (ADS)

Xiao, Hua; Shao, Huai-Zong; Peng, Qi-Cong

In this paper, a robust sound source localization approach is proposed. The approach retains good performance even when model errors exist. Compared with previous work in this field, the contributions of this paper are as follows. First, an improved broad-band and near-field array model is proposed. It takes array gain, phase perturbations into account and is based on the actual positions of the elements. It can be used in arbitrary planar geometry arrays. Second, a subspace model errors estimation algorithm and a Weighted 2-Dimension Multiple Signal Classification (W2D-MUSIC) algorithm are proposed. The subspace model errors estimation algorithm estimates unknown parameters of the array model, i. e., gain, phase perturbations, and positions of the elements, with high accuracy. The performance of this algorithm is improved with the increasing of SNR or number of snapshots. The W2D-MUSIC algorithm based on the improved array model is implemented to locate sound sources. These two algorithms compose the robust sound source approach. The more accurate steering vectors can be provided for further processing such as adaptive beamforming algorithm. Numerical examples confirm effectiveness of this proposed approach.
An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

NASA Astrophysics Data System (ADS)

Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

2017-12-01

Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.
Aerodynamic Performance of Hand Launch Glider

NASA Astrophysics Data System (ADS)

Koike, Masaru; Ishii, Mitsuru

In recent years Micro Air Vehicles (MAV) for disaster aerial video are developed vigorously. In order to improve aerodynamic performance of MAV wing performance in low Reynolds numbers (Re) need to be improved, but research on the theme are very rare. In category of Hand Launch Glider, a kind of model aircraft, glide performance are competed, as a result high performance airfoils in Re is around 20,000 are developed. Therefore for MAV's aerodynamic performance improvement airfoils of Hand Launch Gliders should be referred and aerodynamic characteristics of the airfoils desired to be studied. So in this research, aerodynamic characteristics of the gliders are measured in wind tunnel. And also consistency between wind tunnel test and glide test in calm air is examined to confirm reliability of wind tunnel test. Comparison of different airfoils and flow visualization are also performed.
Research and development of energy-efficient appliance motor-compressors. Volume IV. Production demonstration and field test

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

Middleton, M.G.; Sauber, R.S.

Two models of a high-efficiency compressor were manufactured in a pilot production run. These compressors were for low back-pressure applications. While based on a production compressor, there were many changes that required production process changes. Some changes were performed within our company and others were made by outside vendors. The compressors were used in top mount refrigerator-freezers and sold in normal distribution channels. Forty units were placed in residences for a one-year field test. Additional compressors were built so that a life test program could be performed. The results of the field test reveal a 27.0% improvement in energy consumptionmore » for the 18 ft/sup 3/ high-efficiency model and a 15.6% improvement in the 21 ft/sup 3/ improvement in the 21 ft/sup 3/ high-efficiency model as compared to the standard production unit.« less
Validation of X1 motorcycle model in industrial plant layout by using WITNESSTM simulation software

NASA Astrophysics Data System (ADS)

Hamzas, M. F. M. A.; Bareduan, S. A.; Zakaria, M. Z.; Tan, W. J.; Zairi, S.

2017-09-01

This paper demonstrates a case study on simulation, modelling and analysis for X1 Motorcycles Model. In this research, a motorcycle assembly plant has been selected as a main place of research study. Simulation techniques by using Witness software were applied to evaluate the performance of the existing manufacturing system. The main objective is to validate the data and find out the significant impact on the overall performance of the system for future improvement. The process of validation starts when the layout of the assembly line was identified. All components are evaluated to validate whether the data is significance for future improvement. Machine and labor statistics are among the parameters that were evaluated for process improvement. Average total cycle time for given workstations is used as criterion for comparison of possible variants. From the simulation process, the data used are appropriate and meet the criteria for two-sided assembly line problems.
Hypothesis generation using network structures on community health center cancer-screening performance.

PubMed

Carney, Timothy Jay; Morgan, Geoffrey P; Jones, Josette; McDaniel, Anna M; Weaver, Michael T; Weiner, Bryan; Haggstrom, David A

2015-10-01

Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments. Copyright © 2015 Elsevier Inc. All rights reserved.
Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.

PubMed

Fong, Allan; Harriott, Nicole; Walters, Donna M; Foley, Hanan; Morrissey, Richard; Ratwani, Raj R

2017-08-01

Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety. Reviewing and analyzing these reports is often time consuming and resource intensive because of both the quantity of reports and length of free-text descriptions in the reports. Natural language processing (NLP) experts collaborated with clinical experts on a patient safety committee to assist in the identification and analysis of medication related patient safety events. Different NLP algorithmic approaches were developed to identify four types of medication related patient safety events and the models were compared. Well performing NLP models were generated to categorize medication related events into pharmacy delivery delays, dispensing errors, Pyxis discrepancies, and prescriber errors with receiver operating characteristic areas under the curve of 0.96, 0.87, 0.96, and 0.81 respectively. We also found that modeling the brief without the resolution text generally improved model performance. These models were integrated into a dashboard visualization to support the patient safety committee review process. We demonstrate the capabilities of various NLP models and the use of two text inclusion strategies at categorizing medication related patient safety events. The NLP models and visualization could be used to improve the efficiency of patient safety event data review and analysis. Copyright © 2017 Elsevier B.V. All rights reserved.
An improved large signal model of InP HEMTs

NASA Astrophysics Data System (ADS)

Li, Tianhao; Li, Wenjun; Liu, Jun

2018-05-01

An improved large signal model for InP HEMTs is proposed in this paper. The channel current and charge model equations are constructed based on the Angelov model equations. Both the equations for channel current and gate charge models were all continuous and high order drivable, and the proposed gate charge model satisfied the charge conservation. For the strong leakage induced barrier reduction effect of InP HEMTs, the Angelov current model equations are improved. The channel current model could fit DC performance of devices. A 2 × 25 μm × 70 nm InP HEMT device is used to demonstrate the extraction and validation of the model, in which the model has predicted the DC I–V, C–V and bias related S parameters accurately. Project supported by the National Natural Science Foundation of China (No. 61331006).
Economic Benefits of Improved Information on Worldwide Crop Production: An Optimal Decision Model of Production and Distribution with Application to Wheat, Corn, and Soybeans

NASA Technical Reports Server (NTRS)

Andrews, J.

1977-01-01

An optimal decision model of crop production, trade, and storage was developed for use in estimating the economic consequences of improved forecasts and estimates of worldwide crop production. The model extends earlier distribution benefits models to include production effects as well. Application to improved information systems meeting the goals set in the large area crop inventory experiment (LACIE) indicates annual benefits to the United States of $200 to $250 million for wheat, $50 to $100 million for corn, and $6 to $11 million for soybeans, using conservative assumptions on expected LANDSAT system performance.
Improved LTVMPC design for steering control of autonomous vehicle

NASA Astrophysics Data System (ADS)

Velhal, Shridhar; Thomas, Susy

2017-01-01

An improved linear time varying model predictive control for steering control of autonomous vehicle running on slippery road is presented. Control strategy is designed such that the vehicle will follow the predefined trajectory with highest possible entry speed. In linear time varying model predictive control, nonlinear vehicle model is successively linearized at each sampling instant. This linear time varying model is used to design MPC which will predict the future horizon. By incorporating predicted input horizon in each successive linearization the effectiveness of controller has been improved. The tracking performance using steering with front wheel and braking at four wheels are presented to illustrate the effectiveness of the proposed method.
Effect of citizen engagement levels in flood forecasting by assimilating crowdsourced observations in hydrological models

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Cortes Arevalo, Juliette; Alfonso, Leonardo; Wehn, Uta; Norbiato, Daniele; Monego, Martina; Ferri, Michele; Solomatine, Dimitri

2017-04-01

In the past years, a number of methods have been proposed to reduce uncertainty in flood prediction by means of model updating techniques. Traditional physical observations are usually integrated into hydrological and hydraulic models to improve model performances and consequent flood predictions. Nowadays, low-cost sensors can be used for crowdsourced observations. Different type of social sensors can measure, in a more distributed way, physical variables such as precipitation and water level. However, these crowdsourced observations are not integrated into a real-time fashion into water-system models due to their varying accuracy and random spatial-temporal coverage. We assess the effect in model performance due to the assimilation of crowdsourced observations of water level. Our method consists in (1) implementing a Kalman filter into a cascade of hydrological and hydraulic models. (2) defining observation errors depending on the type of sensor either physical or social. Randomly distributed errors are based on accuracy ranges that slightly improve according to the citizens' expertise level. (3) Using a simplified social model to realistically represent citizen engagement levels based on population density and citizens' motivation scenarios. To test our method, we synthetically derive crowdsourced observations for different citizen engagement levels from a distributed network of physical and social sensors. The observations are assimilated during a particular flood event occurred in the Bacchiglione catchment, Italy. The results of this study demonstrate that sharing crowdsourced water level observations (often motivated by a feeling of belonging to a community of friends) can help in improving flood prediction. On the other hand, a growing participation of individual citizens or weather enthusiasts sharing hydrological observations in cities can help to improve model performance. This study is a first step to assess the effects of crowdsourced observations in flood model predictions. Effective communication and feedback about the quality of observations from water authorities to engaged citizens are further required to minimize their intrinsic low-variable accuracy.
Invited review: A position on the Global Livestock Environmental Assessment Model (GLEAM).

PubMed

MacLeod, M J; Vellinga, T; Opio, C; Falcucci, A; Tempio, G; Henderson, B; Makkar, H; Mottet, A; Robinson, T; Steinfeld, H; Gerber, P J

2018-02-01

The livestock sector is one of the fastest growing subsectors of the agricultural economy and, while it makes a major contribution to global food supply and economic development, it also consumes significant amounts of natural resources and alters the environment. In order to improve our understanding of the global environmental impact of livestock supply chains, the Food and Agriculture Organization of the United Nations has developed the Global Livestock Environmental Assessment Model (GLEAM). The purpose of this paper is to provide a review of GLEAM. Specifically, it explains the model architecture, methods and functionality, that is the types of analysis that the model can perform. The model focuses primarily on the quantification of greenhouse gases emissions arising from the production of the 11 main livestock commodities. The model inputs and outputs are managed and produced as raster data sets, with spatial resolution of 0.05 decimal degrees. The Global Livestock Environmental Assessment Model v1.0 consists of five distinct modules: (a) the Herd Module; (b) the Manure Module; (c) the Feed Module; (d) the System Module; (e) the Allocation Module. In terms of the modelling approach, GLEAM has several advantages. For example spatial information on livestock distributions and crops yields enables rations to be derived that reflect the local availability of feed resources in developing countries. The Global Livestock Environmental Assessment Model also contains a herd model that enables livestock statistics to be disaggregated and variation in livestock performance and management to be captured. Priorities for future development of GLEAM include: improving data quality and the methods used to perform emissions calculations; extending the scope of the model to include selected additional environmental impacts and to enable predictive modelling; and improving the utility of GLEAM output.
Phase 2 and 3 wind tunnel tests of the J-97 powered, external augmentor V/STOL model. [at Ames 40 by 80 wind tunnel

NASA Technical Reports Server (NTRS)

Garland, D. B.; Harris, J. L.

1980-01-01

Static and forward speed tests were made in a 40 multiplied by 80 foot wind tunnel of a large-scale, ejector-powered V/STOL aircraft model. Modifications were made to the model following earlier tests primarily to improve longitudinal acceleration capability during transition from hovering to wingborne flight. A rearward deflection of the fuselage augmentor thrust vector was shown to be beneficial in this regard. Other augmentor modifications were tested, notably the removal of both endplates, which improved acceleration performance at the higher transition speeds. The model tests again demonstrated minimal interference of the fuselage augmentor on aerodynamic lift. A flapped canard surface also showed negligible influence on the performance of the wing and of the fuselage augmentor.
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.
Regional Seismic Travel-Time Prediction, Uncertainty, and Location Improvement in Western Eurasia

NASA Astrophysics Data System (ADS)

Flanagan, M. P.; Myers, S. C.

2004-12-01

We investigate our ability to improve regional travel-time prediction and seismic event location using an a priori, three-dimensional velocity model of Western Eurasia and North Africa: WENA1.0 [Pasyanos et al., 2004]. Our objective is to improve the accuracy of seismic location estimates and calculate representative location uncertainty estimates. As we focus on the geographic region of Western Eurasia, the Middle East, and North Africa, we develop, test, and validate 3D model-based travel-time prediction models for 30 stations in the study region. Three principal results are presented. First, the 3D WENA1.0 velocity model improves travel-time prediction over the iasp91 model, as measured by variance reduction, for regional Pg, Pn, and P phases recorded at the 30 stations. Second, a distance-dependent uncertainty model is developed and tested for the WENA1.0 model. Third, an end-to-end validation test based on 500 event relocations demonstrates improved location performance over the 1-dimensional iasp91 model. Validation of the 3D model is based on a comparison of approximately 11,000 Pg, Pn, and P travel-time predictions and empirical observations from ground truth (GT) events. Ray coverage for the validation dataset is chosen to provide representative, regional-distance sampling across Eurasia and North Africa. The WENA1.0 model markedly improves travel-time predictions for most stations with an average variance reduction of 25% for all ray paths. We find that improvement is station dependent, with some stations benefiting greatly from WENA1.0 predictions (52% at APA, 33% at BKR, and 32% at NIL), some stations showing moderate improvement (12% at KEV, 14% at BOM, and 12% at TAM), some benefiting only slightly (6% at MOX, and 4% at SVE), and some are degraded (-6% at MLR and -18% at QUE). We further test WENA1.0 by comparing location accuracy with results obtained using the iasp91 model. Again, relocation of these events is dependent on ray paths that evenly sample WENA1.0 and therefore provide an unbiased assessment of location performance. A statistically significant sample is achieved by generating 500 location realizations based on 5 events with location accuracy between 1 km and 5 km. Each realization is a randomly selected event with location determined by randomly selecting 5 stations from the available network. In 340 cases (68% of the instances), locations are improved, and average mislocation is reduced from 31 km to 26 km. Preliminary test of uncertainty estimates suggest that our uncertainty model produces location uncertainty ellipses that are representative of location accuracy. These results highlight the importance of accurate GT datasets in assessing regional travel-time models and demonstrate that an a priori 3D model can markedly improve our ability to locate small magnitude events in a regional monitoring context. This work was performed under the auspices of the U.S. Department of Energy by the University of California Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48, Contribution UCRL-CONF-206386.
The contribution of temporary storage and executive processes to category learning.

PubMed

Wang, Tengfei; Ren, Xuezhu; Schweizer, Karl

2015-09-01

Three distinctly different working memory processes, temporary storage, mental shifting and inhibition, were proposed to account for individual differences in category learning. A sample of 213 participants completed a classic category learning task and two working memory tasks that were experimentally manipulated for tapping specific working memory processes. Fixed-links models were used to decompose data of the category learning task into two independent components representing basic performance and improvement in performance in category learning. Processes of working memory were also represented by fixed-links models. In a next step the three working memory processes were linked to components of category learning. Results from modeling analyses indicated that temporary storage had a significant effect on basic performance and shifting had a moderate effect on improvement in performance. In contrast, inhibition showed no effect on any component of the category learning task. These results suggest that temporary storage and the shifting process play different roles in the course of acquiring new categories. Copyright © 2015 Elsevier B.V. All rights reserved.
A Stewart isolator with high-static-low-dynamic stiffness struts based on negative stiffness magnetic springs

NASA Astrophysics Data System (ADS)

Zheng, Yisheng; Li, Qingpin; Yan, Bo; Luo, Yajun; Zhang, Xinong

2018-05-01

In order to improve the isolation performance of passive Stewart platforms, the negative stiffness magnetic spring (NSMS) is employed to construct high static low dynamic stiffness (HSLDS) struts. With the NSMS, the resonance frequencies of the platform can be reduced effectively without deteriorating its load bearing capacity. The model of the Stewart isolation platform with HSLDS struts is presented and the stiffness characteristic of its struts is studied firstly. Then the nonlinear dynamic model of the platform including both geometry nonlinearity and stiffness nonlinearity is established; and its simplified dynamic model is derived under the condition of small vibration. The effect of nonlinearity on the isolation performance is also evaluated. Finally, a prototype is built and the isolation performance is tested. Both simulated and experimental results demonstrate that, by using the NSMS, the resonance frequencies of the Stewart isolator are reduced and the isolation performance in all six directions is improved: the isolation frequency band is increased and extended to a lower-frequency level.
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
Short-term Wind Forecasting at Wind Farms using WRF-LES and Actuator Disk Model

NASA Astrophysics Data System (ADS)

Kirkil, Gokhan

2017-04-01

Short-term wind forecasts are obtained for a wind farm on a mountainous terrain using WRF-LES. Multi-scale simulations are also performed using different PBL parameterizations. Turbines are parameterized using Actuator Disc Model. LES models improved the forecasts. Statistical error analysis is performed and ramp events are analyzed. Complex topography of the study area affects model performance, especially the accuracy of wind forecasts were poor for cross valley-mountain flows. By means of LES, we gain new knowledge about the sources of spatial and temporal variability of wind fluctuations such as the configuration of wind turbines.

UAS in the NAS Project: Large-Scale Communication Architecture Simulations with NASA GRC Gen5 Radio Model

NASA Technical Reports Server (NTRS)

Kubat, Gregory

2016-01-01

This report provides a description and performance characterization of the large-scale, Relay architecture, UAS communications simulation capability developed for the NASA GRC, UAS in the NAS Project. The system uses a validated model of the GRC Gen5 CNPC, Flight-Test Radio model. Contained in the report is a description of the simulation system and its model components, recent changes made to the system to improve performance, descriptions and objectives of sample simulations used for test and verification, and a sampling and observations of results and performance data.
An improved predictive functional control method with application to PMSM systems

NASA Astrophysics Data System (ADS)

Li, Shihua; Liu, Huixian; Fu, Wenshu

2017-01-01

In common design of prediction model-based control method, usually disturbances are not considered in the prediction model as well as the control design. For the control systems with large amplitude or strong disturbances, it is difficult to precisely predict the future outputs according to the conventional prediction model, and thus the desired optimal closed-loop performance will be degraded to some extent. To this end, an improved predictive functional control (PFC) method is developed in this paper by embedding disturbance information into the system model. Here, a composite prediction model is thus obtained by embedding the estimated value of disturbances, where disturbance observer (DOB) is employed to estimate the lumped disturbances. So the influence of disturbances on system is taken into account in optimisation procedure. Finally, considering the speed control problem for permanent magnet synchronous motor (PMSM) servo system, a control scheme based on the improved PFC method is designed to ensure an optimal closed-loop performance even in the presence of disturbances. Simulation and experimental results based on a hardware platform are provided to confirm the effectiveness of the proposed algorithm.
Artificial intelligence techniques for modeling database user behavior

NASA Technical Reports Server (NTRS)

Tanner, Steve; Graves, Sara J.

1990-01-01

The design and development of the adaptive modeling system is described. This system models how a user accesses a relational database management system in order to improve its performance by discovering use access patterns. In the current system, these patterns are used to improve the user interface and may be used to speed data retrieval, support query optimization and support a more flexible data representation. The system models both syntactic and semantic information about the user's access and employs both procedural and rule-based logic to manipulate the model.
Market and plan characteristics related to HMO quality and improvement.

PubMed

Scanlon, Dennis P; Swaminathan, Shailender; Chernew, Michael; Lee, Woolton

2006-12-01

Existing research on health plan performance examines whether variation in plans' scores is related to enrollee and health plan traits, primarily using cross-sectional research designs. This study extends that literature by incorporating data on market characteristics using a longitudinal framework. We estimate multivariate growth models that relate plan performance on standard measures to market and HMO characteristics using an unbalanced panel of data for 1998 to 2002. We find that HMO competition is not associated with better performance or greater rates of improvement in performance on the HEDIS chronic care measures. HMO penetration, on the other hand, is positively associated with HEDIS performance in several of the chronic care process-and-outcomes measures but not with a greater rate of improvement through time. Our analysis indicates that a significant percentage of the unexplained variation in quality improvement is because of permanent, unobserved plan-level characteristics that future research should strive to identify.
The Project Protect Infection Prevention Fellowship: A model for advancing infection prevention competency, quality improvement, and patient safety.

PubMed

Reisinger, Janine D; Wojcik, Anna; Jenkins, Ian; Edson, Barbara; Pegues, David A; Greene, Linda

2017-08-01

The Centers for Disease Control and Prevention 2016 Healthcare-Associated Infections (HAI) Progress Report documented no change in catheter-associated urinary tract infections (CAUTIs) between 2009 and 2014. There is a need for investment in additional efforts to reduce HAIs, specifically CAUTI. Quality improvement fellowships are 1 approach to expand the capacity of dedicated leaders and infection prevention champions. The fellowship used a model that expanded collaboration among disciplines and focused on partnership by recruiting a diverse cohort of fellows and by providing 1-on-1 mentoring to enhance leadership development. The curriculum supported the Association for Professionals in Infection Control and Prevention Competency Model in 2 domains: leadership and performance improvement and implementation science. The fellowship was successful. The fellows and mentors had self-reported high level of satisfaction, fellows' knowledge increased, and they demonstrated leadership, quality improvement, and implementation science competency within the completed capstone projects. A model encompassing diverse educational topics, discussions, workshops, and mentorship can serve as a template for developing infection prevention champions. Although this project focused on CAUTI, this template can be used in a variety of settings and applied to a range of other HAIs and performance improvement projects. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. All rights reserved.
Transfer matrix modeling and experimental validation of cellular porous material with resonant inclusions.

PubMed

Doutres, Olivier; Atalla, Noureddine; Osman, Haisam

2015-06-01

Porous materials are widely used for improving sound absorption and sound transmission loss of vibrating structures. However, their efficiency is limited to medium and high frequencies of sound. A solution for improving their low frequency behavior while keeping an acceptable thickness is to embed resonant structures such as Helmholtz resonators (HRs). This work investigates the absorption and transmission acoustic performances of a cellular porous material with a two-dimensional periodic arrangement of HR inclusions. A low frequency model of a resonant periodic unit cell based on the parallel transfer matrix method is presented. The model is validated by comparison with impedance tube measurements and simulations based on both the finite element method and a homogenization based model. At the HR resonance frequency (i) the transmission loss is greatly improved and (ii) the sound absorption of the foam can be either decreased or improved depending on the HR tuning frequency and on the thickness and properties of the host foam. Finally, the diffuse field sound absorption and diffuse field sound transmission loss performance of a 2.6 m(2) resonant cellular material are measured. It is shown that the improvements observed at the Helmholtz resonant frequency on a single cell are confirmed at a larger scale.
The use of self-modeling to improve the swimming performance of spina bifida children.

PubMed Central

Dowrick, P W; Dove, C

1980-01-01

The use of edited videotape replay (which showed only "positive" behaviors) to improve the water skills of three spina bifida children, aged 5 to 10 years was examined. A multiple baseline across subjects design was used, and behavioral changes were observed to occur in close association with intervention. One child was given successive reapplications of videotaped self-modeling with continuing improvements. It appears that a useful practical technique has been developed. PMID:6988381
Improved P300 speller performance using electrocorticography, spectral features, and natural language processing.

PubMed

Speier, William; Fried, Itzhak; Pouratian, Nader

2013-07-01

The P300 speller is a system designed to restore communication to patients with advanced neuromuscular disorders. This study was designed to explore the potential improvement from using electrocorticography (ECoG) compared to the more traditional usage of electroencephalography (EEG). We tested the P300 speller on two epilepsy patients with temporary subdural electrode arrays over the occipital and temporal lobes respectively. We then performed offline analysis to determine the accuracy and bit rate of the system and integrated spectral features into the classifier and used a natural language processing (NLP) algorithm to further improve the results. The subject with the occipital grid achieved an accuracy of 82.77% and a bit rate of 41.02, which improved to 96.31% and 49.47 respectively using a language model and spectral features. The temporal grid patient achieved an accuracy of 59.03% and a bit rate of 18.26 with an improvement to 75.81% and 27.05 respectively using a language model and spectral features. Spatial analysis of the individual electrodes showed best performance using signals generated and recorded near the occipital pole. Using ECoG and integrating language information and spectral features can improve the bit rate of a P300 speller system. This improvement is sensitive to the electrode placement and likely depends on visually evoked potentials. This study shows that there can be an improvement in BCI performance when using ECoG, but that it is sensitive to the electrode location. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
A multiscale modeling framework model (superparameterized CAM5) with a higher-order turbulence closure: Model description and low-cloud simulations

DOE PAGES

Wang, Minghuai; Larson, Vincent E.; Ghan, Steven; ...

2015-04-18

In this study, a higher-order turbulence closure scheme, called Cloud Layers Unified by Binormals (CLUBB), is implemented into a Multi-scale Modeling Framework (MMF) model to improve low cloud simulations. The performance of CLUBB in MMF simulations with two different microphysics configurations (one-moment cloud microphysics without aerosol treatment and two-moment cloud microphysics coupled with aerosol treatment) is evaluated against observations and further compared with results from the Community Atmosphere Model, Version 5 (CAM5) with conventional cloud parameterizations. CLUBB is found to improve low cloud simulations in the MMF, and the improvement is particularly evident in the stratocumulus-to-cumulus transition regions. Compared tomore » the single-moment cloud microphysics, CLUBB with two-moment microphysics produces clouds that are closer to the coast, and agrees better with observations. In the stratocumulus-to cumulus transition regions, CLUBB with two-moment cloud microphysics produces shortwave cloud forcing in better agreement with observations, while CLUBB with single moment cloud microphysics overestimates shortwave cloud forcing. CLUBB is further found to produce quantitatively similar improvements in the MMF and CAM5, with slightly better performance in the MMF simulations (e.g., MMF with CLUBB generally produces low clouds that are closer to the coast than CAM5 with CLUBB). As a result, improved low cloud simulations in MMF make it an even more attractive tool for studying aerosol-cloud-precipitation interactions.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)

Rabiti, Cristian; Alfonsi, Andrea; Huang, Dongli

This report collect the effort performed to improve the reliability analysis capabilities of the RAVEN code and explore new opportunity in the usage of surrogate model by extending the current RAVEN capabilities to multi physics surrogate models and construction of surrogate models for high dimensionality fields.
Pulse Detonation Rocket Engine Research at NASA Marshall

NASA Technical Reports Server (NTRS)

Morris, Christopher I.

2003-01-01

Pulse detonation rocket engines (PDREs) offer potential performance improvements over conventional designs, but represent a challenging modeling task. A quasi 1-D, finite-rate chemistry CFD model for a PDRE is described and implemented. A parametric study of the effect of blowdown pressure ratio on the performance of an optimized, fixed PDRE nozzle configuration is reported. The results are compared to a steady-state rocket system using similar modeling assumptions.
AERMOD: A DISPERSION MODEL FOR INDUSTRIAL SOURCE APPLICATIONS PART II: MODEL PERFORMANCE AGAINST 17 FIELD STUDY DATABASES

EPA Science Inventory

The formulations of the AMS/EPA Regulatory Model Improvement Committee's applied air dispersion model (AERMOD) are described. This is the second in a series of three articles. Part I describes the model's methods for characterizing the atmospheric boundary layer and complex ter...
Mirror neuron system and observational learning: behavioral and neurophysiological evidence.

PubMed

Lago-Rodriguez, Angel; Lopez-Alonso, Virginia; Fernández-del-Olmo, Miguel

2013-07-01

Three experiments were performed to study observational learning using behavioral, perceptual, and neurophysiological data. Experiment 1 investigated whether observing an execution model, during physical practice of a transitive task that only presented one execution strategy, led to performance improvements compared with physical practice alone. Experiment 2 investigated whether performing an observational learning protocol improves subjects' action perception. In experiment 3 we evaluated whether the type of practice performed determined the activation of the Mirror Neuron System during action observation. Results showed that, compared with physical practice, observing an execution model during a task that only showed one execution strategy does not provide behavioral benefits. However, an observational learning protocol allows subjects to predict more precisely the outcome of the learned task. Finally, intersperse observation of an execution model with physical practice results in changes of primary motor cortex activity during the observation of the motor pattern previously practiced, whereas modulations in the connectivity between primary and non primary motor areas (PMv-M1; PPC-M1) were not affected by the practice protocol performed by the observer. Copyright © 2013 Elsevier B.V. All rights reserved.
An overview of the model integration process: From pre-integration assessment to testing

EPA Science Inventory

Integration of models requires linking models which can be developed using different tools, methodologies, and assumptions. We performed a literature review with the aim of improving our understanding of model integration process, and also presenting better strategies for buildin...
Evaluation of a Pharmacokinetic-Pharmacodynamic Model for Hypouricemic Effects of Febuxostat Using Datasets Obtained from Real-world Patients.

PubMed

Hirai, Toshinori; Itoh, Toshimasa; Kimura, Toshimi; Echizen, Hirotoshi

2018-06-06

Febuxostat is an active xanthine oxidase (XO) inhibitor that is widely used in the hyperuricemia treatment. We aimed to evaluate the predictive performance of a pharmacokinetic-pharmacodynamic (PK-PD) model for hypouricemic effects of febuxostat. Previously, we have formulated a PK--PD model for predicting hypouricemic effects of febuxostat as a function of baseline serum urate levels, body weight, renal function, and drug dose using datasets reported in preapproval studies (Hirai T et al., Biol Pharm Bull 2016; 39: 1013-21). Using an updated model with sensitivity analysis, we examined the predictive performance of the PK-PD model using datasets obtained from the medical records of patients who received febuxostat from March 2011 to December 2015 at Tokyo Women's Medical University Hospital. Multivariate regression analysis was performed to explore clinical variables to improve the predictive performance of the model. A total of 1,199 serum urate data were retrieved from 168 patients (age: 60.5 ±17.7 years, 71.4% males) who received febuxostat as hyperuricemia treatment. There was a significant correlation (r=0.68, p<0.01) between serum urate levels observed and those predicted by the modified PK-PD model. A multivariate regression analysis revealed that the predictive performance of the model may be improved further by considering comorbidities, such as diabetes mellitus, estimated glomerular filtration rate (eGFR), and co-administration of loop diuretics (r = 0.77, p<0.01). The PK-PD model may be useful for predicting individualized maintenance doses of febuxostat in real-world patients. This article is protected by copyright. All rights reserved.
Assessing competencies: an evaluation of ASTD's Certified Professional in Learning and Performance (CPLP) designation.

PubMed

Kwon, Seolim; Wadholm, Robert R; Carmody, Laurie E

2014-06-01

The American Society of Training and Development's (ASTD) Certified Professional in Learning and Performance (CPLP) program is purported to be based on the ASTD's competency model, a model which outlines foundational competencies, roles, and areas of expertise in the field of training and performance improvement. This study seeks to uncover the relationship between the competency model and the CPLP knowledge exam questions and work product submissions (two of the major instruments used to test for competency of CPLP applicants). A mixed qualitative-quantitative approach is used to identify themes, quantify relationships, and assess questions and guidelines. Multiple raters independently analyzed the data and identified key themes, and Fleiss' Kappa coefficient was used in measuring inter-rater agreement. The study concludes that several discrepancies exist between the competency model and the knowledge exam and work product submission guidelines. Recommendations are given for possible improvement of the CPLP program. Copyright © 2014 Elsevier Ltd. All rights reserved.
Theoretical design study of the MSFC wind-wheel turbine

NASA Technical Reports Server (NTRS)

Frost, W.; Kessel, P. A.

1982-01-01

A wind wheel turbine (WWT) is studied. Evaluation of the probable performance, possible practical applications, and economic viability as compared to other conventional wind energy systems is discussed. The WWT apparatus is essentially a bladed wheel which is directly exposed to the wind on the upper half and exposed to wind through multiple ducting on the lower half. The multiple ducts consist of a forward duct (front concentrator) and two side ducts (side concentrators). The forced rotation of the wheel is then converted to power through appropriate subsystems. Test results on two simple models, a paper model and a stainless steel model, are reported. Measured values of power coefficients over wind speeds ranging from 4 to 16 m/s are given. An analytical model of a four bladed wheel is also developed. Overall design features of the wind turbine are evaluated and discussed. Turbine sizing is specified for a 5 and 25 kW machine. Suggested improvements to the original design to increase performance and performance predictions for an improved WWT design are given.
An analysis of aerodynamic requirements for coordinated bank-to-turn autopilots

NASA Technical Reports Server (NTRS)

Arrow, A.

1982-01-01

Two planar missile airframes were compared having the potential for improved bank-to-turn control but having different aerodynamic properties. The comparison was made with advanced level autopilots using both linear and nonlinear 3-D aerodynamic models to obtain realistic missile body angular rates and control surface incidence. Cortical cross-coupling effects are identified and desirable aerodynamics are recommended for improved coordinated (BTT) (CBTT) performance. In addition, recommendations are made for autopilot control law analyses and design techniques for improving CBTT performance.
Changes in Blade Configuration Improve Turbopump

NASA Technical Reports Server (NTRS)

Meng, S. Y.; Bache, G. E.

1987-01-01

Cavitation reduced while suction increased. Tests conducted with model liquid-oxygen turbopump using water as pumped fluid confirms performance improved by "tandem" arrangement of blades. Findings expected to apply to other pumps having two adjacent rotor rows.
A perturbative approach for enhancing the performance of time series forecasting.

PubMed

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.
Evaluation and Improvement of Polar WRF simulations using the observed atmospheric profiles in the Arctic seasonal ice zone

NASA Astrophysics Data System (ADS)

Liu, Z.; Schweiger, A. J. B.

2016-12-01

We use the Polar Weather Research and Forecasting (WRF) model to simulate atmospheric conditions during the Seasonal Ice Zone Reconnaissance Survey (SIZRS) over the Beaufort Sea in the summer since 2013. With the 119 SIZRS dropsondes in the18 cross sections along the 150W and 140W longitude lines, we evaluate the performance of WRF simulations and two forcing data sets, the ERA-Interim reanalysis and the Global Forecast System (GFS) analysis, and explore the improvement of the Polar WRF performance when the dropsonde data are assimilated using observation nudging. Polar WRF, ERA-Interim, and GFS can reproduce the general features of the observed mean atmospheric profiles, such as low-level temperature inversion, low-level jet (LLJ) and specific humidity inversion. The Polar WRF significantly improves the mean LLJ, with a lower and stronger jet and a larger turning angle than the forcing, which is likely related to the lower values of the boundary layer diffusion in WRF than in the global models such as ECMWF and GFS. The Polar WRF simulated relative humidity closely resembles the forcing datasets while having large biases compared to observations. This suggests that the performance of Polar WRF and its forecasts in this region are limited by the quality of the forcing dataset and that the assimilation of more and better-calibrated observations, such as humidity data, is critical for their improvement. We investigate the potential of assimilating the SIZRS dropsonde dataset in improving the weather forecast over the Beaufort Sea. A simple local nudging approach is adopted. Along SIZRS flight cross sections, a set of Polar WRF simulations are performed with varying number of variables and dropsonde profiles assimilated. Different model physics are tested to examine the sensitivity of different aspects of model physics, such as boundary layer schemes, cloud microphysics, and radiation parameterization, to data assimilation. The comparison of the Polar WRF runs with assimilation and the runs without assimilation demonstrates the importance of SIZRS dropsonde data to the improvement of atmospheric analysis and reanalysis such as GFS and ERA-Interim, and consequently to the improvement of weather forecast in this region.
Structural-Thermal-Optical-Performance (STOP) Model Development and Analysis of a Field-widened Michelson Interferometer

NASA Technical Reports Server (NTRS)

Scola, Salvatore J.; Osmundsen, James F.; Murchison, Luke S.; Davis, Warren T.; Fody, Joshua M.; Boyer, Charles M.; Cook, Anthony L.; Hostetler, Chris A.; Seaman, Shane T.; Miller, Ian J.;

2014-01-01

An integrated Structural-Thermal-Optical-Performance (STOP) model was developed for a field-widened Michelson interferometer which is being built and tested for the High Spectral Resolution Lidar (HSRL) project at NASA Langley Research Center (LaRC). The performance of the interferometer is highly sensitive to thermal expansion, changes in refractive index with temperature, temperature gradients, and deformation due to mounting stresses. Hand calculations can only predict system performance for uniform temperature changes, under the assumption that coefficient of thermal expansion (CTE) mismatch effects are negligible. An integrated STOP model was developed to investigate the effects of design modifications on the performance of the interferometer in detail, including CTE mismatch, and other three- dimensional effects. The model will be used to improve the design for a future spaceflight version of the interferometer. The STOP model was developed using the Comet SimApp'TM' Authoring Workspace which performs automated integration between Pro-Engineer®, Thermal Desktop®, MSC Nastran'TM', SigFit'TM', Code V'TM', and MATLAB®. This is the first flight project for which LaRC has utilized Comet, and it allows a larger trade space to be studied in a shorter time than would be possible in a traditional STOP analysis. This paper describes the development of the STOP model, presents a comparison of STOP results for simple cases with hand calculations, and presents results of the correlation effort to bench-top testing of the interferometer. A trade study conducted with the STOP model which demonstrates a few simple design changes that can improve the performance seen in the lab is also presented.

A neural network controller for automated composite manufacturing

NASA Technical Reports Server (NTRS)

Lichtenwalner, Peter F.

1994-01-01

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.
Real-time economic nonlinear model predictive control for wind turbine control

NASA Astrophysics Data System (ADS)

Gros, Sebastien; Schild, Axel

2017-12-01

Nonlinear model predictive control (NMPC) is a strong candidate to handle the control challenges emerging in the modern wind energy industry. Recent research suggested that wind turbine (WT) control based on economic NMPC (ENMPC) can improve the closed-loop performance and simplify the task of controller design when compared to a classical NMPC approach. This paper establishes a formal relationship between the ENMPC controller and the classic NMPC approach, and compares empirically their closed-loop nominal behaviour and performance. The robustness of the performance is assessed for an inaccurate modelling of the tower fore-aft main frequency. Additionally, though a perfect wind preview is assumed here, the effect of having a limited horizon of preview of the wind speed via the LIght Detection And Ranging (LIDAR) sensor is investigated. Finally, this paper provides new algorithmic solutions for deploying ENMPC for WT control, and report improved computational times.
Making the Case for a Positive Approach to Improving Organizational Performance in Higher Education Institutions: The Community College Abundance Model

ERIC Educational Resources Information Center

Shults, Christopher

2008-01-01

Increasingly hostile and turbulent environments have rendered top-down, problem-focused management structures inadequate for competing in the ever-changing postsecondary knowledge industry. The community college abundance model (CCAM), a strengths-based approach to performance enhancement in community colleges, is presented as a viable…
Using Clickers to Facilitate Interactive Engagement Activities in a Lecture Room for Improved Performance by Students

ERIC Educational Resources Information Center

Tlhoaele, Malefyane; Hofman, Adriaan; Naidoo, Ari; Winnips, Koos

2014-01-01

What impact can interactive engagement (IE) activities using clickers have on students' motivation and academic performance during lectures as compared to attending traditional types of lectures? This article positions the research on IE within the comprehensive model of educational effectiveness and Gagné's instructional events model. For the…
Pay for performance in neonatal-perinatal medicine--will the quality of health care improve in the neonatal intensive care unit? A business model for improving outcomes in the neonatal intensive care unit.

PubMed

Spitzer, Alan R

2010-03-01

Because neonatal medicine is such an expensive contributor to health care in the United States--with a small population of infants accounting for very high health care costs--there has been a fair amount of attention given to this group of patients. An idea that has received increasing attention in this discussion is pay for performance. This article discusses the concept of pay for performance, examines what potential benefits and risks exist in this model, and investigates how it might achieve the desired goals if implemented in a thoughtful way. Copyright 2010 Elsevier Inc. All rights reserved.
Flood loss model transfer: on the value of additional data

NASA Astrophysics Data System (ADS)

Schröter, Kai; Lüdtke, Stefan; Vogel, Kristin; Kreibich, Heidi; Thieken, Annegret; Merz, Bruno

2017-04-01

The transfer of models across geographical regions and flood events is a key challenge in flood loss estimation. Variations in local characteristics and continuous system changes require regional adjustments and continuous updating with current evidence. However, acquiring data on damage influencing factors is expensive and therefore assessing the value of additional data in terms of model reliability and performance improvement is of high relevance. The present study utilizes empirical flood loss data on direct damage to residential buildings available from computer aided telephone interviews that were carried out after the floods in 2002, 2005, 2006, 2010, 2011 and 2013 mainly in the Elbe and Danube catchments in Germany. Flood loss model performance is assessed for incrementally increased numbers of loss data which are differentiated according to region and flood event. Two flood loss modeling approaches are considered: (i) a multi-variable flood loss model approach using Random Forests and (ii) a uni-variable stage damage function. Both model approaches are embedded in a bootstrapping process which allows evaluating the uncertainty of model predictions. Predictive performance of both models is evaluated with regard to mean bias, mean absolute and mean squared errors, as well as hit rate and sharpness. Mean bias and mean absolute error give information about the accuracy of model predictions; mean squared error and sharpness about precision and hit rate is an indicator for model reliability. The results of incremental, regional and temporal updating demonstrate the usefulness of additional data to improve model predictive performance and increase model reliability, particularly in a spatial-temporal transfer setting.
Data Fusion of Gridded Snow Products Enhanced with Terrain Covariates and a Simple Snow Model

NASA Astrophysics Data System (ADS)

Snauffer, A. M.; Hsieh, W. W.; Cannon, A. J.

2017-12-01

Hydrologic planning requires accurate estimates of regional snow water equivalent (SWE), particularly areas with hydrologic regimes dominated by spring melt. While numerous gridded data products provide such estimates, accurate representations are particularly challenging under conditions of mountainous terrain, heavy forest cover and large snow accumulations, contexts which in many ways define the province of British Columbia (BC), Canada. One promising avenue of improving SWE estimates is a data fusion approach which combines field observations with gridded SWE products and relevant covariates. A base artificial neural network (ANN) was constructed using three of the best performing gridded SWE products over BC (ERA-Interim/Land, MERRA and GLDAS-2) and simple location and time covariates. This base ANN was then enhanced to include terrain covariates (slope, aspect and Terrain Roughness Index, TRI) as well as a simple 1-layer energy balance snow model driven by gridded bias-corrected ANUSPLIN temperature and precipitation values. The ANN enhanced with all aforementioned covariates performed better than the base ANN, but most of the skill improvement was attributable to the snow model with very little contribution from the terrain covariates. The enhanced ANN improved station mean absolute error (MAE) by an average of 53% relative to the composing gridded products over the province. Interannual peak SWE correlation coefficient was found to be 0.78, an improvement of 0.05 to 0.18 over the composing products. This nonlinear approach outperformed a comparable multiple linear regression (MLR) model by 22% in MAE and 0.04 in interannual correlation. The enhanced ANN has also been shown to estimate better than the Variable Infiltration Capacity (VIC) hydrologic model calibrated and run for four BC watersheds, improving MAE by 22% and correlation by 0.05. The performance improvements of the enhanced ANN are statistically significant at the 5% level across the province and in four out of five physiographic regions.
Biosecurity through Public Health System Design.

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

Beyeler, Walter E.; Finley, Patrick D.; Arndt, William

We applied modeling and simulation to examine the real-world tradeoffs between developingcountry public-health improvement and the need to improve the identification, tracking, and security of agents with bio-weapons potential. Traditionally, the international community has applied facility-focused strategies for improving biosecurity and biosafety. This work examines how system-level assessments and improvements can foster biosecurity and biosafety. We modeled medical laboratory resources and capabilities to identify scenarios where biosurveillance goals are transparently aligned with public health needs, and resource are distributed in a way that maximizes their ability to serve patients while minimizing security a nd safety risks. Our modeling platform simulatesmore » key processes involved in healthcare system operation, such as sample collection, transport, and analysis at medical laboratories. The research reported here extends the prior art by provided two key compone nts for comparative performance assessment: a model of patient interaction dynamics, and the capability to perform uncertainty quantification. In addition, we have outlined a process for incorporating quantitative biosecurity and biosafety risk measures. Two test problems were used to exercise these research products examine (a) Systemic effects of technological innovation and (b) Right -sizing of laboratory networks.« less
Evaluating and improving the representation of heteroscedastic errors in hydrological models

NASA Astrophysics Data System (ADS)

McInerney, D. J.; Thyer, M. A.; Kavetski, D.; Kuczera, G. A.

2013-12-01

Appropriate representation of residual errors in hydrological modelling is essential for accurate and reliable probabilistic predictions. In particular, residual errors of hydrological models are often heteroscedastic, with large errors associated with high rainfall and runoff events. Recent studies have shown that using a weighted least squares (WLS) approach - where the magnitude of residuals are assumed to be linearly proportional to the magnitude of the flow - captures some of this heteroscedasticity. In this study we explore a range of Bayesian approaches for improving the representation of heteroscedasticity in residual errors. We compare several improved formulations of the WLS approach, the well-known Box-Cox transformation and the more recent log-sinh transformation. Our results confirm that these approaches are able to stabilize the residual error variance, and that it is possible to improve the representation of heteroscedasticity compared with the linear WLS approach. We also find generally good performance of the Box-Cox and log-sinh transformations, although as indicated in earlier publications, the Box-Cox transform sometimes produces unrealistically large prediction limits. Our work explores the trade-offs between these different uncertainty characterization approaches, investigates how their performance varies across diverse catchments and models, and recommends practical approaches suitable for large-scale applications.
Thinking outside the boxes: Using current reading models to assess and treat developmental surface dyslexia.

PubMed

Law, Caroline; Cupples, Linda

2017-03-01

Improving the reading performance of children with developmental surface dyslexia has proved challenging, with limited generalisation of reading skills typically reported after intervention. The aim of this study was to provide tailored, theoretically motivated intervention to two children with developmental surface dyslexia. Our objectives were to improve their reading performance, and to evaluate the utility of current reading models in therapeutic practice. Detailed reading and cognitive profiles for two male children with developmental surface dyslexia were compared to the results obtained by age-matched control groups. The specific area of single-word reading difficulty for each child was identified within the dual route model (DRM) of reading, following which a theoretically motivated intervention programme was devised. Both children showed significant improvements in single-word reading ability after training, with generalisation effects observed for untrained words. However, the assessment and intervention results also differed for each child, reinforcing the view that the causes and consequences of developmental dyslexia, even within subtypes, are not homogeneous. Overall, the results of the interventions corresponded more closely with the DRM than other current reading models, in that real word reading improved in the absence of enhanced nonword reading for both children.
Primary Care Reform: Can Quebec's Family Medicine Group Model Benefit from the Experience of Ontario's Family Health Teams?

PubMed Central

Breton, Mylaine; Lévesque, Jean-Frédéric; Pineault, Raynald; Hogg, William

2011-01-01

Canadian politicians, decision-makers, clinicians and researchers have come to agree that reforming primary care services is a key strategy for improving healthcare system performance. However, it is only more recently that real transformative initiatives have been undertaken in different Canadian provinces. One model that offers promise for improving primary care service delivery is the family medicine group (FMG) model developed in Quebec. A FMG is a group of physicians working closely with nurses in the provision of services to enrolled patients on a non-geographic basis. The objectives of this paper are to analyze the FMG's potential as a lever for improving healthcare system performance and to discuss how it could be improved. First, we briefly review the history of primary care in Quebec. Then we present the FMG model in relation to the four key healthcare system functions identified by the World Health Organization: (a) funding, (b) generating human and technological resources, (c) providing services to individuals and communities and (d) governance. Next, we discuss possible ways of advancing primary care reform, looking particularly at the family health team (FHT) model implemented in the province of Ontario. We conclude with recommendations to inspire other initiatives aimed at transforming primary care. PMID:23115575
Application of WRF/Chem over East Asia: Part II. Model improvement and sensitivity simulations

NASA Astrophysics Data System (ADS)

Zhang, Yang; Zhang, Xin; Wang, Kai; Zhang, Qiang; Duan, Fengkui; He, Kebin

2016-01-01

To address the problems and limitations identified through a comprehensive evaluation in Part I paper, several modifications are made in model inputs, treatments, and configurations and sensitivity simulations with improved model inputs and treatments are performed in this Part II paper. The use of reinitialization of meteorological variables reduces the biases and increases the spatial correlations in simulated temperature at 2-m (T2), specific humidity at 2-m (Q2), wind speed at 10-m (WS10), and precipitation (Precip). The use of a revised surface drag parameterization further reduces the biases in simulated WS10. The adjustment of only the magnitudes of anthropogenic emissions in the surface layer does not help improve overall model performance, whereas the adjustment of both the magnitudes and vertical distributions of anthropogenic emissions shows moderate to large improvement in simulated surface concentrations and column mass abundances of species in terms of domain mean performance statistics, hourly and monthly mean concentrations, and vertical profiles of concentrations at individual sites. The revised and more advanced dust emission schemes can help improve PM predictions. Using revised upper boundary conditions for O3 significantly improves the column O3 abundances. Using a simple SOA formation module further improves the predictions of organic carbon and PM2.5. The sensitivity simulation that combines all above model improvements greatly improves the overall model performance. For example, the sensitivity simulation gives the normalized mean biases (NMBs) of -6.1% to 23.8% for T2, 2.7-13.8% for Q2, 22.5-47.6% for WS10, and -9.1% to 15.6% for Precip, comparing to -9.8% to 75.6% for T2, 0.4-23.4% for Q2, 66.5-101.0% for WS10, and 11.4%-92.7% for Precip from the original simulation without those improvements. It also gives the NMBs for surface predictions of -68.2% to -3.7% for SO2, -73.8% to -20.6% for NO2, -8.8%-128.7% for O3, -61.4% to -26.5% for PM2.5, and -64.0% to 7.2% for PM10, comparing to -84.2% to -44.5% for SO2, -88.1% to -44.0% for NO2, -11.0%-160.3% for O3, -63.9% to -25.2% for PM2.5, and -68.9%-33.3% for PM10 from the original simulation. The improved WRF/Chem is applied to estimate the impact of anthropogenic aerosols on regional climate and air quality in East Asia. Anthropogenic aerosols can increase cloud condensation nuclei, aerosol optical depth, cloud droplet number concentrations, and cloud optical depth. They can decrease surface net radiation, temperature at 2-m, wind speed at 10-m, planetary boundary layer height, and precipitation through various direct and indirect effects. These changes in turn lead to changes in chemical predictions in a variety of ways.
Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Goebel, Kai Frank

2010-01-01

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

PubMed Central

Park, Jeongyoung; Konetzka, R Tamara; Werner, Rachel M

2011-01-01

Objective To examine whether high performance or improvement on quality measures leads to economic rewards for nursing homes in the presence of public reporting. Data Sources Data from 6,286 freestanding Medicare-certified nursing homes between 1999 and 2005 were identified in Medicare Cost Reports, Minimum Data Set, and Online Survey and Certification Reporting System. Study Design Using a facility-level fixed-effects model, the effect of public reporting on financial performance was measured by comparing each of four financial outcomes (revenues, expenses, operating, and total profit margins) before (1999–2002) to after (2003–2005) public reporting was initiated. The effects were estimated separately by level of performance and improvement over time. Principal Findings Facilities that improved on publicly reported performance had increased revenues and higher profit margins after public reporting, mainly through increased Medicare admissions. High-scoring facilities showed similar patterns, though differences were not statistically significant. Conclusions Providers that improve their performance under public reporting may receive a return on their investment in quality improvement. This supports the business case for public reporting. PMID:21029093
Improvements to an earth observing statistical performance model with applications to LWIR spectral variability

NASA Astrophysics Data System (ADS)

Zhao, Runchen; Ientilucci, Emmett J.

2017-05-01

Hyperspectral remote sensing systems provide spectral data composed of hundreds of narrow spectral bands. Spectral remote sensing systems can be used to identify targets, for example, without physical interaction. Often it is of interested to characterize the spectral variability of targets or objects. The purpose of this paper is to identify and characterize the LWIR spectral variability of targets based on an improved earth observing statistical performance model, known as the Forecasting and Analysis of Spectroradiometric System Performance (FASSP) model. FASSP contains three basic modules including a scene model, sensor model and a processing model. Instead of using mean surface reflectance only as input to the model, FASSP transfers user defined statistical characteristics of a scene through the image chain (i.e., from source to sensor). The radiative transfer model, MODTRAN, is used to simulate the radiative transfer based on user defined atmospheric parameters. To retrieve class emissivity and temperature statistics, or temperature / emissivity separation (TES), a LWIR atmospheric compensation method is necessary. The FASSP model has a method to transform statistics in the visible (ie., ELM) but currently does not have LWIR TES algorithm in place. This paper addresses the implementation of such a TES algorithm and its associated transformation of statistics.
A wave model test bed study for wave energy resource characterization

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

Yang, Zhaoqing; Neary, Vincent S.; Wang, Taiping

This paper presents a test bed study conducted to evaluate best practices in wave modeling to characterize energy resources. The model test bed off the central Oregon Coast was selected because of the high wave energy and available measured data at the site. Two third-generation spectral wave models, SWAN and WWIII, were evaluated. A four-level nested-grid approach—from global to test bed scale—was employed. Model skills were assessed using a set of model performance metrics based on comparing six simulated wave resource parameters to observations from a wave buoy inside the test bed. Both WWIII and SWAN performed well at themore » test bed site and exhibited similar modeling skills. The ST4 package with WWIII, which represents better physics for wave growth and dissipation, out-performed ST2 physics and improved wave power density and significant wave height predictions. However, ST4 physics tended to overpredict the wave energy period. The newly developed ST6 physics did not improve the overall model skill for predicting the six wave resource parameters. Sensitivity analysis using different wave frequencies and direction resolutions indicated the model results were not sensitive to spectral resolutions at the test bed site, likely due to the absence of complex bathymetric and geometric features.« less
Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

DOE PAGES

Liu, Y.; Liu, Z.; Zhang, S.; ...

2014-09-10

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parametermore » estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.« less

Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions

NASA Astrophysics Data System (ADS)

Lahmers, T. M.; Castro, C. L.; Gupta, H. V.; Gochis, D.; Dugger, A. L.; Smith, M.

2016-12-01

The NOAA National Water Model (NWM), which is based on the WRF-Hydro architecture, became operational in June of 2016 to produce streamflow forecasts nationwide. In order to improve the physical process representation of NWM/WRF-Hydro, a parameterized channel infiltration function is added to the Muskingum-Cunge channel routing scheme. Representation of transmission losses along streams was previously not supported by WRF-Hydro, even though most channels in the southwest CONUS have a high depth to groundwater, and are consequently a source for recharge throughout the region. The LSM, routing grid, baseflow bucket model, and channel parameters of the modified version of NWM/WRF-Hydro are calibrated using spatial regularization in selected basins in the Midwest and Southwest CONUS. WRF-Hydro is calibrated and tested in the Verde, San Pedro, Little Sioux, Nishnabotna, and Wapsipinicon basins. The model is forced with NCEP Stage-IV and NLDAS-2 precipitation for calibration, and the effects of the precipitation climatology, including extreme events, on model performance are considered. This work advances the regional performance of WRF-Hydro through process enhancement and calibration that is highly relevant for improving model fidelity in semi-arid climates.
Comparison of Individualized Covert Modeling, Self-Control Desensitization, and Study Skills Training for Alleviation of Test Anxiety.

ERIC Educational Resources Information Center

Harris, Gina; Johhson, Suzanne Bennett

1980-01-01

Individualized covert modeling and self-control desensitization substantially reduced self-reported test anxiety. However, the individualized covert modeling group was the only treatment group that showed significant improvement in academic performance. (Author)
A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes.

PubMed

Mushkudiani, Nino A; Hukkelhoven, Chantal W P M; Hernández, Adrián V; Murray, Gordon D; Choi, Sung C; Maas, Andrew I R; Steyerberg, Ewout W

2008-04-01

To describe the modeling techniques used for early prediction of outcome in traumatic brain injury (TBI) and to identify aspects for potential improvements. We reviewed key methodological aspects of studies published between 1970 and 2005 that proposed a prognostic model for the Glasgow Outcome Scale of TBI based on admission data. We included 31 papers. Twenty-four were single-center studies, and 22 reported on fewer than 500 patients. The median of the number of initially considered predictors was eight, and on average five of these were selected for the prognostic model, generally including age, Glasgow Coma Score (or only motor score), and pupillary reactivity. The most common statistical technique was logistic regression with stepwise selection of predictors. Model performance was often quantified by accuracy rate rather than by more appropriate measures such as the area under the receiver-operating characteristic curve. Model validity was addressed in 15 studies, but mostly used a simple split-sample approach, and external validation was performed in only four studies. Although most models agree on the three most important predictors, many were developed on small sample sizes within single centers and hence lack generalizability. Modeling strategies have to be improved, and include external validation.
Performance Evaluation of the microPET®—FOCUS-F120

NASA Astrophysics Data System (ADS)

Laforest, Richard; Longford, Desmond; Siegel, Stefan; Newport, Danny F.; Yap, Jeffrey

2007-02-01

microPETreg-Focus-F120 is the latest model of dedicated small animal PET scanners from CTI-Concorde Microsystems LLC, (Knoxville, TN). This scanner, based on the geometry of the microPET-R4, takes advantage of several detector modifications to the coincidence processing electronics that improve the image resolution, sensitivity, and counting rate performance as compared to the predecessor models. This work evaluates the performance of the Focus-F120 system and shows its improvement over the earlier models. In particular, the spatial resolution is shown to improve from 2.32 to 1.69 mm at 5 mm radial distance and the peak absolute sensitivity increases from 4.1% to 7.1% compared to the microPET-R4. The counting rate capability, expressed in noise equivalent counting rate (NEC-1R), was shown to peak at over 800 kcps at 88 MBq for both systems using a mouse phantom. For this small phantom, the NECR counting rate is limited by the data transmission bandwidth between the scanner and the acquisition console. The rat-like phantom showed peak NEC-1R value at 300 kcps at 140 MBq. Evaluation of image quality and quantitation accuracy was also performed using specially designed phantoms and animal experiments
Performance Improvement of Receivers Based on Ultra-Tight Integration in GNSS-Challenged Environments

PubMed Central

Qin, Feng; Zhan, Xingqun; Du, Gang

2013-01-01

Ultra-tight integration was first proposed by Abbott in 2003 with the purpose of integrating a global navigation satellite system (GNSS) and an inertial navigation system (INS). This technology can improve the tracking performances of a receiver by reconfiguring the tracking loops in GNSS-challenged environments. In this paper, the models of all error sources known to date in the phase lock loops (PLLs) of a standard receiver and an ultra-tightly integrated GNSS/INS receiver are built, respectively. Based on these models, the tracking performances of the two receivers are compared to verify the improvement due to the ultra-tight integration. Meanwhile, the PLL error distributions of the two receivers are also depicted to analyze the error changes of the tracking loops. These results show that the tracking error is significantly reduced in the ultra-tightly integrated GNSS/INS receiver since the receiver's dynamics are estimated and compensated by an INS. Moreover, the mathematical relationship between the tracking performances of the ultra-tightly integrated GNSS/INS receiver and the quality of the selected inertial measurement unit (IMU) is derived from the error models and proved by the error comparisons of four ultra-tightly integrated GNSS/INS receivers aided by different grade IMUs.
Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept.

PubMed

Srinivas, T R; Taber, D J; Su, Z; Zhang, J; Mour, G; Northrup, D; Tripathi, A; Marsden, J E; Moran, W P; Mauldin, P D

2017-03-01

We sought proof of concept of a Big Data Solution incorporating longitudinal structured and unstructured patient-level data from electronic health records (EHR) to predict graft loss (GL) and mortality. For a quality improvement initiative, GL and mortality prediction models were constructed using baseline and follow-up data (0-90 days posttransplant; structured and unstructured for 1-year models; data up to 1 year for 3-year models) on adult solitary kidney transplant recipients transplanted during 2007-2015 as follows: Model 1: United Network for Organ Sharing (UNOS) data; Model 2: UNOS & Transplant Database (Tx Database) data; Model 3: UNOS, Tx Database & EHR comorbidity data; and Model 4: UNOS, Tx Database, EHR data, Posttransplant trajectory data, and unstructured data. A 10% 3-year GL rate was observed among 891 patients (2007-2015). Layering of data sources improved model performance; Model 1: area under the curve (AUC), 0.66; (95% confidence interval [CI]: 0.60, 0.72); Model 2: AUC, 0.68; (95% CI: 0.61-0.74); Model 3: AUC, 0.72; (95% CI: 0.66-077); Model 4: AUC, 0.84, (95 % CI: 0.79-0.89). One-year GL (AUC, 0.87; Model 4) and 3-year mortality (AUC, 0.84; Model 4) models performed similarly. A Big Data approach significantly adds efficacy to GL and mortality prediction models and is EHR deployable to optimize outcomes. © 2016 The American Society of Transplantation and the American Society of Transplant Surgeons.
Determinant Factors of Long-Term Performance Development in Young Swimmers.

PubMed

Morais, Jorge E; Silva, António J; Marinho, Daniel A; Lopes, Vítor P; Barbosa, Tiago M

2017-02-01

To develop a performance predictor model based on swimmers' biomechanical profile, relate the partial contribution of the main predictors with the training program, and analyze the time effect, sex effect, and time × sex interaction. 91 swimmers (44 boys, 12.04 ± 0.81 y; 47 girls, 11.22 ± 0.98 y) evaluated during a 3-y period. The decimal age and anthropometric, kinematic, and efficiency features were collected 10 different times over 3 seasons (ie, longitudinal research). Hierarchical linear modeling was the procedure used to estimate the performance predictors. Performance improved between season 1 early and season 3 late for both sexes (boys 26.9% [20.88;32.96], girls 16.1% [10.34;22.54]). Decimal age (estimate [EST] -2.05, P < .001), arm span (EST -0.59, P < .001), stroke length (EST 3.82; P = .002), and propelling efficiency (EST -0.17, P = .001) were entered in the final model. Over 3 consecutive seasons young swimmers' performance improved. Performance is a multifactorial phenomenon where anthropometrics, kinematics, and efficiency were the main determinants. The change of these factors over time was coupled with the training plans of this talent identification and development program.
Smart Caching Based on Mobile Agent of Power WebGIS Platform

PubMed Central

Wang, Xiaohui; Wu, Kehe; Chen, Fei

2013-01-01

Power information construction is developing towards intensive, platform, distributed direction with the expansion of power grid and improvement of information technology. In order to meet the trend, power WebGIS was designed and developed. In this paper, we first discuss the architecture and functionality of power WebGIS, and then we study caching technology in detail, which contains dynamic display cache model, caching structure based on mobile agent, and cache data model. We have designed experiments of different data capacity to contrast performance between WebGIS with the proposed caching model and traditional WebGIS. The experimental results showed that, with the same hardware environment, the response time of WebGIS with and without caching model increased as data capacity growing, while the larger the data was, the higher the performance of WebGIS with proposed caching model improved. PMID:24288504
Implement balanced scorecard to translate strategic plan into actionable objectives.

PubMed

2004-09-01

Faced with challenges ranging from declining reimbursement to staff shortages, health care organizations--integrated delivery systems, physician group practices, disease management providers, and others--increasingly are turning to general business models to map out step-by-step action plans for performance measurement and process improvement. Creating a "balanced scorecard" is an obvious starting point for assessing and improving clinical and financial performance.
Characterization of a Robotic Manipulator for Dynamic Wind Tunnel Applications

DTIC Science & Technology

2015-03-26

further enhancements would need to be performed individually for each joint. This research effort focused on the improvement of the MTA wrist roll ...Measurement Unit ( IMU ), was used to validate the Euler angle output calculated by the MTA Computer using forward kinematics. Additionally, fast-response...61 3.7 Modeling the Wrist Roll Motor and Controller . . . . . . . . . . . . . . . . . . . . . 64 3.8 Proportional Control for Improved Performance
Known Good Substrates Year 1

DTIC Science & Technology

2007-12-05

yield record setting carrier lifetime values and very low concentrations of point defects. Epiwafers delivered for fabrication of RF static induction ...boules and on improved furnace uniformity (adding rotation, etc.). Pareto analysis was performed on wafer yield loss at the start of every quarter...100mm PVT process. Work focused on modeling the process for longer (50 mm) boules and on improved furnace uniformity. Pareto analysis was performed
Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

NASA Astrophysics Data System (ADS)

Johnson, Nicholas E.; Bonczak, Bartosz; Kontokosta, Constantine E.

2018-07-01

The increased availability and improved quality of new sensing technologies have catalyzed a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments. Air quality, in particular, has received greater attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of air pollution in developed and developing countries and cities around the world. Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the performance and stability of these tools has not been adequately evaluated in complex urban environments, and further research is needed. In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 aerosol monitor and examine the suitability of the sensor for deployment in a dense heterogeneous urban environment. We assess the sensor's performance during a field calibration campaign from February 7th to March 25th 2017 with a reference instrument in New York City, and present a novel calibration approach using a machine learning method that incorporates publicly available meteorological data in order to improve overall sensor performance. We find that while the PPD42 performs well in relation to the reference instrument using linear regression (R2 = 0.36-0.51), a gradient boosting regression tree model can significantly improve device calibration (R2 = 0.68-0.76). We discuss the sensor's performance and reliability when deployed in a dense, heterogeneous urban environment during a period of significant variation in weather conditions, and important considerations when using machine learning techniques to improve the performance of low-cost air quality monitors.
Using a shared governance structure to evaluate the implementation of a new model of care: the shared experience of a performance improvement committee.

PubMed

Myers, Mary; Parchen, Debra; Geraci, Marilla; Brenholtz, Roger; Knisely-Carrigan, Denise; Hastings, Clare

2013-10-01

Sustaining change in the behaviors and habits of experienced practicing nurses can be frustrating and daunting, even when changes are based on evidence. Partnering with an active shared governance structure to communicate change and elicit feedback is an established method to foster partnership, equity, accountability, and ownership. Few recent exemplars in the literature link shared governance, change management, and evidence-based practice to transitions in care models. This article describes an innovative staff-driven approach used by nurses in a shared governance performance improvement committee to use evidence-based practice in determining the best methods to evaluate the implementation of a new model of care.
An application of locally linear model tree algorithm with combination of feature selection in credit scoring

NASA Astrophysics Data System (ADS)

Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad

2014-10-01

Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.
Using a Shared Governance Structure to Evaluate the Implementation of a New Model of Care: The Shared Experience of a Performance Improvement Committee

PubMed Central

Myers, Mary; Parchen, Debra; Geraci, Marilla; Brenholtz, Roger; Knisely-Carrigan, Denise; Hastings, Clare

2013-01-01

Sustaining change in the behaviors and habits of experienced practicing nurses can be frustrating and daunting, even when changes are based on evidence. Partnering with an active shared governance structure to communicate change and elicit feedback is an established method to foster partnership, equity, accountability and ownership. Few recent exemplars in the literature link shared governance, change management and evidence-based practice to transitions in care models. This article describes an innovative staff-driven approach used by nurses in a shared governance performance improvement committee to use evidence based practice in determining the best methods to evaluate the implementation of a new model of care. PMID:24061583
Regression modeling of particle size distributions in urban storm water: advancements through improved sample collection methods

USGS Publications Warehouse

Fienen, Michael N.; Selbig, William R.

2012-01-01

A new sample collection system was developed to improve the representation of sediment entrained in urban storm water by integrating water quality samples from the entire water column. The depth-integrated sampler arm (DISA) was able to mitigate sediment stratification bias in storm water, thereby improving the characterization of suspended-sediment concentration and particle size distribution at three independent study locations. Use of the DISA decreased variability, which improved statistical regression to predict particle size distribution using surrogate environmental parameters, such as precipitation depth and intensity. The performance of this statistical modeling technique was compared to results using traditional fixed-point sampling methods and was found to perform better. When environmental parameters can be used to predict particle size distributions, environmental managers have more options when characterizing concentrations, loads, and particle size distributions in urban runoff.
Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model

NASA Astrophysics Data System (ADS)

Zheng, Fei; Zhu, Jiang

2017-04-01

How to design a reliable ensemble prediction strategy with considering the major uncertainties of a forecasting system is a crucial issue for performing an ensemble forecast. In this study, a new stochastic perturbation technique is developed to improve the prediction skills of El Niño-Southern Oscillation (ENSO) through using an intermediate coupled model. We first estimate and analyze the model uncertainties from the ensemble Kalman filter analysis results through assimilating the observed sea surface temperatures. Then, based on the pre-analyzed properties of model errors, we develop a zero-mean stochastic model-error model to characterize the model uncertainties mainly induced by the missed physical processes of the original model (e.g., stochastic atmospheric forcing, extra-tropical effects, Indian Ocean Dipole). Finally, we perturb each member of an ensemble forecast at each step by the developed stochastic model-error model during the 12-month forecasting process, and add the zero-mean perturbations into the physical fields to mimic the presence of missing processes and high-frequency stochastic noises. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-yr hindcast experiments, which are initialized from the same initial conditions and differentiated by whether they consider the stochastic perturbations. The comparison results show that the stochastic perturbations have a significant effect on improving the ensemble-mean prediction skills during the entire 12-month forecasting process. This improvement occurs mainly because the nonlinear terms in the model can form a positive ensemble-mean from a series of zero-mean perturbations, which reduces the forecasting biases and then corrects the forecast through this nonlinear heating mechanism.
LPJmL4 - a dynamic global vegetation model with managed land - Part 2: Model evaluation

NASA Astrophysics Data System (ADS)

Schaphoff, Sibyll; Forkel, Matthias; Müller, Christoph; Knauer, Jürgen; von Bloh, Werner; Gerten, Dieter; Jägermeyr, Jonas; Lucht, Wolfgang; Rammig, Anja; Thonicke, Kirsten; Waha, Katharina

2018-04-01

The dynamic global vegetation model LPJmL4 is a process-based model that simulates climate and land use change impacts on the terrestrial biosphere, agricultural production, and the water and carbon cycle. Different versions of the model have been developed and applied to evaluate the role of natural and managed ecosystems in the Earth system and the potential impacts of global environmental change. A comprehensive model description of the new model version, LPJmL4, is provided in a companion paper (Schaphoff et al., 2018c). Here, we provide a full picture of the model performance, going beyond standard benchmark procedures and give hints on the strengths and shortcomings of the model to identify the need for further model improvement. Specifically, we evaluate LPJmL4 against various datasets from in situ measurement sites, satellite observations, and agricultural yield statistics. We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO2 concentration, while the permafrost scheme improves estimates of carbon stocks. The full LPJmL4 code including the new developments will be supplied open source through https://gitlab.pik-potsdam.de/lpjml/LPJmL. We hope that this will lead to new model developments and applications that improve the model performance and possibly build up a new understanding of the terrestrial biosphere.
Software reliability studies

NASA Technical Reports Server (NTRS)

Hoppa, Mary Ann; Wilson, Larry W.

1994-01-01

There are many software reliability models which try to predict future performance of software based on data generated by the debugging process. Our research has shown that by improving the quality of the data one can greatly improve the predictions. We are working on methodologies which control some of the randomness inherent in the standard data generation processes in order to improve the accuracy of predictions. Our contribution is twofold in that we describe an experimental methodology using a data structure called the debugging graph and apply this methodology to assess the robustness of existing models. The debugging graph is used to analyze the effects of various fault recovery orders on the predictive accuracy of several well-known software reliability algorithms. We found that, along a particular debugging path in the graph, the predictive performance of different models can vary greatly. Similarly, just because a model 'fits' a given path's data well does not guarantee that the model would perform well on a different path. Further we observed bug interactions and noted their potential effects on the predictive process. We saw that not only do different faults fail at different rates, but that those rates can be affected by the particular debugging stage at which the rates are evaluated. Based on our experiment, we conjecture that the accuracy of a reliability prediction is affected by the fault recovery order as well as by fault interaction.
Adaptive control method for core power control in TRIGA Mark II reactor

NASA Astrophysics Data System (ADS)

Sabri Minhat, Mohd; Selamat, Hazlina; Subha, Nurul Adilla Mohd

2018-01-01

The 1MWth Reactor TRIGA PUSPATI (RTP) Mark II type has undergone more than 35 years of operation. The existing core power control uses feedback control algorithm (FCA). It is challenging to keep the core power stable at the desired value within acceptable error bands to meet the safety demand of RTP due to the sensitivity of nuclear research reactor operation. Currently, the system is not satisfied with power tracking performance and can be improved. Therefore, a new design core power control is very important to improve the current performance in tracking and regulate reactor power by control the movement of control rods. In this paper, the adaptive controller and focus on Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) were applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, adaptive controller model, and control rods selection programming. The mathematical models of the reactor core were based on point kinetics model, thermal hydraulic models, and reactivity models. The adaptive control model was presented using Lyapunov method to ensure stable close loop system and STC Generalised Minimum Variance (GMV) Controller was not necessary to know the exact plant transfer function in designing the core power control. The performance between proposed adaptive control and FCA will be compared via computer simulation and analysed the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

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