GOPET: A tool for automated predictions of Gene Ontology terms

Directory of Open Access Journals (Sweden)

Glatting Karl-Heinz

2006-03-01

Full Text Available Abstract Background Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. Description We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO. Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool. It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar Conclusion Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.

RNA-SSPT: RNA Secondary Structure Prediction Tools.

Science.gov (United States)

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

2013-01-01

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

SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

Energy Technology Data Exchange (ETDEWEB)

Palefsky, S; Roper, J; Elder, E; Dhabaan, A [Winship Cancer Institute of Emory University, Atlanta, GA (United States)

2015-06-15

Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites.

SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

International Nuclear Information System (INIS)

Palefsky, S; Roper, J; Elder, E; Dhabaan, A

2015-01-01

Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites

Common features of microRNA target prediction tools

Directory of Open Access Journals (Sweden)

Sarah M. Peterson

2014-02-01

Full Text Available The human genome encodes for over 1800 microRNAs, which are short noncoding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one microRNA to target multiple gene transcripts, microRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of microRNA targets is a critical initial step in identifying microRNA:mRNA target interactions for experimental validation. The available tools for microRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to microRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all microRNA target prediction tools, four main aspects of the microRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MicroRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

Developing a comprehensive training curriculum for integrated predictive maintenance

Science.gov (United States)

Wurzbach, Richard N.

2002-03-01

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

Prediction of ttt curves of cold working tool steels using support vector machine model

Science.gov (United States)

Pillai, Nandakumar; Karthikeyan, R., Dr.

2018-04-01

The cold working tool steels are of high carbon steels with metallic alloy additions which impart higher hardenability, abrasion resistance and less distortion in quenching. The microstructure changes occurring in tool steel during heat treatment is of very much importance as the final properties of the steel depends upon these changes occurred during the process. In order to obtain the desired performance the alloy constituents and its ratio plays a vital role as the steel transformation itself is complex in nature and depends very much upon the time and temperature. The proper treatment can deliver satisfactory results, at the same time process deviation can completely spoil the results. So knowing time temperature transformation (TTT) of phases is very critical which varies for each type depending upon its constituents and proportion range. To obtain adequate post heat treatment properties the percentage of retained austenite should be lower and metallic carbides obtained should be fine in nature. Support vector machine is a computational model which can learn from the observed data and use these to predict or solve using mathematical model. Back propagation feedback network will be created and trained for further solutions. The points on the TTT curve for the known transformations curves are used to plot the curves for different materials. These data will be trained to predict TTT curves for other steels having similar alloying constituents but with different proportion range. The proposed methodology can be used for prediction of TTT curves for cold working steels and can be used for prediction of phases for different heat treatment methods.

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction.

Science.gov (United States)

Wu, X; Lund, M S; Sun, D; Zhang, Q; Su, G

2015-10-01

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less-related animals is favourable for reliability of genomic prediction. © 2015 Blackwell Verlag GmbH.

TSSPlant: a new tool for prediction of plant Pol II promoters

KAUST Repository

Shahmuradov, Ilham A.

2017-01-13

Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). Moreover, TSS selection depends on cell/tissue, development stage and environmental conditions. Such complex promoter structures make their computational identification notoriously difficult. Here, we present TSSPlant, a novel tool that predicts both TATA and TATA-less promoters in sequences of a wide spectrum of plant genomes. The tool was developed by using large promoter collections from ppdb and PlantProm DB. It utilizes eighteen significant compositional and signal features of plant promoter sequences selected in this study, that feed the artificial neural network-based model trained by the backpropagation algorithm. TSSPlant achieves significantly higher accuracy compared to the next best promoter prediction program for both TATA promoters (MCC≃0.84 and F1-score≃0.91 versus MCC≃0.51 and F1-score≃0.71) and TATA-less promoters (MCC≃0.80, F1-score≃0.89 versus MCC≃0.29 and F1-score≃0.50). TSSPlant is available to download as a standalone program at http://www.cbrc.kaust.edu.sa/download/.

TSSPlant: a new tool for prediction of plant Pol II promoters

KAUST Repository

Shahmuradov, Ilham A.; Umarov, Ramzan; Solovyev, Victor V.

2017-01-01

Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). Moreover, TSS selection depends on cell/tissue, development stage and environmental conditions. Such complex promoter structures make their computational identification notoriously difficult. Here, we present TSSPlant, a novel tool that predicts both TATA and TATA-less promoters in sequences of a wide spectrum of plant genomes. The tool was developed by using large promoter collections from ppdb and PlantProm DB. It utilizes eighteen significant compositional and signal features of plant promoter sequences selected in this study, that feed the artificial neural network-based model trained by the backpropagation algorithm. TSSPlant achieves significantly higher accuracy compared to the next best promoter prediction program for both TATA promoters (MCC≃0.84 and F1-score≃0.91 versus MCC≃0.51 and F1-score≃0.71) and TATA-less promoters (MCC≃0.80, F1-score≃0.89 versus MCC≃0.29 and F1-score≃0.50). TSSPlant is available to download as a standalone program at http://www.cbrc.kaust.edu.sa/download/.

TSORT - an automated tool for allocating tasks to training strategies

International Nuclear Information System (INIS)

Carter, R.J.; Jorgensen, C.C.

1986-01-01

An automated tool (TSORT) that can aid training system developers in determining which training strategy should be applied to a particular task and in grouping similar tasks into training categories has been developed. This paper describes the rationale for TSORT's development and addresses its structure, including training categories, task description dimensions, and categorization metrics. It also provides some information on TSORT's application

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

A new tool to evaluate postgraduate training posts: the Job Evaluation Survey Tool (JEST).

Science.gov (United States)

Wall, David; Goodyear, Helen; Singh, Baldev; Whitehouse, Andrew; Hughes, Elizabeth; Howes, Jonathan

2014-10-02

Three reports in 2013 about healthcare and patient safety in the UK, namely Berwick, Francis and Keogh have highlighted the need for junior doctors' views about their training experience to be heard. In the UK, the General Medical Council (GMC) quality assures medical training programmes and requires postgraduate deaneries to undertake quality management and monitoring of all training posts in their area. The aim of this study was to develop a simple trainee questionnaire for evaluation of postgraduate training posts based on the GMC, UK standards and to look at the reliability and validity including comparison with a well-established and internationally validated tool, the Postgraduate Hospital Educational Environment Measure (PHEEM). The Job Evaluation Survey Tool (JEST), a fifteen item job evaluation questionnaire was drawn up in 2006, piloted with Foundation doctors (2007), field tested with specialist paediatric registrars (2008) and used over a three year period (2008-11) by Foundation Doctors. Statistical analyses including descriptives, reliability, correlation and factor analysis were undertaken and JEST compared with PHEEM. The JEST had a reliability of 0.91 in the pilot study of 76 Foundation doctors, 0.88 in field testing of 173 Paediatric specialist registrars and 0.91 in three years of general use in foundation training with 3367 doctors completing JEST. Correlation of JEST with PHEEM was 0.80 (p training posts.

Training for vigilance: using predictive power to evaluate feedback effectiveness.

Science.gov (United States)

Szalma, James L; Hancock, Peter A; Warm, Joel S; Dember, William N; Parsons, Kelley S

2006-01-01

We examined the effects of knowledge of results (KR) on vigilance accuracy and report the first use of positive and negative predictive power (PPP and NPP) to assess vigilance training effectiveness. Training individuals to detect infrequent signals among a plethora of nonsignals is critical to success in many failure-intolerant monitoring technologies. KR has been widely used for vigilance training, but the effect of the schedule of KR presentation on accuracy has been neglected. Previous research on training for vigilance has used signal detection metrics or hits and false alarms. In this study diagnosticity measures were applied to augment traditional analytic methods. We examined the effects of continuous KR and a partial-KR regimen versus a no-KR control on decision diagnosticity. Signal detection theory (SDT) analysis indicated that KR induced conservatism in responding but did not enhance sensitivity. However, KR in both forms equally enhanced PPP while selectively impairing NPP. There is a trade-off in the effectiveness of KR in reducing false alarms and misses. Together, SDT and PPP/NPP measures provide a more complete portrait of performance effects. PPP and NPP together provide another assessment technique for vigilance performance, and as additional diagnostic tools, these measures are potentially useful to the human factors community.

New Tool to Predict Glaucoma

Science.gov (United States)

... In This Section A New Tool to Predict Glaucoma email Send this article to a friend by ... Close Send Thanks for emailing that article! Tweet Glaucoma can be difficult to detect and diagnose. Measurement ...

Toward a powerful problem-based training tool for harnessing lessons learned

International Nuclear Information System (INIS)

May, W. E.; Richards, R. E.

2006-01-01

One of the purposes of this paper is to describe a training method and supporting technologies (tool) to increase personnel awareness of error precursors. This model-centered method may be an effective 'part task training' to precede more expensive fall-blown multimedia scenarios (behavioral simulations). The training is designed to efficiently increase positive transfer from lessons learned case studies. The tool enables developers to take actual instances and inexpensively frame them within an interactive computer learning environment. The learning is engaging and helps model the kind of causal thinking needed to prevent future occurrences. This method and tool could help workers 'see' the problems and feel the need to take action when they are in real settings. Another purpose of this paper is to present the results of research into the process of designing and developing this training software. The benefits for attendees are insights that may enable more rapid proto-typing of software for training the workforce. The authors will present the models, methodologies, and patterns in the participants' design activities. Results show that participants used a cyclical, spiral process that revisited design topics until they were clarified. (authors)

An evidence-based decision assistance model for predicting training outcome in juvenile guide dogs.

Science.gov (United States)

Harvey, Naomi D; Craigon, Peter J; Blythe, Simon A; England, Gary C W; Asher, Lucy

2017-01-01

Working dog organisations, such as Guide Dogs, need to regularly assess the behaviour of the dogs they train. In this study we developed a questionnaire-style behaviour assessment completed by training supervisors of juvenile guide dogs aged 5, 8 and 12 months old (n = 1,401), and evaluated aspects of its reliability and validity. Specifically, internal reliability, temporal consistency, construct validity, predictive criterion validity (comparing against later training outcome) and concurrent criterion validity (comparing against a standardised behaviour test) were evaluated. Thirty-nine questions were sourced either from previously published literature or created to meet requirements identified via Guide Dogs staff surveys and staff feedback. Internal reliability analyses revealed seven reliable and interpretable trait scales named according to the questions within them as: Adaptability; Body Sensitivity; Distractibility; Excitability; General Anxiety; Trainability and Stair Anxiety. Intra-individual temporal consistency of the scale scores between 5-8, 8-12 and 5-12 months was high. All scales excepting Body Sensitivity showed some degree of concurrent criterion validity. Predictive criterion validity was supported for all seven scales, since associations were found with training outcome, at at-least one age. Thresholds of z-scores on the scales were identified that were able to distinguish later training outcome by identifying 8.4% of all dogs withdrawn for behaviour and 8.5% of all qualified dogs, with 84% and 85% specificity. The questionnaire assessment was reliable and could detect traits that are consistent within individuals over time, despite juvenile dogs undergoing development during the study period. By applying thresholds to scores produced from the questionnaire this assessment could prove to be a highly valuable decision-making tool for Guide Dogs. This is the first questionnaire-style assessment of juvenile dogs that has shown value in predicting

PISCES: A Tool for Predicting Software Testability

Science.gov (United States)

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

1991-01-01

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

What predicts performance during clinical psychology training?

OpenAIRE

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

2013-01-01

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

Laser safety tools and training

CERN Document Server

Barat, Ken

2008-01-01

Lasers perform many unique functions in a plethora of applications, but there are many inherent risks with this continually burgeoning technology. Laser Safety: Tools and Training presents simple, effective ways for users in a variety of facilities to evaluate the hazards of any laser procedure and ensure they are following documented laser safety standards.Designed for use as either a stand-alone volume or a supplement to Laser Safety Management, this text includes fundamental laser and laser safety information and critical laser use information rarely found in a single source. The first lase

Empirical comparison of web-based antimicrobial peptide prediction tools.

Science.gov (United States)

Gabere, Musa Nur; Noble, William Stafford

2017-07-01

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

Tools & training for more secure software

CERN Multimedia

CERN. Geneva

2017-01-01

Just by fate of nature, software today is shipped out as “beta”, coming with vulnerabilities and weaknesses, which should already have been fixed at the programming stage. This presentation will show the consequences of suboptimal software, why good programming, thorough software design, and a proper software development process is imperative for the overall security of the Organization, and how a few simple tools and training are supposed to make CERN software more secure.

An intelligent tool for the training of nuclear plant operators

International Nuclear Information System (INIS)

Cordier, B.

1990-01-01

A new type of pedagogical tool has been developped for the training of nuclear power plant operation. This tool combines simulation and expert system. The first process developped is about Steam Generator Tube Rupture (S.G.T.R.). All nuclear power plants will be equiped with this system in 1989 and 1990. After this first experiment, others processes will be developped for this tool

Updating risk prediction tools: a case study in prostate cancer.

Science.gov (United States)

Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

2012-01-01

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Simulation-Based Tool for Traffic Management Training, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — Both the current NAS, as well as NextGen, need successful use of advanced tools. Successful training is required today because more information gathering and...

Simulation-Based Tool for Traffic Management Training, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Both the current NAS, as well as NextGen, need successful use of advanced tools. Successful training is required today because more information gathering and...

The Predictive Validity of the ABFM's In-Training Examination.

Science.gov (United States)

O'Neill, Thomas R; Li, Zijia; Peabody, Michael R; Lybarger, Melanie; Royal, Kenneth; Puffer, James C

2015-05-01

Our objective was to examine the predictive validity of the American Board of Family Medicine's (ABFM) In-Training Examination (ITE) with regard to predicting outcomes on the ABFM certification examination. This study used a repeated measures design across three levels of medical training (PGY1--PGY2, PGY2--PGY3, and PGY3--initial certification) with three different cohorts (2010--2011, 2011--2012, and 2012--2013) to examine: (1) how well the residents' ITE scores correlated with their test scores in the following year, (2) what the typical score increase was across training years, and (3) what was the sensitivity, specificity, positive predictive value, and negative predictive value of the PGY3 scores with regard to predicting future results on the MC-FP Examination. ITE scores generally correlate at about .7 with the following year's ITE or with the following year's certification examination. The mean growth from PGY1 to PGY2 was 52 points, from PGY2 to PGY3 was 34 points, and from PGY3 to initial certification was 27 points. The sensitivity, specificity, positive predictive value, and negative predictive value were .91, .47, .96, and .27, respectively. The ITE is a useful predictor of future ITE and initial certification examination performance.

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

Directory of Open Access Journals (Sweden)

Jaroslav Bendl

2014-01-01

Full Text Available Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

Science.gov (United States)

Komiyama, Yusuke; Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

2016-03-15

Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. shimizu@bi.a.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Development and content validation of the power mobility training tool.

Science.gov (United States)

Kenyon, Lisa K; Farris, John P; Cain, Brett; King, Emily; VandenBerg, Ashley

2018-01-01

This paper outlines the development and content validation of the power mobility training tool (PMTT), an observational tool designed to assist therapists in developing power mobility training programs for children who have multiple, severe impairments. Initial items on the PMTT were developed based on a literature review and in consultation with therapists experienced in the use of power mobility. Items were trialled in clinical settings, reviewed, and refined. Items were then operationalized and an administration manual detailing scoring for each item was created. Qualitative and quantitative methods were used to establish content validity via a 15 member, international expert panel. The content validity ratio (CVR) was determined for each possible item. Of the 19 original items, 10 achieved minimum required CVR values and were included in the final version of the PMTT. Items related to manoeuvring a power mobility device were merged and an item related to the number of switches used concurrently to operate a power mobility device were added to the PMTT. The PMTT may assist therapists in developing training programs that facilitate the acquisition of beginning power mobility skills in children who have multiple, severe impairments. Implications for Rehabilitation The Power Mobility Training Tool (PMTT) was developed to help guide the development of power mobility intervention programs for children who have multiple, severe impairments. The PMTT can be used with children who access a power mobility device using either a joystick or a switch. Therapists who have limited experience with power mobility may find the PMTT to be helpful in setting up and conducting power mobility training interventions as a feasible aspect of a plan of care for children who have multiple, severe impairments.

A tool for assessing cultural competence training in dental education.

Science.gov (United States)

Holyfield, Lavern J; Miller, Barbara H

2013-08-01

Policies exist to promote fairness and equal access to opportunities and services that address basic human needs of all U.S. citizens. Nonetheless, health disparities continue to persist among certain subpopulations, including those of racial, ethnic, geographic, socioeconomic, and other cultural identity groups. The Commission on Dental Accreditation (CODA) has added standards to address this concern. According to the most recent standards, adopted in 2010 for implementation in July 2013, CODA stipulates that "students should learn about factors and practices associated with disparities in health." Thus, it is imperative that dental schools develop strategies to comply with this addition. One key strategy for compliance is the inclusion of cultural competence training in the dental curriculum. A survey, the Dental Tool for Assessing Cultural Competence Training (D-TACCT), based on the Association of American Medical Colleges' Tool for Assessing Cultural Competence Training (TACCT), was sent to the academic deans at seventy-one U.S. and Canadian dental schools to determine best practices for cultural competence training. The survey was completed by thirty-seven individuals, for a 52 percent response rate. This article describes the use of this survey as a guide for developing culturally competent strategies and enhancing cultural competence training in dental schools.

The Human Factor: Training and Professional Development as a Policy Tool

Directory of Open Access Journals (Sweden)

Lucian CIOLAN

2014-11-01

Full Text Available In this paper, we try to make a case for the risky approach of many decision-makers and pol- icy specialists to overuse authority and regula- tion-based tools, while neglecting the ones more focused on human capacity and persuasion. Especially in fields like education, we consider that the human factor should be at the core of any policy mix, and a tool like training and pro- fessional development should gain a more visible and persistent role in policy interventions. Firstly, we try to analyze the distribution of policy tools on the authority-complexity axes. The value we see in the mapping of policy tools is that it can be used for investigating and positioning the activity of a specific governing body or central gover- nance. Thus, a fundamental question remains as to what really influences the choice of policy tools or instruments, as a basis for better understand- ing the rationales behind a specific policy mix. We argue that policy failure could be ex- plained in many cases by the incapacity to ad- dress in a consistent and professional way the human capacities needed for implementation. Thus, training and professional development are, at least, poorly used from the perspective of the potential they have. As an argument, we tried to look at training and professional development in the specific area of teachers in pre-university education in Romania, situating it in the broader context of European policies in lifelong learning and participation of adults in continuing educa- tion and training, but also in the local policy en- vironment. The results of the research led us to the conclusion that educational policies should be among the first in the broader spectrum of public policies valuing and emphasizing learning, through training and professional development of the stakeholders involved in policy change together with adding more value to the Human Factor in educational policies. 

Gaussian process regression for tool wear prediction

Science.gov (United States)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

The development of a myoelectric training tool for above-elbow amputees.

Science.gov (United States)

Dawson, Michael R; Fahimi, Farbod; Carey, Jason P

2012-01-01

The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis. The training tool consists of a physical and simulated robotic arm, signal acquisition hardware, controller software, and a graphical user interface. The MTT improves over earlier training systems by allowing a targeted muscle reinnervation (TMR) patient to control up to two degrees of freedom simultaneously. The training tool has also been designed to function as a research prototype for novel myoelectric controllers. A preliminary experiment was performed in order to evaluate the effectiveness of the MTT as a learning tool and to identify any issues with the system. Five able-bodied participants performed a motor-learning task using the EMG controlled robotic arm with the goal of moving five balls from one box to another as quickly as possible. The results indicate that the subjects improved their skill in myoelectric control over the course of the trials. A usability survey was administered to the subjects after their trials. Results from the survey showed that the shoulder degree of freedom was the most difficult to control.

Daylight prediction techniques in energy design tools

Energy Technology Data Exchange (ETDEWEB)

Milne, M.; Zurick, J. [California Univ., Los Angeles, Dept. of Architecture, CA (United States)

1998-09-01

Four different whole-building energy design tool systems that calculate energy savings from daylighting and that display annual performance on an-hour-by-hour basis, have been tested. The nature of design tools, the sources of hourly outdoor illuminance data, the ways of predicting indoor illumination, the assumptions of each tool, and the resulting energy savings of the design tools tested are discussed. The tests were carried out with the essential criteria for evaluating whole-building daylighting and energy design tools in mind. These have been identified as user confidence, accuracy, response time, and the amount of detail. Results of the tests, all four of them run on a single elementary school classroom for the sake of comparability, were provided. 9 refs., 2 figs.

Biochemical assessment of physical training: a tool to sports dietitians-nutritionists

Directory of Open Access Journals (Sweden)

Aritz Urdampilleta

2013-06-01

Full Text Available The high demand in athletes creates the need to control the process of adaptation to training. The aim of this review is to analyze the biochemical parameters of utility for biological control of the athlete, and provide tools to sports dietitian-nutritionist in the follow-up of the training.Glucose and lipid profile parameters are widely used but insufficient to control training. The lactic acid level in the plasma is the most common tool to assess training load, where values higher than 4 mmol/l, suggest an intensive training. Other enzymes in high concentrations such as creatine kinase (CK, lactate dehydrogenase (LDH and two transaminases: glutamic oxaloacetic transaminase (GOT or aspartate transaminase (AST or aspartate aminotransferase (AAT and glutamic pyruvic transaminase (GPT or alanine transaminase or aminotransferase (ALT suggest that the training load was high producing microscopic tearing of the muscle fibers. Determination of other substrates such as ammonia, glutamine, or testosterone/cortisol ratio, used to detect a possible overtraining syndrome. Likewise the latest research suggest that high cortisol levels decrease the immune system.Moreover, an increase of urea, alanine or ketone bodies are related to muscle glycogen stores depleted. Therefore, the information provided by these parameters is useful for the sports dietitian-nutritionist for dietary and nutritional interventions to achieve more effective in function of the training goals.

Radiation protection education and training infrastructure. Open and distance learning tools for training in radiation protection

Energy Technology Data Exchange (ETDEWEB)

Marco, M.; Rodriguez, M.; Gonzalez Giralda, C.G.; Bailador Ferreras, A.B. [CIEMAT, Madrid (Spain); Coeck, M.C. [Studiecentrum voor Kernenergie - Centre d' Etude de l' Energie Nucleaire, Mol (Belgium); Etard, C.E. [CEA Saclay, 91 - Gif sur Yvette (France). INSTN, Institut National des Sciences et Techniques Nucleaires; Moebius, S.M. [FZK -FTU, Munich (Germany); Schmitt-Hanning, A.S. [BfS, Karlsruhe (Germany); Luciani, A.I. [ENEA, Bologna (Italy); Van Der Steen, J.V. [NRG, Petten (Netherlands)

2006-07-01

Full text: A sustainable Education and Training (E.T.) infrastructure for Radiation Protection is an essential component to combat the decline in expertise and to ensure the continuation of the high level of radiation protection knowledge in the future. Such infrastructure has to be built in such a way that both the initial training (Education) and the unceasing maintenance of the level of competencies (referred to as 'Training') are available. The E.N.E.T.R.A.P. project intends to develop the E.T. infrastructure mentioned. To achieve the aims of the different tasks and activities, the work programme for the E.N.E.T.R.A.P. Network is divided in eight work packages developed by 11 partners: Each partner will assume responsibility for the W.P.s. C.I.E.M.A.T. is involved in the W.P.-5 'New concepts and new tools for an E.R.P.C.'. The tasks of the W.P.-5 are focussed in the investigation of the electronic tools used in R.P. training and education. This paper presents the first results of this working group. The first task is an approach to the development and usage of learning resources. A review on the e-learning methodologies, the present state of art and its evolution, are being carried out. Results will be used to select the best way to host learning activities in the framework of the E.N.E.T.R.A.P. project. Another important task is to identify, analyse and evaluate the Open and Distance learning tools and material existing for train ing in Radiation Protection. A review on the evolutions, approaches and methodologies aiming to provide education and training in radiation protection, will be carried out. The results of this task will be a summary of links referred to the most interesting R.P. e-learning. Finally, taking in account the previous results a pilot R.P. module of E.R.P.C. should be prepared. (authors)

Radiation protection education and training infrastructure. Open and distance learning tools for training in radiation protection

International Nuclear Information System (INIS)

Marco, M.; Rodriguez, M.; Gonzalez Giralda, C.G.; Bailador Ferreras, A.B.; Coeck, M.C.; Etard, C.E.; Schmitt-Hanning, A.S.; Luciani, A.I.; Van Der Steen, J.V.

2006-01-01

Full text: A sustainable Education and Training (E.T.) infrastructure for Radiation Protection is an essential component to combat the decline in expertise and to ensure the continuation of the high level of radiation protection knowledge in the future. Such infrastructure has to be built in such a way that both the initial training (Education) and the unceasing maintenance of the level of competencies (referred to as 'Training') are available. The E.N.E.T.R.A.P. project intends to develop the E.T. infrastructure mentioned. To achieve the aims of the different tasks and activities, the work programme for the E.N.E.T.R.A.P. Network is divided in eight work packages developed by 11 partners: Each partner will assume responsibility for the W.P.s. C.I.E.M.A.T. is involved in the W.P.-5 'New concepts and new tools for an E.R.P.C.'. The tasks of the W.P.-5 are focussed in the investigation of the electronic tools used in R.P. training and education. This paper presents the first results of this working group. The first task is an approach to the development and usage of learning resources. A review on the e-learning methodologies, the present state of art and its evolution, are being carried out. Results will be used to select the best way to host learning activities in the framework of the E.N.E.T.R.A.P. project. Another important task is to identify, analyse and evaluate the Open and Distance learning tools and material existing for train ing in Radiation Protection. A review on the evolutions, approaches and methodologies aiming to provide education and training in radiation protection, will be carried out. The results of this task will be a summary of links referred to the most interesting R.P. e-learning. Finally, taking in account the previous results a pilot R.P. module of E.R.P.C. should be prepared. (authors)

Flight Experiment Verification of Shuttle Boundary Layer Transition Prediction Tool

Science.gov (United States)

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

2016-01-01

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

idSpace Tooling and Training for collaborative distributed product innovation

NARCIS (Netherlands)

Rutjens, Marjo; Bitter-Rijpkema, Marlies; Grube, Pascal; Heider, Thomas

2009-01-01

Rutjens, M., Bitter-Rijpkema, M., Grube, P. P., & Heider, T. (2009). idSpace Tooling and Training for collaborative distributed product innovation. Workshop during the e-Learning Baltic conference. June, 17-19, 2009, Rostock, Germany.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

Science.gov (United States)

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance

Training warning flags

International Nuclear Information System (INIS)

Miller, Richard C.

2003-01-01

Problems in accredited training programmes at US nuclear stations have resulted in several programmes having their accreditation status designated as probationary. A limited probationary period allows time for problem resolution before the programmes are again reviewed by the National Nuclear Accrediting Board. A careful study of these problems has resulted in the identification of several 'Training Warning Flags' that singularly, or in concert, may indicate or predict degraded training programme effectiveness. These training warning flags have been used by several US nuclear stations as a framework for self-assessments, as a reference in making changes to training programmes, and as a tool in considering student and management feedback on training activities. Further analysis and consideration of the training warning flags has developed precursors for each of the training warning flags. Although more subjective than the training warning flags, the precursors may represent early indicators of factors that may lead to or contribute to degraded training programme effectiveness. Used as evaluative tools, the training warning flags and the precursors may help identify areas for improvements in training programmes and help prioritize training programme improvement efforts. (author)

USE OF MULTIMEDIA TOOLS IN THE TRAINING OF PEDAGOGICAL COLLEGES STUDENTS

Directory of Open Access Journals (Sweden)

Olga M. Naumenko

2010-08-01

Full Text Available Current questions concerning the introduction of information and communication technologies tools into the pedagogical college teacher training using the essentially new ways of informative activity are considered. On the basis of the researches having been spent in the pedagogical colleges of Kyiv, there are analyzed possible scenarios of use of such multimedia tools in educational process.

USE OF MULTIMEDIA TOOLS IN THE TRAINING OF PEDAGOGICAL COLLEGES STUDENTS

OpenAIRE

Olga M. Naumenko

2010-01-01

Current questions concerning the introduction of information and communication technologies tools into the pedagogical college teacher training using the essentially new ways of informative activity are considered. On the basis of the researches having been spent in the pedagogical colleges of Kyiv, there are analyzed possible scenarios of use of such multimedia tools in educational process.

Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural network

International Nuclear Information System (INIS)

Mirvakili, S.M.; Faghihi, F.; Khalafi, H.

2012-01-01

Highlights: ► Thermal–hydraulics parameters of a VVER-1000 core based on neural network (ANN), are carried out. ► Required data for ANN training are found based on modified COBRA-EN code and then linked each other using MATLAB software. ► Based on ANN method, average and maximum temperature of fuel and clad as well as MDNBR of each FA are predicted. -- Abstract: The main goal of the present article is to design a computational tool to predict physical parameters of the VVER-1000 nuclear reactor core based on artificial neural network (ANN), taking into account a detailed physical model of the fuel rods and coolant channels in a fuel assembly. Predictions of thermal characteristics of fuel, clad and coolant are performed using cascade feed forward ANN based on linear fission power distribution and power peaking factors of FAs and hot channels factors (which are found based on our previous neutronic calculations). A software package has been developed to prepare the required data for ANN training which applies a modified COBRA-EN code for sub-channel analysis and links the codes using the MATLAB software. Based on the current estimation system, five main core TH parameters are predicted, which include the average and maximum temperatures of fuel and clad as well as the minimum departure from nucleate boiling ratio (MDNBR) for each FA. To get the best conditions for the considered ANNs training, a comprehensive sensitivity study has been performed to examine the effects of variation of hidden neurons, hidden layers, transfer functions, and the learning algorithms on the training and simulation results. Performance evaluation results show that the developed ANN can be trained to estimate the core TH parameters of a typical VVER-1000 reactor quickly without loss of accuracy.

Training using a new multidirectional reach tool improves balance in individuals with stroke.

Science.gov (United States)

Khumsapsiri, Numpung; Siriphorn, Akkradate; Pooranawatthanakul, Kanokporn; Oungphalachai, Tanyarut

2018-04-01

Previous studies suggested that limits of stability (LOS) training with visual feedback using commercial equipment could be used to improve balance ability in individuals with stroke. However, this system is expensive. In this study, we created a new tool from inexpensive elements based on LOS training using visual feedback. The aim of this study was to investigate the effect of training using a new multidirectional reach tool on balance in individuals with stroke. A single-blind randomized control trial was conducted. Individuals with stroke (n = 16; age range 38-72 years) were recruited. Participants in the experimental group were trained with the multidirectional reach training for 30 min and conventional physical therapy for 30 min per day, 3 days a week for 4 weeks. Participants in the control group received conventional physical therapy for 30 min per day, 3 days a week for 4 weeks. The outcomes were LOS, weight-bearing squat, and Fullerton Advanced Balance scale. All of the outcome measures were measured at pretraining, post-training, and 1 month follow-up. At post-training and 1-month follow-up, the participants in the experimental group had an improvement of dynamic balance than the control group. Furthermore, the activity assessment by Fullerton Advanced Balance scale was more improved at 1 month follow-up in the experimental group than control group. The results of this study provide evidence that training using a new multidirectional reach tool is effective for improving balance in individuals with stroke. Copyright © 2018 John Wiley & Sons, Ltd.

GAPIT: genome association and prediction integrated tool.

Science.gov (United States)

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

2012-09-15

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

Evaluating the utility of workplace-based assessment tools for speciality training.

Science.gov (United States)

Setna, Z; Jha, V; Boursicot, K A M; Roberts, T E

2010-12-01

Workplace assessment has been incorporated into speciality training in the UK following changes in the training and work patterns within the National Health Service (NHS). There are various types of assessment tools that have been adopted to assess the clinical competence of trainees. In obstetrics and gynaecology, these include mini-CEX, Objective Structured Assessment of Technical skills (OSATS) and case-based discussion (CbDs). This review provides a theoretical background of workplace assessment and the educational framework that may be adopted to evaluate their effectiveness. It summarises current evidence for the utility of these tools with regard to reliability, validity, acceptability, educational impact and cost. Copyright © 2010 Elsevier Ltd. All rights reserved.

Chronic condition self-management support for Aboriginal people: Adapting tools and training.

Science.gov (United States)

Battersby, Malcolm; Lawn, Sharon; Kowanko, Inge; Bertossa, Sue; Trowbridge, Coral; Liddicoat, Raylene

2018-04-22

Chronic conditions are major health problems for Australian Aboriginal people. Self-management programs can improve health outcomes. However, few health workers are skilled in self-management support and existing programs are not always appropriate in Australian Aboriginal contexts. The goal was to increase the capacity of the Australian health workforce to support Australian Aboriginal people to self-manage their chronic conditions by adapting the Flinders Program of chronic condition self-management support for Australian Aboriginal clients and develop and deliver training for health professionals to implement the program. Feedback from health professionals highlighted that the Flinders Program assessment and care planning tools needed to be adapted to suit Australian Aboriginal contexts. Through consultation with Australian Aboriginal Elders and other experts, the tools were condensed into an illustrated booklet called 'My Health Story'. Associated training courses and resources focusing on cultural safety and effective engagement were developed. A total of 825 health professionals  across Australia was trained and 61 people qualified as accredited trainers in the program, ensuring sustainability. The capacity and skills of the Australian health workforce to engage with and support Australian Aboriginal people to self-manage their chronic health problems significantly increased as a result of this project. The adapted tools and training were popular and appreciated by the health care organisations, health professionals and clients involved. The adapted tools have widespread appeal for cultures that do not have Western models of health care and where there are health literacy challenges. My Health Story has already been used internationally. © 2018 National Rural Health Alliance Ltd.

Predictability of psychic outcome for exercise training and exercise training including relaxation therapy after myocardial infarction

NARCIS (Netherlands)

H.J. Duivenvoorden (Hugo); J. van Dixhoorn (J.)

1991-01-01

markdownabstractAbstract Predictability of the psychic outcome for two cardiac rehabilitation programmes was investigated in 119 myocardial infarction patients. They were randomly assigned to either a five-week daily exercise training or to an identical training in combination with six sessions

NEEMO 20: Science Training, Operations, and Tool Development

Science.gov (United States)

Graff, T.; Miller, M.; Rodriguez-Lanetty, M.; Chappell, S.; Naids, A.; Hood, A.; Coan, D.; Abell, P.; Reagan, M.; Janoiko, B.

2016-01-01

The 20th mission of the National Aeronautics and Space Administration (NASA) Extreme Environment Mission Operations (NEEMO) was a highly integrated evaluation of operational protocols and tools designed to enable future exploration beyond low-Earth orbit. NEEMO 20 was conducted from the Aquarius habitat off the coast of Key Largo, FL in July 2015. The habitat and its surroundings provide a convincing analog for space exploration. A crew of six (comprised of astronauts, engineers, and habitat technicians) lived and worked in and around the unique underwater laboratory over a mission duration of 14-days. Incorporated into NEEMO 20 was a diverse Science Team (ST) comprised of geoscientists from the Astromaterials Research and Exploration Science (ARES/XI) Division from the Johnson Space Center (JSC), as well as marine scientists from the Department of Biological Sciences at Florida International University (FIU). This team trained the crew on the science to be conducted, defined sampling techniques and operational procedures, and planned and coordinated the science focused Extra Vehicular Activities (EVAs). The primary science objectives of NEEMO 20 was to study planetary sampling techniques and tools in partial gravity environments under realistic mission communication time delays and operational pressures. To facilitate these objectives two types of science sites were employed 1) geoscience sites with available rocks and regolith for testing sampling procedures and tools and, 2) marine science sites dedicated to specific research focused on assessing the photosynthetic capability of corals and their genetic connectivity between deep and shallow reefs. These marine sites and associated research objectives included deployment of handheld instrumentation, context descriptions, imaging, and sampling; thus acted as a suitable proxy for planetary surface exploration activities. This abstract briefly summarizes the scientific training, scientific operations, and tool

Development of Web tools to predict axillary lymph node metastasis and pathological response to neoadjuvant chemotherapy in breast cancer patients.

Science.gov (United States)

Sugimoto, Masahiro; Takada, Masahiro; Toi, Masakazu

2014-12-09

Nomograms are a standard computational tool to predict the likelihood of an outcome using multiple available patient features. We have developed a more powerful data mining methodology, to predict axillary lymph node (AxLN) metastasis and response to neoadjuvant chemotherapy (NAC) in primary breast cancer patients. We developed websites to use these tools. The tools calculate the probability of AxLN metastasis (AxLN model) and pathological complete response to NAC (NAC model). As a calculation algorithm, we employed a decision tree-based prediction model known as the alternative decision tree (ADTree), which is an analog development of if-then type decision trees. An ensemble technique was used to combine multiple ADTree predictions, resulting in higher generalization abilities and robustness against missing values. The AxLN model was developed with training datasets (n=148) and test datasets (n=143), and validated using an independent cohort (n=174), yielding an area under the receiver operating characteristic curve (AUC) of 0.768. The NAC model was developed and validated with n=150 and n=173 datasets from a randomized controlled trial, yielding an AUC of 0.787. AxLN and NAC models require users to input up to 17 and 16 variables, respectively. These include pathological features, including human epidermal growth factor receptor 2 (HER2) status and imaging findings. Each input variable has an option of "unknown," to facilitate prediction for cases with missing values. The websites developed facilitate the use of these tools, and serve as a database for accumulating new datasets.

Tool-use training in a species of rodent: the emergence of an optimal motor strategy and functional understanding.

Directory of Open Access Journals (Sweden)

Kazuo Okanoya

Full Text Available BACKGROUND: Tool use is defined as the manipulation of an inanimate object to change the position or form of a separate object. The expansion of cognitive niches and tool-use capabilities probably stimulated each other in hominid evolution. To understand the causes of cognitive expansion in humans, we need to know the behavioral and neural basis of tool use. Although a wide range of animals exhibit tool use in nature, most studies have focused on primates and birds on behavioral or psychological levels and did not directly address questions of which neural modifications contributed to the emergence of tool use. To investigate such questions, an animal model suitable for cellular and molecular manipulations is needed. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrated for the first time that rodents can be trained to use tools. Through a step-by-step training procedure, we trained degus (Octodon degus to use a rake-like tool with their forelimbs to retrieve otherwise out-of-reach rewards. Eventually, they mastered effective use of the tool, moving it in an elegant trajectory. After the degus were well trained, probe tests that examined whether they showed functional understanding of the tool were performed. Degus did not hesitate to use tools of different size, colors, and shapes, but were reluctant to use the tool with a raised nonfunctional blade. Thus, degus understood the functional and physical properties of the tool after extensive training. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that tool use is not a specific faculty resulting from higher intelligence, but is a specific combination of more general cognitive faculties. Studying the brains and behaviors of trained rodents can provide insights into how higher cognitive functions might be broken down into more general faculties, and also what cellular and molecular mechanisms are involved in the emergence of such cognitive functions.

Basic Botany On-Line: A Training Tool for the Master Gardener Program.

Science.gov (United States)

VanDerZanden, Ann Marie; Rost, Bob; Eckel, Rick

2002-01-01

A noncredit, online training module on botany was offered to participants in the Oregon Master Gardener program. The 48 participants felt the module was a useful training tool. They also noted that the convenience of completing the material at their own pace and during a time that fit into their schedule. (SK)

Prediction of the maximum absorption wavelength of azobenzene dyes by QSPR tools

Science.gov (United States)

Xu, Xuan; Luan, Feng; Liu, Huitao; Cheng, Jianbo; Zhang, Xiaoyun

2011-12-01

The maximum absorption wavelength ( λmax) of a large data set of 191 azobenzene dyes was predicted by quantitative structure-property relationship (QSPR) tools. The λmax was correlated with the 4 molecular descriptors calculated from the structure of the dyes alone. The multiple linear regression method (MLR) and the non-linear radial basis function neural network (RBFNN) method were applied to develop the models. The statistical parameters provided by the MLR model were R2 = 0.893, Radj2=0.893, qLOO2=0.884, F = 1214.871, RMS = 11.6430 for the training set; and R2 = 0.849, Radj2=0.845, qext2=0.846, F = 207.812, RMS = 14.0919 for the external test set. The RBFNN model gave even improved statistical results: R2 = 0.920, Radj2=0.919, qLOO2=0.898, F = 1664.074, RMS = 9.9215 for the training set, and R2 = 0.895, Radj2=0.892, qext2=0.895, F = 314.256, RMS = 11.6427 for the external test set. This theoretical method provides a simple, precise and an alternative method to obtain λmax of azobenzene dyes.

Development of a Portable Training Tool for Simulating Visceral Angiographic Procedures for Beginners

International Nuclear Information System (INIS)

Yamagami, Takuji; Osuga, Keigo; Yoshimatsu, Rika; Matsumoto, Tomohiro; Miura, Hiroshi; Terayama, Koshi; Anai, Hiroshi; Takahashi, Masahide; Hasebe, Terumitsu; Nishimura, Tsunehiko

2009-01-01

The purpose of this study was to evaluate the usefulness of a tool that we developed to simulate performance of visceral angiography to train beginners in the field of splanchnic angiography. Seven residents and two fellows who were rotating within the Division of Interventional Radiology at our institution between June and August 2008 participated in the evaluation of this tool. They had no experience in performing visceral angiography as an operator. Time for selection of arterial branches arising from the celiac axis on the model was measured before and after training. After such training, the participants performed actual visceral angiography as an operator with instructors beside them. Success of the trainees in selecting visceral arterial branches was evaluated in these real-life cases. In the first test using the model, seven of nine trainees (77.8%) succeeded in selecting all required arteries, while the remaining two failed to select all required arteries. After training using the model, all trainees succeeded in selecting all required arteries just before the actual angiographic study. In the actual angiography, the catheter was successfully inserted from the femoral artery and advanced to the superior mesenteric, celiac, splenic, common hepatic, gastroduodenal, and right and left hepatic arteries by all trainees with only two exceptions. In conclusion, this tool is helpful for training beginners in visceral angiographic procedures.

Virtual Beach: Decision Support Tools for Beach Pathogen Prediction

Science.gov (United States)

The Virtual Beach Managers Tool (VB) is decision-making software developed to help local beach managers make decisions as to when beaches should be closed due to predicted high levels of water borne pathogens. The tool is being developed under the umbrella of EPA's Advanced Monit...

Predictions of titanium alloy properties using thermodynamic modeling tools

Science.gov (United States)

Zhang, F.; Xie, F.-Y.; Chen, S.-L.; Chang, Y. A.; Furrer, D.; Venkatesh, V.

2005-12-01

Thermodynamic modeling tools have become essential in understanding the effect of alloy chemistry on the final microstructure of a material. Implementation of such tools to improve titanium processing via parameter optimization has resulted in significant cost savings through the elimination of shop/laboratory trials and tests. In this study, a thermodynamic modeling tool developed at CompuTherm, LLC, is being used to predict β transus, phase proportions, phase chemistries, partitioning coefficients, and phase boundaries of multicomponent titanium alloys. This modeling tool includes Pandat, software for multicomponent phase equilibrium calculations, and PanTitanium, a thermodynamic database for titanium alloys. Model predictions are compared with experimental results for one α-β alloy (Ti-64) and two near-β alloys (Ti-17 and Ti-10-2-3). The alloying elements, especially the interstitial elements O, N, H, and C, have been shown to have a significant effect on the β transus temperature, and are discussed in more detail herein.

Using Web-Based Technologies and Tools in Future Choreographers' Training: British Experience

Science.gov (United States)

Bidyuk, Dmytro

2016-01-01

In the paper the problem of using effective web-based technologies and tools in teaching choreography in British higher education institutions has been discussed. Researches on the usage of web-based technologies and tools for practical dance courses in choreographers' professional training at British higher education institutions by such British…

PRmePRed: A protein arginine methylation prediction tool.

Directory of Open Access Journals (Sweden)

Pawan Kumar

Full Text Available Protein methylation is an important Post-Translational Modification (PTMs of proteins. Arginine methylation carries out and regulates several important biological functions, including gene regulation and signal transduction. Experimental identification of arginine methylation site is a daunting task as it is costly as well as time and labour intensive. Hence reliable prediction tools play an important task in rapid screening and identification of possible methylation sites in proteomes. Our preliminary assessment using the available prediction methods on collected data yielded unimpressive results. This motivated us to perform a comprehensive data analysis and appraisal of features relevant in the context of biological significance, that led to the development of a prediction tool PRmePRed with better performance. The PRmePRed perform reasonably well with an accuracy of 84.10%, 82.38% sensitivity, 83.77% specificity, and Matthew's correlation coefficient of 66.20% in 10-fold cross-validation. PRmePRed is freely available at http://bioinfo.icgeb.res.in/PRmePRed/.

Omics AnalySIs System for PRecision Oncology (OASISPRO): A Web-based Omics Analysis Tool for Clinical Phenotype Prediction.

Science.gov (United States)

Yu, Kun-Hsing; Fitzpatrick, Michael R; Pappas, Luke; Chan, Warren; Kung, Jessica; Snyder, Michael

2017-09-12

Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients' clinical profiles, executes machine-learning algorithms of choice on the omics data, and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients' survival outcomes in many cancer types, including mesothelioma and adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. This web-based tool is available at http://tinyurl.com/oasispro ;source codes are available at http://tinyurl.com/oasisproSourceCode . © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Web tools for predictive toxicology model building.

Science.gov (United States)

Jeliazkova, Nina

2012-07-01

The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

Dairy Tool Box Talks: a comprehensive worker training in dairy farming

Directory of Open Access Journals (Sweden)

Maristela Rovai

2016-07-01

Full Text Available Today’s dairies are growing rapidly, with increasing dependence on Latino immigrant workers. This requires new educational strategies for improving milk quality and introduction to state-of-the-art dairy farming practices. It also creates knowledge gaps pertaining to the health of animals and workers, mainly due to lack of time and language barriers. Owners, managers and herdsmen assign training duties to more experienced employees, which may not promote ‘best practices’ and may perpetuate bad habits. A comprehensive and periodic training program administered by qualified personnel is currently needed and will enhance the sustainability of the dairy industry. Strategic management and employee satisfaction will be achieved through proper training in the employee’s language, typically Spanish. The training needs to address not only current industry standards but also social and cultural differences. An innovative training course was developed following the same structure used by the engineering and construction industries, giving farm workers basic understanding of animal care and handling, cow comfort and personal safety. The Dairy Tool Box Talks program was conducted over a ten week period with nine 30-minute sessions according to farm’s various employee work shifts. Bulk milk bacterial counts and somatic cell count were used to evaluate milk quality on the three dairy farms participating in the program.Dairy Tool Box Talks resulted in a general sense of employee satisfaction, significant learning outcomes, and enthusiasm about the topics covered. We conclude this article by highlighting the importance of educational programs aimed at improving overall cross-cultural training.

Dairy Tool Box Talks: A Comprehensive Worker Training in Dairy Farming.

Science.gov (United States)

Rovai, Maristela; Carroll, Heidi; Foos, Rebecca; Erickson, Tracey; Garcia, Alvaro

2016-01-01

Today's dairies are growing rapidly, with increasing dependence on Latino immigrant workers. This requires new educational strategies for improving milk quality and introduction to state-of-the-art dairy farming practices. It also creates knowledge gaps pertaining to the health of animals and workers, mainly due to the lack of time and language barriers. Owners, managers, and herdsmen assign training duties to more experienced employees, which may not promote "best practices" and may perpetuate bad habits. A comprehensive and periodic training program administered by qualified personnel is currently needed and will enhance the sustainability of the dairy industry. Strategic management and employee satisfaction will be achieved through proper training in the employee's language, typically Spanish. The training needs to address not only current industry standards but also social and cultural differences. An innovative training course was developed following the same structure used by the engineering and construction industries, giving farm workers basic understanding of animal care and handling, cow comfort, and personal safety. The "Dairy Tool Box Talks" program was conducted over a 10-week period with nine sessions according to farm's various employee work shifts. Bulk milk bacterial counts and somatic cell counts were used to evaluate milk quality on the three dairy farms participating in the program. "Dairy Tool Box Talks" resulted in a general sense of employee satisfaction, significant learning outcomes, and enthusiasm about the topics covered. We conclude this article by highlighting the importance of educational programs aimed at improving overall cross-cultural training.

Getting nowhere fast: trade-off between speed and precision in training to execute image-guided hand-tool movements

Directory of Open Access Journals (Sweden)

Anil Ufuk Batmaz

2016-11-01

Full Text Available Abstract Background The speed and precision with which objects are moved by hand or hand-tool interaction under image guidance depend on a specific type of visual and spatial sensorimotor learning. Novices have to learn to optimally control what their hands are doing in a real-world environment while looking at an image representation of the scene on a video monitor. Previous research has shown slower task execution times and lower performance scores under image-guidance compared with situations of direct action viewing. The cognitive processes for overcoming this drawback by training are not yet understood. Methods We investigated the effects of training on the time and precision of direct view versus image guided object positioning on targets of a Real-world Action Field (RAF. Two men and two women had to learn to perform the task as swiftly and as precisely as possible with their dominant hand, using a tool or not and wearing a glove or not. Individuals were trained in sessions of mixed trial blocks with no feed-back. Results As predicted, image-guidance produced significantly slower times and lesser precision in all trainees and sessions compared with direct viewing. With training, all trainees get faster in all conditions, but only one of them gets reliably more precise in the image-guided conditions. Speed-accuracy trade-offs in the individual performance data show that the highest precision scores and steepest learning curve, for time and precision, were produced by the slowest starter. Fast starters produced consistently poorer precision scores in all sessions. The fastest starter showed no sign of stable precision learning, even after extended training. Conclusions Performance evolution towards optimal precision is compromised when novices start by going as fast as they can. The findings have direct implications for individual skill monitoring in training programmes for image-guided technology applications with human operators.

SitesIdentify: a protein functional site prediction tool

Directory of Open Access Journals (Sweden)

Doig Andrew J

2009-11-01

Full Text Available Abstract Background The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application. Results Here we present a functional site prediction tool (SitesIdentify, based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites. Conclusion SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/

Popularity Prediction Tool for ATLAS Distributed Data Management

Science.gov (United States)

Beermann, T.; Maettig, P.; Stewart, G.; Lassnig, M.; Garonne, V.; Barisits, M.; Vigne, R.; Serfon, C.; Goossens, L.; Nairz, A.; Molfetas, A.; Atlas Collaboration

2014-06-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.

Popularity prediction tool for ATLAS distributed data management

International Nuclear Information System (INIS)

Beermann, T; Maettig, P; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2014-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.

Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

Science.gov (United States)

Magarey, Roger; Newton, Leslie; Hong, Seung C.; Takeuchi, Yu; Christie, Dave; Jarnevich, Catherine S.; Kohl, Lisa; Damus, Martin; Higgins, Steven I.; Miller, Leah; Castro, Karen; West, Amanda; Hastings, John; Cook, Gericke; Kartesz, John; Koop, Anthony

2018-01-01

This study compares four models for predicting the potential distribution of non-indigenous weed species in the conterminous U.S. The comparison focused on evaluating modeling tools and protocols as currently used for weed risk assessment or for predicting the potential distribution of invasive weeds. We used six weed species (three highly invasive and three less invasive non-indigenous species) that have been established in the U.S. for more than 75 years. The experiment involved providing non-U. S. location data to users familiar with one of the four evaluated techniques, who then developed predictive models that were applied to the United States without knowing the identity of the species or its U.S. distribution. We compared a simple GIS climate matching technique known as Proto3, a simple climate matching tool CLIMEX Match Climates, the correlative model MaxEnt, and a process model known as the Thornley Transport Resistance (TTR) model. Two experienced users ran each modeling tool except TTR, which had one user. Models were trained with global species distribution data excluding any U.S. data, and then were evaluated using the current known U.S. distribution. The influence of weed species identity and modeling tool on prevalence and sensitivity effects was compared using a generalized linear mixed model. Each modeling tool itself had a low statistical significance, while weed species alone accounted for 69.1 and 48.5% of the variance for prevalence and sensitivity, respectively. These results suggest that simple modeling tools might perform as well as complex ones in the case of predicting potential distribution for a weed not yet present in the United States. Considerations of model accuracy should also be balanced with those of reproducibility and ease of use. More important than the choice of modeling tool is the construction of robust protocols and testing both new and experienced users under blind test conditions that approximate operational conditions.

Evaluation of insight training of ambulance drivers in Sweden using DART, a new e-learning tool.

Science.gov (United States)

Albertsson, Pontus; Sundström, Anna

2011-12-01

The aim of the study was to evaluate whether a new e-learning tool for insight training of ambulance drivers can have an effect on drivers' driving behaviors, perceived driving competence, competence to assess risks, self-reflection, and safety attitudes. A quasi-experimental study design, with participants nonrandomly assigned into a control and intervention group, was used. The intervention group participated in the insight-training course and the control group did not. Both groups completed a self- and peer assessment online questionnaire before and after the training. The main finding is that the ambulance drivers assessed themselves through the instruments after the training, with the e-learning tool Driver Access Recording Tool (DART), as safer drivers in the areas of speed adaptation, closing up, and overtaking. In the answers from the group-based evaluation, the ambulance drivers responded that they were more reflective/analytical, had increased their risk awareness, and had changed their driving behaviors. After insight training, the ambulance drivers in this study assessed themselves as safer drivers in several important areas, including speed adaptation, closing up, and overtaking. In future training of ambulance drivers there should be more focus on insight training instead of previous training focusing on maneuvering capabilities.

Implementation of a digital preparation validation tool in dental skills laboratory training.

Science.gov (United States)

Kozarovska, A; Larsson, C

2018-05-01

To describe the implementation of a digital tool for preparation validation and evaluate it as an aid in students' self-assessment. Students at the final semester of skills laboratory training were asked to use a digital preparation validation tool (PVT) when performing two different tasks; preparation of crowns for teeth 11 and 21. The students were divided into two groups. Group A self-assessed and scanned all three attempts at 21 ("prep-and-scan"). Group B self-assessed all attempts chose the best one and scanned it ("best-of-three"). The situation was reversed for 11. The students assessed five parameters of the preparation and marked them as approved (A) or failed (F). These marks were compared with the information from the PVT. The students also completed a questionnaire. Each question was rated from 1 to 5. Teachers' opinions were collected at staff meetings throughout the project. Most students in the "prep-and-scan" groups showed an increase in agreement between their self-assessment and the information from the PVT, whereas students in the "best-of-three" groups showed lower levels of agreement. All students rated the PVT positively. Most strongly agreed that the tool was helpful in developing skills (mean 4.15), easy to use (mean 4.23) and that it added benefits in comparison to existing assessment tools (mean 4.05). They did not however, fully agree that the tool is time efficient (mean 2.55), and they did not consider it a substitute for verbal teacher feedback. Teachers' feedback suggested advantages of the tool in the form of ease of use, visual aid and increasing interest and motivation during skills laboratory training however, they did not notice a reduction in need of verbal feedback. Within the limitations of the study, our conclusion is that a digital PVT may be a valuable adjunct to other assessment tools in skills laboratory training. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Methodology to develop a training program as a tool for energy management

Directory of Open Access Journals (Sweden)

Mónica Rosario Berenguer-Ungaro

2017-12-01

Full Text Available The paperaims to present the methodology to develop a training program improve labor skills that enhance the efficient use of energy resources, which aims to make training a timely and meet the training needs as they arise and that the protagonist of it is he who receives training. It is based on the training-action and action research method and model for evaluating training Krikpatrick, it evaluates four levels, reaction, learning, behavior and results. The methodology is structured in three stages: 1 diagnosis of knowledge, 2 intervention based on the results and 3 evaluation and feedback for continuous improvement. Each stage has identified the objectives and implementation tools. Evaluation is transverse to the entire program and it is through it that decisions for feedback loops are taken.

TNO-ADVANCE: a modular power train simulation and design tool

NARCIS (Netherlands)

Venne, J.W.C. van de; Hendriksen, P.; Smokers, R.T.M.; Verkiel, M.

1998-01-01

To support its activities in the field of conventional and hybrid vehicles, TNO has developed ADVANCE, a modular simulation tool for the design and evaluation of advanced power trains. In this paper the various features and the potential of ADVANCE are described and illustrated by means of two case

Natural training tools of informatics in conditions of embodied and mental approach realization

Directory of Open Access Journals (Sweden)

Daria A. Barkhatova

2017-01-01

Full Text Available Modern processes of globalization and informatization of human activity cause the necessity of change of the educational paradigm in the field of information training of a person, focused on the formation of the strong fundamental knowledge and abilities, which are necessary for person’s information activities and self-education during all life.In connection with these requirements, it is necessary to pay attention to new approaches in education, based on achievements of cognitive science and modern pedagogic. One of such approaches is embodied and mental approach. The paper is devoted to the description of a way of realization of embodied and mental approach in training of informatics through application of the natural tools, providing the fullest and deep understanding of the educational material, and development of cognitive abilities of students.In the paper the theoretical analysis of psychology-pedagogical and methodical literature on a research subject is carried out, results are generalized, natural tools are modeled and results of their partial approbation are described. Achievement of necessary quality of education is offered due to the use of modern techniques, focused on the development of cognitive abilities and improvement of quality of the knowledge. In the conditions of information education, the combination of embodied and mental approaches will allow to acquaint students with the essence of the studied subject due to activation of motor area of the memory and the kinesthetic and visual perception channels. The instrument of realization of this idea is offered to use natural tools in informatics, what is actualized by age features of cognitive abilities of students and individual requirements to ways of perception and mastering of the material, matched according to the level of their knowledge.The research results describe the models of natural tools, developed by students and lecturers of the basic Department of Informatics

Popularity Prediction Tool for ATLAS Distributed Data Management

CERN Document Server

Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2013-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...

Popularity Prediction Tool for ATLAS Distributed Data Management

CERN Document Server

Beermann, T; The ATLAS collaboration; Stewart, G; Lassnig, M; Garonne, V; Barisits, M; Vigne, R; Serfon, C; Goossens, L; Nairz, A; Molfetas, A

2014-01-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distri...

Predictive Modeling in Race Walking

Directory of Open Access Journals (Sweden)

Krzysztof Wiktorowicz

2015-01-01

Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

Prefrontal Cortex Structure Predicts Training-Induced Improvements in Multitasking Performance.

Science.gov (United States)

Verghese, Ashika; Garner, K G; Mattingley, Jason B; Dux, Paul E

2016-03-02

The ability to perform multiple, concurrent tasks efficiently is a much-desired cognitive skill, but one that remains elusive due to the brain's inherent information-processing limitations. Multitasking performance can, however, be greatly improved through cognitive training (Van Selst et al., 1999, Dux et al., 2009). Previous studies have examined how patterns of brain activity change following training (for review, see Kelly and Garavan, 2005). Here, in a large-scale human behavioral and imaging study of 100 healthy adults, we tested whether multitasking training benefits, assessed using a standard dual-task paradigm, are associated with variability in brain structure. We found that the volume of the rostral part of the left dorsolateral prefrontal cortex (DLPFC) predicted an individual's response to training. Critically, this association was observed exclusively in a task-specific training group, and not in an active-training control group. Our findings reveal a link between DLPFC structure and an individual's propensity to gain from training on a task that taps the limits of cognitive control. Cognitive "brain" training is a rapidly growing, multibillion dollar industry (Hayden, 2012) that has been touted as the panacea for a variety of disorders that result in cognitive decline. A key process targeted by such training is "cognitive control." Here, we combined an established cognitive control measure, multitasking ability, with structural brain imaging in a sample of 100 participants. Our goal was to determine whether individual differences in brain structure predict the extent to which people derive measurable benefits from a cognitive training regime. Ours is the first study to identify a structural brain marker-volume of left hemisphere dorsolateral prefrontal cortex-associated with the magnitude of multitasking performance benefits induced by training at an individual level. Copyright © 2016 the authors 0270-6474/16/362638-08$15.00/0.

Training in positivity for stroke? A qualitative study of acceptability of use of Positive Mental Training (PosMT) as a tool to assist stroke survivors with post-stroke psychological problems and in coping with rehabilitation.

Science.gov (United States)

Mavaddat, Nahal; Ross, Sheila; Dobbin, Alastair; Williams, Kate; Graffy, Jonathan; Mant, Jonathan

2017-01-01

Post-stroke psychological problems predict poor recovery, while positive affect enables patients to focus on rehabilitation and may improve functional outcomes. Positive Mental Training (PosMT), a guided self-help audio shows promise as a tool in promoting positivity, optimism and resilience. To assess acceptability of training in positivity with PosMT for prevention and management of post-stroke psychological problems and to help with coping with rehabilitation. A modified PosMT tool consisted of 12 audio tracks each lasting 18 minutes, one listened to every day for a week. Survivors and carers were asked to listen for 4 weeks, but could volunteer to listen for more. Interviews took place about experiences of the tool after 4 and 12 weeks. 10 stroke survivors and 5 carers from Stroke Support Groups in the UK. Three stroke survivors did not engage with the tool. The remainder reported positive physical and psychological benefits including improved relaxation, better sleep and reduced anxiety after four weeks. Survivors who completed the programme gained a positive outlook on the future, increased motivation, confidence and ability to cope with rehabilitation. No adverse effects were reported. The PosMT shows potential as a tool for coping with rehabilitation and overcoming post-stroke psychological problems including anxiety and depression.

Analytical Model of Underground Train Induced Vibrations on Nearby Building Structures in Cameroon: Assessment and Prediction

Directory of Open Access Journals (Sweden)

Lezin Seba MINSILI

2013-11-01

Full Text Available The purpose of this research paper was to assess and predict the effect of vibrations induced by an underground railway on nearby-existing buildings prior to the construction of projected new railway lines of the National Railway Master Plan of Cameroon and after upgrading of the railway conceded to CAMRAIL linking the two most densely populated cities of Cameroon: Douala and Yaoundé. With the source-transmitter-receiver mathematical model as the train-soil-structure interaction model, taking into account sub-model parameters such as type of the train-railway system, typical geotechnical conditions of the ground and the sensitivity of the nearby buildings, the analysis is carried out over the entire system using the dynamic finite element method in the time domain. This subdivision of the model is a powerful tool that allows to consider different alternatives of sub-models with different characteristics, and thus to determine any critical excessive vibration impact. Based on semi-empirical analytical results obtained from presented models, the present work assesses and predicts characteristics of traffic-induced vibrations as a function of time duration, intensity and vehicle speed, as well as their influence on buildings at different levels.

A comparison of text and technology based training tools to improve cognitive skills in older adults.

Science.gov (United States)

Power, Kevin; Kirwan, Grainne; Palmer, Marion

2011-01-01

Research has indicated that use of cognitive skills training tools can produce positive benefits with older adults. However, little research has compared the efficacy of technology-based interventions and more traditional, text-based interventions which are also available. This study aimed to investigate cognitive skills improvements experienced by 40 older adults using cognitive skills training tools. A Solomon 4 group design was employed to determine which intervention demonstrated the greatest improvement. Participants were asked to use the interventions for 5-10 minutes per day, over a period of 60 days. Pre and post-tests consisted of measures of numerical ability, self-reported memory and intelligence. Following training, older adults indicated significant improvements on numerical ability and intelligence regardless of intervention type. No improvement in selfreported memory was observed. This research provides a critical appraisal of brain training tools and can help point the way for future improvements in the area. Brain training improvements could lead to improved quality of life, and perhaps, have financial and independent living ramifications for older adults.

Bronchoscopy Simulation Training as a Tool in Medical School Education.

Science.gov (United States)

Gopal, Mallika; Skobodzinski, Alexus A; Sterbling, Helene M; Rao, Sowmya R; LaChapelle, Christopher; Suzuki, Kei; Litle, Virginia R

2018-07-01

Procedural simulation training is rare at the medical school level and little is known about its usefulness in improving anatomic understanding and procedural confidence in students. Our aim is to assess the impact of bronchoscopy simulation training on bronchial anatomy knowledge and technical skills in medical students. Medical students were recruited by email, consented, and asked to fill out a survey regarding their baseline experience. Two thoracic surgeons measured their knowledge of bronchoscopy on a virtual reality bronchoscopy simulator using the Bronchoscopy Skills and Tasks Assessment Tool (BSTAT), a validated 65-point checklist (46 for anatomy, 19 for simulation). Students performed four self-directed training sessions of 15 minutes per week. A posttraining survey and BSTAT were completed afterward. Differences between pretraining and posttraining scores were analyzed with paired Student's t tests and random intercept linear regression models accounting for baseline BSTAT score, total training time, and training year. The study was completed by 47 medical students with a mean training time of 81.5 ± 26.8 minutes. Mean total BSTAT score increased significantly from 12.3 ± 5.9 to 48.0 ± 12.9 (p training time and frequency of training did not have a significant impact on level of improvement. Self-driven bronchoscopy simulation training in medical students led to improvements in bronchial anatomy knowledge and bronchoscopy skills. Further investigation is under way to determine the impact of bronchoscopy simulation training on future specialty interest and long-term skills retention. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

INFORMATION TECHNOLOGIES AS THE TOOL OF EFFICIENCY IMPROVING OF FUTURE PHYSICS TEACHERS TRAINING TO LABORATORY SESSION IN OPTICS

Directory of Open Access Journals (Sweden)

Goncharenko T.

2017-12-01

Full Text Available The analysis of the problem of the use of information technologies implementation as the tool of the efficiency improving of future physics teachers training to execution of laboratory session in Optics is considered in the article. The problems and contradictions concerning ICT tools use in higher education institutions, the work of which is aimed at future physics teachers training are described. Due to the specifics of future teachers training in higher education institutions, labor market requirements and public procurement, the main ICT tools are identified, that are effective in students’ self-activity work to laboratory session execution. The developed list of electronic resources is divided into blocks according to the topics of laboratory works in Optics. The methodology of using of ICT tools at future students training for laboratory session on the example of individual topics is considered.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

Directory of Open Access Journals (Sweden)

Ashok K. Sharma

2017-11-01

Full Text Available The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93% and Matthews's correlation coefficient (0.84. The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87 on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84 than the multi-linear regression (MLR and partial least square regression (PLSR models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2 performed better (R2 = 0.68 in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity

Computer-Assisted Visual Search/Decision Aids as a Training Tool for Mammography

National Research Council Canada - National Science Library

Nodine, Calvin

1999-01-01

The primary goal of the project is to develop a computer-assisted visual search (CAVS) mammography training tool that will improve the perceptual and cognitive skills of trainees leading to mammographic expertise...

Computer-Assisted Visual Search/Decision Aids as a Training Tool for Mammography

National Research Council Canada - National Science Library

Nodine, Calvin

1998-01-01

The primary goal of the project is to develop a computer-assisted visual search (CAVS) mammography training tool that will improve the perceptual and cognitive skills of trainees leading to mammographic expertise...

Computer-Assisted Visual Search/Decision Aids as a Training Tool for Mammography

National Research Council Canada - National Science Library

Nodine, Calvin

2000-01-01

The primary goal of the project is to develop a computer-assisted visual search (CAVS) mammography training tool that will improve the perceptual and cognitive skills of trainees leading to mammographic expertise...

GameTeen: new tools for evaluating and training emotional regulation strategies

OpenAIRE

Rodriguez Ortega, Alejandro; Rey, Beatriz; Alcañiz Raya, Mariano Luis; BAÑOS, R.; Guixeres Provinciale, Jaime; Wrzesien, Maja; Gómez Martínez, Mario; Pérez Lopez, David Clemente; Rasal, Paloma; Parra Vargas, Elena

2012-01-01

The aim of this paper is to describe GameTeen, a novel instrument for the assessment and training of Emotional Regulation (ER) strategies in adolescent population. These new tools are based on the use of 3D serious games that can be played under different settings. The evolution of ER strategies will be monitored in two ways depending on the setting where the tool is presented. Firstly, in the laboratory, physiological signals and facial expressions of participants will be recorded. Secondly,...

Prediction of Running Injuries from Training Load: a Machine Learning Approach.

NARCIS (Netherlands)

Dijkhuis, Talko; Otter, Ruby; Velthuijsen, H.; Lemmink, Koen A.P.M.

2017-01-01

The prediction of the running injuries based on selfreported training data on load is difficult. At present, coaches and researchers have no validated system to predict if a runner has an increased risk of injuries. We aim to develop an algorithm to predict the increase of the risk of a runner to

Turning inspection regulations into training tools

International Nuclear Information System (INIS)

Finley, D.; Wheatcraft, D.

1996-01-01

In response to suggestions from internal and State of California auditors, the Hazardous Waste Management Division (HWM) at Lawrence Livermore National Laboratory prepared an Inspection Schedule and Guidance Document that summarizes the Laboratory's inspection schedule and procedures for waste treatment, storage, and disposal facilities (TSDFs). Because it explains and comments in detail on the inspection schedule, forms, and procedures, this document is a centralized reference for HWM managers and personnel performing TSDF inspections at the Laboratory. It is also a training tool for experienced and new inspectors, standardizing the inspections of personnel with experience and explaining to novices what to look for and why. This poster presentation traces the team effort that created this document and provides specific examples of how the document was developed and how it is used

Echo simulator with novel training and competency testing tools.

Science.gov (United States)

Sheehan, Florence H; Otto, Catherine M; Freeman, Rosario V

2013-01-01

We developed and validated an echo simulator with three novel tools that facilitate training and enable quantitative and objective measurement of psychomotor as well as cognitive skill. First, the trainee can see original patient images - not synthetic or simulated images - that morph in real time as the mock transducer is manipulated on the mannequin. Second, augmented reality is used for Visual Guidance, a tool that assists the trainee in scanning by displaying the target organ in 3-dimensions (3D) together with the location of the current view plane and the plane of the anatomically correct view. Third, we introduce Image Matching, a tool that leverages the aptitude of the human brain for recognizing similarities and differences to help trainees learn to perform visual assessment of ultrasound images. Psychomotor competence is measured in terms of the view plane angle error. The construct validity of the simulator for competency testing was established by demonstrating its ability to discriminate novices vs. experts.

AgMIP Training in Multiple Crop Models and Tools

Science.gov (United States)

Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

2015-01-01

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

Science.gov (United States)

Jen, Chian-Li; Tilwick, Leon

2000-01-01

This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

Science.gov (United States)

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

Effect of standardized training on the reliability of the Cochrane risk of bias assessment tool: a study protocol.

Science.gov (United States)

da Costa, Bruno R; Resta, Nina M; Beckett, Brooke; Israel-Stahre, Nicholas; Diaz, Alison; Johnston, Bradley C; Egger, Matthias; Jüni, Peter; Armijo-Olivo, Susan

2014-12-13

The Cochrane risk of bias (RoB) tool has been widely embraced by the systematic review community, but several studies have reported that its reliability is low. We aim to investigate whether training of raters, including objective and standardized instructions on how to assess risk of bias, can improve the reliability of this tool. We describe the methods that will be used in this investigation and present an intensive standardized training package for risk of bias assessment that could be used by contributors to the Cochrane Collaboration and other reviewers. This is a pilot study. We will first perform a systematic literature review to identify randomized clinical trials (RCTs) that will be used for risk of bias assessment. Using the identified RCTs, we will then do a randomized experiment, where raters will be allocated to two different training schemes: minimal training and intensive standardized training. We will calculate the chance-corrected weighted Kappa with 95% confidence intervals to quantify within- and between-group Kappa agreement for each of the domains of the risk of bias tool. To calculate between-group Kappa agreement, we will use risk of bias assessments from pairs of raters after resolution of disagreements. Between-group Kappa agreement will quantify the agreement between the risk of bias assessment of raters in the training groups and the risk of bias assessment of experienced raters. To compare agreement of raters under different training conditions, we will calculate differences between Kappa values with 95% confidence intervals. This study will investigate whether the reliability of the risk of bias tool can be improved by training raters using standardized instructions for risk of bias assessment. One group of inexperienced raters will receive intensive training on risk of bias assessment and the other will receive minimal training. By including a control group with minimal training, we will attempt to mimic what many review authors

WPPT, a tool for on-line wind power prediction

Energy Technology Data Exchange (ETDEWEB)

Skov Nielsen, T. [Dept. of Mathematical Modelling (IMM-DTU), Kgs. Lyngby (Denmark); Madsen, H. [Dept. of Mathematical Modelling (IMM-DTU) Kgs. Lyngby (Denmark); Toefting, J. [Elsam, Fredericia (Denmark)

2004-07-01

This paper dsecribes VPPT (Wind Power Prediction Tool), an application for assessing the future available wind power up to 36 hours ahead in time. WPPT has been installed in the Eltra/Elsam central dispatch center since October 1997. The paper describes the prediction model used, the actual implementation of WPPT as well as the experience gained by the operators in the dispatch center (au)

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction.

Science.gov (United States)

Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta

2018-01-01

The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning) predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

Cardiodynamicsgram: a novel tool for monitoring cardiac function in exercise training.

Science.gov (United States)

Wen, Xu; Guo, Bokai; Gong, Yinglan; Xia, Ling; Yu, Jie

2018-04-27

This study evaluated the feasibility of cardiodynamicsgram (CDG) for monitoring the cardiac functions of athletes and exercisers. CDG could provide an effective, simple, and economical tool for exercise training. Seventeen middle-distance race athletes aged 14-28 years old were recruited. CDG tests and blood test including creatine kinase (CK), CK-MB isoenzyme, and high-sensitivity troponin I (hsTnI) were performed before a high-intensity prolonged training, as well as 2 and 14 h after training, respectively. The CDG test result was unsatisfactory when the CK test result was used as standard. However, the accuracy of CDG test was about 80% when CK-MB and hsTnI were used as standards. Thus, CDG offers a noninvasive, simple, and economical approach for monitoring the cardiac function of athletes and exercisers during exercise training. Nonetheless, the applicability of CDG needs further investigation.

Effect of standardized training on the reliability of the Cochrane risk of bias assessment tool: a prospective study.

Science.gov (United States)

da Costa, Bruno R; Beckett, Brooke; Diaz, Alison; Resta, Nina M; Johnston, Bradley C; Egger, Matthias; Jüni, Peter; Armijo-Olivo, Susan

2017-03-03

The Cochrane risk of bias tool is commonly criticized for having a low reliability. We aimed to investigate whether training of raters, with objective and standardized instructions on how to assess risk of bias, can improve the reliability of the Cochrane risk of bias tool. In this pilot study, four raters inexperienced in risk of bias assessment were randomly allocated to minimal or intensive standardized training for risk of bias assessment of randomized trials of physical therapy treatments for patients with knee osteoarthritis pain. Two raters were experienced risk of bias assessors who served as reference. The primary outcome of our study was between-group reliability, defined as the agreement of the risk of bias assessments of inexperienced raters with the reference assessments of experienced raters. Consensus-based assessments were used for this purpose. The secondary outcome was within-group reliability, defined as the agreement of assessments within pairs of inexperienced raters. We calculated the chance-corrected weighted Kappa to quantify agreement within and between groups of raters for each of the domains of the risk of bias tool. A total of 56 trials were included in our analysis. The Kappa for the agreement of inexperienced raters with reference across items of the risk of bias tool ranged from 0.10 to 0.81 for the minimal training group and from 0.41 to 0.90 for the standardized training group. The Kappa values for the agreement within pairs of inexperienced raters across the items of the risk of bias tool ranged from 0 to 0.38 for the minimal training group and from 0.93 to 1 for the standardized training group. Between-group differences in Kappa for the agreement of inexperienced raters with reference always favored the standardized training group and was most pronounced for incomplete outcome data (difference in Kappa 0.52, p training on risk of bias assessment may significantly improve the reliability of the Cochrane risk of bias tool.

HostPhinder: A Phage Host Prediction Tool

Directory of Open Access Journals (Sweden)

Julia Villarroel

2016-05-01

Full Text Available The current dramatic increase of antibiotic resistant bacteria has revitalised the interest in bacteriophages as alternative antibacterial treatment. Meanwhile, the development of bioinformatics methods for analysing genomic data places high-throughput approaches for phage characterization within reach. Here, we present HostPhinder, a tool aimed at predicting the bacterial host of phages by examining the phage genome sequence. Using a reference database of 2196 phages with known hosts, HostPhinder predicts the host species of a query phage as the host of the most genomically similar reference phages. As a measure of genomic similarity the number of co-occurring k-mers (DNA sequences of length k is used. Using an independent evaluation set, HostPhinder was able to correctly predict host genus and species for 81% and 74% of the phages respectively, giving predictions for more phages than BLAST and significantly outperforming BLAST on phages for which both had predictions. HostPhinder predictions on phage draft genomes from the INTESTI phage cocktail corresponded well with the advertised targets of the cocktail. Our study indicates that for most phages genomic similarity correlates well with related bacterial hosts. HostPhinder is available as an interactive web service [1] and as a stand alone download from the Docker registry [2].

A training and educational tool for neutron coincidence measurements

International Nuclear Information System (INIS)

Huszti, J.; Bagi, J.; Langner, D.

2009-01-01

Neutron coincidence counting techniques are widely used for nuclear safeguards inspection. They are based on the detection of time correlated neutrons created from spontaneous or induced fission of plutonium and some other actinides. IAEA inspectors are trained to know and to use this technique, but it is not easy to illustrate and explain the basics of the neutron coincidence counting. The traditional shift registers or multiplicity counters give only multiplicity distributions and the singles, doubles and triples count rates. Using the list mode method for the recording and evaluation of neutron coincidence data makes it easier to teach this technique. List mode acquisition is a relatively new way to collect data in neutron coincidence counting. It is based on the recording of the follow-up times of neutron pulses originating from a neutron detector into a file. The recorded pulse train can be evaluated with special software after the measurement. Hardware and software for list mode neutron coincidence acquisition have been developed in the Institute of Isotopes and is called a Pulse Train Reader. A system called Virtual Source for replaying pulse trains registered with the list mode device has also been developed. The list mode device and the pulse train 're-player' together build a good educational tool for teaching the basics of neutron coincidence counting. Some features of the follow-up time, multiplicity and Rossi-alpha distributions can be well demonstrated by replaying artificially generated or pre-recorded pulse trains. The choice of real sources is stored on DVD. There is no need to transport and maintain real sources for the training. Virtual sources also give the possibility of investigating rare sources that trainees would not have access to otherwise. (authors)

The Predictive Accuracy of PREDICT : A Personalized Decision-Making Tool for Southeast Asian Women With Breast Cancer

NARCIS (Netherlands)

Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M.; Hartman, Mikael; Bhoo Pathy, N

Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480

Atomic Oxygen Erosion Yield Predictive Tool for Spacecraft Polymers in Low Earth Orbit

Science.gov (United States)

Bank, Bruce A.; de Groh, Kim K.; Backus, Jane A.

2008-01-01

A predictive tool was developed to estimate the low Earth orbit (LEO) atomic oxygen erosion yield of polymers based on the results of the Polymer Erosion and Contamination Experiment (PEACE) Polymers experiment flown as part of the Materials International Space Station Experiment 2 (MISSE 2). The MISSE 2 PEACE experiment accurately measured the erosion yield of a wide variety of polymers and pyrolytic graphite. The 40 different materials tested were selected specifically to represent a variety of polymers used in space as well as a wide variety of polymer chemical structures. The resulting erosion yield data was used to develop a predictive tool which utilizes chemical structure and physical properties of polymers that can be measured in ground laboratory testing to predict the in-space atomic oxygen erosion yield of a polymer. The properties include chemical structure, bonding information, density and ash content. The resulting predictive tool has a correlation coefficient of 0.914 when compared with actual MISSE 2 space data for 38 polymers and pyrolytic graphite. The intent of the predictive tool is to be able to make estimates of atomic oxygen erosion yields for new polymers without requiring expensive and time consumptive in-space testing.

Workplace-based assessments of junior doctors: do scores predict training difficulties?

Science.gov (United States)

Mitchell, Colin; Bhat, Sarita; Herbert, Anne; Baker, Paul

2011-12-01

Workplace-based assessment (WPBA) is an increasingly important part of postgraduate medical training and its results may be used as evidence of professional competence. This study evaluates the ability of WPBA to distinguish UK Foundation Programme (FP) doctors with training difficulties and its effectiveness as a surrogate marker for deficiencies in professional competence. We conducted a retrospective observational study using anonymised records for 1646 trainees in a single UK postgraduate deanery. Data for WPBAs conducted from August 2005 to April 2009 were extracted from the e-portfolio database. These data included all scores submitted by trainees in FP years 1 and 2 on mini-clinical evaluation exercise (mini-CEX), case-based discussion (CbD), direct observation of procedural skills (DOPS) and mini-peer assessment tool (mini-PAT) assessments. Records of trainees in difficulty, as identified by their educational supervisors, were tagged as index cases. Main outcome measures were odds ratios (ORs) for associations between mean WPBA scores and training difficulties. Further analyses by the reported aetiology of the training difficulty (health-, conduct- or performance-related) were performed. Of the 1646 trainees, 92 had been identified as being in difficulty. Mean CbD and mini-CEX scores were lower for trainees in difficulty and an association was found between identified training difficulties and average scores on the mini-CEX (OR = 0.54; p = 0.034) and CbD (OR = 0.39; p = 0.002). A receiver operator characteristic curve analysis of mean WPBA scores for diagnosing 'in difficulty' status yielded an area under the curve of 0.64, indicating weak predictive value. There was no statistical evidence that mean scores on DOPS and mini-PAT assessments differed between the two groups. Analysis of a large dataset of WPBA scores revealed significant associations between training difficulties and lower mean scores on both the mini-CEX and CbD. Models show that using WPBA

Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction

Directory of Open Access Journals (Sweden)

Eiji Watanabe

2018-03-01

Full Text Available The cerebral cortex predicts visual motion to adapt human behavior to surrounding objects moving in real time. Although the underlying mechanisms are still unknown, predictive coding is one of the leading theories. Predictive coding assumes that the brain's internal models (which are acquired through learning predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models. In the past year, deep neural networks based on predictive coding were reported for a video prediction machine called PredNet. If the theory substantially reproduces the visual information processing of the cerebral cortex, then PredNet can be expected to represent the human visual perception of motion. In this study, PredNet was trained with natural scene videos of the self-motion of the viewer, and the motion prediction ability of the obtained computer model was verified using unlearned videos. We found that the computer model accurately predicted the magnitude and direction of motion of a rotating propeller in unlearned videos. Surprisingly, it also represented the rotational motion for illusion images that were not moving physically, much like human visual perception. While the trained network accurately reproduced the direction of illusory rotation, it did not detect motion components in negative control pictures wherein people do not perceive illusory motion. This research supports the exciting idea that the mechanism assumed by the predictive coding theory is one of basis of motion illusion generation. Using sensory illusions as indicators of human perception, deep neural networks are expected to contribute significantly to the development of brain research.

Interactive Media and Simulation Tools for Technical Training

Science.gov (United States)

Gramoll, Kurt

1997-01-01

Over the last several years, integration of multiple media sources into a single information system has been rapidly developing. It has been found that when sound, graphics, text, animations, and simulations are skillfully integrated, the sum of the parts exceeds the individual parts for effective learning. In addition, simulations can be used to design and understand complex engineering processes. With the recent introduction of many high-level authoring, animation, modeling, and rendering programs for personal computers, significant multimedia programs can be developed by practicing engineers, scientists and even managers for both training and education. However, even with these new tools, a considerable amount of time is required to produce an interactive multimedia program. The development of both CD-ROM and Web-based programs are discussed in addition to the use of technically oriented animations. Also examined are various multimedia development tools and how they are used to develop effective engineering education courseware. Demonstrations of actual programs in engineering mechanics are shown.

Development of an Evaluation Tool for Online Food Safety Training Programs

Science.gov (United States)

Neal, Jack A., Jr.; Murphy, Cheryl A.; Crandall, Philip G.; O'Bryan, Corliss A.; Keifer, Elizabeth; Ricke, Steven C.

2011-01-01

The objective of this study was to provide the person in charge and food safety instructors an assessment tool to help characterize, identify strengths and weaknesses, determine the completeness of the knowledge gained by the employee, and evaluate the level of content presentation and usability of current retail food safety training platforms. An…

Don't Be a Stranger-Designing a Digital Intercultural Sensitivity Training Tool that is Culture General

NARCIS (Netherlands)

Degens, Nick; Hofstede, Gert Jan; Beulens, Adrie; Krumhuber, E.; Kappas, Arvid

2016-01-01

Digital intercultural training tools play an important role in helping people to mediate cultural misunderstandings. In recent years, these tools were made to teach about specific cultures, but there has been little attention for the design of a tool to teach about differences across a wide range

iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

Science.gov (United States)

Somerville, J; Stuart, L; Sernagor, E; Borisyuk, R

2010-12-15

Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis). Copyright © 2010

Development of a simple tool to predict the risk of postpartum diabetes in women with gestational diabetes mellitus.

Science.gov (United States)

Köhler, M; Ziegler, A G; Beyerlein, A

2016-06-01

Women with gestational diabetes mellitus (GDM) have an increased risk of diabetes postpartum. We developed a score to predict the long-term risk of postpartum diabetes using clinical and anamnestic variables recorded during or shortly after delivery. Data from 257 GDM women who were prospectively followed for diabetes outcome over 20 years of follow-up were used to develop and validate the risk score. Participants were divided into training and test sets. The risk score was calculated using Lasso Cox regression and divided into four risk categories, and its prediction performance was assessed in the test set. Postpartum diabetes developed in 110 women. The computed training set risk score of 5 × body mass index in early pregnancy (per kg/m(2)) + 132 if GDM was treated with insulin (otherwise 0) + 44 if the woman had a family history of diabetes (otherwise 0) - 35 if the woman lactated (otherwise 0) had R (2) values of 0.23, 0.25, and 0.33 at 5, 10, and 15 years postpartum, respectively, and a C-Index of 0.75. Application of the risk score in the test set resulted in observed risk of postpartum diabetes at 5 years of 11 % for low risk scores ≤140, 29 % for scores 141-220, 64 % for scores 221-300, and 80 % for scores >300. The derived risk score is easy to calculate, allows accurate prediction of GDM-related postpartum diabetes, and may thus be a useful prediction tool for clinicians and general practitioners.

A Tool for Balance Control Training Using Muscle Synergies and Multimodal Interfaces

Directory of Open Access Journals (Sweden)

D. Galeano

2014-01-01

Full Text Available Balance control plays a key role in neuromotor rehabilitation after stroke or spinal cord injuries. Computerized dynamic posturography (CDP is a classic technological tool to assess the status of balance control and to identify potential disorders. Despite the more accurate diagnosis generated by these tools, the current strategies to promote rehabilitation are still limited and do not take full advantage of the technologies available. This paper presents a novel balance training platform which combines a CDP device made from low-cost interfaces, such as the Nintendo Wii Balance Board and the Microsoft Kinect. In addition, it integrates a custom electrical stimulator that uses the concept of muscle synergies to promote natural interaction. The aim of the platform is to support the exploration of innovative multimodal therapies. Results include the technical validation of the platform using mediolateral and anteroposterior sways as basic balance training therapies.

lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

Science.gov (United States)

Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

2015-01-01

Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

Integrating Contemplative Tools into Biomedical Science Education and Research Training Programs

Directory of Open Access Journals (Sweden)

Rodney R. Dietert

2014-01-01

Full Text Available Academic preparation of science researchers and/or human or veterinary medicine clinicians through the science, technology, engineering, and mathematics (STEM curriculum has usually focused on the students (1 acquiring increased disciplinary expertise, (2 learning needed methodologies and protocols, and (3 expanding their capacity for intense, persistent focus. Such educational training is effective until roadblocks or problems arise via this highly-learned approach. Then, the health science trainee may have few tools available for effective problem solving. Training to achieve flexibility, adaptability, and broadened perspectives using contemplative practices has been rare among biomedical education programs. To address this gap, a Cornell University-based program involving formal biomedical science coursework, and health science workshops has been developed to offer science students, researchers and health professionals a broader array of personal, contemplation-based, problem-solving tools. This STEM educational initiative includes first-person exercises designed to broaden perceptional awareness, decrease emotional drama, and mobilize whole-body strategies for creative problem solving. Self-calibration and journaling are used for students to evaluate the personal utility of each exercise. The educational goals are to increase student self-awareness and self-regulation and to provide trainees with value-added tools for career-long problem solving. Basic elements of this educational initiative are discussed using the framework of the Tree of Contemplative Practices.

The assessment of training efficiency. A management tool in Sta. Ma de Garona NPP

International Nuclear Information System (INIS)

Carretero, Felipe; Sanchez Alvarez, Francisco J.; Soriano, Santiago Lucas

2003-01-01

Nowadays, one of the critical factors which determines the success of the companies is their adaptability to changes and the development of key competencies in a more global and competitive market. The acquisition, maintenance and updating of those competencies is a fundamental concern of the training managers. Training management implies also to answer, as accurate as possible, questions as following: Which is the quality of the training activities? Are the taught knowledge, skills and attitudes fully assimilated? Are they transferred to job position? Is it possible to correlate training effort and company results? Which is the comprehensive cost of the training programme? and finally, Is the cost reasonable according to the results?, in other words, has been the training programme efficient enough?The present paper introduces the experience in implementing an assessment system of training efficiency, in Sta. M a de Garona, as a training management tool for decision making. (author)

Translating and validating a Training Needs Assessment tool into Greek

OpenAIRE

Markaki, Adelais; Antonakis, Nikos; Hicks, Carolyn M; Lionis, Christos

2007-01-01

Abstract Background The translation and cultural adaptation of widely accepted, psychometrically tested tools is regarded as an essential component of effective human resource management in the primary care arena. The Training Needs Assessment (TNA) is a widely used, valid instrument, designed to measure professional development needs of health care professionals, especially in primary health care. This study aims to describe the translation, adaptation and validation of the TNA questionnaire...

Using of pH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network

Directory of Open Access Journals (Sweden)

Mahmoud Nasr

2014-01-01

Full Text Available Monitoring of groundwater quality is one of the important tools to provide adequate information about water management. In the present study, artificial neural network (ANN with a feed-forward back-propagation was designed to predict groundwater salinity, expressed by total dissolved solids (TDS, using pH as an input parameter. Groundwater samples were collected from a 36 m depth well located in the experimental farm of the City of Scientific Researches and Technological Applications (SRTA City, New Borg El-Arab City, Alexandria, Egypt. The network structure was 1–5–3–1 and used the default Levenberg–Marquardt algorithm for training. It was observed that, the best validation performance, based on the mean square error, was 14819 at epoch 0, and no major problems or over-fitting occurred with the training step. The simulated output tracked the measured data with a correlation coefficient (R-value of 0.64, 0.67 and 0.90 for training, validation and test, respectively. In this case, the network response was acceptable, and simulation could be used for entering new inputs.

ACL-RSI and KOOS Measures Predict Normal Knee Function after ACL-SPORTS Training

OpenAIRE

White, Kathleen; Zeni, Joseph; Snyder-Mackler, Lynn

2014-01-01

Objectives: After anterior cruciate ligament reconstruction (ACLR) athletes commonly report increased fear of re-injury and below normal knee function. Implementing a post-operative training protocol (ACL-SPORTS Training) to improve patient perceived knee function, may improve short term outcomes after surgery. Identifying pre-training measures that predict normal knee function after training may allow us to determine who may respond to the treatment intervention. The purpose of this study wa...

Psychomotor testing predicts rate of skill acquisition for proficiency-based laparoscopic skills training.

Science.gov (United States)

Stefanidis, Dimitrios; Korndorffer, James R; Black, F William; Dunne, J Bruce; Sierra, Rafael; Touchard, Cheri L; Rice, David A; Markert, Ronald J; Kastl, Peter R; Scott, Daniel J

2006-08-01

Laparoscopic simulator training translates into improved operative performance. Proficiency-based curricula maximize efficiency by tailoring training to meet the needs of each individual; however, because rates of skill acquisition vary widely, such curricula may be difficult to implement. We hypothesized that psychomotor testing would predict baseline performance and training duration in a proficiency-based laparoscopic simulator curriculum. Residents (R1, n = 20) were enrolled in an IRB-approved prospective study at the beginning of the academic year. All completed the following: a background information survey, a battery of 12 innate ability measures (5 motor, and 7 visual-spatial), and baseline testing on 3 validated simulators (5 videotrainer [VT] tasks, 12 virtual reality [minimally invasive surgical trainer-virtual reality, MIST-VR] tasks, and 2 laparoscopic camera navigation [LCN] tasks). Participants trained to proficiency, and training duration and number of repetitions were recorded. Baseline test scores were correlated to skill acquisition rate. Cutoff scores for each predictive test were calculated based on a receiver operator curve, and their sensitivity and specificity were determined in identifying slow learners. Only the Cards Rotation test correlated with baseline simulator ability on VT and LCN. Curriculum implementation required 347 man-hours (6-person team) and 795,000 dollars of capital equipment. With an attendance rate of 75%, 19 of 20 residents (95%) completed the curriculum by the end of the academic year. To complete training, a median of 12 hours (range, 5.5-21), and 325 repetitions (range, 171-782) were required. Simulator score improvement was 50%. Training duration and repetitions correlated with prior video game and billiard exposure, grooved pegboard, finger tap, map planning, Rey Figure Immediate Recall score, and baseline performance on VT and LCN. The map planning cutoff score proved most specific in identifying slow learners

Tools for Predicting Cleaning Efficiency in the LHC

CERN Document Server

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

2003-01-01

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

Users' experiences of an emergency department patient admission predictive tool: A qualitative evaluation.

Science.gov (United States)

Jessup, Melanie; Crilly, Julia; Boyle, Justin; Wallis, Marianne; Lind, James; Green, David; Fitzgerald, Gerard

2016-09-01

Emergency department overcrowding is an increasing issue impacting patients, staff and quality of care, resulting in poor patient and system outcomes. In order to facilitate better management of emergency department resources, a patient admission predictive tool was developed and implemented. Evaluation of the tool's accuracy and efficacy was complemented with a qualitative component that explicated the experiences of users and its impact upon their management strategies, and is the focus of this article. Semi-structured interviews were conducted with 15 pertinent users, including bed managers, after-hours managers, specialty department heads, nurse unit managers and hospital executives. Analysis realised dynamics of accuracy, facilitating communication and enabling group decision-making Users generally welcomed the enhanced potential to predict and plan following the incorporation of the patient admission predictive tool into their daily and weekly decision-making processes. They offered astute feedback with regard to their responses when faced with issues of capacity and communication. Participants reported an growing confidence in making informed decisions in a cultural context that is continually moving from reactive to proactive. This information will inform further patient admission predictive tool development specifically and implementation processes generally. © The Author(s) 2015.

A study on the effectiveness of task manager board game as a training tool in managing project

Science.gov (United States)

Yusof, Shahrul Azmi Mohd; Radzi, Shanizan Herman Md; Din, Sharifah Nadera Syed; Khalid, Nurhafizah

2016-08-01

Nowadays, games have become one of the useful tools in training. Many instructors choose to use games to enhance the way of delivering the subject. Failure to apply the suitable tool in training will lead to discouragement in learning and causing waste to the resources. An effective game will help the student understand the concept quickly. It can also help students to get involve in experiential learning where the student can manage and solve the problem as in the actual situation. This study will focus on the effectiveness of board game as a training tool for managing projects. This game has 4 tasks to be completed by students. They will be divided into a group of 4 or 5. Two methods are used in this study, pilot test, and post-test. These methods are chosen to analyze the effectiveness of using Task Manager Board Game as a teaching tool and the improvement of student's knowledge in project management. Three sub-components assessed were motivation, user experience and learning using case studies on Kirkpatrick's level one base on the perception of the students. The result indicated that the use of Task Manager board game as a training tool for managing project has a positive impact on students. It helps students to experience the situation of managing projects. It is one of the easiest ways for improving time management, human resources and communication skill.

iPat: intelligent prediction and association tool for genomic research.

Science.gov (United States)

Chen, Chunpeng James; Zhang, Zhiwu

2018-06-01

The ultimate goal of genomic research is to effectively predict phenotypes from genotypes so that medical management can improve human health and molecular breeding can increase agricultural production. Genomic prediction or selection (GS) plays a complementary role to genome-wide association studies (GWAS), which is the primary method to identify genes underlying phenotypes. Unfortunately, most computing tools cannot perform data analyses for both GWAS and GS. Furthermore, the majority of these tools are executed through a command-line interface (CLI), which requires programming skills. Non-programmers struggle to use them efficiently because of the steep learning curves and zero tolerance for data formats and mistakes when inputting keywords and parameters. To address these problems, this study developed a software package, named the Intelligent Prediction and Association Tool (iPat), with a user-friendly graphical user interface. With iPat, GWAS or GS can be performed using a pointing device to simply drag and/or click on graphical elements to specify input data files, choose input parameters and select analytical models. Models available to users include those implemented in third party CLI packages such as GAPIT, PLINK, FarmCPU, BLINK, rrBLUP and BGLR. Users can choose any data format and conduct analyses with any of these packages. File conversions are automatically conducted for specified input data and selected packages. A GWAS-assisted genomic prediction method was implemented to perform genomic prediction using any GWAS method such as FarmCPU. iPat was written in Java for adaptation to multiple operating systems including Windows, Mac and Linux. The iPat executable file, user manual, tutorials and example datasets are freely available at http://zzlab.net/iPat. zhiwu.zhang@wsu.edu.

Prediction Of Abrasive And Diffusive Tool Wear Mechanisms In Machining

Science.gov (United States)

Rizzuti, S.; Umbrello, D.

2011-01-01

Tool wear prediction is regarded as very important task in order to maximize tool performance, minimize cutting costs and improve the quality of workpiece in cutting. In this research work, an experimental campaign was carried out at the varying of cutting conditions with the aim to measure both crater and flank tool wear, during machining of an AISI 1045 with an uncoated carbide tool P40. Parallel a FEM-based analysis was developed in order to study the tool wear mechanisms, taking also into account the influence of the cutting conditions and the temperature reached on the tool surfaces. The results show that, when the temperature of the tool rake surface is lower than the activation temperature of the diffusive phenomenon, the wear rate can be estimated applying an abrasive model. In contrast, in the tool area where the temperature is higher than the diffusive activation temperature, the wear rate can be evaluated applying a diffusive model. Finally, for a temperature ranges within the above cited values an adopted abrasive-diffusive wear model furnished the possibility to correctly evaluate the tool wear phenomena.

Technical Training: Technical Training Seminar

CERN Multimedia

2004-01-01

TECHNICAL TRAINING Monique Duval tel. 74924 technical.training@cern.ch Monday 9 February 2004 From 10:00 to 12:00 - IT Auditorium - bldg. 31, 3rd floor ANSOFT High-Frequency Seminar David Prestaux, Application Engineer, ANSOFT F-78535 BUC, France This Technical Training seminar will present two Ansoft application products: Ansoft HFSS and Ansoft Designer. Ansoft HFSS makes use of the Finite Element Method (FEM) to calculate field solutions from first principles. It can accurately predict all high-frequency behaviours such as dispersion, mode conversion, and losses due to materials and radiation. Ansoft Designer is a suite of design tools to fully integrate high-frequency, physics-based electromagnetic simulations into a seamless system-level simulation environment. Ansoft Designer uses a simple interface to give complete control over every design task, by a method allowing multiple solvers, Solver on Demand. • Introduction • Overview of the Ansoft Total solution • Ansoft HFSS 9...

Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor-tracking radiotherapy.

Science.gov (United States)

Mukumoto, Nobutaka; Nakamura, Mitsuhiro; Akimoto, Mami; Miyabe, Yuki; Yokota, Kenji; Matsuo, Yukinori; Mizowaki, Takashi; Hiraoka, Masahiro

2017-08-01

To explore the effect of sampling interval of training data acquisition on the intrafractional prediction error of surrogate signal-based dynamic tumor-tracking using a gimbal-mounted linac. Twenty pairs of respiratory motions were acquired from 20 patients (ten lung, five liver, and five pancreatic cancer patients) who underwent dynamic tumor-tracking with the Vero4DRT. First, respiratory motions were acquired as training data for an initial construction of the prediction model before the irradiation. Next, additional respiratory motions were acquired for an update of the prediction model due to the change of the respiratory pattern during the irradiation. The time elapsed prior to the second acquisition of the respiratory motion was 12.6 ± 3.1 min. A four-axis moving phantom reproduced patients' three dimensional (3D) target motions and one dimensional surrogate motions. To predict the future internal target motion from the external surrogate motion, prediction models were constructed by minimizing residual prediction errors for training data acquired at 80 and 320 ms sampling intervals for 20 s, and at 500, 1,000, and 2,000 ms sampling intervals for 60 s using orthogonal kV x-ray imaging systems. The accuracies of prediction models trained with various sampling intervals were estimated based on training data with each sampling interval during the training process. The intrafractional prediction errors for various prediction models were then calculated on intrafractional monitoring images taken for 30 s at the constant sampling interval of a 500 ms fairly to evaluate the prediction accuracy for the same motion pattern. In addition, the first respiratory motion was used for the training and the second respiratory motion was used for the evaluation of the intrafractional prediction errors for the changed respiratory motion to evaluate the robustness of the prediction models. The training error of the prediction model was 1.7 ± 0.7 mm in 3D for all sampling

The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

Science.gov (United States)

Yang, Min; Wong, Stephen C. P.; Coid, Jeremy

2010-01-01

Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their…

The Effects of Webopac Self Training Tool with Guided Exploration on Information Literacy Skills among First Year Degree Students

Science.gov (United States)

Ismail, Mohd Nasir; Mamat, Nurfaezah; Jamaludin, Adnan

2018-01-01

The purpose of this study is to investigate effects of WebOPAC Self Training Tool with Guided Exploration (WSTTG), WebOPAC Self Training Tool with non-guided exploration (WSTT) and Traditional (T) groups as the learning strategies on information literacy (IL) skills standards among first year degree students in Malaysian public university. The…

Development of METAL-ACTIVE SITE and ZINCCLUSTER tool to predict active site pockets.

Science.gov (United States)

Ajitha, M; Sundar, K; Arul Mugilan, S; Arumugam, S

2018-03-01

The advent of whole genome sequencing leads to increasing number of proteins with known amino acid sequences. Despite many efforts, the number of proteins with resolved three dimensional structures is still low. One of the challenging tasks the structural biologists face is the prediction of the interaction of metal ion with any protein for which the structure is unknown. Based on the information available in Protein Data Bank, a site (METALACTIVE INTERACTION) has been generated which displays information for significant high preferential and low-preferential combination of endogenous ligands for 49 metal ions. User can also gain information about the residues present in the first and second coordination sphere as it plays a major role in maintaining the structure and function of metalloproteins in biological system. In this paper, a novel computational tool (ZINCCLUSTER) is developed, which can predict the zinc metal binding sites of proteins even if only the primary sequence is known. The purpose of this tool is to predict the active site cluster of an uncharacterized protein based on its primary sequence or a 3D structure. The tool can predict amino acids interacting with a metal or vice versa. This tool is based on the occurrence of significant triplets and it is tested to have higher prediction accuracy when compared to that of other available techniques. © 2017 Wiley Periodicals, Inc.

Simulation training tools for nonlethal weapons using gaming environments

Science.gov (United States)

Donne, Alexsana; Eagan, Justin; Tse, Gabriel; Vanderslice, Tom; Woods, Jerry

2006-05-01

Modern simulation techniques have a growing role for evaluating new technologies and for developing cost-effective training programs. A mission simulator facilitates the productive exchange of ideas by demonstration of concepts through compellingly realistic computer simulation. Revolutionary advances in 3D simulation technology have made it possible for desktop computers to process strikingly realistic and complex interactions with results depicted in real-time. Computer games now allow for multiple real human players and "artificially intelligent" (AI) simulated robots to play together. Advances in computer processing power have compensated for the inherent intensive calculations required for complex simulation scenarios. The main components of the leading game-engines have been released for user modifications, enabling game enthusiasts and amateur programmers to advance the state-of-the-art in AI and computer simulation technologies. It is now possible to simulate sophisticated and realistic conflict situations in order to evaluate the impact of non-lethal devices as well as conflict resolution procedures using such devices. Simulations can reduce training costs as end users: learn what a device does and doesn't do prior to use, understand responses to the device prior to deployment, determine if the device is appropriate for their situational responses, and train with new devices and techniques before purchasing hardware. This paper will present the status of SARA's mission simulation development activities, based on the Half-Life gameengine, for the purpose of evaluating the latest non-lethal weapon devices, and for developing training tools for such devices.

Don't Be a Stranger--Designing a Digital Intercultural Sensitivity Training Tool That Is Culture General

Science.gov (United States)

Degens, Nick; Hofstede, Gert Jan; Beulens, Adrie; Krumhuber, Eva; Kappas, Arvid

2016-01-01

Digital intercultural training tools play an important role in helping people to mediate cultural misunderstandings. In recent years, these tools were made to teach about specific cultures, but there has been little attention for the design of a tool to teach about differences across a wide range of cultures. In this work, we take the first steps…

Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses

Directory of Open Access Journals (Sweden)

Arthur C. Oliveira

2017-05-01

Full Text Available Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature—TargetScan (TS, miRanda-mirSVR (MR, Pita, and RNA22 (R22, and we determined the most effective approach for analyzing target prediction data (individual, union, or intersection. For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p and 1,400 genes (700 validated and 700 non-validated as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of in silico prediction analysis of miRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.

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

Science.gov (United States)

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

2018-04-09

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

The use of machine learning and nonlinear statistical tools for ADME prediction.

Science.gov (United States)

Sakiyama, Yojiro

2009-02-01

Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

Integrating Cloud-Based Strategies and Tools in Face-to-Face Training Sessions to Increase the Impact of Professional Development

Science.gov (United States)

Gradel, Kathleen; Edson, Alden J.

2012-01-01

This article is based on the premise that face-to-face training can be augmented with cloud-based technology tools, to potentially extend viable training supports as higher education staff and faculty implement new content/skills in their jobs and classrooms. There are significant benefits to harnessing cloud-based tools that can facilitate both…

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Analysis and Prediction of Micromilling Stability with Variable Tool Geometry

Directory of Open Access Journals (Sweden)

Ziyang Cao

2014-11-01

Full Text Available Micromilling can fabricate miniaturized components using micro-end mill at high rotational speeds. The analysis of machining stability in micromilling plays an important role in characterizing the cutting process, estimating the tool life, and optimizing the process. A numerical analysis and experimental method are presented to investigate the chatter stability in micro-end milling process with variable milling tool geometry. The schematic model of micromilling process is constructed and the calculation formula to predict cutting force and displacements is derived. This is followed by a detailed numerical analysis on micromilling forces between helical ball and square end mills through time domain and frequency domain method and the results are compared. Furthermore, a detailed time domain simulation for micro end milling with straight teeth and helical teeth end mill is conducted based on the machine-tool system frequency response function obtained through modal experiment. The forces and displacements are predicted and the simulation result between variable cutter geometry is deeply compared. The simulation results have important significance for the actual milling process.

Comparison of the Nosocomial Pneumonia Mortality Prediction (NPMP) model with standard mortality prediction tools.

Science.gov (United States)

Srinivasan, M; Shetty, N; Gadekari, S; Thunga, G; Rao, K; Kunhikatta, V

2017-07-01

Severity or mortality prediction of nosocomial pneumonia could aid in the effective triage of patients and assisting physicians. To compare various severity assessment scoring systems for predicting intensive care unit (ICU) mortality in nosocomial pneumonia patients. A prospective cohort study was conducted in a tertiary care university-affiliated hospital in Manipal, India. One hundred patients with nosocomial pneumonia, admitted in the ICUs who developed pneumonia after >48h of admission, were included. The Nosocomial Pneumonia Mortality Prediction (NPMP) model, developed in our hospital, was compared with Acute Physiology and Chronic Health Evaluation II (APACHE II), Mortality Probability Model II (MPM 72  II), Simplified Acute Physiology Score II (SAPS II), Multiple Organ Dysfunction Score (MODS), Sequential Organ Failure Assessment (SOFA), Clinical Pulmonary Infection Score (CPIS), Ventilator-Associated Pneumonia Predisposition, Insult, Response, Organ dysfunction (VAP-PIRO). Data and clinical variables were collected on the day of pneumonia diagnosis. The outcome for the study was ICU mortality. The sensitivity and specificity of the various scoring systems was analysed by plotting receiver operating characteristic (ROC) curves and computing the area under the curve for each of the mortality predicting tools. NPMP, APACHE II, SAPS II, MPM 72  II, SOFA, and VAP-PIRO were found to have similar and acceptable discrimination power as assessed by the area under the ROC curve. The AUC values for the above scores ranged from 0.735 to 0.762. CPIS and MODS showed least discrimination. NPMP is a specific tool to predict mortality in nosocomial pneumonia and is comparable to other standard scores. Copyright © 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites.

Directory of Open Access Journals (Sweden)

Jiangning Song

Full Text Available The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s. Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate

Impact of design research on industrial practice tools, technology, and training

CERN Document Server

Lindemann, Udo

2016-01-01

Showcasing exemplars of how various aspects of design research were successfully transitioned into and influenced, design practice, this book features chapters written by eminent international researchers and practitioners from industry on the Impact of Design Research on Industrial Practice. Chapters written by internationally acclaimed researchers of design analyse the findings (guidelines, methods and tools), technologies/products and educational approaches that have been transferred as tools, technologies and people to transform industrial practice of engineering design, whilst the chapters that are written by industrial practitioners describe their experience of how various tools, technologies and training impacted design practice. The main benefit of this book, for educators, researchers and practitioners in (engineering) design, will be access to a comprehensive coverage of case studies of successful transfer of outcomes of design research into practice; as well as guidelines and platforms for successf...

An Examination of Cultural Competence Training in US Medical Education Guided by the Tool for Assessing Cultural Competence Training.

Science.gov (United States)

Jernigan, Valarie Blue Bird; Hearod, Jordan B; Tran, Kim; Norris, Keith C; Buchwald, Dedra

2016-01-01

In the United States, medical students must demonstrate a standard level of "cultural competence," upon graduation. Cultural competence is most often defined as a set of congruent behaviors, attitudes, and policies that come together in a system, organization, or among professionals that enables effective work in cross-cultural situations. The Association of American Medical Colleges developed the Tool for Assessing Cultural Competence Training (TACCT) to assist schools in developing and evaluating cultural competence curricula to meet these requirements. This review uses the TACCT as a guideline to describe and assess pedagogical approaches to cultural competence training in US medical education and identify content gaps and opportunities for curriculum improvement. A total of 18 programs are assessed. Findings support previous research that cultural competence training can improve the knowledge, attitudes, and skills of medical trainees. However, wide variation in the conceptualization, implementation, and evaluation of cultural competence training programs exists, leading to differences in training quality and outcomes. More research is needed to establish optimal approaches to implementing and evaluating cultural competence training that incorporate cultural humility, the social determinants of health, and broader structural competency within the medical system.

An Examination of Cultural Competence Training in US Medical Education Guided by the Tool for Assessing Cultural Competence Training

Science.gov (United States)

Jernigan, Valarie Blue Bird; Hearod, Jordan B.; Tran, Kim; Norris, Keith C.; Buchwald, Dedra

2015-01-01

In the United States, medical students must demonstrate a standard level of “cultural competence,” upon graduation. Cultural competence is most often defined as a set of congruent behaviors, attitudes, and policies that come together in a system, organization, or among professionals that enables effective work in cross-cultural situations. The Association of American Medical Colleges developed the Tool for Assessing Cultural Competence Training (TACCT) to assist schools in developing and evaluating cultural competence curricula to meet these requirements. This review uses the TACCT as a guideline to describe and assess pedagogical approaches to cultural competence training in US medical education and identify content gaps and opportunities for curriculum improvement. A total of 18 programs are assessed. Findings support previous research that cultural competence training can improve the knowledge, attitudes, and skills of medical trainees. However, wide variation in the conceptualization, implementation, and evaluation of cultural competence training programs exists, leading to differences in training quality and outcomes. More research is needed to establish optimal approaches to implementing and evaluating cultural competence training that incorporate cultural humility, the social determinants of health, and broader structural competency within the medical system. PMID:27818848

Trained student pharmacists’ telephonic collection of patient medication information: Evaluation of a structured interview tool

Science.gov (United States)

Margolis, Amanda R.; Martin, Beth A.; Mott, David A.

2016-01-01

Objective To determine the feasibility and fidelity of student pharmacists collecting patient medication list information using a structured interview tool and the accuracy of documenting the information. The medication lists were used by a community pharmacist to provide a targeted medication therapy management (MTM) intervention. Design Descriptive analysis of patient medication lists collected via telephone interviews. Participants 10 trained student pharmacists collected the medication lists. Intervention Trained student pharmacists conducted audio-recorded telephone interviews with 80 English-speaking community dwelling older adults using a structured interview tool to collect and document medication lists. Main outcome measures Feasibility was measured using the number of completed interviews, the time student pharmacists took to collect the information, and pharmacist feedback. Fidelity to the interview tool was measured by assessing student pharmacists’ adherence to asking all scripted questions and probes. Accuracy was measured by comparing the audio recorded interviews to the medication list information documented in an electronic medical record. Results On average it took student pharmacists 26.7 minutes to collect the medication lists. The community pharmacist said the medication lists were complete and that having the medication lists saved time and allowed him to focus on assessment, recommendations, and education during the targeted MTM session. Fidelity was high with an overall proportion of asked scripted probes of 83.75% (95%CI: 80.62–86.88%). Accuracy was also high for both prescription (95.1%, 95%CI: 94.3–95.8%) and non-prescription (90.5%, 95%CI: 89.4–91.4%) medications. Conclusion Trained student pharmacists were able to use an interview tool to collect and document medication lists with a high degree of fidelity and accuracy. This study suggests that student pharmacists or trained technicians may be able to collect patient medication

Trained student pharmacists' telephonic collection of patient medication information: Evaluation of a structured interview tool.

Science.gov (United States)

Margolis, Amanda R; Martin, Beth A; Mott, David A

2016-01-01

To determine the feasibility and fidelity of student pharmacists collecting patient medication list information using a structured interview tool and the accuracy of documenting the information. The medication lists were used by a community pharmacist to provide a targeted medication therapy management (MTM) intervention. Descriptive analysis of patient medication lists collected with telephone interviews. Ten trained student pharmacists collected the medication lists. Trained student pharmacists conducted audio-recorded telephone interviews with 80 English-speaking, community-dwelling older adults using a structured interview tool to collect and document medication lists. Feasibility was measured using the number of completed interviews, the time student pharmacists took to collect the information, and pharmacist feedback. Fidelity to the interview tool was measured by assessing student pharmacists' adherence to asking all scripted questions and probes. Accuracy was measured by comparing the audio-recorded interviews to the medication list information documented in an electronic medical record. On average, it took student pharmacists 26.7 minutes to collect the medication lists. The community pharmacist said the medication lists were complete and that having the medication lists saved time and allowed him to focus on assessment, recommendations, and education during the targeted MTM session. Fidelity was high, with an overall proportion of asked scripted probes of 83.75% (95% confidence interval [CI], 80.62-86.88%). Accuracy was also high for both prescription (95.1%; 95% CI, 94.3-95.8%) and nonprescription (90.5%; 95% CI, 89.4-91.4%) medications. Trained student pharmacists were able to use an interview tool to collect and document medication lists with a high degree of fidelity and accuracy. This study suggests that student pharmacists or trained technicians may be able to collect patient medication lists to facilitate MTM sessions in the community pharmacy

Does the Risk Assessment and Prediction Tool Predict Discharge Disposition After Joint Replacement?

DEFF Research Database (Denmark)

Hansen, Viktor J.; Gromov, Kirill; Lebrun, Lauren M

2015-01-01

BACKGROUND: Payers of health services and policymakers place a major focus on cost containment in health care. Studies have shown that early planning of discharge is essential in reducing length of stay and achieving financial benefit; tools that can help predict discharge disposition would...... populations is unknown. A low RAPT score is reported to indicate a high risk of needing any form of inpatient rehabilitation after TJA, including short-term nursing facilities. QUESTIONS/PURPOSES: This study attempts (1) to assess predictive accuracy of the RAPT on US patients undergoing total hip and knee....... Based on our findings, the risk categories in our populations should be high risk intermediate risk 7 to 10, and low risk > 10. CONCLUSIONS: The RAPT accurately predicted discharge disposition for high- and low-risk patients in our cohort. Based on our data, intermediate-risk patients should...

A systematic review on popularity, application and characteristics of protein secondary structure prediction tools.

Science.gov (United States)

Kashani-Amin, Elaheh; Tabatabaei-Malazy, Ozra; Sakhteman, Amirhossein; Larijani, Bagher; Ebrahim-Habibi, Azadeh

2018-02-27

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple secondary structure prediction (SSP) options is challenging. The current study is an insight onto currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating a SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight about the recent usage of SSP tools which could be helpful for selecting a proper tool's choice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A survey of diagnostic ultrasound within the physiotherapy profession for the design of future training tools

International Nuclear Information System (INIS)

McKiernan, Sharmaine; Chiarelli, Pauline; Warren-Forward, Helen

2011-01-01

The expansion of diagnostic ultrasound outside of the traditional radiology profession into the physiotherapy profession is occurring. The purpose of this study was to determine if physiotherapists are purchasing diagnostic ultrasound machines, receiving training in the modality and what imaging procedures they are performing. For the design of future training tools, also investigated were the methods of training that physiotherapists might find most beneficial and what content they deem necessary to be covered during such training for the use of diagnostic ultrasound for their profession. An e-mail invitation was sent to physiotherapists throughout Australian who were registered on two databases, asking them to complete a web based survey. The survey was comprised of 18 questions including open and closed items. The data was then categorised into themes in accordance with the purpose of the study. Of the respondents, 39% did not own a diagnostic ultrasound machine, 33% had access to a machine that was owned by their employer and 18% actually owned a machine themselves. Training in diagnostic ultrasound had been received by 61% of the respondents however for 67% of those who had been trained, this training had only lasted for several hours, not days or weeks. For future training in ultrasound the majority of respondents would prefer either a workshop or DVD to cover imaging anatomy, the use of machine controls and scanning the pelvic floor, abdominal muscles and shoulder. From this survey it can be concluded that physiotherapists have an interest in or are using diagnostic ultrasound in their practice. While some form of training is being provided, further training is considered necessary and wanted by the physiotherapists so training tools need to be developed.

A survey of diagnostic ultrasound within the physiotherapy profession for the design of future training tools

Energy Technology Data Exchange (ETDEWEB)

McKiernan, Sharmaine, E-mail: sharmaine.mckiernan@newcastle.edu.a [School of Health Sciences, University of Newcastle, Callaghan, NSW (Australia); Chiarelli, Pauline; Warren-Forward, Helen [School of Health Sciences, University of Newcastle, Callaghan, NSW (Australia)

2011-05-15

The expansion of diagnostic ultrasound outside of the traditional radiology profession into the physiotherapy profession is occurring. The purpose of this study was to determine if physiotherapists are purchasing diagnostic ultrasound machines, receiving training in the modality and what imaging procedures they are performing. For the design of future training tools, also investigated were the methods of training that physiotherapists might find most beneficial and what content they deem necessary to be covered during such training for the use of diagnostic ultrasound for their profession. An e-mail invitation was sent to physiotherapists throughout Australian who were registered on two databases, asking them to complete a web based survey. The survey was comprised of 18 questions including open and closed items. The data was then categorised into themes in accordance with the purpose of the study. Of the respondents, 39% did not own a diagnostic ultrasound machine, 33% had access to a machine that was owned by their employer and 18% actually owned a machine themselves. Training in diagnostic ultrasound had been received by 61% of the respondents however for 67% of those who had been trained, this training had only lasted for several hours, not days or weeks. For future training in ultrasound the majority of respondents would prefer either a workshop or DVD to cover imaging anatomy, the use of machine controls and scanning the pelvic floor, abdominal muscles and shoulder. From this survey it can be concluded that physiotherapists have an interest in or are using diagnostic ultrasound in their practice. While some form of training is being provided, further training is considered necessary and wanted by the physiotherapists so training tools need to be developed.

Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

DEFF Research Database (Denmark)

Simonsen, Anja H; Mattila, Jussi; Hejl, Anne-Mette

2012-01-01

of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical...... significant trend (p classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best...... classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented....

Using Bayesian Network as a tool for coastal storm flood impact prediction at Varna Bay (Bulgaria, Western Black Sea)

Science.gov (United States)

Valchev, Nikolay; Eftimova, Petya; Andreeva, Nataliya; Prodanov, Bogdan

2017-04-01

Coastal zone is among the fastest evolving areas worldwide. Ever increasing population inhabiting coastal settlements develops often conflicting economic and societal activities. The existing imbalance between the expansion of these activities, on one hand, and the potential to accommodate them in a sustainable manner, on the other, becomes a critical problem. Concurrently, coasts are affected by various hydro-meteorological phenomena such as storm surges, heavy seas, strong winds and flash floods, which intensities and occurrence frequency is likely to increase due to the climate change. This implies elaboration of tools capable of quick prediction of impact of those phenomena on the coast and providing solutions in terms of disaster risk reduction measures. One such tool is Bayesian network. Proposed paper describes the set-up of such network for Varna Bay (Bulgaria, Western Black Sea). It relates near-shore storm conditions to their onshore flood potential and ultimately to relevant impact as relative damage on coastal and manmade environment. Methodology for set-up and training of the Bayesian network was developed within RISC-KIT project (Resilience-Increasing Strategies for Coasts - toolKIT). Proposed BN reflects the interaction between boundary conditions, receptors, hazard, and consequences. Storm boundary conditions - maximum significant wave height and peak surge level, were determined on the basis of their historical and projected occurrence. The only hazard considered in this study is flooding characterized by maximum inundation depth. BN was trained with synthetic events created by combining estimated boundary conditions. Flood impact was modeled with the process-based morphodynamical model XBeach. Restaurants, sport and leisure facilities, administrative buildings, and car parks were introduced in the network as receptors. Consequences (impact) are estimated in terms of relative damage caused by given inundation depth. National depth

Who Is Going to College? Predicting Education Training from Pre-VR Consumer Characteristics

Science.gov (United States)

Boutin, Daniel L.; Wilson, Keith B.

2012-01-01

The relationship of receiving college and university training within the state vocational rehabilitation (VR) program to pre-VR consumer characteristics was investigated with a multiple direct logistic regression technique. A model containing 11 pre-VR characteristics predict the reception of college and university training for a multidisability…

RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

Science.gov (United States)

Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

2012-01-01

RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. http://www.cemb.edu.pk/sw.html RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language.

Development of a Bilingual Training Tool to Train Dairy Workers on the Prevention and Management of Non-Ambulatory Cows

Science.gov (United States)

Roman-Muniz, Ivette N.; Van Metre, David C.

2011-01-01

Dairy cows at risk of becoming non-ambulatory or downers represent economic losses and animal well-being issues for the dairy industry. Colorado State University researchers and Extension faculty collaborated with Colorado's dairy industry to create a training tool for the early identification and management of cows at risk of becoming downers on…

Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery

Science.gov (United States)

Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

2014-01-01

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138

USAF Enlisted Air Traffic Controller Selection: Examination of the Predictive Validity of the FAA Air Traffic Selection and Training Battery versus Training Performance

National Research Council Canada - National Science Library

Carretta, Thomas R; King, Raymond E

2008-01-01

.... The current study examined the utility of the FAA Air Traffic Selection and Training (AT-SAT) battery for incrementing the predictiveness of the ASVAB versus several enlisted ATC training criteria...

Academic Training: Predicting Natural Catastrophes

CERN Multimedia

Françoise Benz

2005-01-01

2005-2006 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 12, 13, 14, 15, 16 December from 11:00 to 12:00 - Main Auditorium, bldg. 500 Predicting Natural Catastrophes E. OKAL / Northwestern University, Evanston, USA 1. Tsunamis -- Introduction Definition of phenomenon - basic properties of the waves Propagation and dispersion Interaction with coasts - Geological and societal effects Origin of tsunamis - natural sources Scientific activities in connection with tsunamis. Ideas about simulations 2. Tsunami generation The earthquake source - conventional theory The earthquake source - normal mode theory The landslide source Near-field observation - The Plafker index Far-field observation - Directivity 3. Tsunami warning General ideas - History of efforts Mantle magnitudes and TREMOR algorithms The challenge of 'tsunami earthquakes' Energy-moment ratios and slow earthquakes Implementation and the components of warning centers 4. Tsunami surveys Principles and methodologies Fifteen years of field surveys and re...

Influence of Genotype on Warfarin Maintenance Dose Predictions Produced Using a Bayesian Dose Individualization Tool.

Science.gov (United States)

Saffian, Shamin M; Duffull, Stephen B; Roberts, Rebecca L; Tait, Robert C; Black, Leanne; Lund, Kirstin A; Thomson, Alison H; Wright, Daniel F B

2016-12-01

A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In this study, we aimed (1) to determine whether the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from 2 different clinical settings, (2) to explore the influence of CYP2C9 and VKORC1 genotype on predictive performance of the Bayesian dosing tool, and (3) to determine whether the previous population used to develop the kinetic-pharmacodynamic model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions. The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values. The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations. Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.

Casual Video Games as Training Tools for Attentional Processes in Everyday Life.

Science.gov (United States)

Stroud, Michael J; Whitbourne, Susan Krauss

2015-11-01

Three experiments examined the attentional components of the popular match-3 casual video game, Bejeweled Blitz (BJB). Attentionally demanding, BJB is highly popular among adults, particularly those in middle and later adulthood. In experiment 1, 54 older adults (Mage = 70.57) and 33 younger adults (Mage = 19.82) played 20 rounds of BJB, and completed online tasks measuring reaction time, simple visual search, and conjunction visual search. Prior experience significantly predicted BJB scores for younger adults, but for older adults, both prior experience and simple visual search task scores predicted BJB performance. Experiment 2 tested whether BJB practice alone would result in a carryover benefit to a visual search task in a sample of 58 young adults (Mage = 19.57) who completed 0, 10, or 30 rounds of BJB followed by a BJB-like visual search task with targets present or absent. Reaction times were significantly faster for participants who completed 30 but not 10 rounds of BJB compared with the search task only. This benefit was evident when targets were both present and absent, suggesting that playing BJB improves not only target detection, but also the ability to quit search effectively. Experiment 3 tested whether the attentional benefit in experiment 2 would apply to non-BJB stimuli. The results revealed a similar numerical but not significant trend. Taken together, the findings suggest there are benefits of casual video game playing to attention and relevant everyday skills, and that these games may have potential value as training tools.

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

Directory of Open Access Journals (Sweden)

Okokpujie Imhade Princess

2017-12-01

Full Text Available In recent machining operation, tool life is one of the most demanding tasks in production process, especially in the automotive industry. The aim of this paper is to study tool wear on HSS in end milling of aluminium 6061 alloy. The experiments were carried out to investigate tool wear with the machined parameters and to developed mathematical model using response surface methodology. The various machining parameters selected for the experiment are spindle speed (N, feed rate (f, axial depth of cut (a and radial depth of cut (r. The experiment was designed using central composite design (CCD in which 31 samples were run on SIEG 3/10/0010 CNC end milling machine. After each experiment the cutting tool was measured using scanning electron microscope (SEM. The obtained optimum machining parameter combination are spindle speed of 2500 rpm, feed rate of 200 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.0mm was found out to achieved the minimum tool wear as 0.213 mm. The mathematical model developed predicted the tool wear with 99.7% which is within the acceptable accuracy range for tool wear prediction.

Experimental and Mathematical Modeling for Prediction of Tool Wear on the Machining of Aluminium 6061 Alloy by High Speed Steel Tools

Science.gov (United States)

Okokpujie, Imhade Princess; Ikumapayi, Omolayo M.; Okonkwo, Ugochukwu C.; Salawu, Enesi Y.; Afolalu, Sunday A.; Dirisu, Joseph O.; Nwoke, Obinna N.; Ajayi, Oluseyi O.

2017-12-01

In recent machining operation, tool life is one of the most demanding tasks in production process, especially in the automotive industry. The aim of this paper is to study tool wear on HSS in end milling of aluminium 6061 alloy. The experiments were carried out to investigate tool wear with the machined parameters and to developed mathematical model using response surface methodology. The various machining parameters selected for the experiment are spindle speed (N), feed rate (f), axial depth of cut (a) and radial depth of cut (r). The experiment was designed using central composite design (CCD) in which 31 samples were run on SIEG 3/10/0010 CNC end milling machine. After each experiment the cutting tool was measured using scanning electron microscope (SEM). The obtained optimum machining parameter combination are spindle speed of 2500 rpm, feed rate of 200 mm/min, axial depth of cut of 20 mm, and radial depth of cut 1.0mm was found out to achieved the minimum tool wear as 0.213 mm. The mathematical model developed predicted the tool wear with 99.7% which is within the acceptable accuracy range for tool wear prediction.

Predictive Function Control for Communication-Based Train Control (CBTC Systems

Directory of Open Access Journals (Sweden)

Bing Bu

2013-01-01

Full Text Available In Communication-Based Train Control (CBTC systems, random transmission delays and packet drops are inevitable in the wireless networks, which could result in unnecessary traction, brakes or even emergency brakes of trains, losses of line capacity and passenger dissatisfaction. This paper applies predictive function control technology with a mixed H2/∞ control approach to improve the control performances. The controller is in the state feedback form and satisfies the requirement of quadratic input and state constraints. A linear matrix inequality (LMI approach is developed to solve the control problem. The proposed method attenuates disturbances by incorporating H2/∞ into the control scheme. The control command from the automatic train operation (ATO is included in the reward function to optimize the train's running profile. The influence of transmission delays and packet drops is alleviated through improving the performances of the controller. Simulation results show that the method is effective to improve the performances and robustness of CBTC systems.

Development of Next Generation Multiphase Pipe Flow Prediction Tools

Energy Technology Data Exchange (ETDEWEB)

Cem Sarica; Holden Zhang

2006-05-31

The developments of oil and gas fields in deep waters (5000 ft and more) will become more common in the future. It is inevitable that production systems will operate under multiphase flow conditions (simultaneous flow of gas, oil and water possibly along with sand, hydrates, and waxes). Multiphase flow prediction tools are essential for every phase of hydrocarbon recovery from design to operation. Recovery from deep-waters poses special challenges and requires accurate multiphase flow predictive tools for several applications, including the design and diagnostics of the production systems, separation of phases in horizontal wells, and multiphase separation (topside, seabed or bottom-hole). It is crucial for any multiphase separation technique, either at topside, seabed or bottom-hole, to know inlet conditions such as flow rates, flow patterns, and volume fractions of gas, oil and water coming into the separation devices. Therefore, the development of a new generation of multiphase flow predictive tools is needed. The overall objective of the proposed study is to develop a unified model for gas-oil-water three-phase flow in wells, flow lines, and pipelines to predict flow characteristics such as flow patterns, phase distributions, and pressure gradient encountered during petroleum production at different flow conditions (pipe diameter and inclination, fluid properties and flow rates). In the current multiphase modeling approach, flow pattern and flow behavior (pressure gradient and phase fractions) prediction modeling are separated. Thus, different models based on different physics are employed, causing inaccuracies and discontinuities. Moreover, oil and water are treated as a pseudo single phase, ignoring the distinct characteristics of both oil and water, and often resulting in inaccurate design that leads to operational problems. In this study, a new model is being developed through a theoretical and experimental study employing a revolutionary approach. The

Simulation-Based Laparoscopic Surgery Crisis Resource Management Training-Predicting Technical and Nontechnical Skills.

Science.gov (United States)

Goldenberg, Mitchell G; Fok, Kai H; Ordon, Michael; Pace, Kenneth T; Lee, Jason Y

2017-12-19

To develop a unique simulation-based assessment using a laparoscopic inferior vena cava (IVC) injury scenario that allows for the safe assessment of urology resident's technical and nontechnical skills, and investigate the effect of personality traits performance in a surgical crisis. Urology residents from our institution were recruited to participate in a simulation-based training laparoscopic nephrectomy exercise. Residents completed demographic and multidimensional personality questionnaires and were instructed to play the role of staff urologist. A vasovagal response to pneumoperitoneum and an IVC injury event were scripted into the scenario. Technical and nontechnical skills were assessed by expert laparoscopic surgeons using validated tools (task checklist, GOALS, and NOTSS). Ten junior and five senior urology residents participated. Five residents were unable to complete the exercise safely. Senior residents outperformed juniors on technical (checklist score 15.1 vs 9.9, p Technical performance scores correlated with NOTSS scores (p technical performance (p technical score (p = 0.03) and pass/fail rating (p = 0.04). Resident level of training and laparoscopic experience correlated with technical performance during a simulation-based laparoscopic IVC injury crisis management scenario, as well as multiple domains of nontechnical performance. Personality traits of our surgical residents are similar and did not predict technical skill. Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

USING SECOND LIFE VIRTUAL COMPUTER WORLD AS A TRAINING TOOL FOR THE SUBJECTIVE GLOBAL ASSESSMENT (sga.

Directory of Open Access Journals (Sweden)

G. Clark Connery

2012-06-01

Full Text Available The SGA is a clinical tool used to assess protein energy wasting. Although well validated, it is still not widely incorporated into clinical practice. A barrier to use may be the physical assessment section. Therefore, the purpose of this project was to develop a free and effective tool to train clinicians on performing the SGA. Second Life (SL is a free virtual reality program accessed through the internet using human-like “avatars.” A museum environment was created with panels presenting SGA background information through text, images, and videos of SGA being performed. Users are able to navigate the information by logging onto a provided avatar. After the initial panels, this avatar is able to interact with avatar bots and perform animations which mimic each body assessment within the SGA. Two trial periods were conducted to assess the efficacy of this training tool. The alpha trial consisted of 3 hospital dietitians and 3 nutrition students. These subjects came to the investigators’ facility to test the program. Subjective responses were collected and used to improve the training tool. Feedback was positive regarding the information, delivery, and direction of the project; however, they did complain of difficulty with controlling the avatar. The beta trial consists of users accessing the module remotely. These users include academic and clinical dietitians. Responses are being collected via 5 surveys covering each portion of the module. While 16 dietitians responded to the beta trial, only 4 have completed the training. Current survey responses state: the use of SL is easy and enjoyable; all SGA information was clear and in a desirable format; tactile comparison objects were beneficial for understanding; the in depth description of each assessment is beneficial; the animations that the avatars perform on the bots needs improvement; a patient avatar on which users could perform the full SGA is desirable; the use of SL in the learning

Development of a CME-associated geomagnetic storm intensity prediction tool

Science.gov (United States)

Wu, C. C.; DeHart, J. M.

2015-12-01

From 1995 to 2012, the Wind spacecraft recorded 168 magnetic cloud (MC) events. Among those events, 79 were found to have upstream shock waves and their source locations on the Sun were identified. Using a recipe of interplanetary magnetic field (IMF) Bz initial turning direction after shock (Wu et al., 1996, GRL), it is found that the north-south polarity of 66 (83.5%) out of the 79 events were accurately predicted. These events were tested and further analyzed, reaffirming that the Bz intial turning direction was accurate. The results also indicate that 37 of the 79 MCs originate from the north (of the Sun) averaged a Dst_min of -119 nT, whereas 42 of the MCs originating from the south (of the Sun) averaged -89 nT. In an effort to provide this research to others, a website was built that incorporated various tools and pictures to predict the intensity of the geomagnetic storms. The tool is capable of predicting geomagnetic storms with different ranges of Dst_min (from no-storm to gigantic storms). This work was supported by Naval Research Lab HBCU/MI Internship program and Chief of Naval Research.

Cognitive training with and without additional physical activity in healthy older adults: cognitive effects, neurobiological mechanisms, and prediction of training success

Directory of Open Access Journals (Sweden)

Julia eRahe

2015-10-01

Full Text Available Data is inconsistent concerning the question whether cognitive-physical training (CPT yields stronger cognitive gains than cognitive training (CT. Effects of additional counseling, neurobiological mechanisms, and predictors have scarcely been studied. Healthy older adults were trained with CT (n=20, CPT (n=25, or CPT with counseling (CPT+C; n=23. Cognition, physical fitness, BDNF, IGF-1, and VEGF were assessed at pre- and posttest. No interaction effects were found except for one effect showing that CPT+C led to stronger gains in verbal fluency than CPT (p = .03. However, this superiority could not be assigned to additional physical training gains. Low baseline cognitive performance and BDNF, not carrying apoE4, gains in physical fitness and the moderation of gains in physical fitness x gains in BDNF predicted training success. Although all types of interventions seem successful to enhance cognition, our data do not support the hypotheses that CPT shows superior cognitive training gains compared to CT or that CPT+C adds merit to CPT. However, as CPT leads to additional gains in physical fitness which in turn is known to have positive impact on cognition in the long-term, CPT seems more beneficial. Training success can partly be predicted by neuropsychological, neurobiological, and genetic parameters.http://www.who.int/ictrp; ID: DRKS00005194

Toward competency-based curriculum: Application of workplace-based assessment tools in the National Saudi Arabian Anesthesia Training Program.

Science.gov (United States)

Boker, Ama

2016-01-01

The anesthesia training program of the Saudi Commission for health specialties has introduced a developed competency-based anesthesia residency program starting from 2015 with the utilization of the workplace-based assessment (WBA) tools, namely mini-clinical exercises (mini-CEX), direct observation of procedural skills (DOPS), and case-based discussion (CBD). This work aimed to describe the process of development of anesthesia-specific list of mini-CEX, DOPS, and CBD tools within the Saudi Arabian Anesthesia Training Programs. To introduce the main concepts of formative WBA tools and to develop anesthesia-specific applications for each of the selected WBA tools, four 1-day workshops were held at the level of major training committees at eastern (Dammam), western (Jeddah), and central (Riyadh) regions in the Kingdom were conducted. Sixty-seven faculties participated in these workshops. After conduction of the four workshops, the anesthesia-specific applications setting of mini-CEX, DOPS, and CBD tools among the 5-year levels were fully described. The level of the appropriate consultation skills was divided according to the case complexity adopted from the American Society of Anesthesiologists physical classification for adult and obstetric and pediatric patient as well as the type of the targeted anesthetic procedure. WBA anesthesia-specific lists of mini-CEX, DOPS, and CBD forms were easily incorporated first into guidelines to help the first stage of implementation of formative assessment in the Saudi Arabian Anesthesia Residency Program, and this can be helpful to replicate such program within other various training programs in Saudi Arabia and abroad.

Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

Science.gov (United States)

Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

2011-01-01

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

Developing site-specific interactive environmental management tools: An exciting method of communicating training, procedures, and other information

Energy Technology Data Exchange (ETDEWEB)

Jaeckels, J.M.

1999-07-01

Environmental managers are faced with numerous programs that must be communicated throughout their organizations. Among these are regulatory training programs, internal environmental policy, regulatory guidance/procedures and internal guidance/procedures. Traditional methods of delivering this type of information are typically confined to written materials and classroom training. There are many challenges faced by environmental managers with these traditional approaches including: determining if recipients of written plans or procedures are reading and comprehending the information; scheduling training sessions to reach all affected people across multiple schedules/shifts; and maintaining adequate training records. In addition, current trends toward performance-based or competency-based training requires a more consistent method of measuring and documenting performance. The use of interactive computer applications to present training or procedural information is a new and exciting tool for delivering environmental information to employees. Site-specific pictures, text, sound, and even video can be combined with multimedia software to create informative and highly interactive applications. Some of the applications that can be produced include integrated environmental training, educational pieces, and interactive environmental procedures. They can be executed from a CD-ROM, hard drive, network or a company Intranet. Collectively, the authors refer to these as interactive environmental management tools (IEMTs). This paper focuses on site-specific, interactive training as an example of an IEMT. Interactive training not only delivers a highly effective message, but can also be designed to focus on site-specific environmental issues that are unique to each company. Interactive training also lends itself well to automated record keeping functions and to reaching all affected employees.

Individual Differences in Executive Functioning Predict Preschoolers' Improvement from Theory-of-Mind Training

Science.gov (United States)

Benson, Jeannette E.; Sabbagh, Mark A.; Carlson, Stephanie M.; Zelazo, Philip David

2013-01-01

Twenty-four 3.5-year-old children who initially showed poor performance on false-belief tasks participated in a training protocol designed to promote performance on these tasks. Our aim was to determine whether the extent to which children benefited from training was predicted by their performance on a battery of executive functioning tasks.…

Improving Student Employee Training: A Study of Web 2.0 Social Media Tools as a Delivery Model

Science.gov (United States)

Smith, Sharon D.

2012-01-01

Training student employees in Educational Outreach and Student Services (EOSS) at Arizona State University's West campus is important to maintaining a knowledgeable and productive workforce. This dissertation describes the results of an action research study in which social media tools were utilized as a delivery mechanism for training student…

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins.

Science.gov (United States)

Zhang, Zong Hong; Koh, Judice L Y; Zhang, Guang Lan; Choo, Khar Heng; Tammi, Martti T; Tong, Joo Chuan

2007-02-15

Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

Application of part-whole training methods to evaluate when to introduce NextGen air traffic management tools to students.

Science.gov (United States)

Vu, Kim-Phuong L; Kiken, Ariana; Chiappe, Dan; Strybel, Thomas Z; Battiste, Vernol

2013-01-01

The Next Generation Air Transportation System (NextGen) will use advanced technologies and new concepts of operation to accommodate projected increases in air travel over the next few decades. Use of NextGen tools requires air traffic controllers (ATCos) to use different procedures than those required to manage NextGen-unequipped aircraft, and ATCos will need to integrate the 2 skill sets when managing a sector consisting of NextGen-equipped and unequipped aircraft. The goal of the present study was to determine the effectiveness of 2 procedures in the training of student controllers to manage both equipage types. We applied a variant of the part-whole training paradigm in the present study. Using a quasi-experimental design, we trained students from 2 different labs of an internship course to manage air traffic with potential NextGen tools concurrent with their traditional training (whole-task group) or after they had time to learn traditional air traffic management skills (part-whole group). Participants were then tested in their ability to manage a simulated sector consisting of different percentages of NextGen-equipped and unequipped aircraft at the mid-term and after the final week of their internship. Results showed that it is better to train students in manual ATCo skills before introducing NextGen tools, unless the students are of higher aptitude. For more skilled students, simultaneously introducing NextGen and manual tools into their curriculum had little negative impact.

The predictive validity of selection for entry into postgraduate training in general practice: evidence from three longitudinal studies.

Science.gov (United States)

Patterson, Fiona; Lievens, Filip; Kerrin, Máire; Munro, Neil; Irish, Bill

2013-11-01

The selection methodology for UK general practice is designed to accommodate several thousand applicants per year and targets six core attributes identified in a multi-method job-analysis study To evaluate the predictive validity of selection methods for entry into postgraduate training, comprising a clinical problem-solving test, a situational judgement test, and a selection centre. A three-part longitudinal predictive validity study of selection into training for UK general practice. In sample 1, participants were junior doctors applying for training in general practice (n = 6824). In sample 2, participants were GP registrars 1 year into training (n = 196). In sample 3, participants were GP registrars sitting the licensing examination after 3 years, at the end of training (n = 2292). The outcome measures include: assessor ratings of performance in a selection centre comprising job simulation exercises (sample 1); supervisor ratings of trainee job performance 1 year into training (sample 2); and licensing examination results, including an applied knowledge examination and a 12-station clinical skills objective structured clinical examination (OSCE; sample 3). Performance ratings at selection predicted subsequent supervisor ratings of job performance 1 year later. Selection results also significantly predicted performance on both the clinical skills OSCE and applied knowledge examination for licensing at the end of training. In combination, these longitudinal findings provide good evidence of the predictive validity of the selection methods, and are the first reported for entry into postgraduate training. Results show that the best predictor of work performance and training outcomes is a combination of a clinical problem-solving test, a situational judgement test, and a selection centre. Implications for selection methods for all postgraduate specialties are considered.

Running speed during training and percent body fat predict race time in recreational male marathoners.

Science.gov (United States)

Barandun, Ursula; Knechtle, Beat; Knechtle, Patrizia; Klipstein, Andreas; Rüst, Christoph Alexander; Rosemann, Thomas; Lepers, Romuald

2012-01-01

Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners. Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times. After multivariate regression, running speed of the training units (β = -0.52, P marathon race times. Marathon race time for recreational male runners may be estimated to some extent by using the following equation (r (2) = 0.44): race time ( minutes) = 326.3 + 2.394 × (percent body fat, %) - 12.06 × (speed in training, km/hours). Running speed during training sessions correlated with prerace percent body fat (r = 0.33, P = 0.0002). The model including anthropometric and training variables explained 44% of the variance of marathon race times, whereas running speed during training sessions alone explained 40%. Thus, training speed was more predictive of marathon performance times than anthropometric characteristics. The present results suggest that low body fat and running speed during training close to race pace (about 11 km/hour) are two key factors for a fast marathon race time in recreational male marathoner runners.

3D Cloud Field Prediction using A-Train Data and Machine Learning Techniques

Science.gov (United States)

Johnson, C. L.

2017-12-01

Validation of cloud process parameterizations used in global climate models (GCMs) would greatly benefit from observed 3D cloud fields at the size comparable to that of a GCM grid cell. For the highest resolution simulations, surface grid cells are on the order of 100 km by 100 km. CloudSat/CALIPSO data provides 1 km width of detailed vertical cloud fraction profile (CFP) and liquid and ice water content (LWC/IWC). This work utilizes four machine learning algorithms to create nonlinear regressions of CFP, LWC, and IWC data using radiances, surface type and location of measurement as predictors and applies the regression equations to off-track locations generating 3D cloud fields for 100 km by 100 km domains. The CERES-CloudSat-CALIPSO-MODIS (C3M) merged data set for February 2007 is used. Support Vector Machines, Artificial Neural Networks, Gaussian Processes and Decision Trees are trained on 1000 km of continuous C3M data. Accuracy is computed using existing vertical profiles that are excluded from the training data and occur within 100 km of the training data. Accuracy of the four algorithms is compared. Average accuracy for one day of predicted data is 86% for the most successful algorithm. The methodology for training the algorithms, determining valid prediction regions and applying the equations off-track is discussed. Predicted 3D cloud fields are provided as inputs to the Ed4 NASA LaRC Fu-Liou radiative transfer code and resulting TOA radiances compared to observed CERES/MODIS radiances. Differences in computed radiances using predicted profiles and observed radiances are compared.

Development and assessment of a new 3D neuroanatomy teaching tool for MRI training.

Science.gov (United States)

Drapkin, Zachary A; Lindgren, Kristen A; Lopez, Michael J; Stabio, Maureen E

2015-01-01

A computerized three-dimensional (3D) neuroanatomy teaching tool was developed for training medical students to identify subcortical structures on a magnetic resonance imaging (MRI) series of the human brain. This program allows the user to transition rapidly between two-dimensional (2D) MRI slices, 3D object composites, and a combined model in which 3D objects are overlaid onto the 2D MRI slices, all while rotating the brain in any direction and advancing through coronal, sagittal, or axial planes. The efficacy of this tool was assessed by comparing scores from an MRI identification quiz and survey in two groups of first-year medical students. The first group was taught using this new 3D teaching tool, and the second group was taught the same content for the same amount of time but with traditional methods, including 2D images of brain MRI slices and 3D models from widely used textbooks and online sources. Students from the experimental group performed marginally better than the control group on overall test score (P = 0.07) and significantly better on test scores extracted from questions involving C-shaped internal brain structures (P teaching tool is an effective way to train medical students to read an MRI of the brain and is particularly effective for teaching C-shaped internal brain structures. © 2015 American Association of Anatomists.

Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

NARCIS (Netherlands)

Maaren, M.C. van; Steenbeek, C.D. van; Pharoah, P.D.; Witteveen, A.; Sonke, G.S.; Strobbe, L.J.A.; Poortmans, P.; Siesling, S.

2017-01-01

BACKGROUND: PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. METHODS: All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected

Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

NARCIS (Netherlands)

van Maaren, M. C.; van Steenbeek, C. D.; Pharoah, P. D.P.; Witteveen, A.; Sonke, Gabe S.; Strobbe, L.J.A.; Poortmans, P.M.P.; Siesling, S.

2017-01-01

Background PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. Methods All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected from

Enhancing Nuclear Newcomer Training with 3D Visualization Learning Tools

International Nuclear Information System (INIS)

Gagnon, V.

2016-01-01

Full text: While the nuclear power industry is trying to reinforce its safety and regain public support post-Fukushima, it is also faced with a very real challenge that affects its day-to-day activities: a rapidly aging workforce. Statistics show that close to 40% of the current nuclear power industry workforce will retire within the next five years. For newcomer countries, the challenge is even greater, having to develop a completely new workforce. The workforce replacement effort introduces nuclear newcomers of a new generation with different backgrounds and affinities. Major lifestyle differences between the two generations of workers result, amongst other things, in different learning habits and needs for this new breed of learners. Interactivity, high visual content and quick access to information are now necessary to achieve a high level of retention. To enhance existing training programmes or to support the establishment of new training programmes for newcomer countries, L-3 MAPPS has devised learning tools to enhance these training programmes focused on the “Practice-by-Doing” principle. L-3 MAPPS has coupled 3D computer visualization with high-fidelity simulation to bring real-time, simulation-driven animated components and systems allowing immersive and participatory, individual or classroom learning. (author

Biodiversity in environmental assessment-current practice and tools for prediction

International Nuclear Information System (INIS)

Gontier, Mikael; Balfors, Berit; Moertberg, Ulla

2006-01-01

Habitat loss and fragmentation are major threats to biodiversity. Environmental impact assessment and strategic environmental assessment are essential instruments used in physical planning to address such problems. Yet there are no well-developed methods for quantifying and predicting impacts of fragmentation on biodiversity. In this study, a literature review was conducted on GIS-based ecological models that have potential as prediction tools for biodiversity assessment. Further, a review of environmental impact statements for road and railway projects from four European countries was performed, to study how impact prediction concerning biodiversity issues was addressed. The results of the study showed the existing gap between research in GIS-based ecological modelling and current practice in biodiversity assessment within environmental assessment

Training Theory of Mind and Executive Control: A Tool for Improving School Achievement?

Science.gov (United States)

Kloo, Daniela; Perner, Josef

2008-01-01

In the preschool years, there are marked improvements in theory of mind (ToM) and executive functions. And, children's competence in these two core cognitive domains is associated with their academic achievement. Therefore, training ToM and executive control could be a valuable tool for improving children's success in school. This article reviews…

MONRATE, a descriptive tool for calculation and prediction of re ...

African Journals Online (AJOL)

The objective of the study was to develop an interactive and systematic descriptive tool, MONRATE for calculating and predicting reinfection rates and time of Ascaris lumbricoides following mass chemotherapy using levamisole. Each pupil previously treated was retreated 6 or 7 months after the initial treatment in Ogun ...

About Using Predictive Models and Tools To Assess Chemicals under TSCA

Science.gov (United States)

As part of EPA's effort to promote chemical safety, OPPT provides public access to predictive models and tools which can help inform the public on the hazards and risks of substances and improve chemical management decisions.

Predictive tools for designing new insulins and treatment regimens

DEFF Research Database (Denmark)

Klim, Søren

The thesis deals with the development of "Predictive tools for designing new insulins and treatments regimens" and consists of two parts: A model based approach for bridging properties of new insulin analogues from glucose clamp experiments to meal tolerance tests (MTT) and a second part that des......The thesis deals with the development of "Predictive tools for designing new insulins and treatments regimens" and consists of two parts: A model based approach for bridging properties of new insulin analogues from glucose clamp experiments to meal tolerance tests (MTT) and a second part...... that describes an implemented software program able to handle stochastic differential equations (SDEs) with mixed effects. The thesis is supplemented with scientific papers published during the PhD. Developing an insulin analogue from candidate molecule to a clinical drug consists of a development programme...... and efficacy are investigated. Numerous methods are used to quantify dose and efficacy in Phase II - especially of interest is the 24-hour meal tolerance test as it tries to portray near normal living conditions. Part I describes an integrated model for insulin and glucose which is aimed at simulating 24-hour...

SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

Energy Technology Data Exchange (ETDEWEB)

Sun, W; Jiang, M; Yin, F [Duke University Medical Center, Durham, NC (United States)

2016-06-15

Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results

Predicting malicious behavior tools and techniques for ensuring global security

CERN Document Server

Jackson, Gary M

2012-01-01

A groundbreaking exploration of how to identify and fight security threats at every level This revolutionary book combines real-world security scenarios with actual tools to predict and prevent incidents of terrorism, network hacking, individual criminal behavior, and more. Written by an expert with intelligence officer experience who invented the technology, it explores the keys to understanding the dark side of human nature, various types of security threats (current and potential), and how to construct a methodology to predict and combat malicious behavior. The companion CD demonstrates ava

Bitter or not? BitterPredict, a tool for predicting taste from chemical structure.

Science.gov (United States)

Dagan-Wiener, Ayana; Nissim, Ido; Ben Abu, Natalie; Borgonovo, Gigliola; Bassoli, Angela; Niv, Masha Y

2017-09-21

Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php ), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70-90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

Neuro-Fuzzy Prediction of Cooperation Interaction Profile of Flexible Road Train Based on Hybrid Automaton Modeling

Directory of Open Access Journals (Sweden)

Banjanovic-Mehmedovic Lejla

2016-01-01

Full Text Available Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS, since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS. The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error, coefficient of determination (R2 and Pearson coefficient (r, were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

The interactive graphic simulator (IGS): A helpful tool for an efficient training

International Nuclear Information System (INIS)

Santiago Lucas, A.

1996-01-01

The IGS is a natural response, in the training area, to the high technological advances in the computer disciplines for the graphic representations and advanced process models with a high level of reliability and friendliness. Tecnatom has integrated a representation of advanced models in several workstations which permits, through its high resolution colour screens, the visualization of all the information available in control room with graphical representation of NPP's system. Simultaneously with this, the IGS permits to operate any component in order to change its status, in the same way that operations upon the panels. Through the flexibility of the software for graphic representations and the advanced models. Tecnatom has generated, based in the use of IGS, several training courses which have provided a tutorial worth, to understand complex phenomena, with a man-machine interface more friendly than the full scope simulator. Therefore, the IGS appears as an important flexible tool which can adapt itself to the training of several collectives in a NPP, and it has a special importance for those which the ignorance of panels interferes with the training in a full scope simulator. (author)

Cardiovascular risk prediction tools for populations in Asia.

Science.gov (United States)

Barzi, F; Patel, A; Gu, D; Sritara, P; Lam, T H; Rodgers, A; Woodward, M

2007-02-01

Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear. To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction. Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts. Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US. 172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study. In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men. A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian

piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER.

Science.gov (United States)

Ray, Rishav; Pandey, Priyanka

2017-12-19

With the increasing number of studies focusing on PIWI-interacting RNA (piRNAs), it is now pertinent to develop efficient tools dedicated towards piRNA analysis. We have developed a novel cluster prediction tool called PILFER (PIrna cLuster FindER), which can accurately predict piRNA clusters from small RNA sequencing data. PILFER is an open source, easy to use tool, and can be executed even on a personal computer with minimum resources. It uses a sliding-window mechanism by integrating the expression of the reads along with the spatial information to predict the piRNA clusters. We have additionally defined a piRNA analysis pipeline incorporating PILFER to detect and annotate piRNAs and their clusters from raw small RNA sequencing data and implemented it on publicly available data from healthy germline and somatic tissues. We compared PILFER with other existing piRNA cluster prediction tools and found it to be statistically more accurate and superior in many aspects such as the robustness of PILFER clusters is higher and memory efficiency is more. Overall, PILFER provides a fast and accurate solution to piRNA cluster prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Tools for Predicting Optical Damage on Inertial Confinement Fusion-Class Laser Systems

International Nuclear Information System (INIS)

Nostrand, M.C.; Carr, C.W.; Liao, Z.M.; Honig, J.; Spaeth, M.L.; Manes, K.R.; Johnson, M.A.; Adams, J.J.; Cross, D.A.; Negres, R.A.; Widmayer, C.C.; Williams, W.H.; Matthews, M.J.; Jancaitis, K.S.; Kegelmeyer, L.M.

2010-01-01

Operating a fusion-class laser to its full potential requires a balance of operating constraints. On the one hand, the total laser energy delivered must be high enough to give an acceptable probability for ignition success. On the other hand, the laser-induced optical damage levels must be low enough to be acceptably handled with the available infrastructure and budget for optics recycle. Our research goal was to develop the models, database structures, and algorithmic tools (which we collectively refer to as ''Loop Tools'') needed to successfully maintain this balance. Predictive models are needed to plan for and manage the impact of shot campaigns from proposal, to shot, and beyond, covering a time span of years. The cost of a proposed shot campaign must be determined from these models, and governance boards must decide, based on predictions, whether to incorporate a given campaign into the facility shot plan based upon available resources. Predictive models are often built on damage ''rules'' derived from small beam damage tests on small optics. These off-line studies vary the energy, pulse-shape and wavelength in order to understand how these variables influence the initiation of damage sites and how initiated damage sites can grow upon further exposure to UV light. It is essential to test these damage ''rules'' on full-scale optics exposed to the complex conditions of an integrated ICF-class laser system. Furthermore, monitoring damage of optics on an ICF-class laser system can help refine damage rules and aid in the development of new rules. Finally, we need to develop the algorithms and data base management tools for implementing these rules in the Loop Tools. The following highlights progress in the development of the loop tools and their implementation.

Tools for Predicting Optical Damage on Inertial Confinement Fusion-Class Laser Systems

Energy Technology Data Exchange (ETDEWEB)

Nostrand, M C; Carr, C W; Liao, Z M; Honig, J; Spaeth, M L; Manes, K R; Johnson, M A; Adams, J J; Cross, D A; Negres, R A; Widmayer, C C; Williams, W H; Matthews, M J; Jancaitis, K S; Kegelmeyer, L M

2010-12-20

Operating a fusion-class laser to its full potential requires a balance of operating constraints. On the one hand, the total laser energy delivered must be high enough to give an acceptable probability for ignition success. On the other hand, the laser-induced optical damage levels must be low enough to be acceptably handled with the available infrastructure and budget for optics recycle. Our research goal was to develop the models, database structures, and algorithmic tools (which we collectively refer to as ''Loop Tools'') needed to successfully maintain this balance. Predictive models are needed to plan for and manage the impact of shot campaigns from proposal, to shot, and beyond, covering a time span of years. The cost of a proposed shot campaign must be determined from these models, and governance boards must decide, based on predictions, whether to incorporate a given campaign into the facility shot plan based upon available resources. Predictive models are often built on damage ''rules'' derived from small beam damage tests on small optics. These off-line studies vary the energy, pulse-shape and wavelength in order to understand how these variables influence the initiation of damage sites and how initiated damage sites can grow upon further exposure to UV light. It is essential to test these damage ''rules'' on full-scale optics exposed to the complex conditions of an integrated ICF-class laser system. Furthermore, monitoring damage of optics on an ICF-class laser system can help refine damage rules and aid in the development of new rules. Finally, we need to develop the algorithms and data base management tools for implementing these rules in the Loop Tools. The following highlights progress in the development of the loop tools and their implementation.

Benefits for wind energy in electricity markets from using short term wind power prediction tools: a simulation study

International Nuclear Information System (INIS)

Usaola, J.; Ravelo, O.; Gonzalez, G.; Soto, F.; Davila, M.C.; Diaz-Guerra, B.

2004-01-01

One of the characteristics of wind energy, from the grid point of view, is its non-dispatchability, i.e. generation cannot be ordered, hence integration in electrical networks may be difficult. Short-term wind power prediction-tools could make this integration easier, either by their use by the grid System Operator, or by promoting the participation of wind farms in the electricity markets and using prediction tools to make their bids in the market. In this paper, the importance of a short-term wind power-prediction tool for the participation of wind energy systems in electricity markets is studied. Simulations, according to the current Spanish market rules, have been performed to the production of different wind farms, with different degrees of accuracy in the prediction tool. It may be concluded that income from participation in electricity markets is increased using a short-term wind power prediction-tool of average accuracy. This both marginally increases income and also reduces the impact on system operation with the improved forecasts. (author)

Small-Sided Games : An Optimal Training Tool to Represent Tactical Match Demands in Elite-Standard Youth Soccer Players?

NARCIS (Netherlands)

Olthof, S. B. H.; Frencken, W. G. P.; Lemmink, K. A. P. M.

2016-01-01

Small-sided games are an often used training tool in soccer practices. It has proven to provide a simultaneous physical, technical and tactical training stimulus for soccer players[1]. Small-sided games replicate the tactical character of a match, but in a simplified format with reductions in number

Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

Directory of Open Access Journals (Sweden)

Ivanka Jerić

2011-11-01

Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.

Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics

Directory of Open Access Journals (Sweden)

Xi Wang

2016-01-01

Full Text Available We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs. Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.

Towards Subject-Specific Strength Training Design through Predictive Use of Musculoskeletal Models

Directory of Open Access Journals (Sweden)

Michael Plüss

2018-01-01

Full Text Available Lower extremity dysfunction is often associated with hip muscle strength deficiencies. Detailed knowledge of the muscle forces generated in the hip under specific external loading conditions enables specific structures to be trained. The aim of this study was to find the most effective movement type and loading direction to enable the training of specific parts of the hip muscles using a standing posture and a pulley system. In a novel approach to release the predictive power of musculoskeletal modelling techniques based on inverse dynamics, flexion/extension and ab-/adduction movements were virtually created. To demonstrate the effectiveness of this approach, three hip orientations and an external loading force that was systematically rotated around the body were simulated using a state-of-the art OpenSim model in order to establish ideal designs for training of the anterior and posterior parts of the M. gluteus medius (GM. The external force direction as well as the hip orientation greatly influenced the muscle forces in the different parts of the GM. No setting was found for simultaneous training of the anterior and posterior parts with a muscle force higher than 50% of the maximum. Importantly, this study has demonstrated the use of musculoskeletal models as an approach to predict muscle force variations for different strength and rehabilitation exercise variations.

Competency-based evaluation tools for integrative medicine training in family medicine residency: a pilot study

Directory of Open Access Journals (Sweden)

Schneider Craig

2007-04-01

Full Text Available Abstract Background As more integrative medicine educational content is integrated into conventional family medicine teaching, the need for effective evaluation strategies grows. Through the Integrative Family Medicine program, a six site pilot program of a four year residency training model combining integrative medicine and family medicine training, we have developed and tested a set of competency-based evaluation tools to assess residents' skills in integrative medicine history-taking and treatment planning. This paper presents the results from the implementation of direct observation and treatment plan evaluation tools, as well as the results of two Objective Structured Clinical Examinations (OSCEs developed for the program. Methods The direct observation (DO and treatment plan (TP evaluation tools developed for the IFM program were implemented by faculty at each of the six sites during the PGY-4 year (n = 11 on DO and n = 8 on TP. The OSCE I was implemented first in 2005 (n = 6, revised and then implemented with a second class of IFM participants in 2006 (n = 7. OSCE II was implemented in fall 2005 with only one class of IFM participants (n = 6. Data from the initial implementation of these tools are described using descriptive statistics. Results Results from the implementation of these tools at the IFM sites suggest that we need more emphasis in our curriculum on incorporating spirituality into history-taking and treatment planning, and more training for IFM residents on effective assessment of readiness for change and strategies for delivering integrative medicine treatment recommendations. Focusing our OSCE assessment more narrowly on integrative medicine history-taking skills was much more effective in delineating strengths and weaknesses in our residents' performance than using the OSCE for both integrative and more basic communication competencies. Conclusion As these tools are refined further they will be of value both in improving

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

Science.gov (United States)

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

2017-01-01

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

Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

DEFF Research Database (Denmark)

Meretoja, Tuomo J; Andersen, Kenneth Geving; Bruce, Julie

2017-01-01

are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity......), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC......-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen...

Towards early software reliability prediction for computer forensic tools (case study).

Science.gov (United States)

Abu Talib, Manar

2016-01-01

Versatility, flexibility and robustness are essential requirements for software forensic tools. Researchers and practitioners need to put more effort into assessing this type of tool. A Markov model is a robust means for analyzing and anticipating the functioning of an advanced component based system. It is used, for instance, to analyze the reliability of the state machines of real time reactive systems. This research extends the architecture-based software reliability prediction model for computer forensic tools, which is based on Markov chains and COSMIC-FFP. Basically, every part of the computer forensic tool is linked to a discrete time Markov chain. If this can be done, then a probabilistic analysis by Markov chains can be performed to analyze the reliability of the components and of the whole tool. The purposes of the proposed reliability assessment method are to evaluate the tool's reliability in the early phases of its development, to improve the reliability assessment process for large computer forensic tools over time, and to compare alternative tool designs. The reliability analysis can assist designers in choosing the most reliable topology for the components, which can maximize the reliability of the tool and meet the expected reliability level specified by the end-user. The approach of assessing component-based tool reliability in the COSMIC-FFP context is illustrated with the Forensic Toolkit Imager case study.

Memory self-efficacy predicts responsiveness to inductive reasoning training in older adults.

Science.gov (United States)

Payne, Brennan R; Jackson, Joshua J; Hill, Patrick L; Gao, Xuefei; Roberts, Brent W; Stine-Morrow, Elizabeth A L

2012-01-01

In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood.

eShadow: A tool for comparing closely related sequences

Energy Technology Data Exchange (ETDEWEB)

Ovcharenko, Ivan; Boffelli, Dario; Loots, Gabriela G.

2004-01-15

Primate sequence comparisons are difficult to interpret due to the high degree of sequence similarity shared between such closely related species. Recently, a novel method, phylogenetic shadowing, has been pioneered for predicting functional elements in the human genome through the analysis of multiple primate sequence alignments. We have expanded this theoretical approach to create a computational tool, eShadow, for the identification of elements under selective pressure in multiple sequence alignments of closely related genomes, such as in comparisons of human to primate or mouse to rat DNA. This tool integrates two different statistical methods and allows for the dynamic visualization of the resulting conservation profile. eShadow also includes a versatile optimization module capable of training the underlying Hidden Markov Model to differentially predict functional sequences. This module grants the tool high flexibility in the analysis of multiple sequence alignments and in comparing sequences with different divergence rates. Here, we describe the eShadow comparative tool and its potential uses for analyzing both multiple nucleotide and protein alignments to predict putative functional elements. The eShadow tool is publicly available at http://eshadow.dcode.org/

A national evaluation of workplace-based assessment tools (WPBAs) in foundation dental training: a UK study. Effective and useful but do they provide an equitable training experience?

Science.gov (United States)

Kirton, J A; Palmer, N O A; Grieveson, B; Balmer, M C

2013-03-01

A questionnaire study was undertaken with trainers and trainees from 12 deaneries in England and Northern Ireland in June 2010 to evaluate workplace-based assessments (WPBAs) in foundation training. From the sample consisting of 741 trainers and 643 foundation trainees, experience of WPBAs was positive overall, playing an important role in trainees' learning during foundation training and building confidence. However, there is a need for comprehensive training in the WPBA tools used to ensure their efficacy.

Predictive Maintenance--An Effective Money Saving Tool Being Applied in Industry Today.

Science.gov (United States)

Smyth, Tom

2000-01-01

Looks at preventive/predictive maintenance as it is used in industry. Discusses core preventive maintenance tools that must be understood to prepare students. Includes a list of websites related to the topic. (JOW)

Predicting Dyspnea Inducers by Molecular Topology

Directory of Open Access Journals (Sweden)

María Gálvez-Llompart

2013-01-01

Full Text Available QSAR based on molecular topology (MT is an excellent methodology used in predicting physicochemical and biological properties of compounds. This approach is applied here for the development of a mathematical model capable to recognize drugs showing dyspnea as a side effect. Using linear discriminant analysis, it was found a four-variable regression equations enabling a predictive rate of about 81% and 73% in the training and test sets of compounds, respectively. These results demonstrate that QSAR-MT is an efficient tool to predict the appearance of dyspnea associated with drug consumption.

Overcoming the barriers to patient-centred care: time, tools and training.

Science.gov (United States)

West, Elizabeth; Barron, David N; Reeves, Rachel

2005-04-01

To investigate whether nurses experience barriers to delivering high quality care in areas that are of particular concern to patients and to describe which aspects of care are most affected when nurses lack the required resources, such as time, tools and training to do their job. Patient surveys conducted in the National Health Service of the United Kingdom tend to show there is variation in the extent to which they are satisfied with care in a number of important areas, such as physical comfort, emotional support and the coordination of care. A sample of nurses working in 20 acute London hospitals was asked to complete a postal questionnaire based on a prototype employee survey developed in the United States and adapted by the authors for use in the United Kingdom. Staff in the human resources departments of participating hospitals mailed the questionnaires to nurses' home addresses. After two reminders, 2880 (out of 6160) useable responses were returned, giving a response rate of 47%. Nurses are aware that there are deficits in standards of care in areas that are particularly important to patients. The majority feel overworked (64%) and report that they do not have enough time to perform essential nursing tasks, such as addressing patients' anxieties, fears and concerns and giving patients and relatives information. Their work is often made more difficult by the lack of staff, space, equipment and cleanliness. They are often unable to control noise and temperature in clinical areas. Nurses in acute London hospitals are subject to high levels of aggressive behaviour, mainly from patients and their relatives, but also from other members of staff. More positively, high proportions of the nurses in our survey expressed the desire for further training, particularly in social and interpersonal aspects of care. This paper goes beyond reporting problems with the quality and safety of care to try to understand why patients do not always receive optimum care in areas that

Tools for predicting the PK/PD of therapeutic proteins.

Science.gov (United States)

Diao, Lei; Meibohm, Bernd

2015-07-01

Assessments of the pharmacokinetic/pharmacodynamic (PK/PD) characteristics are an integral part in the development of novel therapeutic agents. Compared with traditional small molecule drugs, therapeutic proteins possess many distinct PK/PD features that necessitate the application of modified or separate approaches for assessing their PK/PD relationships. In this review, the authors discuss tools that are utilized to describe and predict the PK/PD features of therapeutic proteins and that are valuable additions in the armamentarium of drug development approaches to facilitate and accelerate their successful preclinical and clinical development. A variety of state-of-the-art PK/PD tools is currently being applied and has been adjusted to support the development of proteins as therapeutics, including allometric scaling approaches, target-mediated disposition models, first-in-man dose calculations, physiologically based PK models and empirical and semi-mechanistic PK/PD modeling. With the advent of the next generation of biologics including bioengineered antibody constructs being developed, these tools will need to be further refined and adapted to ensure their applicability and successful facilitation of the drug development process for these novel scaffolds.

Development of Innovative Tools and Models for Vocational Education and Training in Central and Western Romania

Directory of Open Access Journals (Sweden)

Liviu Moldovan

2009-12-01

Full Text Available This paper presents an initiative developed by two partner universities from Romania and Norway entitled „Innovative Tools and models for Vocational Education and Training in Central and western Romania” (MoVE-IT financed by SEE mechanism [1]. It has the priority to develop human resource through promotion of education and training, by means of distance learning. The objective, legal issues, outcome and results and envisaged impact of the project are presented.

A design tool for predicting the capillary transport characteristics of fuel cell diffusion media using an artificial neural network

Science.gov (United States)

Kumbur, E. C.; Sharp, K. V.; Mench, M. M.

Developing a robust, intelligent design tool for multivariate optimization of multi-phase transport in fuel cell diffusion media (DM) is of utmost importance to develop advanced DM materials. This study explores the development of a DM design algorithm based on artificial neural network (ANN) that can be used as a powerful tool for predicting the capillary transport characteristics of fuel cell DM. Direct measurements of drainage capillary pressure-saturation curves of the differently engineered DMs (5, 10 and 20 wt.% PTFE) were performed at room temperature under three compressions (0, 0.6 and 1.4 MPa) [E.C. Kumbur, K.V. Sharp, M.M. Mench, J. Electrochem. Soc. 154(12) (2007) B1295-B1304; E.C. Kumbur, K.V. Sharp, M.M. Mench, J. Electrochem. Soc. 154(12) (2007) B1305-B1314; E.C. Kumbur, K.V. Sharp, M.M. Mench, J. Electrochem. Soc. 154(12) (2007) B1315-B1324]. The generated benchmark data were utilized to systematically train a three-layered ANN framework that processes the feed-forward error back propagation methodology. The designed ANN successfully predicts the measured capillary pressures within an average uncertainty of ±5.1% of the measured data, confirming that the present ANN model can be used as a design tool within the range of tested parameters. The ANN simulations reveal that tailoring the DM with high PTFE loading and applying high compression pressure lead to a higher capillary pressure, therefore promoting the liquid water transport within the pores of the DM. Any increase in hydrophobicity of the DM is found to amplify the compression effect, thus yielding a higher capillary pressure for the same saturation level and compression.

KT Training: Introduction to knowledge transfer tools | 7 October

CERN Multimedia

2016-01-01

Target population: All CERN staff and fellows Prerequisites: None Objectives: Get an overview of different forms of knowledge transfer Learn about available tools to: • Facilitate knowledge and technology transfer • Securing ownership and recognition for knowledge and technology Understand what services and support are available to the CERN community from the KT group Content: Why CERN engages in knowledge and technology transfer Modes of knowledge transfer and the general workflow of a knowledge transfer project Introduction to intellectual property with a focus on patents Overview of contracts for knowledge transfer and the basic structure and content of a typical contract Entrepreneurship and available support for starting a company Examples of knowledge transfer projects at CERN For more information, see the Training catalogue.

An applied artificial intelligence approach towards assessing building performance simulation tools

Energy Technology Data Exchange (ETDEWEB)

Yezioro, Abraham [Faculty of Architecture and Town Planning, Technion IIT (Israel); Dong, Bing [Center for Building Performance and Diagnostics, School of Architecture, Carnegie Mellon University (United States); Leite, Fernanda [Department of Civil and Environmental Engineering, Carnegie Mellon University (United States)

2008-07-01

With the development of modern computer technology, a large amount of building energy simulation tools is available in the market. When choosing which simulation tool to use in a project, the user must consider the tool's accuracy and reliability, considering the building information they have at hand, which will serve as input for the tool. This paper presents an approach towards assessing building performance simulation results to actual measurements, using artificial neural networks (ANN) for predicting building energy performance. Training and testing of the ANN were carried out with energy consumption data acquired for 1 week in the case building called the Solar House. The predicted results show a good fitness with the mathematical model with a mean absolute error of 0.9%. Moreover, four building simulation tools were selected in this study in order to compare their results with the ANN predicted energy consumption: Energy{sub 1}0, Green Building Studio web tool, eQuest and EnergyPlus. The results showed that the more detailed simulation tools have the best simulation performance in terms of heating and cooling electricity consumption within 3% of mean absolute error. (author)

Plant training for induced defense against insect pests: a promising tool for integrated pest management in cotton.

Science.gov (United States)

Llandres, Ana L; Almohamad, Raki; Brévault, Thierry; Renou, Alain; Téréta, Idrissa; Jean, Janine; Goebel, François-Regis

2018-04-17

Enhancing cotton pest management using plant natural defenses has been described as a promising way to improve the management of crop pests. We here reviewed different studies on cotton growing systems to illustrate how an ancient technique called plant training, which includes plant topping and pruning, may contribute to this goal. Based on examples from cotton crops, we show how trained plants could be promoted to a state of enhanced defense that causes faster and more robust activation of their defense responses. We revisit agricultural benefits associated to this technique in cotton crops, with a focus on its potential as a supplementary tool for Integrated Pest Management (IPM). Particularly, we examine its role in mediating plant interactions with conspecific neighboring plants, pests and associated natural enemies. We propose a new IPM tool, plant training for induced defense, which involves inducing plant defense by artificial injuries. Experimental evidence from various studies shows that cotton training is a promising technique, particularly for smallholders, which can be used as part of an IPM program to significantly reduce insecticide use and to improve productivity in cotton farming. This article is protected by copyright. All rights reserved.

Training Recollection in Healthy Older Adults: Clear Improvements on the Training Task, but Little Evidence of Transfer

Directory of Open Access Journals (Sweden)

Vess eStamenova

2014-11-01

Full Text Available Normal aging holds negative consequences for memory, in particular for the ability to recollect the precise details of an experience. With this in mind, Jennings and Jacoby (2003 developed a recollection training method using a single-probe recognition memory paradigm in which new items (i.e., foils were repeated during the test phase at increasingly long intervals. In previous reports, this method has appeared to improve older adults’ performance on several non-trained cognitive tasks. We aimed to further examine potential transfer effects of this training paradigm and to determine which cognitive functions might predict training gains. Fifty-one older adults were assigned to either recollection training (n = 30 or an active control condition (n = 21 for six sessions over two weeks. Afterward, the recollection training group showed a greatly enhanced ability to reject the repeated foils. Surprisingly, however, the training and the control groups improved to the same degree as one another in recognition accuracy (d’ on their respective training tasks. Further, despite the recollection group’s significant improvement in rejecting the repeated foils, we observed little evidence of transfer to non-trained tasks (including a temporal source memory test. Age and higher baseline scores on a measure of global cognitive function (as measured by the Montreal Cognitive Assessment tool and working memory (as measured by Digit Span Backward predicted gains made by the recollection training group members.

Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

Science.gov (United States)

Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

2018-02-01

Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

Social Informatics: Natural Tools for Students' Information Training in The Conditions of Embodied and Mental Approaches Being Employed

Directory of Open Access Journals (Sweden)

Daria Barkhatova

2017-09-01

Full Text Available The relevance of the problem under study is due to the society's requirements for the quality information training of a personality which is oriented to forming the solid fundamental knowledge as well as to developing the cognitive capacities that are needed for solving mental tasks. With regard to this, the paper is aimed at finding out the opportunities of applying the natural tools in information training of students from the standpoints of embodied and mental approaches. The main idea of these is integrated studying of an object, beginning with learning it in an "embodied" way and finishing with abstract models formed in the human memory. The leading approach to the research is the integrated one taking into account the psychological and pedagogical, didactic and methodological constituents. It allows identifying the psychological and pedagogical conditions of application of natural tools as well as the possible ways of their use. The authors describe models of natural tools of computer science training in individual sections of the school course as the main results. The materials of the paper are of practical value in methods of teaching computer science to students at various stages of education.

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

Science.gov (United States)

Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Development and Assessment of a New 3D Neuroanatomy Teaching Tool for MRI Training

Science.gov (United States)

Drapkin, Zachary A.; Lindgren, Kristen A.; Lopez, Michael J.; Stabio, Maureen E.

2015-01-01

A computerized three-dimensional (3D) neuroanatomy teaching tool was developed for training medical students to identify subcortical structures on a magnetic resonance imaging (MRI) series of the human brain. This program allows the user to transition rapidly between two-dimensional (2D) MRI slices, 3D object composites, and a combined model in…

In-training factors predictive of choosing and sustaining a productive academic career path in neurological surgery.

Science.gov (United States)

Crowley, R Webster; Asthagiri, Ashok R; Starke, Robert M; Zusman, Edie E; Chiocca, E Antonio; Lonser, Russell R

2012-04-01

Factors during neurosurgical residency that are predictive of an academic career path and promotion have not been defined. To determine factors associated with selecting and sustaining an academic career in neurosurgery by analyzing in-training factors for all graduates of American College of Graduate Medical Education (ACGME)-accredited programs between 1985 and 1990. Neurological surgery residency graduates (between 1985 and 1990) from ACGME-approved training programs were analyzed to determine factors associated with choosing an academic career path and having academic success. Information was available for 717 of the 720 (99%) neurological surgery resident training graduates (678 male, 39 female). One hundred thirty-eight graduates (19.3%) held full-time academic positions. One hundred seven (14.9%) were professors and 35 (4.9%) were department chairs/chiefs. An academic career path/success was associated with more total (5.1 vs 1.9; P female trainees (2.6 vs 0.9 publications; P career but not predictive of becoming professor or chair/chief (P > .05). Defined in-training factors including number of total publications, number of first-author publications, and program size are predictive of residents choosing and succeeding in an academic career path.

Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins

Directory of Open Access Journals (Sweden)

Mallika V

2010-03-01

Full Text Available Type III Polyketide synthases (PKS are family of proteins considered to have significant role in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins show positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence, being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machine (SVM. Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.

Providing access to risk prediction tools via the HL7 XML-formatted risk web service.

Science.gov (United States)

Chipman, Jonathan; Drohan, Brian; Blackford, Amanda; Parmigiani, Giovanni; Hughes, Kevin; Bosinoff, Phil

2013-07-01

Cancer risk prediction tools provide valuable information to clinicians but remain computationally challenging. Many clinics find that CaGene or HughesRiskApps fit their needs for easy- and ready-to-use software to obtain cancer risks; however, these resources may not fit all clinics' needs. The HughesRiskApps Group and BayesMendel Lab therefore developed a web service, called "Risk Service", which may be integrated into any client software to quickly obtain standardized and up-to-date risk predictions for BayesMendel tools (BRCAPRO, MMRpro, PancPRO, and MelaPRO), the Tyrer-Cuzick IBIS Breast Cancer Risk Evaluation Tool, and the Colorectal Cancer Risk Assessment Tool. Software clients that can convert their local structured data into the HL7 XML-formatted family and clinical patient history (Pedigree model) may integrate with the Risk Service. The Risk Service uses Apache Tomcat and Apache Axis2 technologies to provide an all Java web service. The software client sends HL7 XML information containing anonymized family and clinical history to a Dana-Farber Cancer Institute (DFCI) server, where it is parsed, interpreted, and processed by multiple risk tools. The Risk Service then formats the results into an HL7 style message and returns the risk predictions to the originating software client. Upon consent, users may allow DFCI to maintain the data for future research. The Risk Service implementation is exemplified through HughesRiskApps. The Risk Service broadens the availability of valuable, up-to-date cancer risk tools and allows clinics and researchers to integrate risk prediction tools into their own software interface designed for their needs. Each software package can collect risk data using its own interface, and display the results using its own interface, while using a central, up-to-date risk calculator. This allows users to choose from multiple interfaces while always getting the latest risk calculations. Consenting users contribute their data for future

Using social media as a tool to predict syphilis.

Science.gov (United States)

Young, Sean D; Mercer, Neil; Weiss, Robert E; Torrone, Elizabeth A; Aral, Sevgi O

2018-04-01

Syphilis rates have been rapidly rising in the United States. New technologies, such as social media, might be used to anticipate and prevent the spread of disease. Because social media data collection is easy and inexpensive, integration of social media data into syphilis surveillance may be a cost-effective surveillance strategy, especially in low-resource regions. People are increasingly using social media to discuss health-related issues, such as sexual risk behaviors, allowing social media to be a potential tool for public health and medical research. This study mined Twitter data to assess whether social media could be used to predict syphilis cases in 2013 based on 2012 data. We collected 2012 and 2013 county-level primary and secondary (P&S) and early latent syphilis cases reported to the Center for Disease Control and Prevention, along with >8500 geolocated tweets in the United States that were filtered to include sexual risk-related keywords, including colloquial terms for intercourse. We assessed the relationship between syphilis-related tweets and actual case reports by county, controlling for socioeconomic indicators and prior year syphilis cases. We found a significant positive relationship between tweets and cases of P&S and early latent syphilis. This study shows that social media may be an additional tool to enhance syphilis prediction and surveillance. Copyright © 2017 Elsevier Inc. All rights reserved.

A Simple Evaluation Tool (ET-CET) Indicates Increase of Diagnostic Skills From Small Bowel Capsule Endoscopy Training Courses

OpenAIRE

Albert, J.G.; Humbla, O.; McAlindon, M.E.; Davison, C.; Seitz, U.; Fraser, C.; Hagenm?ller, F.; Noetzel, E.; Spada, C.; Riccioni, M.E.; Barnert, J.; Filmann, N.; Keuchel, M.

2015-01-01

Abstract Small bowel capsule endoscopy (SBCE) has become a first line diagnostic tool. Several training courses with a similar format have been established in Europe; however, data on learning curve and training in SBCE remain sparse. Between 2008 and 2011, different basic SBCE training courses were organized internationally in UK (n?=?2), Italy (n?=?2), Germany (n?=?2), Finland (n?=?1), and nationally in Germany (n?=?10), applying similar 8-hour curricula with 50% lectures and 50% hands-on t...

A simple teaching tool for training the pelvic organ prolapse quantification system.

Science.gov (United States)

Geiss, Ingrid M; Riss, Paul A; Hanzal, Engelbert; Dungl, Andrea

2007-09-01

The pelvic organ prolapse quantification (POPQ) system is currently the most common and specific system describing different prolapse stages. Nevertheless, its use is not yet accepted worldwide in routine care. Our aim was to develop a simple teaching tool for the POPQ system capable of simulating different stages of uterovaginal prolapse for use in medical education with hands on training. We constructed a moveable and flexible tool with an inverted Santa Claus' cap, which simulated the vaginal cuff and the tassel at the end representing the cervix. A wooden embroidery frame fixed the cap and served as the hymen, the reference point for all measurements. Inside the cap, we sewed buttons to define the anatomic landmark points Aa and Ap located 3 cm distal from the frame. After explaining the device to the students, we used the three-by-three grid for recording the quantitative description of the pelvic organ support. First, each student had to demonstrate a specific prolapse with his cap device. Then, a prolapse was simulated on the cap, and the student had to take the relevant measurements and record them in the POPQ grid. The main training effect to understand the POPQ system seems to be the possibility for each trainee to simulate a three-dimensional prolapse with this flexible vagina model.

Which screening tools can predict injury to the lower extremities in team sports?: a systematic review.

Science.gov (United States)

Dallinga, Joan M; Benjaminse, Anne; Lemmink, Koen A P M

2012-09-01

Injuries to lower extremities are common in team sports such as soccer, basketball, volleyball, football and field hockey. Considering personal grief, disabling consequences and high costs caused by injuries to lower extremities, the importance for the prevention of these injuries is evident. From this point of view it is important to know which screening tools can identify athletes who are at risk of injury to their lower extremities. The aim of this article is to determine the predictive values of anthropometric and/or physical screening tests for injuries to the leg, anterior cruciate ligament (ACL), knee, hamstring, groin and ankle in team sports. A systematic review was conducted in MEDLINE (1966 to September 2011), EMBASE (1989 to September 2011) and CINAHL (1982 to September 2011). Based on inclusion criteria defined a priori, titles, abstracts and full texts were analysed to find relevant studies. The analysis showed that different screening tools can be predictive for injuries to the knee, ACL, hamstring, groin and ankle. For injuries in general there is some support in the literature to suggest that general joint laxity is a predictive measure for leg injuries. The anterior right/left reach distance >4 cm and the composite reach distance injuries. Furthermore, an increasing age, a lower hamstring/quadriceps (H : Q) ratio and a decreased range of motion (ROM) of hip abduction may predict the occurrence of leg injuries. Hyperextension of the knee, side-to-side differences in anterior-posterior knee laxity and differences in knee abduction moment between both legs are suggested to be predictive tests for sustaining an ACL injury and height was a predictive screening tool for knee ligament injuries. There is some evidence that when age increases, the probability of sustaining a hamstring injury increases. Debate exists in the analysed literature regarding measurement of the flexibility of the hamstring as a predictive screening tool, as well as using the H

Control room design and human factors using a virtual reality based tool for design, test and training

International Nuclear Information System (INIS)

Lirvall, Peter

1998-02-01

This report describes a user-centred approach to control room design adopted by IFE for the nuclear industry. The novelty of this approach is the development of a Control Room Philosophy, and the use of Virtual Reality (VR) technology as a tool in the design process, integrated with a specially developed Design Documentation System (DDS) and a process display prototyping tool PICASSO-3. The control room philosophy identifies all functional aspects of a control centre, to define the baseline principles and guidelines for the design. The use of VR technology enables end-users of the control room design (e.g. control room operators) to specify their preferred design of the new control room, and to replace the need for a physical mock-up to test and evaluate the proposed design. The DDS, integrated with the VR design tool, guides the control room operators, through a structured approach, to document the proposed design in a complete design specification. The VR tool, specially developed by IFE, is called the VR based Design, Test and Training tool (VR DTandT). It is not only intended to visualise the design, but also to test and evaluate the design. When the design is implemented, the same model is re-used as a VR based training simulator for operators. A special feature in the VR DTandT tool is that the verification and validation (VandV) tests, concerning human factors, are according to the regulative standards for nuclear control rooms

A prediction tool for real-time application in the disruption protection system at JET

International Nuclear Information System (INIS)

Cannas, B.; Fanni, A.; Sonato, P.; Zedda, M.K.

2007-01-01

A disruption prediction system, based on neural networks, is presented in this paper. The system is ideally suitable for on-line application in the disruption avoidance and/or mitigation scheme at the JET tokamak. A multi-layer perceptron (MLP) predictor module has been trained on nine plasma diagnostic signals extracted from 86 disruptive pulses, selected from four years of JET experiments in the pulse range 47830-57346 (from 1999 to 2002). The disruption class of the disruptive pulses is available. In particular, the selected pulses belong to four classes (density limit/high radiated power, internal transport barrier, mode lock and h-mode/l-mode). A self-organizing map has been used to select the samples of the pulses to train the MLP predictor module and to determine its target, increasing the prediction capability of the system. The prediction performance has been tested over 86 disruptive and 102 non-disruptive pulses. The test has been performed presenting to the network all the samples of each pulse sampled every 20 ms. The missed alarm rate and the false alarm rate of the predictor, up to 100 ms prior to the disruption time, are 23% and 1%, respectively. Recent plasma configurations might present features different from those observed in the experiments used in the training set. This 'novelty' can lead to incorrect behaviour of the predictor. To improve the robustness and reliability of the system, a novelty detection module has been integrated in the prediction system, increasing the system performance and resulting in a missed alarm rate reduced to 7% and a false alarm rate reduced to 0%

[Multicenter validation of an evaluation tool for clinical training activities (SVAT) of the nursing students].

Science.gov (United States)

Finotto, Sergio; Gradellini, Cinzia; Bandini, Stefania; Burrai, Francesco; Lucchi Casadei, Sandra; Villani, Carolina; Vincenzi, Simone; Mecugni, Daniela

2017-01-01

To evaluate the psychometric characteristics of the Scheda di Valutazione delle Attività di Tirocinio (SVAT). The degree courses in Nursing of the University of Modena and Reggio Emilia, site of Reggio Emilia, the University of Bologna Formative Section BO1, Imola and training center of Cesena, the University of Ferrara training centers of Ferrara and Codigoro were all enrolled in the research. For the content validation the reactive Delphi method was chosen. The panel of experts expressed a qualitative-intuitive judgment on the adequacy of language and on the stimulus material (SVAT). For internal consistency Cronbach's alpha was calculated the. The test-retest method was used for the reliability of stability. all indicators of the SVAT have achieved a degree of consensus not less than 80% demonstrating its content validity. The face validity is demonstrated by an average score equal to or greater than 7 obtained by all indicators. The reliability of internal consistency of the SVAT was appraised by Cronbach's alpha that was 0.987 for the entire instrument. The reliability of the stability has been calculated through the correlation's coefficient expressed by Pearson's r that was 0.983 (p = 1.3E-198). in Italy there is no a "gold standard" tool to evaluate the clinical performance of nursing students during and at the end of their clinical training. The SVAT proves to be a valuable and reliable tool it furthermore could stimulate the discussion and the debate among educators and nurses, so that also in our country, it may be possible develop and refine tools that support the evaluation of clinical skills of nursing students.

Early Antenatal Prediction of Gestational Diabetes in Obese Women: Development of Prediction Tools for Targeted Intervention.

Directory of Open Access Journals (Sweden)

Sara L White

Full Text Available All obese women are categorised as being of equally high risk of gestational diabetes (GDM whereas the majority do not develop the disorder. Lifestyle and pharmacological interventions in unselected obese pregnant women have been unsuccessful in preventing GDM. Our aim was to develop a prediction tool for early identification of obese women at high risk of GDM to facilitate targeted interventions in those most likely to benefit. Clinical and anthropometric data and non-fasting blood samples were obtained at 15+0-18+6 weeks' gestation in 1303 obese pregnant women from UPBEAT, a randomised controlled trial of a behavioural intervention. Twenty one candidate biomarkers associated with insulin resistance, and a targeted nuclear magnetic resonance (NMR metabolome were measured. Prediction models were constructed using stepwise logistic regression. Twenty six percent of women (n = 337 developed GDM (International Association of Diabetes and Pregnancy Study Groups criteria. A model based on clinical and anthropometric variables (age, previous GDM, family history of type 2 diabetes, systolic blood pressure, sum of skinfold thicknesses, waist:height and neck:thigh ratios provided an area under the curve of 0.71 (95%CI 0.68-0.74. This increased to 0.77 (95%CI 0.73-0.80 with addition of candidate biomarkers (random glucose, haemoglobin A1c (HbA1c, fructosamine, adiponectin, sex hormone binding globulin, triglycerides, but was not improved by addition of NMR metabolites (0.77; 95%CI 0.74-0.81. Clinically translatable models for GDM prediction including readily measurable variables e.g. mid-arm circumference, age, systolic blood pressure, HbA1c and adiponectin are described. Using a ≥35% risk threshold, all models identified a group of high risk obese women of whom approximately 50% (positive predictive value later developed GDM, with a negative predictive value of 80%. Tools for early pregnancy identification of obese women at risk of GDM are described

A study of unstable slopes in permafrost areas : Alaskan case studies used as a training tool.

Science.gov (United States)

2011-12-01

This report is the companion to the PowerPoint presentation for the project A Study of Unstable Slopes in Permafrost: Alaskan Case Studies Used as a Training Tool. The objectives of this study are 1) to provide a comprehensive review of literat...

Customised 3D Printing: An Innovative Training Tool for the Next Generation of Orbital Surgeons.

Science.gov (United States)

Scawn, Richard L; Foster, Alex; Lee, Bradford W; Kikkawa, Don O; Korn, Bobby S

2015-01-01

Additive manufacturing or 3D printing is the process by which three dimensional data fields are translated into real-life physical representations. 3D printers create physical printouts using heated plastics in a layered fashion resulting in a three-dimensional object. We present a technique for creating customised, inexpensive 3D orbit models for use in orbital surgical training using 3D printing technology. These models allow trainee surgeons to perform 'wet-lab' orbital decompressions and simulate upcoming surgeries on orbital models that replicate a patient's bony anatomy. We believe this represents an innovative training tool for the next generation of orbital surgeons.

Online Lexicological Tools in ESP – Towards an Approach to Strategy Training

Directory of Open Access Journals (Sweden)

Jaroslaw Krajka

2015-11-01

Full Text Available Together with great proliferation of online resources on the one hand and a striking lack of commercially published materials for specific ESP domains on the other, the ESP teacher needs to reflect on using Internet materials judiciously in the language classroom. An indispensable element of the teaching process in any context is effective resourcing, or the ability to find, evaluate and use reference tools of various kinds. This strategy is also necessary in the ESP context, and the range of available resources goes beyond dictionaries only and encompasses, among others, specialized dictionaries, glossaries, terminology databanks. The purpose of the present paper is to outline the procedure of online resourcing, by giving specific steps for the training of the skill based on ESP materials of various types. The theoretical discussion of strategy training will be substantiated with the practical activities for the procedure.

Predicting SPE Fluxes: Coupled Simulations and Analysis Tools

Science.gov (United States)

Gorby, M.; Schwadron, N.; Linker, J.; Caplan, R. M.; Wijaya, J.; Downs, C.; Lionello, R.

2017-12-01

Presented here is a nuts-and-bolts look at the coupled framework of Predictive Science Inc's Magnetohydrodynamics Around a Sphere (MAS) code and the Energetic Particle Radiation Environment Module (EPREM). MAS simulated coronal mass ejection output from a variety of events can be selected as the MHD input to EPREM and a variety of parameters can be set to run against: bakground seed particle spectra, mean free path, perpendicular diffusion efficiency, etc.. A standard set of visualizations are produced as well as a library of analysis tools for deeper inquiries. All steps will be covered end-to-end as well as the framework's user interface and availability.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

Science.gov (United States)

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

Science.gov (United States)

Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

2010-11-01

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

An Evaluation of Growth Models as Predictive Tools for Estimates at Completion (EAC)

National Research Council Canada - National Science Library

Trahan, Elizabeth N

2009-01-01

...) as the Estimates at Completion (EAC). Our research evaluates the prospect of nonlinear growth modeling as an alternative to the current predictive tools used for calculating EAC, such as the Cost Performance Index (CPI...

Prediction of the wear and evolution of cutting tools in a carbide / titanium-aluminum-vanadium machining tribosystem by volumetric tool wear characterization and modeling

Science.gov (United States)

Kuttolamadom, Mathew Abraham

The objective of this research work is to create a comprehensive microstructural wear mechanism-based predictive model of tool wear in the tungsten carbide / Ti-6Al-4V machining tribosystem, and to develop a new topology characterization method for worn cutting tools in order to validate the model predictions. This is accomplished by blending first principle wear mechanism models using a weighting scheme derived from scanning electron microscopy (SEM) imaging and energy dispersive x-ray spectroscopy (EDS) analysis of tools worn under different operational conditions. In addition, the topology of worn tools is characterized through scanning by white light interferometry (WLI), and then application of an algorithm to stitch and solidify data sets to calculate the volume of the tool worn away. The methodology was to first combine and weight dominant microstructural wear mechanism models, to be able to effectively predict the tool volume worn away. Then, by developing a new metrology method for accurately quantifying the bulk-3D wear, the model-predicted wear was validated against worn tool volumes obtained from corresponding machining experiments. On analyzing worn crater faces using SEM/EDS, adhesion was found dominant at lower surface speeds, while dissolution wear dominated with increasing speeds -- this is in conformance with the lower relative surface speed requirement for micro welds to form and rupture, essentially defining the mechanical load limit of the tool material. It also conforms to the known dominance of high temperature-controlled wear mechanisms with increasing surface speed, which is known to exponentially increase temperatures especially when machining Ti-6Al-4V due to its low thermal conductivity. Thus, straight tungsten carbide wear when machining Ti-6Al-4V is mechanically-driven at low surface speeds and thermally-driven at high surface speeds. Further, at high surface speeds, craters were formed due to carbon diffusing to the tool surface and

Shape: automatic conformation prediction of carbohydrates using a genetic algorithm

Directory of Open Access Journals (Sweden)

Rosen Jimmy

2009-09-01

Full Text Available Abstract Background Detailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. Shape has been developed to improve the availability of modelling in this field. Results The Shape software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction. Conclusion The Shape fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses.

 Running speed during training and percent body fat predict race time in recreational male marathoners

Directory of Open Access Journals (Sweden)

Barandun U

2012-07-01

Full Text Available  Background: Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners.Methods: Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times.Results: After multivariate regression, running speed of the training units (β=-0.52, P<0.0001 and percent body fat (β=0.27, P <0.0001 were the two variables most strongly correlated with marathon race times. Marathon race time for recreational male runners may be estimated to some extent by using the following equation (r2 = 0.44: race time (minutes = 326.3 + 2.394 × (percent body fat, % – 12.06 × (speed in training, km/hours. Running speed during training sessions correlated with prerace percent body fat (r=0.33, P=0.0002. The model including anthropometric and training variables explained 44% of the variance of marathon race times, whereas running speed during training sessions alone explained 40%. Thus, training speed was more predictive of marathon performance times than anthropometric characteristics.Conclusion: The present results suggest that low body fat and running speed during training close to race pace (about 11 km/hour are two key factors for a fast marathon race time in recreational male marathoner runners.Keywords: body fat, skinfold thickness, anthropometry, endurance, athlete

Web-Enabled Training-Development Tool for Pre-Deployment and Deployed Training

National Research Council Canada - National Science Library

Cianciolo, Anna T

2006-01-01

...) researched the Army training process, identified methods for relieving the constraints on rapid, contextualized training development, and developed these methods into a prototype TA capability for feasibility analysis...

Can we predict Acute Medical readmissions using the BOOST tool? A retrospective case note review.

Science.gov (United States)

Lee, Geraldine A; Freedman, Daniel; Beddoes, Penelope; Lyness, Emily; Nixon, Imogen; Srivastava, Vivek

2016-01-01

Readmissions within 30-days of hospital discharge are a problem. The aim was to determine if the Better Outcomes for Older Adults through Safe Transitions (BOOST) risk assessment tool was applicable within the UK. Patients over 65 readmitted were identified retrospectively via a casenote review. BOOST assessment was applied with 1 point for each risk factor. 324 patients were readmitted (mean age 77 years) with a median of 7 days between discharge and readmission. The median BOOST score was 3 (IQR 2-4) with polypharmacy evident in 88% and prior hospitalisation in 70%. The tool correctly predicted 90% of readmissions using two or more risk factors and 99.1% if one risk factor was included. The BOOST assessment tool appears appropriate in predicting readmissions however further analysis is required to determine its precision.

The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.

Science.gov (United States)

Wong, Hoong-Seam; Subramaniam, Shridevi; Alias, Zarifah; Taib, Nur Aishah; Ho, Gwo-Fuang; Ng, Char-Hong; Yip, Cheng-Har; Verkooijen, Helena M; Hartman, Mikael; Bhoo-Pathy, Nirmala

2015-02-01

Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.

Development of 1RM Prediction Equations for Bench Press in Moderately Trained Men.

Science.gov (United States)

Macht, Jordan W; Abel, Mark G; Mullineaux, David R; Yates, James W

2016-10-01

Macht, JW, Abel, MG, Mullineaux, DR, and Yates, JW. Development of 1RM prediction equations for bench press in moderately trained men. J Strength Cond Res 30(10): 2901-2906, 2016-There are a variety of established 1 repetition maximum (1RM) prediction equations, however, very few prediction equations use anthropometric characteristics exclusively or in part, to estimate 1RM strength. Therefore, the purpose of this study was to develop an original 1RM prediction equation for bench press using anthropometric and performance characteristics in moderately trained male subjects. Sixty male subjects (21.2 ± 2.4 years) completed a 1RM bench press and were randomly assigned a load to complete as many repetitions as possible. In addition, body composition, upper-body anthropometric characteristics, and handgrip strength were assessed. Regression analysis was used to develop a performance-based 1RM prediction equation: 1RM = 1.20 repetition weight + 2.19 repetitions to fatigue - 0.56 biacromial width (cm) + 9.6 (R = 0.99, standard error of estimate [SEE] = 3.5 kg). Regression analysis to develop a nonperformance-based 1RM prediction equation yielded: 1RM (kg) = 0.997 cross-sectional area (CSA) (cm) + 0.401 chest circumference (cm) - 0.385%fat - 0.185 arm length (cm) + 36.7 (R = 0.81, SEE = 13.0 kg). The performance prediction equations developed in this study had high validity coefficients, minimal mean bias, and small limits of agreement. The anthropometric equations had moderately high validity coefficient but larger limits of agreement. The practical applications of this study indicate that the inclusion of anthropometric characteristics and performance variables produce a valid prediction equation for 1RM strength. In addition, the CSA of the arm uses a simple nonperformance method of estimating the lifter's 1RM. This information may be used to predict the starting load for a lifter performing a 1RM prediction protocol or a 1RM testing protocol.

Improving training tools for continuing operator qualification in Spain

International Nuclear Information System (INIS)

Marti, F.; San Antonio, S.

1991-01-01

There are currently nine nuclear power plants in service in Spain; the most recent started commercial operation in 1988. Spanish legislation requires operators to have an academic technical background of at least 3 yr. The turnover rate is <5%, and in recent years, symptom-based emergency procedure has been introduced. These facts have given rise to a situation in which Spanish licensed operators are demanding more in-depth training to avoid a stagnant routine and boredom. In responding to this challenge, Tecnatom has had to significantly update its two simulators for boiling water reactor (BWR) and pressurized water reactor (PSR) plants, to ensure coverage of the emergency procedures and has had to create a tool - the Interactive Graphics Simulator - that allows these problems to be ameliorated. With a view to updating its simulators, Tecnatom initiated in 1985 a project known as advanced simulation models (MAS), which was completed at the end of 1990. The TRACS code is a real-time advanced thermohydraulic code for upgrading Tecnatom's nuclear plant simulators. The interactive graphic simulator, (SGI) is a system that provides a graphic display of the models of a full-scope simulator by means of color monitors. The two new tools used are enabling higher levels of motivation to be achieved among the plant operations personnel, especially with respect to requalification

Simulator program as a form of implementation of electronic teaching tools for self-study of foreign students at the stage of pre-university training

Directory of Open Access Journals (Sweden)

Andriy O. Savel'ev

2015-06-01

Full Text Available Questions of empowerment the organization of classroom and extracurricular self-study of foreign students at the stage of pre-university training through the implementation of electronic teaching tools as a component of computer training facilities in educational process are considered. Classification of modern electronic teaching tools as a component of computer hardware training, developed on the basis of modern information and communication technologies is offered. Version of program-simulator "Introductory course" is offered. The program is created by means of WEB-programming and uses training material of introductory course. Introductory course is one of the most important elements of teaching of scientific style of speech within the language training for the foreign students at the preparatory faculty.

"Best Case/Worst Case": Training Surgeons to Use a Novel Communication Tool for High-Risk Acute Surgical Problems.

Science.gov (United States)

Kruser, Jacqueline M; Taylor, Lauren J; Campbell, Toby C; Zelenski, Amy; Johnson, Sara K; Nabozny, Michael J; Steffens, Nicole M; Tucholka, Jennifer L; Kwekkeboom, Kris L; Schwarze, Margaret L

2017-04-01

Older adults often have surgery in the months preceding death, which can initiate postoperative treatments inconsistent with end-of-life values. "Best Case/Worst Case" (BC/WC) is a communication tool designed to promote goal-concordant care during discussions about high-risk surgery. The objective of this study was to evaluate a structured training program designed to teach surgeons how to use BC/WC. Twenty-five surgeons from one tertiary care hospital completed a two-hour training session followed by individual coaching. We audio-recorded surgeons using BC/WC with standardized patients and 20 hospitalized patients. Hospitalized patients and their families participated in an open-ended interview 30 to 120 days after enrollment. We used a checklist of 11 BC/WC elements to measure tool fidelity and surgeons completed the Practitioner Opinion Survey to measure acceptability of the tool. We used qualitative analysis to evaluate variability in tool content and to characterize patient and family perceptions of the tool. Surgeons completed a median of 10 of 11 BC/WC elements with both standardized and hospitalized patients (range 5-11). We found moderate variability in presentation of treatment options and description of outcomes. Three months after training, 79% of surgeons reported BC/WC is better than their usual approach and 71% endorsed active use of BC/WC in clinical practice. Patients and families found that BC/WC established expectations, provided clarity, and facilitated deliberation. Surgeons can learn to use BC/WC with older patients considering acute high-risk surgical interventions. Surgeons, patients, and family members endorse BC/WC as a strategy to support complex decision making. Copyright © 2017 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

The Analysis of User Behaviour of a Network Management Training Tool using a Neural Network

Directory of Open Access Journals (Sweden)

Helen Donelan

2005-10-01

Full Text Available A novel method for the analysis and interpretation of data that describes the interaction between trainee network managers and a network management training tool is presented. A simulation based approach is currently being used to train network managers, through the use of a simulated network. The motivation is to provide a tool for exposing trainees to a life like situation without disrupting a live network. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the trainee network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture is implemented in order to perform an exploratory data analysis of the interaction data. The neural network employs a novel form of continuous self-organisation to discover key features in the data and thus provide new insights into the learning and teaching strategies employed.

Validity of a simple Internet-based outcome-prediction tool in patients with total hip replacement: a pilot study.

Science.gov (United States)

Stöckli, Cornel; Theiler, Robert; Sidelnikov, Eduard; Balsiger, Maria; Ferrari, Stephen M; Buchzig, Beatus; Uehlinger, Kurt; Riniker, Christoph; Bischoff-Ferrari, Heike A

2014-04-01

We developed a user-friendly Internet-based tool for patients undergoing total hip replacement (THR) due to osteoarthritis to predict their pain and function after surgery. In the first step, the key questions were identified by statistical modelling in a data set of 375 patients undergoing THR. Based on multiple regression, we identified the two most predictive WOMAC questions for pain and the three most predictive WOMAC questions for functional outcome, while controlling for comorbidity, body mass index, age, gender and specific comorbidities relevant to the outcome. In the second step, a pilot study was performed to validate the resulting tool against the full WOMAC questionnaire among 108 patients undergoing THR. The mean difference between observed (WOMAC) and model-predicted value was -1.1 points (95% confidence interval, CI -3.8, 1.5) for pain and -2.5 points (95% CI -5.3, 0.3) for function. The model-predicted value was within 20% of the observed value in 48% of cases for pain and in 57% of cases for function. The tool demonstrated moderate validity, but performed weakly for patients with extreme levels of pain and extreme functional limitations at 3 months post surgery. This may have been partly due to early complications after surgery. However, the outcome-prediction tool may be useful in helping patients to become better informed about the realistic outcome of their THR.

Relevance of balance measurement tools and balance training for fall prevention in older adults

OpenAIRE

Majumi M. Noohu, MPTh; Aparajit B. Dey, MD; Mohammed E. Hussain, PhD

2014-01-01

Approximately one in three older people fall each year owing to gait/balance disorder/weakness, the second leading cause of falls in older adults. This review evaluates the capability of different balance measurement tools to predict falls in the elderly, which are used routinely for assessing balance impairment. Balance measurement tools reviewed are the Timed Up and Go test, Berg Balance Scale, Tinetti Performance Oriented Mobility Assessment, Functional Reach Test, Clinical Test of Sensory...

A computational model that predicts behavioral sensitivity to intracortical microstimulation

Science.gov (United States)

Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J.

2017-02-01

Objective. Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber’s law. Significance. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

Predicting the threshold of pulse-train electrical stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

Science.gov (United States)

Xu, Yifang; Collins, Leslie M

2004-04-01

The incorporation of low levels of noise into an electrical stimulus has been shown to improve auditory thresholds in some human subjects (Zeng et al., 2000). In this paper, thresholds for noise-modulated pulse-train stimuli are predicted utilizing a stochastic neural-behavioral model of ensemble fiber responses to bi-phasic stimuli. The neural refractory effect is described using a Markov model for a noise-free pulse-train stimulus and a closed-form solution for the steady-state neural response is provided. For noise-modulated pulse-train stimuli, a recursive method using the conditional probability is utilized to track the neural responses to each successive pulse. A neural spike count rule has been presented for both threshold and intensity discrimination under the assumption that auditory perception occurs via integration over a relatively long time period (Bruce et al., 1999). An alternative approach originates from the hypothesis of the multilook model (Viemeister and Wakefield, 1991), which argues that auditory perception is based on several shorter time integrations and may suggest an NofM model for prediction of pulse-train threshold. This motivates analyzing the neural response to each individual pulse within a pulse train, which is considered to be the brief look. A logarithmic rule is hypothesized for pulse-train threshold. Predictions from the multilook model are shown to match trends in psychophysical data for noise-free stimuli that are not always matched by the long-time integration rule. Theoretical predictions indicate that threshold decreases as noise variance increases. Theoretical models of the neural response to pulse-train stimuli not only reduce calculational overhead but also facilitate utilization of signal detection theory and are easily extended to multichannel psychophysical tasks.

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.

Science.gov (United States)

Furmanchuk, Al'ona; Saal, James E; Doak, Jeff W; Olson, Gregory B; Choudhary, Alok; Agrawal, Ankit

2018-02-05

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off-stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application (http://info.eecs.northwestern.edu/SeebeckCoefficientPredictor) to assist field scientists in the discovery of novel thermoelectric materials. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Feasibility of an Assessment Tool for Children's Competence to Consent to Predictive Genetic Testing: a Pilot Study.

Science.gov (United States)

Hein, Irma M; Troost, Pieter W; Lindeboom, Robert; Christiaans, Imke; Grisso, Thomas; van Goudoever, Johannes B; Lindauer, Ramón J L

2015-12-01

Knowledge on children's capacities to consent to medical treatment is limited. Also, age limits for asking children's consent vary considerably between countries. Decision-making on predictive genetic testing (PGT) is especially complicated, considering the ongoing ethical debate. In order to examine just age limits for alleged competence to consent in children, we evaluated feasibility of a standardized assessment tool, and investigated cutoff ages for children's competence to consent to PGT. We performed a pilot study, including 17 pediatric outpatients between 6 and 18 years at risk for an autosomal dominantly inherited cardiac disease, eligible for predictive genetic testing. The reference standard for competence was established by experts trained in the relevant criteria for competent decision-making. The MacArthur Competence Assessment Tool for Treatment (MacCAT-T) served as index test. Data analysis included raw agreement between competence classifications, difference in mean ages between children judged competent and judged incompetent, and estimation of cutoff ages for judgments of competence. Twelve (71 %) children were considered competent by the reference standard, and 16 (94 %) by the MacCAT-T, with an overall agreement of 76 %. The expert judgments disagreed in most cases, while the MacCAT-T judgments agreed in 65 %. Mean age of children judged incompetent was 9.3 years and of children judged competent 12.1 years (p = .035). With 90 % sensitivity, children younger than 10.0 years were judged incompetent, with 90 % specificity children older than 11.8 years were judged competent. Feasibility of the MacCAT-T in children is confirmed. Initial findings on age cutoffs are indicative for children between the age of 12 and 18 to be judged competent for involvement in the informed consent process. Future research on appropriate age-limits for children's alleged competence to consent is needed.

A new tool to measure training load in soccer training and match play

DEFF Research Database (Denmark)

Rebelo, A; Brito, J; Seabra, A

2012-01-01

-based methods (TRIMP and Edwards' method). 51 soccer players (age 15.6±0.3 years) answered 2 questions to assess perceived exertion and fatigue (VAS1-TL, and VAS2-TL) after training sessions and official matches. Performance in the Yo-Yo tests, VAS scores and heart rate of training sessions and matches......An accurate evaluation of training load is paramount for the planning and periodization of training. The aim of the present study was to evaluate the relationship between a new method to monitor training load in soccer (Visual Analogic Scale training load; VAS-TL), and two established heart rate...

Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools.

Science.gov (United States)

Scruth, Elizabeth Ann; Page, Karen; Cheng, Eugene; Campbell, Michelle; Worrall-Carter, Linda

2012-01-01

The objective of the study was to provide comprehensive information for the clinical nurse specialist (CNS) on commonly used clinical prediction (risk assessment) tools used to estimate risk of a secondary cardiac or noncardiac event and mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). The evolution and widespread adoption of primary PCI represent major advances in the treatment of acute myocardial infarction, specifically STEMI. The American College of Cardiology and the American Heart Association have recommended early risk stratification for patients presenting with acute coronary syndromes using several clinical risk scores to identify patients' mortality and secondary event risk after PCI. Clinical nurse specialists are integral to any performance improvement strategy. Their knowledge and understandings of clinical prediction tools will be essential in carrying out important assessment, identifying and managing risk in patients who have sustained a STEMI, and enhancing discharge education including counseling on medications and lifestyle changes. Over the past 2 decades, risk scores have been developed from clinical trials to facilitate risk assessment. There are several risk scores that can be used to determine in-hospital and short-term survival. This article critiques the most common tools: the Thrombolytic in Myocardial Infarction risk score, the Global Registry of Acute Coronary Events risk score, and the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications risk score. The importance of incorporating risk screening assessment tools (that are important for clinical prediction models) to guide therapeutic management of patients cannot be underestimated. The ability to forecast secondary risk after a STEMI will assist in determining which patients would require the most aggressive level of treatment and monitoring postintervention including

XBeach-G: a tool for predicting gravel barrier response to extreme storm conditions

Science.gov (United States)

Masselink, Gerd; Poate, Tim; McCall, Robert; Roelvink, Dano; Russell, Paul; Davidson, Mark

2014-05-01

Gravel beaches protect low-lying back-barrier regions from flooding during storm events and their importance to society is widely acknowledged. Unfortunately, breaching and extensive storm damage has occurred at many gravel sites and this is likely to increase as a result of sea-level rise and enhanced storminess due to climate change. Limited scientific guidance is currently available to provide beach managers with operational management tools to predict the response of gravel beaches to storms. The New Understanding and Prediction of Storm Impacts on Gravel beaches (NUPSIG) project aims to improve our understanding of storm impacts on gravel coastal environments and to develop a predictive capability by modelling these impacts. The NUPSIG project uses a 5-pronged approach to address its aim: (1) analyse hydrodynamic data collected during a proto-type laboratory experiment on a gravel beach; (2) collect hydrodynamic field data on a gravel beach under a range of conditions, including storm waves with wave heights up to 3 m; (3) measure swash dynamics and beach response on 10 gravel beaches during extreme wave conditions with wave heights in excess of 3 m; (4) use the data collected under 1-3 to develop and validate a numerical model to model hydrodynamics and morphological response of gravel beaches under storm conditions; and (5) develop a tool for end-users, based on the model formulated under (4), for predicting storm response of gravel beaches and barriers. The aim of this presentation is to present the key results of the NUPSIG project and introduce the end-user tool for predicting storm response on gravel beaches. The model is based on the numerical model XBeach, and different forcing scenarios (wave and tides), barrier configurations (dimensions) and sediment characteristics are easily uploaded for model simulations using a Graphics User Interface (GUI). The model can be used to determine the vulnerability of gravel barriers to storm events, but can also be

Automated tool for virtual screening and pharmacology-based pathway prediction and analysis

Directory of Open Access Journals (Sweden)

Sugandh Kumar

2017-10-01

Full Text Available The virtual screening is an effective tool for the lead identification in drug discovery. However, there are limited numbers of crystal structures available as compared to the number of biological sequences which makes (Structure Based Drug Discovery SBDD a difficult choice. The current tool is an attempt to automate the protein structure modelling and automatic virtual screening followed by pharmacology-based prediction and analysis. Starting from sequence(s, this tool automates protein structure modelling, binding site identification, automated docking, ligand preparation, post docking analysis and identification of hits in the biological pathways that can be modulated by a group of ligands. This automation helps in the characterization of ligands selectivity and action of ligands on a complex biological molecular network as well as on individual receptor. The judicial combination of the ligands binding different receptors can be used to inhibit selective biological pathways in a disease. This tool also allows the user to systemically investigate network-dependent effects of a drug or drug candidate.

Predicting intention to attend and actual attendance at a universal parent-training programme: a comparison of social cognition models.

Science.gov (United States)

Thornton, Sarah; Calam, Rachel

2011-07-01

The predictive validity of the Health Belief Model (HBM) and the Theory of Planned Behaviour (TPB) were examined in relation to 'intention to attend' and 'actual attendance' at a universal parent-training intervention for parents of children with behavioural difficulties. A validation and reliability study was conducted to develop two questionnaires (N = 108 parents of children aged 4-7).These questionnaires were then used to investigate the predictive validity of the two models in relation to 'intention to attend' and 'actual attendance' at a parent-training intervention ( N = 53 parents of children aged 4-7). Both models significantly predicted 'intention to attend a parent-training group'; however, the TPB accounted for more variance in the outcome variable compared to the HBM. Preliminary investigations highlighted that attendees were more likely to intend to attend the groups, have positive attitudes towards the groups, perceive important others as having positive attitudes towards the groups, and report elevated child problem behaviour scores. These findings provide useful information regarding the belief-based factors that affect attendance at universal parent-training groups. Possible interventions aimed at increasing 'intention to attend' and 'actual attendance' at parent-training groups are discussed.

A Discussion of Virtual Reality As a New Tool for Training Healthcare Professionals

OpenAIRE

Caroline Fertleman; Caroline Fertleman; Phoebe Aubugeau-Williams; Carmel Sher; Carmel Sher; Ai-Nee Lim; Sophie Lumley; Sylvie Delacroix; Xueni Pan

2018-01-01

BackgroundVirtual reality technology is an exciting and emerging field with vast applications. Our study sets out the viewpoint that virtual reality software could be a new focus of direction in the development of training tools in medical education. We carried out a panel discussion at the Center for Behavior Change 3rd Annual Conference, prompted by the study, “The Responses of Medical General Practitioners to Unreasonable Patient Demand for Antibiotics––A Study of Medical Ethics Using Imme...

A Discussion of Virtual Reality As a New Tool for Training Healthcare Professionals

OpenAIRE

Fertleman, Caroline; Aubugeau-Williams, Phoebe; Sher, Carmel; Lim, Ai-Nee; Lumley, Sophie; Delacroix, Sylvie; Pan, Xueni

2018-01-01

Background: Virtual reality technology is an exciting and emerging field with vast applications. Our study sets out the viewpoint that virtual reality software could be a new focus of direction in the development of training tools in medical education. We carried out a panel discussion at the Center for Behavior Change 3rd Annual Conference, prompted by the study, “The Responses of Medical General Practitioners to Unreasonable Patient Demand for Antibiotics––A Study of Medical Ethics Using Im...

bTSSfinder: a novel tool for the prediction of promoters in Cyanobacteria andEscherichia coli

KAUST Repository

Shahmuradov, Ilham Ayub

2016-09-29

Motivation: The computational search for promoters in prokaryotes remains an attractive problem in bioinformatics. Despite the attention it has received for many years, the problem has not been addressed satisfactorily. In any bacterial genome, the transcription start site is chosen mostly by the sigma (σ) factor proteins, which control the gene activation. The majority of published bacterial promoter prediction tools target σ70 promoters in Escherichia coli. Moreover, no σ-specific classification of promoters is available for prokaryotes other than for E. coli. Results: Here, we introduce bTSSfinder, a novel tool that predicts putative promoters for five classes of σ factors in Cyanobacteria (σA, σC, σH, σG and σF) and for five classes of sigma factors in E. coli (σ70, σ38, σ32, σ28 and σ24). Comparing to currently available tools, bTSSfinder achieves higher accuracy (MCC=0.86, F1-score=0.93) compared to the next best tool with MCC=0.59, F1-score=0.79) and covers multiple classes of promoters.

A Simple Evaluation Tool (ET-CET) Indicates Increase of Diagnostic Skills From Small Bowel Capsule Endoscopy Training Courses

Science.gov (United States)

Albert, J.G.; Humbla, O.; McAlindon, M.E.; Davison, C.; Seitz, U.; Fraser, C.; Hagenmüller, F.; Noetzel, E.; Spada, C.; Riccioni, M.E.; Barnert, J.; Filmann, N.; Keuchel, M.

2015-01-01

Abstract Small bowel capsule endoscopy (SBCE) has become a first line diagnostic tool. Several training courses with a similar format have been established in Europe; however, data on learning curve and training in SBCE remain sparse. Between 2008 and 2011, different basic SBCE training courses were organized internationally in UK (n = 2), Italy (n = 2), Germany (n = 2), Finland (n = 1), and nationally in Germany (n = 10), applying similar 8-hour curricula with 50% lectures and 50% hands-on training. The Given PillCam System was used in 12 courses, the Olympus EndoCapsule system in 5, respectively. A simple evaluation tool for capsule endoscopy training (ET-CET) was developed using 10 short SBCE videos including relevant lesions and normal or irrelevant findings. For each video, delegates were required to record a diagnosis (achievable total score from 0 to 10) and the clinical relevance (achievable total score 0 to 10). ET-CET was performed at baseline before the course and repeated, with videos in altered order, after the course. Two hundred ninety-four delegates (79.3% physicians, 16.3% nurses, 4.4% others) were included for baseline analysis, 268 completed the final evaluation. Forty percent had no previous experience in SBCE, 33% had performed 10 or less procedures. Median scores for correct diagnosis improved from 4.0 (IQR 3) to 7.0 (IQR 3) during the courses (P endoscopy may be useful before attending an SBCE course. PMID:26512623

Exercise Black Skies 2008: Enhancing Live Training Through Virtual Preparation -- Part Two: An Evaluation of Tools and Techniques

Science.gov (United States)

2009-06-01

visualisation tool. These tools are currently in use at the Surveillance and Control Training Unit (SACTU) in Williamtown, New South Wales, and the School...itself by facilitating the brevity and sharpness of learning points. The playback of video and audio was considered an extremely useful method of...The task assessor’s comments were supported by wall projections and audio replays of relevant mission segments that were controlled by an AAR

In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool

Science.gov (United States)

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functiona...

A new methodology for predictive tool wear

Science.gov (United States)

Kim, Won-Sik

turned with various cutting conditions and the results were compared with the proposed analytical wear models. The crater surfaces after machining have been carefully studied to shed light on the physics behind the crater wear. In addition, the abrasive wear mechanism plays a major role in the development of crater wear. Laser shock processing (LSP) has been applied to locally relieve the deleterious tensile residual stresses on the crater surface of a coated tool, thus to improve the hardness of the coating. This thesis shows that LSP has indeed improve wear resistance of CVD coated alumina tool inserts, which has residual stress due to high processing temperature. LSP utilizes a very short laser pulse with high energy density, which induces high-pressure stress wave propagation. The residual stresses are relieved by incident shock waves on the coating surface. Residual stress levels of LSP CVD alumina-coated carbide insert were evaluated by the X-ray diffractometer. Based on these results, LSP parameters such as number of laser pulses and laser energy density can be controlled to reduce residual stress. Crater wear shows that the wear resistance increase with LSP treated tool inserts. Because the hardness data are used to predict the wear, the improvement in hardness and wear resistance shows that the mechanism of crater wear also involves abrasive wear.

Longitudinal Evaluation of Johns Hopkins Fall Risk Assessment Tool and Nurses' Experience.

Science.gov (United States)

Hur, Eun Young; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

The Johns Hopkins Fall Risk Assessment Tool (JHFRAT) is relatively new in Korea, and it has not been fully evaluated. This study revealed that the JHFRAT had good predictive validity throughout the hospitalization period. However, 2 items (fall history and elimination patterns) on the tool were not determinants of falls in this population. Interestingly, the nurses indicated those 2 items were the most difficult items to assess and needed further training to develop the assessment skills.

An Engineering Tool for the Prediction of Internal Dielectric Charging

Science.gov (United States)

Rodgers, D. J.; Ryden, K. A.; Wrenn, G. L.; Latham, P. M.; Sorensen, J.; Levy, L.

1998-11-01

A practical internal charging tool has been developed. It provides an easy-to-use means for satellite engineers to predict whether on-board dielectrics are vulnerable to electrostatic discharge in the outer radiation belt. The tool is designed to simulate irradiation of single-dielectric planar or cylindrical structures with or without shielding. Analytical equations are used to describe current deposition in the dielectric. This is fast and gives charging currents to sufficient accuracy given the uncertainties in other aspects of the problem - particularly material characteristics. Time-dependent internal electric fields are calculated, taking into account the effect on conductivity of electric field, dose rate and temperature. A worst-case model of electron fluxes in the outer belt has been created specifically for the internal charging problem and is built into the code. For output, the tool gives a YES or NO decision on the susceptibility of the structure to internal electrostatic breakdown and if necessary, calculates the required changes to bring the system below the breakdown threshold. A complementary programme of laboratory irradiations has been carried out to validate the tool. The results for Epoxy-fibreglass samples show that the code models electric field realistically for a wide variety of shields, dielectric thicknesses and electron spectra. Results for Teflon samples indicate that some further experimentation is required and the radiation-induced conductivity aspects of the code have not been validated.

Running speed during training and percent body fat predict race time in recreational male marathoners

OpenAIRE

Knechtle, Beat; Barandun,; Knechtle,Patrizia; Klipstein,; Rüst,Christoph Alexander; Rosemann,Thomas; Lepers,Romuald

2012-01-01

 Background: Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners.Methods: Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times.Results...

Sensory Integration Training Tool Design for Children with Autism Spectrum Disorder

Directory of Open Access Journals (Sweden)

Jiang Lijun

2017-01-01

Full Text Available This study aims to design a training tool for therapy of children with autism spectrum disorder (ASDs. Typically, ASDs pass through obstacle track several times with sandbags, which should be picked up from starting point and threw into a box at the end during sensory integration therapy. Counting the sandbags can help ASDs to have concept about the progress of mission. We redesign the counting box named “Skybox” which can help counting by playing sound after detect something throw in it. Aims to probe into the sound preference of two main subjects, an experiment with four kinds of sounds is conducted in this paper by using the method of paired comparisons. The result shows they like animals most, followed by human voice and nature sounds, and music instrument is the last. The material preference experiment shows two subjects like acrylic most, wood and paper are secondary while furry is the last. Skybox shortens their training time for 23.53%, 29.87% and 37.37% in three different projects. We consider that Skybox attracts ASDs therefore reduces their distraction and improves their performance in the usability test.

THE METHODICAL ASPECTS OF MAXIMA USING AS A TOOL FOR FUNDAMENTAL TRAINING OF BACHELORS OF COMPUTER SCIENCE

Directory of Open Access Journals (Sweden)

M. Shyshkina

2014-07-01

Full Text Available Within the formation of the information society, where the pace of scientific progress is rapidly growing, it is difficult to provide the training for immediate inclusion of the person into the production chain at a workplace or in an educational system. There is the way out and it is fundamentalization of informatics education. It is necessary to train the specialist so that he (she could be able to be adapted quickly to the changes occurring in the industry technological development; to give him knowledge, universal in nature, so as the expert may navigate quickly to resolve the professional tasks on this basis. The article describes the trends of systems of computer mathematics (SCM pedagogical use for teaching computer science disciplines. The general characteristics and conditions for effective use of the Maxima as a tool for fundamentalization of the bachelors learning process are outlined. The ways of informatics disciplines teaching methodology are revealed. The peculiarities of cloud based learning solutions are considered. The purpose of the article is the analysis of contemporary approaches to the use of systems of computer mathematics as a tool for fundamentalization of informatics disciplines training courses and identify methodological aspects of these systems application for the teaching of operations research as by the example of SCM Maxima. The object of investigation is the learning process of informatics bachelors with the use of SCM. The subject of investigation is the peculiarities of using the SCM Maxima as a learning tool for informatics courses support

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

International Nuclear Information System (INIS)

Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine; Minca, Eugenia; Filip, Florin

2009-01-01

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

"Score the Core" Web-based pathologist training tool improves the accuracy of breast cancer IHC4 scoring.

Science.gov (United States)

Engelberg, Jesse A; Retallack, Hanna; Balassanian, Ronald; Dowsett, Mitchell; Zabaglo, Lila; Ram, Arishneel A; Apple, Sophia K; Bishop, John W; Borowsky, Alexander D; Carpenter, Philip M; Chen, Yunn-Yi; Datnow, Brian; Elson, Sarah; Hasteh, Farnaz; Lin, Fritz; Moatamed, Neda A; Zhang, Yanhong; Cardiff, Robert D

2015-11-01

Hormone receptor status is an integral component of decision-making in breast cancer management. IHC4 score is an algorithm that combines hormone receptor, HER2, and Ki-67 status to provide a semiquantitative prognostic score for breast cancer. High accuracy and low interobserver variance are important to ensure the score is accurately calculated; however, few previous efforts have been made to measure or decrease interobserver variance. We developed a Web-based training tool, called "Score the Core" (STC) using tissue microarrays to train pathologists to visually score estrogen receptor (using the 300-point H score), progesterone receptor (percent positive), and Ki-67 (percent positive). STC used a reference score calculated from a reproducible manual counting method. Pathologists in the Athena Breast Health Network and pathology residents at associated institutions completed the exercise. By using STC, pathologists improved their estrogen receptor H score and progesterone receptor and Ki-67 proportion assessment and demonstrated a good correlation between pathologist and reference scores. In addition, we collected information about pathologist performance that allowed us to compare individual pathologists and measures of agreement. Pathologists' assessment of the proportion of positive cells was closer to the reference than their assessment of the relative intensity of positive cells. Careful training and assessment should be used to ensure the accuracy of breast biomarkers. This is particularly important as breast cancer diagnostics become increasingly quantitative and reproducible. Our training tool is a novel approach for pathologist training that can serve as an important component of ongoing quality assessment and can improve the accuracy of breast cancer prognostic biomarkers. Copyright © 2015 Elsevier Inc. All rights reserved.

A numerical tool for reproducing driver behaviour: experiments and predictive simulations.

Science.gov (United States)

Casucci, M; Marchitto, M; Cacciabue, P C

2010-03-01

This paper presents the simulation tool called SDDRIVE (Simple Simulation of Driver performance), which is the numerical computerised implementation of the theoretical architecture describing Driver-Vehicle-Environment (DVE) interactions, contained in Cacciabue and Carsten [Cacciabue, P.C., Carsten, O. A simple model of driver behaviour to sustain design and safety assessment of automated systems in automotive environments, 2010]. Following a brief description of the basic algorithms that simulate the performance of drivers, the paper presents and discusses a set of experiments carried out in a Virtual Reality full scale simulator for validating the simulation. Then the predictive potentiality of the tool is shown by discussing two case studies of DVE interactions, performed in the presence of different driver attitudes in similar traffic conditions.

Perioperative Respiratory Adverse Events in Pediatric Ambulatory Anesthesia: Development and Validation of a Risk Prediction Tool.

Science.gov (United States)

Subramanyam, Rajeev; Yeramaneni, Samrat; Hossain, Mohamed Monir; Anneken, Amy M; Varughese, Anna M

2016-05-01

Perioperative respiratory adverse events (PRAEs) are the most common cause of serious adverse events in children receiving anesthesia. Our primary aim of this study was to develop and validate a risk prediction tool for the occurrence of PRAE from the onset of anesthesia induction until discharge from the postanesthesia care unit in children younger than 18 years undergoing elective ambulatory anesthesia for surgery and radiology. The incidence of PRAE was studied. We analyzed data from 19,059 patients from our department's quality improvement database. The predictor variables were age, sex, ASA physical status, morbid obesity, preexisting pulmonary disorder, preexisting neurologic disorder, and location of ambulatory anesthesia (surgery or radiology). Composite PRAE was defined as the presence of any 1 of the following events: intraoperative bronchospasm, intraoperative laryngospasm, postoperative apnea, postoperative laryngospasm, postoperative bronchospasm, or postoperative prolonged oxygen requirement. Development and validation of the risk prediction tool for PRAE were performed using a split sampling technique to split the database into 2 independent cohorts based on the year when the patient received ambulatory anesthesia for surgery and radiology using logistic regression. A risk score was developed based on the regression coefficients from the validation tool. The performance of the risk prediction tool was assessed by using tests of discrimination and calibration. The overall incidence of composite PRAE was 2.8%. The derivation cohort included 8904 patients, and the validation cohort included 10,155 patients. The risk of PRAE was 3.9% in the development cohort and 1.8% in the validation cohort. Age ≤ 3 years (versus >3 years), ASA physical status II or III (versus ASA physical status I), morbid obesity, preexisting pulmonary disorder, and surgery (versus radiology) significantly predicted the occurrence of PRAE in a multivariable logistic regression

A tool model for predicting atmospheric kinetics with sensitivity analysis

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

A package( a tool model) for program of predicting atmospheric chemical kinetics with sensitivity analysis is presented. The new direct method of calculating the first order sensitivity coefficients using sparse matrix technology to chemical kinetics is included in the tool model, it is only necessary to triangularize the matrix related to the Jacobian matrix of the model equation. The Gear type procedure is used to integrate amodel equation and its coupled auxiliary sensitivity coefficient equations. The FORTRAN subroutines of the model equation, the sensitivity coefficient equations, and their Jacobian analytical expressions are generated automatically from a chemical mechanism. The kinetic representation for the model equation and its sensitivity coefficient equations, and their Jacobian matrix is presented. Various FORTRAN subroutines in packages, such as SLODE, modified MA28, Gear package, with which the program runs in conjunction are recommended.The photo-oxidation of dimethyl disulfide is used for illustration.

Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics.

Science.gov (United States)

Ernst, Corinna; Hahnen, Eric; Engel, Christoph; Nothnagel, Michael; Weber, Jonas; Schmutzler, Rita K; Hauke, Jan

2018-03-27

The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer. We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches. PolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer. We show that due to low specificities state-of-the-art in silico

Using Precept-Assist® to predict performance on the American Board of Family Medicine In-Training Examination.

Science.gov (United States)

Post, Robert E; Jamena, Gemma P; Gamble, James D

2014-09-01

Precept-Assist® (PA) is a computer-based program developed by the Virtua Family Medicine Residency where residents receive a score on a Likert-type scale from an attending for each precept based on their knowledge base. The purpose of this study was to attempt to validate this program for precepting family medicine residents. This was a validation study. PA and American Board of Family Medicine (ABFM) In-Training Exam (ITE) scores for all residents from a community-based family medicine residency between the years 2002 and 2011 were included (n=216). Pearson correlation coefficients were calculated between PA scores for the second quarter of the academic year (October 1 to December 31) and scores on the ITE. An ROC curve was also created to determine sensitivity and specificity for various PA scores in predicting residents scoring 500 or above on the ITE. The PA mean (SD) score was 5.18 (0.84) and the ITE mean (SD) score was 425.1 (87.6). The Pearson correlation coefficient between PA and ITE scores was 0.55, which is a moderately positive correlation. The AUC of the ROC curve was 0.783 (95% CI 0.704-0.859). A PA score of 5.5 (between the level of a PGY-2 and PGY-3) was 72% sensitive and 77% specific for scoring 500 or above on the ITE with a positive LR of 3.12. There is a significant correlation between PA scores and ABFM In-Training Exam scores. PA is a valid screening tool that can be used as a predictor for future performance in Family Medicine In-Training exams.

Didactic training vs. computer-based self-learning in the prediction of diminutive colon polyp histology by trainees: a randomized controlled study.

Science.gov (United States)

Khan, Taimur; Cinnor, Birtukan; Gupta, Neil; Hosford, Lindsay; Bansal, Ajay; Olyaee, Mojtaba S; Wani, Sachin; Rastogi, Amit

2017-12-01

Background and study aim  Experts can accurately predict diminutive polyp histology, but the ideal method to train nonexperts is not known. The aim of the study was to compare accuracy in diminutive polyp histology characterization using narrow-band imaging (NBI) between participants undergoing classroom didactic training vs. computer-based self-learning. Participants and methods  Trainees at two institutions were randomized to classroom didactic training or computer-based self-learning. In didactic training, experienced endoscopists reviewed a presentation on NBI patterns for adenomatous and hyperplastic polyps and 40 NBI videos, along with interactive discussion. The self-learning group reviewed the same presentation of 40 teaching videos independently, without interactive discussion. A total of 40 testing videos of diminutive polyps under NBI were then evaluated by both groups. Performance characteristics were calculated by comparing predicted and actual histology. Fisher's exact test was used and P  didactic training and 9 self-learning). A larger proportion of polyps were diagnosed with high confidence in the classroom group (66.5 % vs. 50.8 %; P  didactic training for predicting diminutive polyp histology. This approach can help in widespread training and clinical implementation of real-time polyp histology characterization. © Georg Thieme Verlag KG Stuttgart · New York.

Predicted Interval Plots (PIPS): A Graphical Tool for Data Monitoring of Clinical Trials.

Science.gov (United States)

Li, Lingling; Evans, Scott R; Uno, Hajime; Wei, L J

2009-11-01

Group sequential designs are often used in clinical trials to evaluate efficacy and/or futility. Many methods have been developed for different types of endpoints and scenarios. However, few of these methods convey information regarding effect sizes (e.g., treatment differences) and none uses prediction to convey information regarding potential effect size estimates and associated precision, with trial continuation. To address these limitations, Evans et al. (2007) proposed to use prediction and predicted intervals as a flexible and practical tool for quantitative monitoring of clinical trials. In this article, we reaffirm the importance and usefulness of this innovative approach and introduce a graphical summary, predicted interval plots (PIPS), to display the information obtained in the prediction process in a straightforward yet comprehensive manner. We outline the construction of PIPS and apply this method in two examples. The results and the interpretations of the PIPS are discussed.

Congruency in the prediction of pathogenic missense mutations: state-of-the-art web-based tools.

Science.gov (United States)

Castellana, Stefano; Mazza, Tommaso

2013-07-01

A remarkable degree of genetic variation has been found in the protein-encoding regions of DNA through deep sequencing of samples obtained from thousands of subjects from several populations. Approximately half of the 20 000 single nucleotide polymorphisms present, even in normal healthy subjects, are nonsynonymous amino acid substitutions that could potentially affect protein function. The greatest challenges currently facing investigators are data interpretation and the development of strategies to identify the few gene-coding variants that actually cause or confer susceptibility to disease. A confusing array of options is available to address this problem. Unfortunately, the overall accuracy of these tools at ultraconserved positions is low, and predictions generated by current computational tools may mislead researchers involved in downstream experimental and clinical studies. First, we have presented an updated review of these tools and their primary functionalities, focusing on those that are naturally prone to analyze massive variant sets, to infer some interesting similarities among their results. Additionally, we have evaluated the prediction congruency for real whole-exome sequencing data in a proof-of-concept study on some of these web-based tools.

Interdisciplinary Area of Research Offers Tool of Cross-Cultural Understanding: Cross-Cultural Student Seminar for Communication Training on Biomedical Engineering

Directory of Open Access Journals (Sweden)

Shigehiro Hashimoto

2013-12-01

Full Text Available Misunderstanding often occurs in a multidisciplinary field of study, because each field has its own background of thinking. Communication training is important for students, who have a potential to develop the multidisciplinary field of study. Because each nation has its own cultural background, communication in an international seminar is not easy, either. A cross-cultural student seminar has been designed for communication training in the multidisciplinary field of study. Students from a variety of back grounds have joined in the seminar. Both equations and figures are effective tools for communication in the field of science. The seminar works well for communication training in the multidisciplinary field of study of biomedical engineering. An interdisciplinary area of research offers the tool of cross-cultural understanding. The present study refers to author's several experiences: the student internship abroad, the cross-cultural student camp, multi PhD theses, various affiliations, and the creation of the interdisciplinary department.

STUDENT PREDICTION SYSTEM FOR PLACEMENT TRAINING USING FUZZY INFERENCE SYSTEM

Directory of Open Access Journals (Sweden)

Ravi Kumar Rathore

2017-04-01

Full Text Available Proposed student prediction system is most vital approach which may be used to differentiate the student data/information on the basis of the student performance. Managing placement and training records in any larger organization is quite difficult as the student number are high; in such condition differentiation and classification on different categories becomes tedious. Proposed fuzzy inference system will classify the student data with ease and will be helpful to many educational organizations. There are lots of classification algorithms and statistical base technique which may be taken as good assets for classify the student data set in the education field. In this paper, Fuzzy Inference system has been applied to predict student performance which will help to identify performance of the students and also provides an opportunity to improve to performance. For instance, here we will classify the student’s data set for placement and non-placement classes.

Can 3D Gamified Simulations Be Valid Vocational Training Tools for Persons with Intellectual Disability? An Experiment Based on a Real-life Situation.

Science.gov (United States)

von Barnekow, Ariel; Bonet-Codina, Núria; Tost, Dani

2017-03-23

To investigate if 3D gamified simulations can be valid vocational training tools for persons with intellectual disability. A 3D gamified simulation composed by a set of training tasks for cleaning in hostelry was developed in collaboration with professionals of a real hostel and pedagogues of a special needs school. The learning objectives focus on the acquisition of vocabulary skills, work procedures, social abilities and risk prevention. Several accessibility features were developed to make the tasks easy to do from a technological point-of-view. A pilot experiment was conducted to test the pedagogical efficacy of this tool on intellectually disabled workers and students. User scores in the gamified simulation follow a curve of increasing progression. When confronted with reality, they recognized the scenario and tried to reproduce what they had learned in the simulation. Finally, they were interested in the tool, they showed a strong feeling of immersion and engagement, and they reported having fun. On the basis of this experiment we believe that 3D gamified simulations can be efficient tools to train social and professional skills of persons with intellectual disabilities contributing thus to foster their social inclusion through work.

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

Science.gov (United States)

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

2006-06-12

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

Human Splicing Finder: an online bioinformatics tool to predict splicing signals

OpenAIRE

Desmet, Francois-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Beroud, Gwenaelle; Claustres, Mireille; Beroud, Christophe

2009-01-01

International audience; Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effec...

bTSSfinder: a novel tool for the prediction of promoters in Cyanobacteria andEscherichia coli

KAUST Repository

Shahmuradov, Ilham; Mohamad Razali, Rozaimi; Bougouffa, Salim; Radovanovic, Aleksandar; Bajic, Vladimir B.

2016-01-01

Results: Here, we introduce bTSSfinder, a novel tool that predicts putative promoters for five classes of σ factors in Cyanobacteria (σA, σC, σH, σG and σF) and for five classes of sigma factors in E. coli (σ70, σ38, σ32, σ28 and σ24). Comparing to currently available tools, bTSSfinder achieves higher accuracy (MCC=0.86, F1-score=0.93) compared to the next best tool with MCC=0.59, F1-score=0.79) and covers multiple classes of promoters.

Modeling and evaluating of surface roughness prediction in micro-grinding on soda-lime glass considering tool characterization

Science.gov (United States)

Cheng, Jun; Gong, Yadong; Wang, Jinsheng

2013-11-01

The current research of micro-grinding mainly focuses on the optimal processing technology for different materials. However, the material removal mechanism in micro-grinding is the base of achieving high quality processing surface. Therefore, a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography is proposed in this paper. The differences of material removal mechanism between convention grinding process and micro-grinding process are analyzed. Topography characterization has been done on micro-grinding tools which are fabricated by electroplating. Models of grain density generation and grain interval are built, and new predicting model of micro-grinding surface roughness is developed. In order to verify the precision and application effect of the surface roughness prediction model proposed, a micro-grinding orthogonally experiment on soda-lime glass is designed and conducted. A series of micro-machining surfaces which are 78 nm to 0.98 μm roughness of brittle material is achieved. It is found that experimental roughness results and the predicting roughness data have an evident coincidence, and the component variable of describing the size effects in predicting model is calculated to be 1.5×107 by reverse method based on the experimental results. The proposed model builds a set of distribution to consider grains distribution densities in different protrusion heights. Finally, the characterization of micro-grinding tools which are used in the experiment has been done based on the distribution set. It is concluded that there is a significant coincidence between surface prediction data from the proposed model and measurements from experiment results. Therefore, the effectiveness of the model is demonstrated. This paper proposes a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion

Does training novices to criteria and does rapid acquisition of skills on laparoscopic simulators have predictive validity or are we just playing video games?

Science.gov (United States)

Hogle, Nancy J; Widmann, Warren D; Ude, Aku O; Hardy, Mark A; Fowler, Dennis L

2008-01-01

To determine whether LapSim training (version 3.0; Surgical Science Ltd, Göteborg, Sweden) to criteria for novice PGY1 surgical residents had predictive validity for improvement in the performance of laparoscopic cholecystectomy. In all, 21 PGY1 residents performed laparoscopic cholecystectomies in pigs after minimal training; their performance was evaluated by skilled laparoscopic surgeons using the validated tool GOALS (global operative assessment of laparoscopic operative skills: depth perception, bimanual dexterity, efficiency, tissue handling, and overall competence). From the group, 10 residents trained to competency on the LapSim Basic Skills Programs (camera navigation, instrument navigation, coordination, grasping, lifting and grasping, cutting, and clip applying). All 21 PGY1 residents again performed laparoscopic cholecystectomies on pigs; their performance was again evaluated by skilled laparoscopic surgeons using GOALS. Additionally, we studied the rate of learning to determine whether the slow or fast learners on the LapSim performed equivalently when performing actual cholecystectomies in pigs. Finally, 6 categorical residents were tracked, and their clinical performance on all of the laparoscopic cholecystectomies in which they were "surgeon, junior" was prospectively evaluated using the GOALS criteria. We found a statistical improvement of depth perception in the operative performance of cholecystectomies in pigs in the group trained on the LapSim. In the other 4 domains, a trend toward improvement was observed. No correlation between being a fast learner and the ultimate skill was demonstrated in the clinical performance of laparoscopic cholecystectomies. We did find that the fast learners on LapSim all were past or current video game players ("gamers"); however, that background did not translate into better clinical performance. Using current criteria, we doubt that the time and effort spent training novice PGY1 Surgical Residents on the basic

Development and Application of Predictive Tools for MHD Stability Limits in Tokamaks

Energy Technology Data Exchange (ETDEWEB)

Brennan, Dylan [Princeton Univ., NJ (United States); Miller, G. P. [Univ. of Tulsa, Tulsa, AZ (United States)

2016-10-03

This is a project to develop and apply analytic and computational tools to answer physics questions relevant to the onset of non-ideal magnetohydrodynamic (MHD) instabilities in toroidal magnetic confinement plasmas. The focused goal of the research is to develop predictive tools for these instabilities, including an inner layer solution algorithm, a resistive wall with control coils, and energetic particle effects. The production phase compares studies of instabilities in such systems using analytic techniques, PEST- III and NIMROD. Two important physics puzzles are targeted as guiding thrusts for the analyses. The first is to form an accurate description of the physics determining whether the resistive wall mode or a tearing mode will appear first as β is increased at low rotation and low error fields in DIII-D. The second is to understand the physical mechanism behind recent NIMROD results indicating strong damping and stabilization from energetic particle effects on linear resistive modes. The work seeks to develop a highly relevant predictive tool for ITER, advance the theoretical description of this physics in general, and analyze these instabilities in experiments such as ASDEX Upgrade, DIII-D, JET, JT-60U and NTSX. The awardee on this grant is the University of Tulsa. The research efforts are supervised principally by Dr. Brennan. Support is included for two graduate students, and a strong collaboration with Dr. John M. Finn of LANL. The work includes several ongoing collaborations with General Atomics, PPPL, and the NIMROD team, among others.

Development and Application of Predictive Tools for MHD Stability Limits in Tokamaks

International Nuclear Information System (INIS)

Brennan, Dylan; Miller, G. P.

2016-01-01

This is a project to develop and apply analytic and computational tools to answer physics questions relevant to the onset of non-ideal magnetohydrodynamic (MHD) instabilities in toroidal magnetic confinement plasmas. The focused goal of the research is to develop predictive tools for these instabilities, including an inner layer solution algorithm, a resistive wall with control coils, and energetic particle effects. The production phase compares studies of instabilities in such systems using analytic techniques, PEST- III and NIMROD. Two important physics puzzles are targeted as guiding thrusts for the analyses. The first is to form an accurate description of the physics determining whether the resistive wall mode or a tearing mode will appear first as β is increased at low rotation and low error fields in DIII-D. The second is to understand the physical mechanism behind recent NIMROD results indicating strong damping and stabilization from energetic particle effects on linear resistive modes. The work seeks to develop a highly relevant predictive tool for ITER, advance the theoretical description of this physics in general, and analyze these instabilities in experiments such as ASDEX Upgrade, DIII-D, JET, JT-60U and NTSX. The awardee on this grant is the University of Tulsa. The research efforts are supervised principally by Dr. Brennan. Support is included for two graduate students, and a strong collaboration with Dr. John M. Finn of LANL. The work includes several ongoing collaborations with General Atomics, PPPL, and the NIMROD team, among others.

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

Science.gov (United States)

Jones, Stephen; Cournane, Seán; Sheehy, Niall; Hederman, Lucy

2016-12-01

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

Virtual physiological human: training challenges.

Science.gov (United States)

Lawford, Patricia V; Narracott, Andrew V; McCormack, Keith; Bisbal, Jesus; Martin, Carlos; Bijnens, Bart; Brook, Bindi; Zachariou, Margarita; Freixa, Jordi Villà I; Kohl, Peter; Fletcher, Katherine; Diaz-Zuccarini, Vanessa

2010-06-28

The virtual physiological human (VPH) initiative encompasses a wide range of activities, including structural and functional imaging, data mining, knowledge discovery tool and database development, biomedical modelling, simulation and visualization. The VPH community is developing from a multitude of relatively focused, but disparate, research endeavours into an integrated effort to bring together, develop and translate emerging technologies for application, from academia to industry and medicine. This process initially builds on the evolution of multi-disciplinary interactions and abilities, but addressing the challenges associated with the implementation of the VPH will require, in the very near future, a translation of quantitative changes into a new quality of highly trained multi-disciplinary personnel. Current strategies for undergraduate and on-the-job training may soon prove insufficient for this. The European Commission seventh framework VPH network of excellence is exploring this emerging need, and is developing a framework of novel training initiatives to address the predicted shortfall in suitably skilled VPH-aware professionals. This paper reports first steps in the implementation of a coherent VPH training portfolio.

In 'big bang' major incidents do triage tools accurately predict clinical priority?: a systematic review of the literature.

Science.gov (United States)

Kilner, T M; Brace, S J; Cooke, M W; Stallard, N; Bleetman, A; Perkins, G D

2011-05-01

The term "big bang" major incidents is used to describe sudden, usually traumatic,catastrophic events, involving relatively large numbers of injured individuals, where demands on clinical services rapidly outstrip the available resources. Triage tools support the pre-hospital provider to prioritise which patients to treat and/or transport first based upon clinical need. The aim of this review is to identify existing triage tools and to determine the extent to which their reliability and validity have been assessed. A systematic review of the literature was conducted to identify and evaluate published data validating the efficacy of the triage tools. Studies using data from trauma patients that report on the derivation, validation and/or reliability of the specific pre-hospital triage tools were eligible for inclusion.Purely descriptive studies, reviews, exercises or reports (without supporting data) were excluded. The search yielded 1982 papers. After initial scrutiny of title and abstract, 181 papers were deemed potentially applicable and from these 11 were identified as relevant to this review (in first figure). There were two level of evidence one studies, three level of evidence two studies and six level of evidence three studies. The two level of evidence one studies were prospective validations of Clinical Decision Rules (CDR's) in children in South Africa, all the other studies were retrospective CDR derivation, validation or cohort studies. The quality of the papers was rated as good (n=3), fair (n=7), poor (n=1). There is limited evidence for the validity of existing triage tools in big bang major incidents.Where evidence does exist it focuses on sensitivity and specificity in relation to prediction of trauma death or severity of injury based on data from single or small number patient incidents. The Sacco system is unique in combining survivability modelling with the degree by which the system is overwhelmed in the triage decision system. The

Can we predict final outcome of internal medicine residents with in-training evaluation.

Science.gov (United States)

Chierakul, Nitipatana; Pongprasobchai, Supot; Boonyapisit, Kanokwan; Chinthammitr, Yingyong; Pithukpakorn, Manop; Maneesai, Adisak; Srivijitkamol, Apiradee; Koomanachai, Pornpan; Koolvisoot, Ajchara; Tanwandee, Tawesak; Shayakul, Chairat; Kachintorn, Udom

2011-02-01

To assess the predictive value of in-training evaluation for determining future success in the internal medicine board certifying examination. Ninety-seven internal medicine residents from Faculty of Medicine Siriraj Hospital who undertake the Thai Board examination during the academic year 2006-2008 were enrolled. Correlation between the scores during internal medicine rotation and final scores in board examination were then examined. Significant positive linear correlation was found between scores from both written and clinical parts of board certifying examination and scores from the first-year summative written and clinical examinations and also the second-year formative written examination (r = 0.43-0.68, p evaluation by attending staffs was less well correlated (r = 0.29-0.36) and the evaluation by nurses or medical students demonstrated inverse relationship (r = -0.2, p = 0.27 and r = -0.13, p = 0.48). Some methods of in-training evaluation can predict successful outcome of board certifying examination. Multisource assessments cannot well extrapolate some aspects of professional competences and qualities.

Computational tools for genome-wide miRNA prediction and study

KAUST Repository

Malas, T.B.

2012-11-02

MicroRNAs (miRNAs) are single-stranded non-coding RNA susually of 22 nucleotidesin length that play an important post-transcriptional regulation role in many organisms. MicroRNAs bind a seed sequence to the 3-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein level. This regulatory mechanism is central to many biological processes and perturbation could lead to diseases such as cancer. Given the biological importance, of miRNAs, there is a great need to identify and study their targets and functions. However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. Next-generation sequencing coupled with new computational tools has recently evolved to help researchers efficiently identify large numbers of novel miRNAs. In this review, we describe recent miRNA prediction tools and discuss their priorities, advantages and disadvantages. Malas and Ravasi.

Computational tools for genome-wide miRNA prediction and study

KAUST Repository

Malas, T.B.; Ravasi, Timothy

2012-01-01

MicroRNAs (miRNAs) are single-stranded non-coding RNA susually of 22 nucleotidesin length that play an important post-transcriptional regulation role in many organisms. MicroRNAs bind a seed sequence to the 3-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein level. This regulatory mechanism is central to many biological processes and perturbation could lead to diseases such as cancer. Given the biological importance, of miRNAs, there is a great need to identify and study their targets and functions. However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. Next-generation sequencing coupled with new computational tools has recently evolved to help researchers efficiently identify large numbers of novel miRNAs. In this review, we describe recent miRNA prediction tools and discuss their priorities, advantages and disadvantages. Malas and Ravasi.

A Multi-Center Prospective Derivation and Validation of a Clinical Prediction Tool for Severe Clostridium difficile Infection.

LENUS (Irish Health Repository)

Na, Xi

2015-04-23

Prediction of severe clinical outcomes in Clostridium difficile infection (CDI) is important to inform management decisions for optimum patient care. Currently, treatment recommendations for CDI vary based on disease severity but validated methods to predict severe disease are lacking. The aim of the study was to derive and validate a clinical prediction tool for severe outcomes in CDI.

Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing.

Science.gov (United States)

Haarman, Juliet A M; Maartens, Erik; van der Kooij, Herman; Buurke, Jaap H; Reenalda, Jasper; Rietman, Johan S

2017-12-02

During gait training, physical therapists continuously supervise stroke survivors and provide physical support to their pelvis when they judge that the patient is unable to keep his balance. This paper is the first in providing quantitative data about the corrective forces that therapists use during gait training. It is assumed that changes in the acceleration of a patient's COM are a good predictor for therapeutic balance assistance during the training sessions Therefore, this paper provides a method that predicts the timing of therapeutic balance assistance, based on acceleration data of the sacrum. Eight sub-acute stroke survivors and seven therapists were included in this study. Patients were asked to perform straight line walking as well as slalom walking in a conventional training setting. Acceleration of the sacrum was captured by an Inertial Magnetic Measurement Unit. Balance-assisting corrective forces applied by the therapist were collected from two force sensors positioned on both sides of the patient's hips. Measures to characterize the therapeutic balance assistance were the amount of force, duration, impulse and the anatomical plane in which the assistance took place. Based on the acceleration data of the sacrum, an algorithm was developed to predict therapeutic balance assistance. To validate the developed algorithm, the predicted events of balance assistance by the algorithm were compared with the actual provided therapeutic assistance. The algorithm was able to predict the actual therapeutic assistance with a Positive Predictive Value of 87% and a True Positive Rate of 81%. Assistance mainly took place over the medio-lateral axis and corrective forces of about 2% of the patient's body weight (15.9 N (11), median (IQR)) were provided by therapists in this plane. Median duration of balance assistance was 1.1 s (0.6) (median (IQR)) and median impulse was 9.4Ns (8.2) (median (IQR)). Although therapists were specifically instructed to aim for the

International multicenter tool to predict the risk of nonsentinel node metastases in breast cancer

DEFF Research Database (Denmark)

Meretoja, Tuomo J; Leidenius, Marjut H K; Heikkilä, Päivi S

2012-01-01

predicting nonsentinel node involvement were identified in logistic regression analysis. A multivariable predictive model was developed and validated by area under the receiver operating characteristics curve (AUC), first internally in 500 additional patients and then externally in 1068 patients from other...... centers. All statistical tests were two-sided. Results Nine tumor- and sentinel node-specific variables were identified as statistically significant factors predicting nonsentinel node involvement in logistic regression analysis. A resulting predictive model applied to the internal validation series...... resulted in an AUC of 0.714 (95% confidence interval [CI] = 0.665 to 0.763). For the external validation series, the AUC was 0.719 (95% CI = 0.689 to 0.750). The model was well calibrated in the external validation series. Conclusions We present a novel, international, multicenter, predictive tool...

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

Directory of Open Access Journals (Sweden)

Jeng-Fung Chen

2014-10-01

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

Development of Next Generation Multiphase Pipe Flow Prediction Tools

Energy Technology Data Exchange (ETDEWEB)

Tulsa Fluid Flow

2008-08-31

The developments of fields in deep waters (5000 ft and more) is a common occurrence. It is inevitable that production systems will operate under multiphase flow conditions (simultaneous flow of gas-oil-and water possibly along with sand, hydrates, and waxes). Multiphase flow prediction tools are essential for every phase of the hydrocarbon recovery from design to operation. The recovery from deep-waters poses special challenges and requires accurate multiphase flow predictive tools for several applications including the design and diagnostics of the production systems, separation of phases in horizontal wells, and multiphase separation (topside, seabed or bottom-hole). It is very crucial to any multiphase separation technique that is employed either at topside, seabed or bottom-hole to know inlet conditions such as the flow rates, flow patterns, and volume fractions of gas, oil and water coming into the separation devices. The overall objective was to develop a unified model for gas-oil-water three-phase flow in wells, flow lines, and pipelines to predict the flow characteristics such as flow patterns, phase distributions, and pressure gradient encountered during petroleum production at different flow conditions (pipe diameter and inclination, fluid properties and flow rates). The project was conducted in two periods. In Period 1 (four years), gas-oil-water flow in pipes were investigated to understand the fundamental physical mechanisms describing the interaction between the gas-oil-water phases under flowing conditions, and a unified model was developed utilizing a novel modeling approach. A gas-oil-water pipe flow database including field and laboratory data was formed in Period 2 (one year). The database was utilized in model performance demonstration. Period 1 primarily consisted of the development of a unified model and software to predict the gas-oil-water flow, and experimental studies of the gas-oil-water project, including flow behavior description and

Numerical tools to predict the environmental loads for offshore structures under extreme weather conditions

Science.gov (United States)

Wu, Yanling

2018-05-01

In this paper, the extreme waves were generated using the open source computational fluid dynamic (CFD) tools — OpenFOAM and Waves2FOAM — using linear and nonlinear NewWave input. They were used to conduct the numerical simulation of the wave impact process. Numerical tools based on first-order (with and without stretching) and second-order NewWave are investigated. The simulation to predict force loading for the offshore platform under the extreme weather condition is implemented and compared.

Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems.

Science.gov (United States)

DiMaio, Frank

2017-01-01

Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.

 Running speed during training and percent body fat predict race time in recreational male marathoners

OpenAIRE

Barandun U; Knechtle B; Knechtle P; Klipstein A; Rust CA; Rosemann T; Lepers R

2012-01-01

 Background: Recent studies have shown that personal best marathon time is a strong predictor of race time in male ultramarathoners. We aimed to determine variables predictive of marathon race time in recreational male marathoners by using the same characteristics of anthropometry and training as used for ultramarathoners.Methods: Anthropometric and training characteristics of 126 recreational male marathoners were bivariately and multivariately related to marathon race times.Results...

Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance runners.

Science.gov (United States)

Vesterinen, V; Häkkinen, K; Hynynen, E; Mikkola, J; Hokka, L; Nummela, A

2013-03-01

The aim of this study was to investigate whether nocturnal heart rate variability (HRV) can be used to predict changes in endurance performance during 28 weeks of endurance training. The training was divided into 14 weeks of basic training (BTP) and 14 weeks of intensive training periods (ITP). Endurance performance characteristics, nocturnal HRV, and serum hormone concentrations were measured before and after both training periods in 28 recreational endurance runners. During the study peak treadmill running speed (Vpeak ) improved by 7.5 ± 4.5%. No changes were observed in HRV indices after BTP, but after ITP, these indices increased significantly (HFP: 1.9%, P=0.026; TP: 1.7%, P=0.007). Significant correlations were observed between the change of Vpeak and HRV indices (TP: r=0.75, PHRV among recreational endurance runners, it seems that moderate- and high-intensity training are needed. This study showed that recreational endurance runners with a high HRV at baseline improved their endurance running performance after ITP more than runners with low baseline HRV. © 2011 John Wiley & Sons A/S.

Semi-structured interview is a reliable and feasible tool for selection of doctors for general practice specialist training.

Science.gov (United States)

Isaksen, Jesper Hesselbjerg; Hertel, Niels Thomas; Kjær, Niels Kristian

2013-09-01

In order to optimise the selection process for admission to specialist training in family medicine, we developed a new design for structured applications and selection interviews. The design contains semi-structured interviews, which combine individualised elements from the applications with standardised behaviour-based questions. This paper describes the design of the tool, and offers reflections concerning its acceptability, reliability and feasibility. We used a combined quantitative and qualitative evaluation method. Ratings obtained by the applicants in two selection rounds were analysed for reliability and generalisability using the GENOVA programme. Applicants and assessors were randomly selected for individual semi-structured in-depth interviews. The qualitative data were analysed in accordance with the grounded theory method. Quantitative analysis yielded a high Cronbach's alpha of 0.97 for the first round and 0.90 for the second round, and a G coefficient of the first round of 0.74 and of the second round of 0.40. Qualitative analysis demonstrated high acceptability and fairness and it improved the assessors' judgment. Applicants reported concerns about loss of personality and some anxiety. The applicants' ability to reflect on their competences was important. The developed selection tool demonstrated an acceptable level of reliability, but only moderate generalisability. The users found that the tool provided a high degree of acceptability; it is a feasible and useful tool for -selection of doctors for specialist training if combined with work-based assessment. Studies on the benefits and drawbacks of this tool compared with other selection models are relevant. not relevant. not relevant.

Summary of Training Workshop on the Use of NASA tools for Coastal Resource Management in the Gulf of Mexico

Energy Technology Data Exchange (ETDEWEB)

Judd, Chaeli; Judd, Kathleen S.; Gulbransen, Thomas C.; Thom, Ronald M.

2009-03-01

A two-day training workshop was held in Xalapa, Mexico from March 10-11 2009 with the goal of training end users from the southern Gulf of Mexico states of Campeche and Veracruz in the use of tools to support coastal resource management decision-making. The workshop was held at the computer laboratory of the Institute de Ecologia, A.C. (INECOL). This report summarizes the results of that workshop and is a deliverable to our NASA client.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 11: Computer-Aided Manufacturing & Advanced CNC, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

Science.gov (United States)

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Predictive value of the DASH tool for predicting return to work of injured workers with musculoskeletal disorders of the upper extremity.

Science.gov (United States)

Armijo-Olivo, Susan; Woodhouse, Linda J; Steenstra, Ivan A; Gross, Douglas P

2016-12-01

To determine whether the Disabilities of the Arm, Shoulder, and Hand (DASH) tool added to the predictive ability of established prognostic factors, including patient demographic and clinical outcomes, to predict return to work (RTW) in injured workers with musculoskeletal (MSK) disorders of the upper extremity. A retrospective cohort study using a population-based database from the Workers' Compensation Board of Alberta (WCB-Alberta) that focused on claimants with upper extremity injuries was used. Besides the DASH, potential predictors included demographic, occupational, clinical and health usage variables. Outcome was receipt of compensation benefits after 3 months. To identify RTW predictors, a purposeful logistic modelling strategy was used. A series of receiver operating curve analyses were performed to determine which model provided the best discriminative ability. The sample included 3036 claimants with upper extremity injuries. The final model for predicting RTW included the total DASH score in addition to other established predictors. The area under the curve for this model was 0.77, which is interpreted as fair discrimination. This model was statistically significantly different than the model of established predictors alone (pmodels (p=0.34). The DASH tool together with other established predictors significantly helped predict RTW after 3 months in participants with upper extremity MSK disorders. An appealing result for clinicians and busy researchers is that DASH item 23 has equal predictive ability to the total DASH score. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

External validation of approaches to prediction of falls during hospital rehabilitation stays and development of a new simpler tool

Directory of Open Access Journals (Sweden)

Angela Vratsistas-Curto

2017-12-01

Full Text Available Objectives: To test the external validity of 4 approaches to fall prediction in a rehabilitation setting (Predict_FIRST, Ontario Modified STRATIFY (OMS, physiotherapists’ judgement of fall risk (PT_Risk, and falls in the past year (Past_Falls, and to develop and test the validity of a simpler tool for fall prediction in rehabilitation (Predict_CM2. Participants: A total of 300 consecutively-admitted rehabilitation inpatients. Methods: Prospective inception cohort study. Falls during the rehabilitation stay were monitored. Potential predictors were extracted from medical records. Results: Forty-one patients (14% fell during their rehabilitation stay. The external validity, area under the receiver operating characteristic curve (AUC, for predicting future fallers was: 0.71 (95% confidence interval (95% CI: 0.61–0.81 for OMS (Total_Score; 0.66 (95% CI: 0.57–0.74 for Predict_FIRST; 0.65 (95% CI 0.57–0.73 for PT_Risk; and 0.52 for Past_Falls (95% CI: 0.46–0.60. A simple 3-item tool (Predict_CM2 was developed from the most predictive individual items (impaired mobility/transfer ability, impaired cognition, and male sex. The accuracy of Predict_CM2 was 0.73 (95% CI: 0.66–0.81, comparable to OMS (Total_Score (p = 0.52, significantly better than Predict_FIRST (p = 0.04, and Past_Falls (p < 0.001, and approaching significantly better than PT_Risk (p = 0.09. Conclusion: Predict_CM2 is a simpler screening tool with similar accuracy for predicting fallers in rehabilitation to OMS (Total_Score and better accuracy than Predict_FIRST or Past_Falls. External validation of Predict_CM2 is required.

Comparison of various tool wear prediction methods during end milling of metal matrix composite

Science.gov (United States)

Wiciak, Martyna; Twardowski, Paweł; Wojciechowski, Szymon

2018-02-01

In this paper, the problem of tool wear prediction during milling of hard-to-cut metal matrix composite Duralcan™ was presented. The conducted research involved the measurements of acceleration of vibrations during milling with constant cutting conditions, and evaluation of the flank wear. Subsequently, the analysis of vibrations in time and frequency domain, as well as the correlation of the obtained measures with the tool wear values were conducted. The validation of tool wear diagnosis in relation to selected diagnostic measures was carried out with the use of one variable and two variables regression models, as well as with the application of artificial neural networks (ANN). The comparative analysis of the obtained results enable.

Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.).

Science.gov (United States)

Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, Hartwig H; Piepho, Hans-Peter; Gordillo, Andres; Wilde, Peer; Bauer, Eva; Schön, Chris-Carolin

2016-11-01

Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S 2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S 2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS  = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time.

Human Factors in Training: Space Medical Proficiency Training

Science.gov (United States)

Byrne, Vicky E.; Barshi, I.; Arsintescu, L.; Connell, E.

2010-01-01

The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and the International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to the ISS, medical equipment will be located on the ISS, and carried into CEV in the event of an emergency. Flight surgeons (FS) on the ground in Mission Control will be expected to direct the crew medical officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). This is a joint project consisting of human factors team from the Ames Research Center (ARC) with Immanuel Barshi as Principal Investigator and the Johnson Space Center (JSC). Human factors researchers at JSC have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. Work on medical training has been conducted in collaboration with the Medical Training Group at the Johnson Space Center (JSC) and with Wyle Laboratories that provides medical training to crew members, biomedical engineers (BMEs), and to flight surgeons under the Bioastronautics contract. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. A second area of research involves FS performance support tools. Information needed by the FS during the ISS mission

Assessing communication skills in dietetic consultations: the development of the reliable and valid DIET-COMMS tool.

Science.gov (United States)

Whitehead, K A; Langley-Evans, S C; Tischler, V A; Swift, J A

2014-04-01

There is an increasing emphasis on the development of communication skills for dietitians but few evidence-based assessment tools available. The present study aimed to develop a dietetic-specific, short, reliable and valid assessment tool for measuring communication skills in patient consultations: DIET-COMMS. A literature review and feedback from 15 qualified dietitians were used to establish face and content validity during the development of DIET-COMMS. In total, 113 dietetic students and qualified dietitians were video-recorded undertaking mock consultations, assessed using DIET-COMMS by the lead author, and used to establish intra-rater reliability, as well as construct and predictive validity. Twenty recorded consultations were reassessed by nine qualified dietitians to assess inter-rater reliability: eight of these assessors were interviewed to determine user evaluation. Significant improvements in DIET-COMMS scores were achieved as students and qualified staff progressed through their training and gained experience, demonstrating construct validity, and also by qualified staff attending a training course, indicating predictive validity (P skills in practice was questioned. DIET-COMMS is a short, user-friendly, reliable and valid tool for measuring communication skills in patient consultations with both pre- and post-registration dietitians. Additional work is required to develop a training package for assessors and to identify how DIET-COMMS assessment can acceptably be incorporated into practice. © 2013 The British Dietetic Association Ltd.

32 CFR 806b.53 - Training tools.

Science.gov (United States)

2010-07-01

... Justice Privacy web pages. Go to http://www.foia.af.mil. Click on “Resources.” (b) “The Privacy Act of... Privacy Act 101 and is available on-line at http://www.foia.af.mil. (d) Training slides for use by the... http://www.foia.af.mil, under “Resources.” Note: Formal school training groups that develop or modify...

The Training Evaluation Inventory (TEI)--Evaluation of Training Design and Measurement of Training Outcomes for Predicting Training Success

Science.gov (United States)

Ritzmann, Sandrina; Hagemann, Vera; Kluge, Annette

2014-01-01

Training evaluation in research and organisational contexts is vital to ensure informed decisions regarding the value of training. The present study describes the development of a valid and reliable training evaluation inventory (TEI), as it does not exist so far. The objectives were a) to construct an instrument that is theoretically and…

Link practical-oriented research and education: New training tools for a sustainable use of plant protection products.

Science.gov (United States)

Sacchettini, G; Calliera, M

2017-02-01

In the Horizon 2020 work programme 2016-17 it is stated that in 2010, 71% of European farm managers were operating on the basis of practical experience only. Education levels greatly vary depending on country, farm managers' age and gender, or farm structures, and this can hamper innovation. Transition towards a more sustainable agriculture requires a renewal and strengthening of the technical skills of all the actors involved and - as a consequence - of the educational system. The EU Directive on the sustainable use of pesticides (EU, 128/2009/EC) requires European Member States to develop training activities targeting occupational exposure to pesticides. The objective of this study is to develop new training tools for operators, addressing the new legal requirements and taking into account what is already available. For this reason, the outcomes of different European and national research projects developed by the Opera Research Centre were used, involving stakeholders in the decision making process, but also considering the real behaviours and perceptions of the final users. As a result, an e-learning tool able to build personalized training programmes, by collecting and integrating existing training material on Plant Protection Products use was developed, together with an e-learning course, with the aim to help operators, advisors and distributors to get prepared for their national certificate test. This work highlights the opportunity to create long-term added value through enhanced collaboration between educators and researchers, and identifies a common set of priorities that has to be taken into account in order to nudge the changes required to achieve a more sustainable use of pesticide and, more in general, sustainable development. Copyright © 2016 Elsevier B.V. All rights reserved.

Development of nonlinear acoustic propagation analysis tool toward realization of loud noise environment prediction in aeronautics

Energy Technology Data Exchange (ETDEWEB)

Kanamori, Masashi, E-mail: kanamori.masashi@jaxa.jp; Takahashi, Takashi, E-mail: takahashi.takashi@jaxa.jp; Aoyama, Takashi, E-mail: aoyama.takashi@jaxa.jp [Japan Aerospace Exploration Agency, 7-44-1, Jindaijihigashi-machi, Chofu, Tokyo (Japan)

2015-10-28

Shown in this paper is an introduction of a prediction tool for the propagation of loud noise with the application to the aeronautics in mind. The tool, named SPnoise, is based on HOWARD approach, which can express almost exact multidimensionality of the diffraction effect at the cost of back scattering. This paper argues, in particular, the prediction of the effect of atmospheric turbulence on sonic boom as one of the important issues in aeronautics. Thanks to the simple and efficient modeling of the atmospheric turbulence, SPnoise successfully re-creates the feature of the effect, which often emerges in the region just behind the front and rear shock waves in the sonic boom signature.

New Technologies to Assist Training in Hospitality Sector

Science.gov (United States)

Balta, Sabah

2007-01-01

Hospitality sector needs new technological training tools, which can assist to improve sector employees' skills and services quality. The sector might be more interactive when these technological training tools used on the job-training program. This study addresses to issue of illumination of new technologic tools that enforce training in which…

Physics-based Modeling Tools for Life Prediction and Durability Assessment of Advanced Materials, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The technical objectives of this program are: (1) to develop a set of physics-based modeling tools to predict the initiation of hot corrosion and to address pit and...

Effectiveness of Cooperative Learning Instructional Tools With Predict-Observe-Explain Strategy on the Topic of Cuboid and Cube Volume

Science.gov (United States)

Nurhuda; Lukito, A.; Masriyah

2018-01-01

This study aims to develop instructional tools and implement it to see the effectiveness. The method used in this research referred to Designing Effective Instruction. Experimental research with two-group pretest-posttest design method was conducted. The instructional tools have been developed is cooperative learning model with predict-observe-explain strategy on the topic of cuboid and cube volume which consist of lesson plans, POE tasks, and Tests. Instructional tools were of good quality by criteria of validity, practicality, and effectiveness. These instructional tools was very effective for teaching the volume of cuboid and cube. Cooperative instructional tool with predict-observe-explain (POE) strategy was good of quality because the teacher was easy to implement the steps of learning, students easy to understand the material and students’ learning outcomes completed classically. Learning by using this instructional tool was effective because learning activities were appropriate and students were very active. Students’ learning outcomes were completed classically and better than conventional learning. This study produced a good instructional tool and effectively used in learning. Therefore, these instructional tools can be used as an alternative to teach volume of cuboid and cube topics.

An ensemble model of QSAR tools for regulatory risk assessment.

Science.gov (United States)

Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

2016-01-01

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0

Application of electronic learning tools for training of specialists in the field of information technologies for enterprises of mineral resources sector

Directory of Open Access Journals (Sweden)

Е. В. Катунцов

2017-08-01

Full Text Available The article shows the advantages of using modern electronic learning tools in the training of specialists for the mineral and raw materials complex and considers the basic principles of organizing training using these tools. The experience of using electronic learning tools using foreign teaching materials and involving foreign professors is described. A special attention is given to the electronic learning environment of the Cisco Networking Academy – Cisco NetAcad. The experience of teaching at the Networking Academy of the Saint-Petersburg Mining University is described. Details are given to modern virtual environments for laboratory work, such as Cisco Packet Tracer, GNS3 and Emulated Virtual Environment. The experience of using electronic learning technologies at the University of Economics of Bratislava is considered. It actively cooperates with a number of universities of other countries, such as the University of International Business (Almaty, the Eurasian National University named after LN Gumilyov (Astana and the Institute of Social and Humanitarian Knowledge (Kazan.

Artificial neural network as the tool in prediction rheological features of raw minced meat.

Science.gov (United States)

Balejko, Jerzy A; Nowak, Zbigniew; Balejko, Edyta

2012-01-01

The aim of the study was to elaborate a method of modelling and forecasting rheological features which could be applied to raw minced meat at the stage of mixture preparation with a given ingredient composition. The investigated material contained pork and beef meat, pork fat, fat substitutes, ice and curing mixture in various proportions. Seven texture parameters were measured for each sample of raw minced meat. The data obtained were processed using the artificial neural network module in Statistica 9.0 software. The model that reached the lowest training error was a multi-layer perceptron MLP with three neural layers and architecture 7:7-11-7:7. Correlation coefficients between the experimental and calculated values in training, verification and testing subsets were similar and rather high (around 0.65) which indicated good network performance. High percentage of the total variance explained in PCA analysis (73.5%) indicated that the percentage composition of raw minced meat can be successfully used in the prediction of its rheological features. Statistical analysis of the results revealed, that artificial neural network model is able to predict rheological parameters and thus a complete texture profile of raw minced meat.

Performance on the adult rheumatology in-training examination and relationship to outcomes on the rheumatology certification examination.

Science.gov (United States)

Lohr, Kristine M; Clauser, Amanda; Hess, Brian J; Gelber, Allan C; Valeriano-Marcet, Joanne; Lipner, Rebecca S; Haist, Steven A; Hawley, Janine L; Zirkle, Sarah; Bolster, Marcy B

2015-11-01

The American College of Rheumatology (ACR) Adult Rheumatology In-Training Examination (ITE) is a feedback tool designed to identify strengths and weaknesses in the content knowledge of individual fellows-in-training and the training program curricula. We determined whether scores on the ACR ITE, as well as scores on other major standardized medical examinations and competency-based ratings, could be used to predict performance on the American Board of Internal Medicine (ABIM) Rheumatology Certification Examination. Between 2008 and 2012, 629 second-year fellows took the ACR ITE. Bivariate correlation analyses of assessment scores and multiple linear regression analyses were used to determine whether ABIM Rheumatology Certification Examination scores could be predicted on the basis of ACR ITE scores, United States Medical Licensing Examination scores, ABIM Internal Medicine Certification Examination scores, fellowship directors' ratings of overall clinical competency, and demographic variables. Logistic regression was used to evaluate whether these assessments were predictive of a passing outcome on the Rheumatology Certification Examination. In the initial linear model, the strongest predictors of the Rheumatology Certification Examination score were the second-year fellows' ACR ITE scores (β = 0.438) and ABIM Internal Medicine Certification Examination scores (β = 0.273). Using a stepwise model, the strongest predictors of higher scores on the Rheumatology Certification Examination were second-year fellows' ACR ITE scores (β = 0.449) and ABIM Internal Medicine Certification Examination scores (β = 0.276). Based on the findings of logistic regression analysis, ACR ITE performance was predictive of a pass/fail outcome on the Rheumatology Certification Examination (odds ratio 1.016 [95% confidence interval 1.011-1.021]). The predictive value of the ACR ITE score with regard to predicting performance on the Rheumatology Certification Examination

Simulator training effectiveness: instructor training and qualifications

International Nuclear Information System (INIS)

Scholand, G.W.

1985-01-01

Nuclear power plant simulators have become the most important tool in training nuclear power plant operators. Yet, as these machines continue to become even more sophisticated, highly trained and experienced instructors with unique skills and insights are still essential in order to achieve effective and meaningful training. The making of a qualified simulator instructor involves training and techniques that exceed the traditional programs required of a Senior Reactor Operator (SRO). This paper discusses (i) the training necessary to produce a competent simulator instructor; and (ii) the continuing task of maintaining his or her proficiency. (author)

Obtaining Global Picture From Single Point Observations by Combining Data Assimilation and Machine Learning Tools

Science.gov (United States)

Shprits, Y.; Zhelavskaya, I. S.; Kellerman, A. C.; Spasojevic, M.; Kondrashov, D. A.; Ghil, M.; Aseev, N.; Castillo Tibocha, A. M.; Cervantes Villa, J. S.; Kletzing, C.; Kurth, W. S.

2017-12-01

Increasing volume of satellite measurements requires deployment of new tools that can utilize such vast amount of data. Satellite measurements are usually limited to a single location in space, which complicates the data analysis geared towards reproducing the global state of the space environment. In this study we show how measurements can be combined by means of data assimilation and how machine learning can help analyze large amounts of data and can help develop global models that are trained on single point measurement. Data Assimilation: Manual analysis of the satellite measurements is a challenging task, while automated analysis is complicated by the fact that measurements are given at various locations in space, have different instrumental errors, and often vary by orders of magnitude. We show results of the long term reanalysis of radiation belt measurements along with fully operational real-time predictions using data assimilative VERB code. Machine Learning: We present application of the machine learning tools for the analysis of NASA Van Allen Probes upper-hybrid frequency measurements. Using the obtained data set we train a new global predictive neural network. The results for the Van Allen Probes based neural network are compared with historical IMAGE satellite observations. We also show examples of predictions of geomagnetic indices using neural networks. Combination of machine learning and data assimilation: We discuss how data assimilation tools and machine learning tools can be combine so that physics-based insight into the dynamics of the particular system can be combined with empirical knowledge of it's non-linear behavior.

Exercise training raises daily activity stronger than predicted from exercise capacity in patients with COPD.

Science.gov (United States)

Behnke, Michaela; Wewel, Alexandra R; Kirsten, Detlef; Jörres, Rudolf A; Magnussen, Helgo

2005-06-01

The 6-min walking (6MWD) and 6-min treadmill distance (6MTD) are often used as measures of exercise performance in patients with COPD. The aim of our study was to assess their relationship to daily activity in the course of an exercise training program. Eighty-eight patients with stable COPD (71m/17f; mean +/- SD age, 60 +/-8 year; FEV1, 43+/-14% pred) were recruited, 66 of whom performed a hospital-based 10-day walking training, whereas 22 were treated as control. On day 16MTD, and on days 8 and 10, 6MTD and 6MWD were determined. In addition, patients used an accelerometer (TriTrac-R3D) to record 24 h-activity, whereby training sessions were excluded. In both groups there was a linear relationship (r > or = 0.84 and P daily activity did not markedly vary with exercise capacity under baseline conditions. Participation in a training program increased activity significantly stronger than predicted from the gain in exercise capacity. This underlines the importance of non-physiological, patient-centered factors associated with training in COPD.

An Event-Based Approach to Design a Teamwork Training Scenario and Assessment Tool in Surgery.

Science.gov (United States)

Nguyen, Ngan; Watson, William D; Dominguez, Edward

2016-01-01

Simulation is a technique recommended for teaching and measuring teamwork, but few published methodologies are available on how best to design simulation for teamwork training in surgery and health care in general. The purpose of this article is to describe a general methodology, called event-based approach to training (EBAT), to guide the design of simulation for teamwork training and discuss its application to surgery. The EBAT methodology draws on the science of training by systematically introducing training exercise events that are linked to training requirements (i.e., competencies being trained and learning objectives) and performance assessment. The EBAT process involves: Of the 4 teamwork competencies endorsed by the Agency for Healthcare Research Quality and Department of Defense, "communication" was chosen to be the focus of our training efforts. A total of 5 learning objectives were defined based on 5 validated teamwork and communication techniques. Diagnostic laparoscopy was chosen as the clinical context to frame the training scenario, and 29 KSAs were defined based on review of published literature on patient safety and input from subject matter experts. Critical events included those that correspond to a specific phase in the normal flow of a surgical procedure as well as clinical events that may occur when performing the operation. Similar to the targeted KSAs, targeted responses to the critical events were developed based on existing literature and gathering input from content experts. Finally, a 29-item EBAT-derived checklist was created to assess communication performance. Like any instructional tool, simulation is only effective if it is designed and implemented appropriately. It is recognized that the effectiveness of simulation depends on whether (1) it is built upon a theoretical framework, (2) it uses preplanned structured exercises or events to allow learners the opportunity to exhibit the targeted KSAs, (3) it assesses performance, and (4

Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

Directory of Open Access Journals (Sweden)

Swagata Payra

2014-01-01

Full Text Available The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

Medical support to military airborne training and operations.

Science.gov (United States)

Starkey, Kerry J; Lyon, J; Sigman, E; Pynn, H J; Nordmann, G

2018-05-01

Airborne operations enable large numbers of military forces to deploy on the ground in the shortest possible time. This however must be balanced by an increased risk of injury. The aim of this paper is to review the current UK military drop zone medical estimate process, which may help to predict the risk of potential injury and assist in planning appropriate levels of medical support. In spring 2015, a British Airborne Battlegroup (UKBG) deployed on a 7-week overseas interoperability training exercise in the USA with their American counterparts (USBG). This culminated in a 7-day Combined Joint Operations Access Exercise, which began with an airborne Joint Forcible Entry (JFE) of approximately 2100 paratroopers.The predicted number of jump-related injuries was estimated using Parachute Order Number 8 (PO No 8). Such injuries were defined as injuries occurring from the time the paratrooper exited the aircraft until they released their parachute harness on the ground. Overall, a total of 53 (2.5%) casualties occurred in the JFE phase of the exercise, lower than the predicted number of 168 (8%) using the PO No 8 tool. There was a higher incidence of back (30% actual vs 20% estimated) and head injuries (21% actual vs 5% estimated) than predicted with PO No 8. The current method for predicting the incidence of medical injuries after a parachute drop using the PO No 8 tool is potentially not accurate enough for current requirements. Further research into injury rate, influencing factors and injury type are urgently required in order to provide an evidence base to ensure optimal medical logistical and clinical planning for airborne training and operations in the future. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes

Directory of Open Access Journals (Sweden)

Mikuláš Gangur

2014-05-01

Full Text Available Electronic virtual markets can serve as an alternative tool for collecting information that is spread among numerous experts. This is the principal market functionality from the operators’ point of view. On the other hand it is profits that are the main interest of the market participants. What they expect from the market is liquidity as high as possible and the opportunity for unrestricted trading. Both the operator and the electronic market participant can be considered consumers of this particular market with reference to the requirements for the accuracy of its outputs but also for the market liquidity. Both the above mentioned groups of consumers (the operators and the participants themselves expect protection of their specific consumer rights, i.e. securing the two above mentioned functionalities of the market. These functionalities of the electronic market are, however, influenced by many factors, among others by participants’ activity. The article deals with the motivation tools that may improve the quality of the prediction market. In the prediction electronic virtual market there may be situations in which the commonly used tools for increasing business activities described in the published literature are not significantly effective. For such situations we suggest a new type of motivation incentive consisting in penalizing the individual market participants whose funds are not placed in the market. The functionality of the proposed motivation incentive is presented on the example of the existing data gained from the electronic virtual prediction market which is actively operated.

Integrated Decision Tools for Sustainable Watershed/Ground Water and Crop Health using Predictive Weather, Remote Sensing, and Irrigation Decision Tools

Science.gov (United States)

Jones, A. S.; Andales, A.; McGovern, C.; Smith, G. E. B.; David, O.; Fletcher, S. J.

2017-12-01

US agricultural and Govt. lands have a unique co-dependent relationship, particularly in the Western US. More than 30% of all irrigated US agricultural output comes from lands sustained by the Ogallala Aquifer in the western Great Plains. Six US Forest Service National Grasslands reside within the aquifer region, consisting of over 375,000 ha (3,759 km2) of USFS managed lands. Likewise, National Forest lands are the headwaters to many intensive agricultural regions. Our Ogallala Aquifer team is enhancing crop irrigation decision tools with predictive weather and remote sensing data to better manage water for irrigated crops within these regions. An integrated multi-model software framework is used to link irrigation decision tools, resulting in positive management benefits on natural water resources. Teams and teams-of-teams can build upon these multi-disciplinary multi-faceted modeling capabilities. For example, the CSU Catalyst for Innovative Partnerships program has formed a new multidisciplinary team that will address "Rural Wealth Creation" focusing on the many integrated links between economic, agricultural production and management, natural resource availabilities, and key social aspects of govt. policy recommendations. By enhancing tools like these with predictive weather and other related data (like in situ measurements, hydrologic models, remotely sensed data sets, and (in the near future) linking to agro-economic and life cycle assessment models) this work demonstrates an integrated data-driven future vision of inter-meshed dynamic systems that can address challenging multi-system problems. We will present the present state of the work and opportunities for future involvement.

Virtual Reality as an Educational and Training Tool for Medicine.

Science.gov (United States)

Izard, Santiago González; Juanes, Juan A; García Peñalvo, Francisco J; Estella, Jesús Mª Gonçalvez; Ledesma, Mª José Sánchez; Ruisoto, Pablo

2018-02-01

Until very recently, we considered Virtual Reality as something that was very close, but it was still science fiction. However, today Virtual Reality is being integrated into many different areas of our lives, from videogames to different industrial use cases and, of course, it is starting to be used in medicine. There are two great general classifications for Virtual Reality. Firstly, we find a Virtual Reality in which we visualize a world completely created by computer, three-dimensional and where we can appreciate that the world we are visualizing is not real, at least for the moment as rendered images are improving very fast. Secondly, there is a Virtual Reality that basically consists of a reflection of our reality. This type of Virtual Reality is created using spherical or 360 images and videos, so we lose three-dimensional visualization capacity (until the 3D cameras are more developed), but on the other hand we gain in terms of realism in the images. We could also mention a third classification that merges the previous two, where virtual elements created by computer coexist with 360 images and videos. In this article we will show two systems that we have developed where each of them can be framed within one of the previous classifications, identifying the technologies used for their implementation as well as the advantages of each one. We will also analize how these systems can improve the current methodologies used for medical training. The implications of these developments as tools for teaching, learning and training are discussed.

PBPK Modeling - A Predictive, Eco-Friendly, Bio-Waiver Tool for Drug Research.

Science.gov (United States)

De, Baishakhi; Bhandari, Koushik; Mukherjee, Ranjan; Katakam, Prakash; Adiki, Shanta K; Gundamaraju, Rohit; Mitra, Analava

2017-01-01

The world has witnessed growing complexities in disease scenario influenced by the drastic changes in host-pathogen- environment triadic relation. Pharmaceutical R&Ds are in constant search of novel therapeutic entities to hasten transition of drug molecules from lab bench to patient bedside. Extensive animal studies and human pharmacokinetics are still the "gold standard" in investigational new drug research and bio-equivalency studies. Apart from cost, time and ethical issues on animal experimentation, burning questions arise relating to ecological disturbances, environmental hazards and biodiversity issues. Grave concerns arises when the adverse outcomes of continued studies on one particular disease on environment gives rise to several other pathogenic agents finally complicating the total scenario. Thus Pharma R&Ds face a challenge to develop bio-waiver protocols. Lead optimization, drug candidate selection with favorable pharmacokinetics and pharmacodynamics, toxicity assessment are vital steps in drug development. Simulation tools like Gastro Plus™, PK Sim®, SimCyp find applications for the purpose. Advanced technologies like organ-on-a chip or human-on-a chip where a 3D representation of human organs and systems can mimic the related processes and activities, thereby linking them to major features of human biology can be successfully incorporated in the drug development tool box. PBPK provides the State of Art to serve as an optional of animal experimentation. PBPK models can successfully bypass bio-equivalency studies, predict bioavailability, drug interactions and on hyphenation with in vitro-in vivo correlation can be extrapolated to humans thus serving as bio-waiver. PBPK can serve as an eco-friendly bio-waiver predictive tool in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System

CERN Document Server

Barreiro Megino, Fernando Harald; The ATLAS collaboration

2017-01-01

Having information such as an estimation of the processing time or possibility of system outage (abnormal behaviour) helps to assist to monitor system performance and to predict its next state. The current cyber-infrastructure presents computing conditions in which contention for resources among high-priority data analysis happens routinely, that might lead to significant workload and data handling interruptions. The lack of the possibility to monitor and to predict the behaviour of the analysis process (its duration) and system’s state itself caused to focus on design of the built-in situational awareness analytic tools.

Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.

Science.gov (United States)

Leong, Ivone U S; Stuckey, Alexander; Lai, Daniel; Skinner, Jonathan R; Love, Donald R

2015-05-13

Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1-3 gene variants. The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either "pathogenic" (283) or "benign" (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined. The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico

Forecasting the Value of Training

Science.gov (United States)

Basarab, Dave

2011-01-01

The Predictive Evaluation (PE) model is a training and evaluation approach with the element of prediction. PE allows trainers and business leaders to predict the results, value, intention, adoption, and impact of training, allowing them to make smarter, more strategic training and evaluation investments. PE is invaluable for companies that…

ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins.

Science.gov (United States)

Konc, Janez; Janežič, Dušanka

2017-09-01

ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tool Sequence Trends in Minimally Invasive Surgery: Statistical Analysis and Implications for Predictive Control of Multifunction Instruments

Directory of Open Access Journals (Sweden)

Carl A. Nelson

2012-01-01

Full Text Available This paper presents an analysis of 67 minimally invasive surgical procedures covering 11 different procedure types to determine patterns of tool use. A new graph-theoretic approach was taken to organize and analyze the data. Through grouping surgeries by type, trends of common tool changes were identified. Using the concept of signal/noise ratio, these trends were found to be statistically strong. The tool-use trends were used to generate tool placement patterns for modular (multi-tool, cartridge-type surgical tool systems, and the same 67 surgeries were numerically simulated to determine the optimality of these tool arrangements. The results indicate that aggregated tool-use data (by procedure type can be employed to predict tool-use sequences with good accuracy, and also indicate the potential for artificial intelligence as a means of preoperative and/or intraoperative planning. Furthermore, this suggests that the use of multifunction surgical tools can be optimized to streamline surgical workflow.

Use of Artificial Neural Networks for Prediction of Convective Heat Transfer in Evaporative Units

Directory of Open Access Journals (Sweden)

Romero-Méndez Ricardo

2014-01-01

Full Text Available Convective heat transfer prediction of evaporative processes is more complicated than the heat transfer prediction of single-phase convective processes. This is due to the fact that physical phenomena involved in evaporative processes are very complex and vary with the vapor quality that increases gradually as more fluid is evaporated. Power-law correlations used for prediction of evaporative convection have proved little accuracy when used in practical cases. In this investigation, neural-network-based models have been used as a tool for prediction of the thermal performance of evaporative units. For this purpose, experimental data were obtained in a facility that includes a counter-flow concentric pipes heat exchanger with R134a refrigerant flowing inside the circular section and temperature controlled warm water moving through the annular section. This work also included the construction of an inverse Rankine refrigeration cycle that was equipped with measurement devices, sensors and a data acquisition system to collect the experimental measurements under different operating conditions. Part of the data were used to train several neural-network configurations. The best neural-network model was then used for prediction purposes and the results obtained were compared with experimental data not used for training purposes. The results obtained in this investigation reveal the convenience of using artificial neural networks as accurate predictive tools for determining convective heat transfer rates of evaporative processes.

The Smart Wheelchair: is it an appropriate mobility training tool for children with physical disabilities?

Science.gov (United States)

McGarry, Sarah; Moir, Lois; Girdler, Sonya

2012-09-01

To describe the impact of a mobility training program using the Smart Wheelchair on the driving skills and psychosocial outcomes of children with physical disabilities. A multiple case study design using mixed methods was used. Four children with physical disabilities were recruited through The Centre for Cerebral Palsy in Western Australia. The intervention was a 16 session Smart Wheelchair mobility training program. Data was collected using a quantitative driving skills assessment, field notes and qualitative parent interviews. Three out of four children gained independence in at least three driving skills or more, whilst one child was competent with verbal prompts. Three out of four mothers reported positive changes in their child's confidence, motivation and affect. The Smart Wheelchair has the ability to uncover learning potential and facilitate the recognition of abilities in children previously excluded from access to independent mobility. Given the significant limitation that restrictions in mobility pose to participation for children with physical disabilities, therapists must begin to understand the effectiveness of interventions such as the Smart Wheelchair. The descriptive findings of this study allow for future, more rigorous research, to be conducted on the effectiveness of the Smart Wheelchair as a mobility training tool.

Training Plan (M29 Revision)

Science.gov (United States)

Online Submission, 2008

2008-01-01

The objectives of training activities as stated in the DoW are: 1. to organize training within the project for the participants to learn to use the necessary tools; 2. to support training activities of the partners when they deliver and take in use the tools and practices during extended pilots. The approach with regards to the first point is that…

Webcams as a tool for teaching in Optometry training

Science.gov (United States)

Gargallo, A.; Arines, J.

2015-04-01

Clinical Optometry lab training is devoted to develop the students skills needed in eye healthcare professional practice. Nevertheless, students always find difficulties in the management of some optometric instruments and in the understanding of the evaluation techniques. Moreover, teachers also have problems in explaining the eye evaluation tests or making demonstrations of instruments handling. In order to facilitate the learning process, webcams adapted to the optometric devices represent a helpful and useful tool. In this work we present the use of webcams in some of the most common clinical test in Optometry as ocular refraction, colour vision test, eye health evaluation with slip-lamp, retinoscopy, ophthalmoscopy and contact lens fitting. Our experience shows that with this simple approach we can do things easier: show the instrument handling to all the students at the same time; take pictures or videos of different eye health conditions or exploratory routines for posterior visualization with all the students; recreate visual experience of the patient during optometric exam; simulate colour vision pathologies; increase the interactions between students allowing them to help and correct each other; and also record the final routine exam in order to make possible its revision with the students.

2B-Alert Web: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance.

Science.gov (United States)

Reifman, Jaques; Kumar, Kamal; Wesensten, Nancy J; Tountas, Nikolaos A; Balkin, Thomas J; Ramakrishnan, Sridhar

2016-12-01

Computational tools that predict the effects of daily sleep/wake amounts on neurobehavioral performance are critical components of fatigue management systems, allowing for the identification of periods during which individuals are at increased risk for performance errors. However, none of the existing computational tools is publicly available, and the commercially available tools do not account for the beneficial effects of caffeine on performance, limiting their practical utility. Here, we introduce 2B-Alert Web, an open-access tool for predicting neurobehavioral performance, which accounts for the effects of sleep/wake schedules, time of day, and caffeine consumption, while incorporating the latest scientific findings in sleep restriction, sleep extension, and recovery sleep. We combined our validated Unified Model of Performance and our validated caffeine model to form a single, integrated modeling framework instantiated as a Web-enabled tool. 2B-Alert Web allows users to input daily sleep/wake schedules and caffeine consumption (dosage and time) to obtain group-average predictions of neurobehavioral performance based on psychomotor vigilance tasks. 2B-Alert Web is accessible at: https://2b-alert-web.bhsai.org. The 2B-Alert Web tool allows users to obtain predictions for mean response time, mean reciprocal response time, and number of lapses. The graphing tool allows for simultaneous display of up to seven different sleep/wake and caffeine schedules. The schedules and corresponding predicted outputs can be saved as a Microsoft Excel file; the corresponding plots can be saved as an image file. The schedules and predictions are erased when the user logs off, thereby maintaining privacy and confidentiality. The publicly accessible 2B-Alert Web tool is available for operators, schedulers, and neurobehavioral scientists as well as the general public to determine the impact of any given sleep/wake schedule, caffeine consumption, and time of day on performance of a

Controller Strategies for Automation Tool Use under Varying Levels of Trajectory Prediction Uncertainty

Science.gov (United States)

Morey, Susan; Prevot, Thomas; Mercer, Joey; Martin, Lynne; Bienert, Nancy; Cabrall, Christopher; Hunt, Sarah; Homola, Jeffrey; Kraut, Joshua

2013-01-01

A human-in-the-loop simulation was conducted to examine the effects of varying levels of trajectory prediction uncertainty on air traffic controller workload and performance, as well as how strategies and the use of decision support tools change in response. This paper focuses on the strategies employed by two controllers from separate teams who worked in parallel but independently under identical conditions (airspace, arrival traffic, tools) with the goal of ensuring schedule conformance and safe separation for a dense arrival flow in en route airspace. Despite differences in strategy and methods, both controllers achieved high levels of schedule conformance and safe separation. Overall, results show that trajectory uncertainties introduced by wind and aircraft performance prediction errors do not affect the controllers' ability to manage traffic. Controller strategies were fairly robust to changes in error, though strategies were affected by the amount of delay to absorb (scheduled time of arrival minus estimated time of arrival). Using the results and observations, this paper proposes an ability to dynamically customize the display of information including delay time based on observed error to better accommodate different strategies and objectives.

FSPP: A Tool for Genome-Wide Prediction of smORF-Encoded Peptides and Their Functions

Directory of Open Access Journals (Sweden)

Hui Li

2018-04-01

Full Text Available smORFs are small open reading frames of less than 100 codons. Recent low throughput experiments showed a lot of smORF-encoded peptides (SEPs played crucial rule in processes such as regulation of transcription or translation, transportation through membranes and the antimicrobial activity. In order to gather more functional SEPs, it is necessary to have access to genome-wide prediction tools to give profound directions for low throughput experiments. In this study, we put forward a functional smORF-encoded peptides predictor (FSPP which tended to predict authentic SEPs and their functions in a high throughput method. FSPP used the overlap of detected SEPs from Ribo-seq and mass spectrometry as target objects. With the expression data on transcription and translation levels, FSPP built two co-expression networks. Combing co-location relations, FSPP constructed a compound network and then annotated SEPs with functions of adjacent nodes. Tested on 38 sequenced samples of 5 human cell lines, FSPP successfully predicted 856 out of 960 annotated proteins. Interestingly, FSPP also highlighted 568 functional SEPs from these samples. After comparison, the roles predicted by FSPP were consistent with known functions. These results suggest that FSPP is a reliable tool for the identification of functional small peptides. FSPP source code can be acquired at https://www.bioinfo.org/FSPP.

THE ROLE OF SELF-EFFICACY IN PREDICTING USE OF DISTANCE EDUCATION TOOLS AND LEARNING MANAGEMENT SYSTEMS

Directory of Open Access Journals (Sweden)

Ibrahim ARPACI

2017-01-01

Full Text Available This study aims to investigate the role of self-efficacy in predicting students’ use of distance education tools and learning management systems (LMSs. A total of 124 undergraduate students who enrolled in a course on Distance Education and selected using convenience sampling willingly participated in the study. The participants had little prior knowledge about distance education tools and LMSs. Therefore, they received instructions from the researcher over the course of a semester. The study proposed a research model based on the Technology Acceptance Model that has been widely used to predict user acceptance and use. Structural equation modelling was used to test the research model against the data collected through questionnaire surveys. Pretest-posttest results suggested that the students had significant learning by participating in the instruction. The results of the main analysis also suggested that self-efficacy positively affects perceived ease of use, while usefulness and ease of use perceptions positively affect attitudes toward using distance education tools and systems. Implications are provided along with limitations of the study discussed.

Training Materials for Release 3

DEFF Research Database (Denmark)

Wake, Jo Dugstad; Hansen, Cecilie; Debus, Kolja

This document, D7.4 – training materials for release 3, provides an overview of the training material for version 3 of the NEXT-TELL tools and methods. Previous documents submitted as part of work package 7, which is about teacher training, are D7.1 – Training Concept, D7.2 – Training Materials...... for Release 1 and D7.3 – Training Materials for Release 2. D7.4 builds on D7.1 and D7.2 and D7.3. D7.4 contains further development of previous work within WP7, essentially a revised theoretical approach to the teacher training, and expansion of the notion of tool training. The media in use have been expanded...

Acceptability of the Predicting Abusive Head Trauma (PredAHT) clinical prediction tool: A qualitative study with child protection professionals.

Science.gov (United States)

Cowley, Laura E; Maguire, Sabine; Farewell, Daniel M; Quinn-Scoggins, Harriet D; Flynn, Matthew O; Kemp, Alison M

2018-05-09

The validated Predicting Abusive Head Trauma (PredAHT) tool estimates the probability of abusive head trauma (AHT) based on combinations of six clinical features: head/neck bruising; apnea; seizures; rib/long-bone fractures; retinal hemorrhages. We aimed to determine the acceptability of PredAHT to child protection professionals. We conducted qualitative semi-structured interviews with 56 participants: clinicians (25), child protection social workers (10), legal practitioners (9, including 4 judges), police officers (8), and pathologists (4), purposively sampled across southwest United Kingdom. Interviews were recorded, transcribed and imported into NVivo for thematic analysis (38% double-coded). We explored participants' evaluations of PredAHT, their opinions about the optimal way to present the calculated probabilities, and their interpretation of probabilities in the context of suspected AHT. Clinicians, child protection social workers and police thought PredAHT would be beneficial as an objective adjunct to their professional judgment, to give them greater confidence in their decisions. Lawyers and pathologists appreciated its value for prompting multidisciplinary investigations, but were uncertain of its usefulness in court. Perceived disadvantages included: possible over-reliance and false reassurance from a low score. Interpretations regarding which percentages equate to 'low', 'medium' or 'high' likelihood of AHT varied; participants preferred a precise % probability over these general terms. Participants would use PredAHT with provisos: if they received multi-agency training to define accepted risk thresholds for consistent interpretation; with knowledge of its development; if it was accepted by colleagues. PredAHT may therefore increase professionals' confidence in their decision-making when investigating suspected AHT, but may be of less value in court. Copyright © 2018 Elsevier Ltd. All rights reserved.

The effects of Crew Resource Management (CRM) training on flight attendants' safety attitudes.

Science.gov (United States)

Ford, Jane; Henderson, Robert; O'Hare, David

2014-02-01

A number of well-known incidents and accidents had led the aviation industry to introduce Crew Resource Management (CRM) training designed specifically for flight attendants, and joint (pilot and flight attendant) CRM training as a way to improve teamwork and communication. The development of these new CRM training programs during the 1990s highlighted the growing need for programs to be evaluated using research tools that had been validated for the flight attendant population. The FSAQ (Flight Safety Attitudes Questionnaire-Flight Attendants) was designed specifically to obtain safety attitude data from flight attendants working for an Asia-Pacific airline. Flight attendants volunteered to participate in a study before receiving CRM training (N=563) and again (N=526) after CRM training. Almost half (13) of the items from the 36-item FSAQ showed highly significant changes following CRM training. Years of experience, crew position, seniority, leadership roles, flight attendant crew size, and length of route flown were all predictive of safety attitudes. CRM training for flight attendants is a valuable tool for increasing positive teamwork behaviors between the flight attendant and pilot sub-groups. Joint training sessions, where flight attendants and pilots work together to find solutions to in-flight emergency scenarios, provide a particularly useful strategy in breaking down communication barriers between the two sub-groups. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Using Search Engine Data as a Tool to Predict Syphilis.

Science.gov (United States)

Young, Sean D; Torrone, Elizabeth A; Urata, John; Aral, Sevgi O

2018-07-01

Researchers have suggested that social media and online search data might be used to monitor and predict syphilis and other sexually transmitted diseases. Because people at risk for syphilis might seek sexual health and risk-related information on the internet, we investigated associations between internet state-level search query data (e.g., Google Trends) and reported weekly syphilis cases. We obtained weekly counts of reported primary and secondary syphilis for 50 states from 2012 to 2014 from the US Centers for Disease Control and Prevention. We collected weekly internet search query data regarding 25 risk-related keywords from 2012 to 2014 for 50 states using Google Trends. We joined 155 weeks of Google Trends data with 1-week lag to weekly syphilis data for a total of 7750 data points. Using the least absolute shrinkage and selection operator, we trained three linear mixed models on the first 10 weeks of each year. We validated models for 2012 and 2014 for the following 52 weeks and the 2014 model for the following 42 weeks. The models, consisting of different sets of keyword predictors for each year, accurately predicted 144 weeks of primary and secondary syphilis counts for each state, with an overall average R of 0.9 and overall average root mean squared error of 4.9. We used Google Trends search data from the prior week to predict cases of syphilis in the following weeks for each state. Further research could explore how search data could be integrated into public health monitoring systems.

Assessment of Lightning Transients on a De-Iced Rotor Blade with Predictive Tools and Coaxial Return Measurements

Science.gov (United States)

Guillet, S.; Gosmain, A.; Ducoux, W.; Ponçon, M.; Fontaine, G.; Desseix, P.; Perraud, P.

2012-05-01

The increasing use of composite materials in aircrafts primary structures has led to different problematics in the field of safety of flight in lightning conditions. The consequences of this technological mutation, which occurs in a parallel context of extension of electrified critical functions, are addressed by aircraft manufacturers through the enhancement of their available assessment means of lightning transient. On the one hand, simulation tools, provided an accurate description of aircraft design, are today valuable assessment tools, in both predictive and operative terms. On the other hand, in-house test means allow confirmation and consolidation of design office hardening solutions. The combined use of predictive simulation tools and in- house test means offers an efficient and reliable support for all aircraft developments in their various life-time stages. The present paper provides PREFACE research project results that illustrate the above introduced strategy on the de-icing system of the NH90 composite main rotor blade.

Prediction Of Tensile And Shear Strength Of Friction Surfaced Tool Steel Deposit By Using Artificial Neural Networks

Science.gov (United States)

Manzoor Hussain, M.; Pitchi Raju, V.; Kandasamy, J.; Govardhan, D.

2018-04-01

Friction surface treatment is well-established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, we present the prediction of tensile and shear strength of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing contribution process parameters essentially friction pressure, rotational speed and welding speed. The simulation is performed by a 33-factor design that takes into account the maximum and least limits of the experimental work performed with the 23-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict tensile and shear strength of tool steel sediments caused by friction.

Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.

Science.gov (United States)

Lin, Hongli; Wang, Weisheng; Luo, Jiawei; Yang, Xuedong

2014-12-01

The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

An Open-Source Web-Based Tool for Resource-Agnostic Interactive Translation Prediction

Directory of Open Access Journals (Sweden)

Daniel Torregrosa

2014-09-01

Full Text Available We present a web-based open-source tool for interactive translation prediction (ITP and describe its underlying architecture. ITP systems assist human translators by making context-based computer-generated suggestions as they type. Most of the ITP systems in literature are strongly coupled with a statistical machine translation system that is conveniently adapted to provide the suggestions. Our system, however, follows a resource-agnostic approach and suggestions are obtained from any unmodified black-box bilingual resource. This paper reviews our ITP method and describes the architecture of Forecat, a web tool, partly based on the recent technology of web components, that eases the use of our ITP approach in any web application requiring this kind of translation assistance. We also evaluate the performance of our method when using an unmodified Moses-based statistical machine translation system as the bilingual resource.

Active Video Games as a Training Tool for Individuals With Chronic Respiratory Diseases: A SYSTEMATIC REVIEW.

Science.gov (United States)

Butler, Stacey J; Lee, Annemarie L; Goldstein, Roger S; Brooks, Dina

2018-02-26

Exercise is an effective treatment for reducing symptom severity and improving quality of life for patients with chronic respiratory diseases. Active video games offer a new and enjoyable way to exercise and have gained popularity in a rehabilitation setting. However, it is unclear whether they achieve comparable physiological and clinical effects as traditional exercise training. A systematic literature search was performed to identify studies that included an active video game component as a form of exercise training and a comparator group in chronic respiratory disease. Two assessors independently reviewed study quality using the Cochrane risk of bias tool and extracted data for exercise capacity, quality of life, and preference of exercise model. Six studies were included in this review. Because of the heterogeneity of the populations, study designs, length of intervention, and outcome measures, meta-analysis could not be performed. Active video game training resulted in comparable training maximal heart rate and dyspnea levels to those achieved when exercising using a treadmill or cycle (n = 5). There was insufficient evidence (n = 3) to determine whether active video game training improved exercise capacity as measured by 6-min walk test or treadmill endurance walking. Although the quality of evidence was low, in a small number of studies active video games induced peak heart rates and dyspnea levels comparable with traditional exercise training. Larger and longer-term randomized controlled trials are needed to establish the impact of video game training for individuals with chronic respiratory diseases.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Prediction and Control of Cutting Tool Vibration in Cnc Lathe with Anova and Ann

Directory of Open Access Journals (Sweden)

S. S. Abuthakeer

2011-06-01

Full Text Available Machining is a complex process in which many variables can deleterious the desired results. Among them, cutting tool vibration is the most critical phenomenon which influences dimensional precision of the components machined, functional behavior of the machine tools and life of the cutting tool. In a machining operation, the cutting tool vibrations are mainly influenced by cutting parameters like cutting speed, depth of cut and tool feed rate. In this work, the cutting tool vibrations are controlled using a damping pad made of Neoprene. Experiments were conducted in a CNC lathe where the tool holder is supported with and without damping pad. The cutting tool vibration signals were collected through a data acquisition system supported by LabVIEW software. To increase the buoyancy and reliability of the experiments, a full factorial experimental design was used. Experimental data collected were tested with analysis of variance (ANOVA to understand the influences of the cutting parameters. Empirical models have been developed using analysis of variance (ANOVA. Experimental studies and data analysis have been performed to validate the proposed damping system. Multilayer perceptron neural network model has been constructed with feed forward back-propagation algorithm using the acquired data. On the completion of the experimental test ANN is used to validate the results obtained and also to predict the behavior of the system under any cutting condition within the operating range. The onsite tests show that the proposed system reduces the vibration of cutting tool to a greater extend.

CERAPP: Collaborative estrogen receptor activity prediction project

DEFF Research Database (Denmark)

Mansouri, Kamel; Abdelaziz, Ahmed; Rybacka, Aleksandra

2016-01-01

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

Validation of mathematical models for the prediction of organs-at-risk dosimetric metrics in high-dose-rate gynecologic interstitial brachytherapy

Energy Technology Data Exchange (ETDEWEB)

Damato, Antonio L.; Viswanathan, Akila N.; Cormack, Robert A. [Dana-Farber Cancer Institute and Brigham and Women' s Hospital, Boston, Massachusetts 02115 (United States)

2013-10-15

Purpose: Given the complicated nature of an interstitial gynecologic brachytherapy treatment plan, the use of a quantitative tool to evaluate the quality of the achieved metrics compared to clinical practice would be advantageous. For this purpose, predictive mathematical models to predict the D{sub 2cc} of rectum and bladder in interstitial gynecologic brachytherapy are discussed and validated.Methods: Previous plans were used to establish the relationship between D2cc and the overlapping volume of the organ at risk with the targeted area (C0) or a 1-cm expansion of the target area (C1). Three mathematical models were evaluated: D{sub 2cc}=α*C{sub 1}+β (LIN); D{sub 2cc}=α– exp(–β*C{sub 0}) (EXP); and a mixed approach (MIX), where both C{sub 0} and C{sub 1} were inputs of the model. The parameters of the models were optimized on a training set of patient data, and the predictive error of each model (predicted D{sub 2cc}− real D{sub 2cc}) was calculated on a validation set of patient data. The data of 20 patients were used to perform a K-fold cross validation analysis, with K = 2, 4, 6, 8, 10, and 20.Results: MIX was associated with the smallest mean prediction error <6.4% for an 18-patient training set; LIN had an error <8.5%; EXP had an error <8.3%. Best case scenario analysis shows that an error ≤5% can be achieved for a ten-patient training set with MIX, an error ≤7.4% for LIN, and an error ≤6.9% for EXP. The error decreases with the increase in training set size, with the most marked decrease observed for MIX.Conclusions: The MIX model can predict the D{sub 2cc} of the organs at risk with an error lower than 5% with a training set of ten patients or greater. The model can be used in the development of quality assurance tools to identify treatment plans with suboptimal sparing of the organs at risk. It can also be used to improve preplanning and in the development of real-time intraoperative planning tools.

Postgraduate training in Ireland: expectations and experience.

LENUS (Irish Health Repository)

Bennett, D

2014-01-05

Postgraduate medical training in Ireland has been compared unfavourably with training abroad and blamed for an "exodus" of graduates of Irish medical schools. Exploration of features of a good training environment and development of tools to measure it have been the focus of much published research. There have been no Irish studies examining training environment using such validated tools.

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

DEFF Research Database (Denmark)

Greenbaum, Jason A.; Andersen, Pernille; Blythe, Martin

2007-01-01

and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington...

Habit strength is predicted by activity dynamics in goal-directed brain systems during training.

Science.gov (United States)

Zwosta, Katharina; Ruge, Hannes; Goschke, Thomas; Wolfensteller, Uta

2018-01-15

Previous neuroscientific research revealed insights into the brain networks supporting goal-directed and habitual behavior, respectively. However, it remains unclear how these contribute to inter-individual differences in habit strength which is relevant for understanding not only normal behavior but also more severe dysregulations between these types of action control, such as in addiction. In the present fMRI study, we trained subjects on approach and avoidance behavior for an extended period of time before testing the habit strength of the acquired stimulus-response associations. We found that stronger habits were associated with a stronger decrease in inferior parietal lobule activity for approach and avoidance behavior and weaker vmPFC activity at the end of training for avoidance behavior, areas associated with the anticipation of outcome identity and value. VmPFC in particular showed markedly different activity dynamics during the training of approach and avoidance behavior. Furthermore, while ongoing training was accompanied by increasing functional connectivity between posterior putamen and premotor cortex, consistent with previous assumptions about the neural basis of increasing habitualization, this was not predictive of later habit strength. Together, our findings suggest that inter-individual differences in habitual behavior are driven by differences in the persistent involvement of brain areas supporting goal-directed behavior during training. Copyright © 2017. Published by Elsevier Inc.

Novel inter and intra prediction tools under consideration for the emerging AV1 video codec

Science.gov (United States)

Joshi, Urvang; Mukherjee, Debargha; Han, Jingning; Chen, Yue; Parker, Sarah; Su, Hui; Chiang, Angie; Xu, Yaowu; Liu, Zoe; Wang, Yunqing; Bankoski, Jim; Wang, Chen; Keyder, Emil

2017-09-01

Google started the WebM Project in 2010 to develop open source, royalty- free video codecs designed specifically for media on the Web. The second generation codec released by the WebM project, VP9, is currently served by YouTube, and enjoys billions of views per day. Realizing the need for even greater compression efficiency to cope with the growing demand for video on the web, the WebM team embarked on an ambitious project to develop a next edition codec AV1, in a consortium of major tech companies called the Alliance for Open Media, that achieves at least a generational improvement in coding efficiency over VP9. In this paper, we focus primarily on new tools in AV1 that improve the prediction of pixel blocks before transforms, quantization and entropy coding are invoked. Specifically, we describe tools and coding modes that improve intra, inter and combined inter-intra prediction. Results are presented on standard test sets.

A clinical risk stratification tool for predicting treatment resistance in major depressive disorder.

Science.gov (United States)

Perlis, Roy H

2013-07-01

Early identification of depressed individuals at high risk for treatment resistance could be helpful in selecting optimal setting and intensity of care. At present, validated tools to facilitate this risk stratification are rarely used in psychiatric practice. Data were drawn from the first two treatment levels of a multicenter antidepressant effectiveness study in major depressive disorder, the STAR*D (Sequenced Treatment Alternatives to Relieve Depression) cohort. This cohort was divided into training, testing, and validation subsets. Only clinical or sociodemographic variables available by or readily amenable to self-report were considered. Multivariate models were developed to discriminate individuals reaching remission with a first or second pharmacological treatment trial from those not reaching remission despite two trials. A logistic regression model achieved an area under the receiver operating characteristic curve exceeding .71 in training, testing, and validation cohorts and maintained good calibration across cohorts. Performance of three alternative models with machine learning approaches--a naïve Bayes classifier and a support vector machine, and a random forest model--was less consistent. Similar performance was observed between more and less severe depression, men and women, and primary versus specialty care sites. A web-based calculator was developed that implements this tool and provides graphical estimates of risk. Risk for treatment resistance among outpatients with major depressive disorder can be estimated with a simple model incorporating baseline sociodemographic and clinical features. Future studies should examine the performance of this model in other clinical populations and its utility in treatment selection or clinical trial design. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Human Splicing Finder: an online bioinformatics tool to predict splicing signals.

Science.gov (United States)

Desmet, François-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Béroud, Gwenaëlle; Claustres, Mireille; Béroud, Christophe

2009-05-01

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-beta Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5' and 3' splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.

Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.

Science.gov (United States)

Veeravagu, Anand; Li, Amy; Swinney, Christian; Tian, Lu; Moraff, Adrienne; Azad, Tej D; Cheng, Ivan; Alamin, Todd; Hu, Serena S; Anderson, Robert L; Shuer, Lawrence; Desai, Atman; Park, Jon; Olshen, Richard A; Ratliff, John K

2017-07-01

OBJECTIVE The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort. METHODS The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery. RESULTS The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60-0.74] in RAT, 0.669 [95% CI 0.60-0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48-0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently

Semi-structured interview is a reliable and feasible tool for selection of doctors for general practice specialist training

DEFF Research Database (Denmark)

Isaksen, Jesper; Hertel, Niels Thomas; Kjær, Niels Kristian

2013-01-01

In order to optimise the selection process for admission to specialist training in family medicine, we developed a new design for structured applications and selection interviews. The design contains semi-structured interviews, which combine individualised elements from the applications...... with standardised behaviour-based questions. This paper describes the design of the tool, and offers reflections concerning its acceptability, reliability and feasibility....

Previous injuries and some training characteristics predict running-related injuries in recreational runners: a prospective cohort study.

Science.gov (United States)

Hespanhol Junior, Luiz Carlos; Pena Costa, Leonardo Oliveira; Lopes, Alexandre Dias

2013-12-01

What is the incidence of running-related injuries (RRIs) in recreational runners? Which personal and training characteristics predict RRIs in recreational runners? Prospective cohort study. A total of 200 recreational runners answered a fortnightly online survey containing questions about their running routine, races, and presence of RRI. These runners were followed-up for a period of 12 weeks. The primary outcome of this study was running-related injury. The incidence of injuries was calculated taking into account the exposure to running and was expressed by RRI/1000 hours. The association between potential predictive factors and RRIs was estimated using generalised estimating equation models. A total of 84 RRIs were registered in 60 (31%) of the 191 recreational runners who completed all follow-up surveys. Of the injured runners 30% (n=18/60) developed two or more RRIs, with 5/18 (28%) being recurrences. The incidence of RRI was 10 RRI/1000 hours of running exposure. The main type of RRI observed was muscle injuries (30%, n=25/84). The knee was the most commonly affected anatomical region (19%, n=16/84). The variables associated with RRI were: previous RRI (OR 1.88, 95% CI 1.01 to 3.51), duration of training although the effect was very small (OR 1.01, 95% CI 1.00 to 1.02), speed training (OR 1.46, 95% CI 1.02 to 2.10), and interval training (OR 0.61, 95% CI 0.43 to 0.88). Physiotherapists should be aware and advise runners that past RRI and speed training are associated with increased risk of further RRI, while interval training is associated with lower risk, although these associations may not be causative. Copyright © 2013 Australian Physiotherapy Association. Published by Elsevier B.V. All rights reserved.

Predicting Knowledge Workers' Participation in Voluntary Learning with Employee Characteristics and Online Learning Tools

Science.gov (United States)

Hicks, Catherine

2018-01-01

Purpose: This paper aims to explore predicting employee learning activity via employee characteristics and usage for two online learning tools. Design/methodology/approach: Statistical analysis focused on observational data collected from user logs. Data are analyzed via regression models. Findings: Findings are presented for over 40,000…

The development of a quick-running prediction tool for the assessment of human injury owing to terrorist attack within crowded metropolitan environments.

Science.gov (United States)

Pope, Daniel J

2011-01-27

In the aftermath of the London '7/7' attacks in 2005, UK government agencies required the development of a quick-running tool to predict the weapon and injury effects caused by the initiation of a person borne improvised explosive device (PBIED) within crowded metropolitan environments. This prediction tool, termed the HIP (human injury predictor) code, was intended to:--assist the security services to encourage favourable crowd distributions and densities within scenarios of 'sensitivity'; --provide guidance to security engineers concerning the most effective location for protection systems; --inform rescue services as to where, in the case of such an event, individuals with particular injuries will be located; --assist in training medical personnel concerning the scope and types of injuries that would be sustained as a consequence of a particular attack; --assist response planners in determining the types of medical specialists (burns, traumatic amputations, lungs, etc.) required and thus identify the appropriate hospitals to receive the various casualty types. This document describes the algorithms used in the development of this tool, together with the pertinent underpinning physical processes. From its rudimentary beginnings as a simple spreadsheet, the HIP code now has a graphical user interface (GUI) that allows three-dimensional visualization of results and intuitive scenario set-up. The code is underpinned by algorithms that predict the pressure and momentum outputs produced by PBIEDs within open and confined environments, as well as the trajectories of shrapnel deliberately placed within the device to increase injurious effects. Further logic has been implemented to transpose these weapon effects into forms of human injury depending on where individuals are located relative to the PBIED. Each crowd member is subdivided into representative body parts, each of which is assigned an abbreviated injury score after a particular calculation cycle. The injury

Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

Science.gov (United States)

Han, Youngmahn; Kim, Dongsup

2017-12-28

Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this study, we demonstrated that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions. Nonapeptide-HLA-A and -B binding data were encoded into ILA data. A DCNN, as a pan-specific prediction model, was trained on the ILA data. The DCNN showed higher performance than other prediction tools for the latest benchmark datasets, which consist of 43 datasets for 15 HLA-A alleles and 25 datasets for 10 HLA-B alleles. In particular, the DCNN outperformed other tools for alleles belonging to the HLA-A3 supertype. The F1 scores of the DCNN were 0.86, 0.94, and 0.67 for HLA-A*31:01, HLA-A*03:01, and HLA-A*68:01 alleles, respectively, which were significantly higher than those of other tools. We found that the DCNN was able to recognize locally-clustered interactions that could synergistically stabilize peptide binding. We developed ConvMHC, a web server to provide user-friendly web interfaces for peptide-MHC class I binding predictions using the DCNN. ConvMHC web server can be accessible via http://jumong.kaist.ac.kr:8080/convmhc

Examining the Relationship Between Mental, Physical, and Organizational Factors Associated With Attrition During Maritime Forces Training.

Science.gov (United States)

Binsch, Olaf; Banko, Katherine M; Veenstra, Bertil J; Valk, Pierre J L

2015-11-01

For infantry units of the Dutch Ministry of Defence, high attrition rates (varying from 42 to 68%) during initial training are a persisting problem. The reasons for this attrition are diverse. Having better insight into the causes of attrition is a prerequisite for implementing preventive measures. To achieve this, a monitoring assessment system was developed that integrated the effects of physical, mental, and organizational determinants on operational readiness. The aim of this study was to implement the monitoring tools and to establish the set of determinants that best predicted attrition during infantry training of new recruits. Eighty-five recruits were monitored over a 24-week infantry training course. Before the training, recruits were screened for medical, psychological, and physical wellness. During the monitoring phase, mental, physiological, and organizational indicants were obtained using an array of tools such as questionnaires, chest belt monitors (for heart rate, acceleration, and skin temperature measurements), and computerized tests (e.g., vigilance, long-term memory). Survival analyses were used to tease out the determinants of individual and grouped predictors of attrition. Nearly half the recruits (47%) failed the training. Attrition was predicted by both physiological and mental determinants. However, the organizational determinant "trainers' judgment" on the "recruits' military quality" dominated the physiological and mental determinants. It was concluded that the monitoring system was successfully implemented during infantry training, and that the survival analysis method emphasized on single effects and interactions between the different determinants. Based on the current findings, we recommend several steps to successfully implement a monitoring method in settings with high demands.

On-Line Flutter Prediction Tool for Wind Tunnel Flutter Testing using Parameter Varying Estimation Methodology, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes to develop an on-line flutter prediction tool for wind tunnel model using the parameter varying estimation (PVE) technique to...

Translating and validating a Training Needs Assessment tool into Greek

Directory of Open Access Journals (Sweden)

Hicks Carolyn M

2007-05-01

Full Text Available Abstract Background The translation and cultural adaptation of widely accepted, psychometrically tested tools is regarded as an essential component of effective human resource management in the primary care arena. The Training Needs Assessment (TNA is a widely used, valid instrument, designed to measure professional development needs of health care professionals, especially in primary health care. This study aims to describe the translation, adaptation and validation of the TNA questionnaire into Greek language and discuss possibilities of its use in primary care settings. Methods A modified version of the English self-administered questionnaire consisting of 30 items was used. Internationally recommended methodology, mandating forward translation, backward translation, reconciliation and pretesting steps, was followed. Tool validation included assessing item internal consistency, using the alpha coefficient of Cronbach. Reproducibility (test – retest reliability was measured by the kappa correlation coefficient. Criterion validity was calculated for selected parts of the questionnaire by correlating respondents' research experience with relevant research item scores. An exploratory factor analysis highlighted how the items group together, using a Varimax (oblique rotation and subsequent Cronbach's alpha assessment. Results The psychometric properties of the Greek version of the TNA questionnaire for nursing staff employed in primary care were good. Internal consistency of the instrument was very good, Cronbach's alpha was found to be 0.985 (p 1.0, KMO (Kaiser-Meyer-Olkin measure of sampling adequacy = 0.680 and Bartlett's test of sphericity, p Conclusion The translated and adapted Greek version is comparable with the original English instrument in terms of validity and reliability and it is suitable to assess professional development needs of nursing staff in Greek primary care settings.

Translating and validating a Training Needs Assessment tool into Greek

Science.gov (United States)

Markaki, Adelais; Antonakis, Nikos; Hicks, Carolyn M; Lionis, Christos

2007-01-01

Background The translation and cultural adaptation of widely accepted, psychometrically tested tools is regarded as an essential component of effective human resource management in the primary care arena. The Training Needs Assessment (TNA) is a widely used, valid instrument, designed to measure professional development needs of health care professionals, especially in primary health care. This study aims to describe the translation, adaptation and validation of the TNA questionnaire into Greek language and discuss possibilities of its use in primary care settings. Methods A modified version of the English self-administered questionnaire consisting of 30 items was used. Internationally recommended methodology, mandating forward translation, backward translation, reconciliation and pretesting steps, was followed. Tool validation included assessing item internal consistency, using the alpha coefficient of Cronbach. Reproducibility (test – retest reliability) was measured by the kappa correlation coefficient. Criterion validity was calculated for selected parts of the questionnaire by correlating respondents' research experience with relevant research item scores. An exploratory factor analysis highlighted how the items group together, using a Varimax (oblique) rotation and subsequent Cronbach's alpha assessment. Results The psychometric properties of the Greek version of the TNA questionnaire for nursing staff employed in primary care were good. Internal consistency of the instrument was very good, Cronbach's alpha was found to be 0.985 (p 1.0, KMO (Kaiser-Meyer-Olkin) measure of sampling adequacy = 0.680 and Bartlett's test of sphericity, p < 0.001. Conclusion The translated and adapted Greek version is comparable with the original English instrument in terms of validity and reliability and it is suitable to assess professional development needs of nursing staff in Greek primary care settings. PMID:17474989

Evaluation of an Automated Analysis Tool for Prostate Cancer Prediction Using Multiparametric Magnetic Resonance Imaging.

Directory of Open Access Journals (Sweden)

Matthias C Roethke

Full Text Available To evaluate the diagnostic performance of an automated analysis tool for the assessment of prostate cancer based on multiparametric magnetic resonance imaging (mpMRI of the prostate.A fully automated analysis tool was used for a retrospective analysis of mpMRI sets (T2-weighted, T1-weighted dynamic contrast-enhanced, and diffusion-weighted sequences. The software provided a malignancy prediction value for each image pixel, defined as Malignancy Attention Index (MAI that can be depicted as a colour map overlay on the original images. The malignancy maps were compared to histopathology derived from a combination of MRI-targeted and systematic transperineal MRI/TRUS-fusion biopsies.In total, mpMRI data of 45 patients were evaluated. With a sensitivity of 85.7% (with 95% CI of 65.4-95.0, a specificity of 87.5% (with 95% CI of 69.0-95.7 and a diagnostic accuracy of 86.7% (with 95% CI of 73.8-93.8 for detection of prostate cancer, the automated analysis results corresponded well with the reported diagnostic accuracies by human readers based on the PI-RADS system in the current literature.The study revealed comparable diagnostic accuracies for the detection of prostate cancer of a user-independent MAI-based automated analysis tool and PI-RADS-scoring-based human reader analysis of mpMRI. Thus, the analysis tool could serve as a detection support system for less experienced readers. The results of the study also suggest the potential of MAI-based analysis for advanced lesion assessments, such as cancer extent and staging prediction.

Understanding health policy leaders' training needs.

Directory of Open Access Journals (Sweden)

Carey Roth Bayer

Full Text Available We assessed the training needs of health policy leaders and practitioners across career stages; identified areas of core content for health policy training programs; and, identified training modalities for health policy leaders.We convened a focus group of health policy leaders at varying career stages to inform the development of the Health Policy Leaders' Training Needs Assessment tool. We piloted and distributed the tool electronically. We used descriptive statistics and thematic coding for analysis.Seventy participants varying in age and stage of career completed the tool. "Cost implications of health policies" ranked highest for personal knowledge development and "intersection of policy and politics" ranked highest for health policy leaders in general. "Effective communication skills" ranked as the highest skill element and "integrity" as the highest attribute element. Format for training varied based on age and career stage.This study highlighted the training needs of health policy leaders personally as well as their perceptions of the needs for training health policy leaders in general. The findings are applicable for current health policy leadership training programs as well as those in development.

Instructor training at the Swedish Nuclear Power Training and Safety Centre

International Nuclear Information System (INIS)

Persson, P.-E.

1988-01-01

In spite of the fact that full-scope simulators are very powerful training tools, the transfer of knowledge and skills to the trainees during simulator training is completely dependent on the instructors' technical competence and their ability to transfer it to the trainees by efficient use of these training tools. Accordingly, the instructor candidates must pass a technical training programme equivalent to that for shift supervisors and have at least a few months of experience in each operator position at a nuclear power plant. To be authorized, the instructors must also pass a teacher training programme consisting of four 2 week instructor courses. To stay authorized the instructors must pass an annual retraining programme consisting of at least two weeks of technical refresher and one week teacher retraining. The retraining programme also includes at least three weeks of operational practice at a nuclear power plant. (author)

Development of a Clinical Tool to Predict Home Death of a Discharged Cancer Patient in Japan: a Case-Control Study.

Science.gov (United States)

Fukui, Sakiko; Morita, Tatsuya; Yoshiuchi, Kazuhiro

2017-08-01

The aim of this study was to investigate the predictive value of a clinical tool to predict whether discharged cancer patients die at home, comparing groups of case who died at home and control who died in hospitals or other facilities. We conducted a nationwide case-control study to identify the determinants of home death for a discharged cancer patient. We randomly selected nurses in charge of 2000 home-visit nursing agencies from all 5813 agencies in Japan by referring to the nationwide databases in January 2013. The nurses were asked to report variables of their patients' place of death, patients' and caregivers' clinical statuses, and their preferences for home death. We used logistic regression analysis and developed a clinical tool to accurately predict it and investigated their predictive values. We identified 466 case and 478 control patients. Five predictive variables of home death were obtained: patients' and caregivers' preferences for home death [OR (95% CI) 2.66 (1.99-3.55)], availability of visiting physicians [2.13 (1.67-2.70)], 24-h contact between physicians and nurses [1.68 (1.30-2.18)], caregivers' experiences of deathwatch at home [1.41 (1.13-1.75)], and patients' insights as to their own prognosis [1.23 (1.02-1.50)]. We calculated the scores predicting home death for each variable (range 6-28). When using a cutoff point of 16, home death was predicted with a sensitivity of 0.72 and a specificity of 0.81 with the Harrell's c-statistic of 0.84. This simple clinical tool for healthcare professionals can help predict whether a discharged patient is likely to die at home.

Predicted impact and evaluation of North Carolina's phosphorus indexing tool.

Science.gov (United States)

Johnson, Amy M; Osmond, Deanna L; Hodges, Steven C

2005-01-01

Increased concern about potential losses of phosphorus (P) from agricultural fields receiving animal waste has resulted in the implementation of new state and federal regulations related to nutrient management. In response to strengthened nutrient management standards that require consideration of P, North Carolina has developed a site-specific P indexing system called the Phosphorus Loss Assessment Tool (PLAT) to predict relative amounts of potential P loss from agricultural fields. The purpose of this study was to apply the PLAT index on farms throughout North Carolina in an attempt to predict the percentage and types of farms that will be forced to change management practices due to implementation of new regulations. Sites from all 100 counties were sampled, with the number of samples taken from each county depending on the proportion of the state's agricultural land that occurs in that county. Results showed that approximately 8% of producers in the state will be required to apply animal waste or inorganic fertilizer on a P rather than nitrogen basis, with the percentage increasing for farmers who apply animal waste (approximately 27%). The PLAT index predicted the greatest amounts of P loss from sites in the Coastal Plain region of North Carolina and from sites receiving poultry waste. Loss of dissolved P through surface runoff tended to be greater than other loss pathways and presents an area of concern as no best management practices (BMPs) currently exist for the reduction of in-field dissolved P. The PLAT index predicted the areas in the state that are known to be disproportionately vulnerable to P loss due to histories of high P applications, high densities of animal units, or soil type and landscapes that are most susceptible to P loss.

Activity Based Training Employed in Quality Assurance Training

Directory of Open Access Journals (Sweden)

Liviu Moldovan

2011-06-01

Full Text Available This paper presents the employment of the Activity Based Training at the “Petru Maior” University of Tîrgu Mureş. The draft of the training activities in each of the 10 modules in the Quality Audit process is illustrated. It is an achievement of the project entitled “Disseminating Open and Innovative Tools and Services for Vocational Education and Training in Quality Assurance” (acronym Do-IT financed by European Commission.

Development of an attrition risk prediction tool.

Science.gov (United States)

Fowler, John; Norrie, Peter

To review lecturers' and students' perceptions of the factors that may lead to attrition from pre-registration nursing and midwifery programmes and to identify ways to reduce the impact of such factors on the student's experience. Comparable attrition rates for nursing and midwifery students across various universities are difficult to monitor accurately; however, estimates that there is approximately a 25% national attrition rate are not uncommon. The financial and human implications of this are significant and worthy of investigation. A study was carried out in one medium-sized UK school of nursing and midwifery, aimed at identifying perceived factors associated with attrition and retention. Thirty-five lecturers were interviewed individually; 605 students completed a questionnaire, and of these, 10 were individually interviewed. Attrition data kept by the student service department were reviewed. Data were collected over an 18-month period in 2007-2008. Regression analysis of the student data identified eight significant predictors. Four of these were 'positive' factors in that they aided student retention and four were 'negative' in that they were associated with students' thoughts of resigning. Student attrition and retention is multifactorial, and, as such, needs to be managed holistically. One aspect of this management could be an attrition risk prediction tool.

NetH2pan: A Computational Tool to Guide MHC peptide prediction on Murine Tumors

DEFF Research Database (Denmark)