Natural Growth Goals and Short-Term Training: A Boomerang Effect.

Science.gov (United States)

Pace, R. Wayne; Regan, Les; Miller, Peter; Dunn, Lee

1998-01-01

Undergraduates were divided into four groups: 76 received training and completed the Natural Growth Goals Inventory and Organizational Learning Survey as pre- and posttests; 76 completed the NGGI only; 30 the OLS only; and 75 were trained and completed posttests. Both pretesting and training had a negative or boomerang effect on perceptions of the…

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

Monitoring and prediction of natural disasters

International Nuclear Information System (INIS)

Kondratyev, K. Ya; Krapivin, V. F.

2004-01-01

The problems of natural disaster predicting and accomplishing a synthesis of environmental monitoring systems to collect, store, and process relevant information for their solution are analysed. A three-level methodology is proposed for making decisions concerning the natural disaster dynamics. The methodology is based on the assessment of environmental indicators and the use of numerical models of the environment

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

Predicting the usefulness and naturalness of color reproductions

NARCIS (Netherlands)

Janssen, T.J.W.M.; Blommaert, F.J.J.

2000-01-01

We present algorithms for predicting the usefulness and naturalness of color reproductions of natural scenes. The algorithms are based on a computational model of the stages that lead to an observer's impression of the usefulness and naturalness of an image. These stages are (1) the perception, or

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.

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

Natural gas consumption prediction in Slovenian industry – a case study

Directory of Open Access Journals (Sweden)

Kovačič Miha

2016-09-01

Full Text Available In accordance with the regulations of the Energy Agency of the Republic of Slovenia, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted and the actually supplied quantities of natural gas. Yearly charges for these differences represent up to 2% of supplied natural gas costs. All the natural gas users, especially industry, have huge problems finding the proper method for efficient natural gas consumption prediction and, consequently, the decreasing of mentioned costs. In this study, prediction of the natural gas consumption in Štore Steel Ltd. (steel plant is presented. On the basis of production data, several models for natural gas consumption have been developed using linear regression, genetic programming and artificial neural network methods. The genetic programming approach outperformed linear regression and artificial neural networks.

Simple webs of natural environment theme as a result of sharing in science teacher training

Science.gov (United States)

Tapilouw, M. C.; Firman, H.; Redjeki, S.; Chandra, D. T.

2018-03-01

Thematic learning is one type of integrated science (Biology, Physics, Chemistry and Earth Science) in Science Education. This study is concerning about simple webs of natural environment theme in science learning, as one of training material in science teacher training program. Making simple web is a goal of first step in teacher training program. Every group explain their web illustration to other group. Twenty Junior High School science teacher above one education foundation participate in science teacher training program. In order to gather simple webs, sharing method was used in this first step of science teacher training. The result of this study is five different simple web of natural environment themes. These webs represent science learning in class VII/Semester I, class VII/Semester II, Class VIII, Class IX/Semester I, Class IX/Semester II based on basic competency in National Curriculum 2013. Each group discussed web of natural environment theme based on their learning experience in real class which basic competency and subject matters are linked with natural environment theme. As a conclusion, simple webs are potential to develop in the next step of science teacher training program and to be implemented in real class.

Natural Gas Vehicle Cylinder Safety, Training and Inspection Project

Energy Technology Data Exchange (ETDEWEB)

Hank Seiff

2008-12-31

Under the auspices of the National Energy Technology Laboratory and the US Department of Energy, the Clean Vehicle Education Foundation conducted a three-year program to increase the understanding of the safe and proper use and maintenance of vehicular compressed natural gas (CNG) fuel systems. High-pressure fuel systems require periodic inspection and maintenance to insure safe and proper operation. The project addressed the needs of CNG fuel containers (cylinders) and associated high-pressure fuel system components related to existing law, codes and standards (C&S), available training and inspection programs, and assured coordination among vehicle users, public safety officials, fueling station operators and training providers. The program included a public and industry awareness campaign, establishment and administration of a cylinder inspector certification training scholarship program, evaluation of current safety training and testing practices, monitoring and investigation of CNG vehicle incidents, evaluation of a cylinder recertification program and the migration of CNG vehicle safety knowledge to the nascent hydrogen vehicle community.

Empirical insights into the frequency and nature of multitasking on Dutch trains

NARCIS (Netherlands)

Waerden, van der P.J.H.J.; Timmermans, H.J.P.; Neerven, van R.J.C.

2008-01-01

This paper describes a study of the frequency and nature of multitasking on Dutch trains. Based on field observations on intercity and regional trains descriptive and model analyses were carried out. It appears that the most occurring task was ‘doing nothing’, followed by ‘Talking socially’ and

Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers

DEFF Research Database (Denmark)

Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2008-01-01

Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate...

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

Natural systems prediction of radionuclide migration

International Nuclear Information System (INIS)

Ewing, R.C.

1991-01-01

This paper reviews the application (and limitations) of data from natural systems to the verification of performance assessments, particularly as they apply to the evaluation of the long-term performance of waste forms, backfill, canister materials, and finally, the integrity of the repository itself. Two specific examples, the corrosion of borosilicate glass and the formation of alteration products of spent fuel, will be discussed. In both cases, inferences are of three types: 1) directly applicable data (i.e. radiation effects, stable phase assemblages): 2) inferences based on the analogous behaviour of the natural and repository systems (e.g. long-term corrosion rate); 3) specific identification of new phenomena that could not have been anticipated from the short term laboratory data (i.e. new mechanisms for the retention or release of radionuclides). The latter can only be derived from the observation of natural systems. Finally, specific attention will be paid to the limitations in the use of natural systems, particularly as the spatial and temporal scales expand, and to the inherent limitations of prediction and verification. (J.P.N.)

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

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

Directory of Open Access Journals (Sweden)

Marc G. Berman

2017-04-01

Full Text Available Previous research has investigated ways to quantify visual information of a scene in terms of a visual processing hierarchy, i.e., making sense of visual environment by segmentation and integration of elementary sensory input. Guided by this research, studies have developed categories for low-level visual features (e.g., edges, colors, high-level visual features (scene-level entities that convey semantic information such as objects, and how models of those features predict aesthetic preference and naturalness. For example, in Kardan et al. (2015a, 52 participants provided aesthetic preference and naturalness ratings, which are used in the current study, for 307 images of mixed natural and urban content. Kardan et al. (2015a then developed a model using low-level features to predict aesthetic preference and naturalness and could do so with high accuracy. What has yet to be explored is the ability of higher-level visual features (e.g., horizon line position relative to viewer, geometry of building distribution relative to visual access to predict aesthetic preference and naturalness of scenes, and whether higher-level features mediate some of the association between the low-level features and aesthetic preference or naturalness. In this study we investigated these relationships and found that low- and high- level features explain 68.4% of the variance in aesthetic preference ratings and 88.7% of the variance in naturalness ratings. Additionally, several high-level features mediated the relationship between the low-level visual features and aaesthetic preference. In a multiple mediation analysis, the high-level feature mediators accounted for over 50% of the variance in predicting aesthetic preference. These results show that high-level visual features play a prominent role predicting aesthetic preference, but do not completely eliminate the predictive power of the low-level visual features. These strong predictors provide powerful insights for

Action oriented training of natural resource management : case study of community- based natural resource management in Wodebeyesus Village, Debaitilatgin Woreda, Ethiopia

NARCIS (Netherlands)

Kassa, H.S.

2008-01-01

Development is largely perceived as a process of building capacities, hence empowering people through training is to able to handle their affairs by them selves. The research examined the practical significance of action oriented training as a basic approach for sustainable management of natural

Prediction to natural circulation in semiscale SBLOCA test, S-NC-8B

International Nuclear Information System (INIS)

Bang, Young Seok; Seul, Kwang Won; Lee, Sukho; Kim, Hho Jung

1995-01-01

Natural circulation and the associated thermal-hydraulic behavior are predicted by RELAP5/MOD3.1 code against the test S-NC-8B, which simulated 0.1% equivalent SBLOCA in PWR. The Semiscale Mod-2A facility and the test-specific initial/boundary condition are modeled. The calculation result is compared with the experiment data in terms of natural circulation characteristic and the code predictability is evaluated on natural circulation. As a result, flow rate during single-and two-phase natural circulation modes is well predicted and slightly overpredicted with oscillation in transition and reflux regimes. Additional sensitivity calculations are attempted with different discharge coefficient and break modeling to investigate the break flow effect

Viscosity Prediction of Natural Gas Using the Friction Theory

DEFF Research Database (Denmark)

Zeberg-Mikkelsen, Claus Kjær; Cisneros, Sergio; Stenby, Erling Halfdan

2002-01-01

Based on the concepts of the friction theory (f-theory) for viscosity modeling, a procedure is introduced for predicting the viscosity of hydrocarbon mixtures rich in one component, which is the case for natural gases. In this procedure, the mixture friction coefficients are estimated with mixing...... rules based on the values of the pure component friction coefficients. Since natural gases contain mainly methane, two f-theory models are combined, where the friction coefficients of methane are estimated by a seven-constant f-theory model directly fitted to methane viscosities, and the friction...... coefficients of the other components are estimated by the one-parameter general f-theory model. The viscosity predictions are performed with the SRK, the PR, and the PRSV equations of state, respectively. For recently measured viscosities of natural gases, the resultant AAD (0.5 to 0.8%) is in excellent...

Use of simplified methods for predicting natural resource damages

International Nuclear Information System (INIS)

Loreti, C.P.; Boehm, P.D.; Gundlach, E.R.; Healy, E.A.; Rosenstein, A.B.; Tsomides, H.J.; Turton, D.J.; Webber, H.M.

1995-01-01

To reduce transaction costs and save time, the US Department of the Interior (DOI) and the National Oceanic and Atmospheric Administration (NOAA) have developed simplified methods for assessing natural resource damages from oil and chemical spills. DOI has proposed the use of two computer models, the Natural Resource Damage Assessment Model for Great Lakes Environments (NRDAM/GLE) and a revised Natural Resource Damage Assessment Model for Coastal and Marine Environments (NRDAM/CME) for predicting monetary damages for spills of oils and chemicals into the Great Lakes and coastal and marine environments. NOAA has used versions of these models to create Compensation Formulas, which it has proposed for calculating natural resource damages for oil spills of up to 50,000 gallons anywhere in the US. Based on a review of the documentation supporting the methods, the results of hundreds of sample runs of DOI's models, and the outputs of the thousands of model runs used to create NOAA's Compensation Formulas, this presentation discusses the ability of these simplified assessment procedures to make realistic damage estimates. The limitations of these procedures are described, and the need for validating the assumptions used in predicting natural resource injuries is discussed

The Gender Issues and the Nature of Science in the Teacher Training

Directory of Open Access Journals (Sweden)

Bettina Heerdt

2016-08-01

Full Text Available The objective of this research is to understand and explain the teaching knowledge mobilized during a process of explicit-reflective formation of the Nature of Science, and the inherent gender relations in this dynamic. To achieve this goal it was elaborated a Didactic Unit entitled construction of scientific knowledge and gender visibility in Science. It took part in the training course 15 teachers in the areas of Humanities and Natural Sciences. This is a qualitative research. During the training process the participants’ speeches were video recorded and after transcribed and analyzed according to the thematic content analysis. Empirical data analysis allowed to show, through deductive inferences, different theoretical perspectives in coexisting traditional and linear discourse and those who perceive Science and Gender as a process of human construction, besides the lack of epistemological discussions and historical aspects of science involving gender issues. Another recurring discourse is the naturalization and the denial of the existence of gender issues in which the male-dominated lines are more articulated, which shows a resistance to feminist perspective. Thus, we reaffirm the need for teacher training explicit and reflective because gender issues are not self-evident, and the continuity of research to discuss these issues.

Homophyly/kinship hypothesis: Natural communities, and predicting in networks

Science.gov (United States)

Li, Angsheng; Li, Jiankou; Pan, Yicheng

2015-02-01

It has been a longstanding challenge to understand natural communities in real world networks. We proposed a community finding algorithm based on fitness of networks, two algorithms for prediction, accurate prediction and confirmation of keywords for papers in the citation network Arxiv HEP-TH (high energy physics theory), and the measures of internal centrality, external de-centrality, internal and external slopes to characterize the structures of communities. We implemented our algorithms on 2 citation and 5 cooperation graphs. Our experiments explored and validated a homophyly/kinship principle of real world networks. The homophyly/kinship principle includes: (1) homophyly is the natural selection in real world networks, similar to Darwin's kinship selection in nature, (2) real world networks consist of natural communities generated by the natural selection of homophyly, (3) most individuals in a natural community share a short list of common attributes, (4) natural communities have an internal centrality (or internal heterogeneity) that a natural community has a few nodes dominating most of the individuals in the community, (5) natural communities have an external de-centrality (or external homogeneity) that external links of a natural community homogeneously distributed in different communities, and (6) natural communities of a given network have typical structures determined by the internal slopes, and have typical patterns of outgoing links determined by external slopes, etc. Our homophyly/kinship principle perfectly matches Darwin's observation that animals from ants to people form social groups in which most individuals work for the common good, and that kinship could encourage altruistic behavior. Our homophyly/kinship principle is the network version of Darwinian theory, and builds a bridge between Darwinian evolution and network science.

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…

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...

Characteristics of muscle dysmorphia in male football, weight training, and competitive natural and non-natural bodybuilding samples.

Science.gov (United States)

Baghurst, Timothy; Lirgg, Cathy

2009-06-01

The purpose of this study was to identify differences in traits associated with muscle dysmorphia between collegiate football players (n=66), weight trainers for physique (n=115), competitive non-natural bodybuilders (n=47), and competitive natural bodybuilders (n=65). All participants completed demographic questionnaires in addition to the Muscle Dysmorphia Inventory (Rhea, Lantz, & Cornelius, 2004). Results revealed a significant main effect for group, and post hoc tests found that the non-natural bodybuilding group did not score significantly higher than the natural bodybuilding group on any subscale except for Pharmacological Use. Both the non-natural and natural bodybuilding groups scored significantly higher than those that weight trained for physique on the Dietary Behavior and Supplement Use subscales. The collegiate football players scored lowest on all subscales of the Muscle Dysmorphia Inventory except for Physique Protection where they scored highest. Findings are discussed with future research expounded.

The in-training examination: an analysis of its predictive value on performance on the general pediatrics certification examination.

Science.gov (United States)

Althouse, Linda A; McGuinness, Gail A

2008-09-01

This study investigates the predictive validity of the In-Training Examination (ITE). Although studies have confirmed the predictive validity of ITEs in other medical specialties, no study has been done for general pediatrics. Each year, residents in accredited pediatric training programs take the ITE as a self-assessment instrument. The ITE is similar to the American Board of Pediatrics General Pediatrics Certifying Examination. First-time takers of the certifying examination over a 5-year period who took at least 1 ITE examination were included in the sample. Regression models analyzed the predictive value of the ITE. The predictive power of the ITE in the first training year is minimal. However, the predictive power of the ITE increases each year, providing the greatest power in the third year of training. Even though ITE scores provide information regarding the likelihood of passing the certification examination, the data should be used with caution, particularly in the first training year. Other factors also must be considered when predicting performance on the certification examination. This study continues to support the ITE as an assessment tool for program directors, as well as a means of providing residents with feedback regarding their acquisition of pediatric knowledge.

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.

Teacher training on the nature of science through action-research

Directory of Open Access Journals (Sweden)

Ángel Vázquez-Alonso

2014-01-01

Full Text Available Nature of science teaching is essential for scientific and technological literacy, but teacher training is poor due to the lack of pedagogical content knowledge (PCK of topics on the nature of science and technology (NS&,T. This article addresses the development of the PCK through the self-training of a teacher, by describing the process of curriculum ownership, change and self-regulation, to teach the students the topic “observation in science”. Since action-research is the frame of this study, the teacher reflects and researches his own practice, with the help of some tools to make explicit the developed PCK. The results show the features of the PCK developed by the teacher, and how the teacher becomes aware that the PCK-NS&,T integrative model, the different teaching contexts in the classroom, and the reflective and explicit teaching processes are effective to teach NS&,T, as they improve students’ understanding of the theory-laden of observations and develop motivation towards consensus argumentation and decision making, autonomous learning, sharing team work, self-reflection and dialogue.

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

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

Training Parents to Use the Natural Language Paradigm to Increase Their Autistic Children's Speech.

Science.gov (United States)

Laski, Karen E.; And Others

1988-01-01

Parents of four nonverbal and four echolalic autistic children, aged five-nine, were trained to increase their children's speech by using the Natural Language Paradigm. Following training, parents increased the frequency with which they required their children to speak, and children increased the frequency of their verbalizations in three…

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.…

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.

Natural conception rates in subfertile couples following fertility awareness training.

Science.gov (United States)

Frank-Herrmann, P; Jacobs, C; Jenetzky, E; Gnoth, C; Pyper, C; Baur, S; Freundl, G; Goeckenjan, M; Strowitzki, T

2017-04-01

To analyze cumulative pregnancy rates of subfertile couples after fertility awareness training. A prospective observational cohort study followed 187 subfertile women, who had received training in self-observation of the fertile phase of the menstrual cycle with the Sensiplan method, for 8 months. The women, aged 21-47 years, had attempted to become pregnant for 3.5 years on average (range 1-8 years) before study entry. Amenorrhea, known tubal occlusion and severe male factor had been excluded. An additional seven women, who had initially been recruited, became pregnant during the cycle immediately prior to Sensiplan training: this is taken to be the spontaneous pregnancy rate per cycle in the cohort in the absence of fertility awareness training. The cumulative pregnancy rate of subfertile couples after fertility awareness training was 38% (95% CI 27-49%; 58 pregnancies) after eight observation months, which is significantly higher than the estimated basic pregnancy rate of 21.6% in untrained couples in the same cohort. For couples who had been seeking to become pregnant for 1-2 years, the pregnancy rate increased to 56% after 8 months. A female age above 35 (cumulative pregnancy rate 25%, p = 0.06), couples who had attempted to become pregnant for more than 2 years (cumulative pregnancy rate 17%, p conceiving naturally at some point. Training women to identify their fertile window in the menstrual cycle seems to be a reasonable first-line therapy in the management of subfertility.

Natural resources youth training program (NRYTP), resource rangers 2010

Energy Technology Data Exchange (ETDEWEB)

NONE

2010-09-15

In 2010, for a second year, the natural resources youth training program (NRYTP) was developed in northern Manitoba thanks to Manitoba Keewatinowi Okimakanak Inc. (MKO) and the collaboration of 42 sponsors. 16 aboriginal youth representing six northern communities took part in the five-week program located at the Egg Lake camp. The objective was to provide these resources rangers with knowledge and training in the most widespread resource sectors in northern Manitoba, including mining, forestry and hydropower. Trainers and experts provided by industry partners offered training sessions, hands-on work experience and other activities to help resource rangers to acquire a better understanding of the employability in this field in the northern region and the knowledge and skills the resource-based careers require. Life and professional skills training was given by the camp staff and local professionals. On-site elders and cultural events also allowed the integration of a northern Cree cultural component. Three staff members, a cook and elders assisted daily the resource rangers. Many improvements and refinements have been made since the success of the 2009 program, including the involvement of a larger number of communities, program contributors and program graduates. The program length has doubled and the number of jobs created has increased, important cultural aspects were introduced and the overall expenses were reduced.

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.

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

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.

Predicting the natural flow regime: Models for assessing hydrological alteration in streams

Science.gov (United States)

Carlisle, D.M.; Falcone, J.; Wolock, D.M.; Meador, M.R.; Norris, R.H.

2009-01-01

Understanding the extent to which natural streamflow characteristics have been altered is an important consideration for ecological assessments of streams. Assessing hydrologic condition requires that we quantify the attributes of the flow regime that would be expected in the absence of anthropogenic modifications. The objective of this study was to evaluate whether selected streamflow characteristics could be predicted at regional and national scales using geospatial data. Long-term, gaged river basins distributed throughout the contiguous US that had streamflow characteristics representing least disturbed or near pristine conditions were identified. Thirteen metrics of the magnitude, frequency, duration, timing and rate of change of streamflow were calculated using a 20-50 year period of record for each site. We used random forests (RF), a robust statistical modelling approach, to develop models that predicted the value for each streamflow metric using natural watershed characteristics. We compared the performance (i.e. bias and precision) of national- and regional-scale predictive models to that of models based on landscape classifications, including major river basins, ecoregions and hydrologic landscape regions (HLR). For all hydrologic metrics, landscape stratification models produced estimates that were less biased and more precise than a null model that accounted for no natural variability. Predictive models at the national and regional scale performed equally well, and substantially improved predictions of all hydrologic metrics relative to landscape stratification models. Prediction error rates ranged from 15 to 40%, but were 25% for most metrics. We selected three gaged, non-reference sites to illustrate how predictive models could be used to assess hydrologic condition. These examples show how the models accurately estimate predisturbance conditions and are sensitive to changes in streamflow variability associated with long-term land-use change. We also

Training of panellists for the sensory control of bottled natural mineral water in connection with water chemical properties.

Science.gov (United States)

Rey-Salgueiro, Ledicia; Gosálbez-García, Aitana; Pérez-Lamela, Concepción; Simal-Gándara, Jesús; Falqué-López, Elena

2013-11-01

As bottled mineral water market is increasing in the world (especially in emergent and developed countries), the development of a simple protocol to train a panel to evaluate sensory properties would be a useful tool for natural drinking water industry. A sensory protocol was developed to evaluate bottled natural mineral water (17 still and 10 carbonated trademarks). The tasting questionnaire included 13 attributes for still water plus overall impression and they were sorted by: colour hues, transparency and brightness, odour/aroma and taste/flavour/texture and 2 more for carbonated waters (bubbles and effervescence). The training lasted two months with, at least, 10 sessions, was adequate to evaluate bottled natural mineral water. To confirm the efficiency of the sensory training procedure two sensory groups formed the whole panel. One trained panel (6 persons) and one professional panel (6 sommeliers) and both participated simultaneously in the water tasting evaluation of 3 sample lots. Similar average scores obtained from trained and professional judges, with the same water trademarks, confirmed the usefulness of the training protocol. The differences obtained for trained panel in the first lot confirm the necessity to train always before a sensory procedure. A sensory water wheel is proposed to guide the training in bottled mineral water used for drinking, in connection with their chemical mineral content. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predicting the locations of naturally fishless lakes

Science.gov (United States)

Schilling, Emily Gaenzle; Loftin, C.S.; Degoosh, K.E.; Huryn, Alexander D.; Webster, K.E.

2008-01-01

1. Fish have been introduced into many previously fishless lakes throughout North America over the past 100+ years. It is difficult to determine the historical distribution of fishless lakes, however, because these introductions have not always been well-documented. 2. Due to its glacial history and low human population density, the state of Maine (U.S.A.) may host the greatest number of naturally fishless lakes in the northeastern United States. However, less than one-quarter of Maine's 6000+ lakes have been surveyed for fish presence, and no accurate assessments of either the historical or current abundance and distribution of fishless lakes exist. 3. We developed methods to assess the abundance and distribution of Maine's naturally fishless lakes (0.6-10.1 ha). We hypothesized that the historical distribution of fishless lakes across a landscape is controlled by geomorphic and geographic conditions. 4. We used ArcGIS to identify landscape-scale geomorphic and geographic factors (e.g. connectivity, surrounding slope) correlated with fish absence in two geomorphic regions of Maine - the western and interior mountains and the eastern lowlands and foothills. By using readily available geographic information systems data our method was not limited to field-visited sites. We estimated the likelihood that a particular lake is fishless with a stepwise logistic regression model developed for each region. 5. The absence of fish from western lakes is related to altitude (+), minimum percent slope in the 500 m buffer (+), maximum percent slope in the 500 m buffer (+) and percent cover of herbaceous-emergent wetland in 1000 m buffer (-). The absence of fish from eastern lakes is related to the lack of a stream within 50 m of the lake. 6. The models predict that a total of 4% (131) of study lakes in the two regions were historically fishless, with the eastern region hosting a greater proportion than the western region. 7. We verified the model predictions with two

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.

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…

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

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.

Earthquake prediction in Japan and natural time analysis of seismicity

Science.gov (United States)

Uyeda, S.; Varotsos, P.

2011-12-01

M9 super-giant earthquake with huge tsunami devastated East Japan on 11 March, causing more than 20,000 casualties and serious damage of Fukushima nuclear plant. This earthquake was predicted neither short-term nor long-term. Seismologists were shocked because it was not even considered possible to happen at the East Japan subduction zone. However, it was not the only un-predicted earthquake. In fact, throughout several decades of the National Earthquake Prediction Project, not even a single earthquake was predicted. In reality, practically no effective research has been conducted for the most important short-term prediction. This happened because the Japanese National Project was devoted for construction of elaborate seismic networks, which was not the best way for short-term prediction. After the Kobe disaster, in order to parry the mounting criticism on their no success history, they defiantly changed their policy to "stop aiming at short-term prediction because it is impossible and concentrate resources on fundamental research", that meant to obtain "more funding for no prediction research". The public were and are not informed about this change. Obviously earthquake prediction would be possible only when reliable precursory phenomena are caught and we have insisted this would be done most likely through non-seismic means such as geochemical/hydrological and electromagnetic monitoring. Admittedly, the lack of convincing precursors for the M9 super-giant earthquake has adverse effect for us, although its epicenter was far out off shore of the range of operating monitoring systems. In this presentation, we show a new possibility of finding remarkable precursory signals, ironically, from ordinary seismological catalogs. In the frame of the new time domain termed natural time, an order parameter of seismicity, κ1, has been introduced. This is the variance of natural time kai weighted by normalised energy release at χ. In the case that Seismic Electric Signals

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.

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.

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.

Predicting Emotions in Facial Expressions from the Annotations in Naturally Occurring First Encounters

DEFF Research Database (Denmark)

Navarretta, Costanza

2014-01-01

This paper deals with the automatic identification of emotions from the manual annotations of the shape and functions of facial expressions in a Danish corpus of video recorded naturally occurring first encounters. More specifically, a support vector classified is trained on the corpus annotations...... to identify emotions in facial expressions. In the classification experiments, we test to what extent emotions expressed in naturally-occurring conversations can be identified automatically by a classifier trained on the manual annotations of the shape of facial expressions and co-occurring speech tokens. We...... also investigate the relation between emotions and the communicative functions of facial expressions. Both emotion labels and their values in a three dimensional space are identified. The three dimensions are Pleasure, Arousal and Dominance. The results of our experiments indicate that the classifiers...

Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies

International Nuclear Information System (INIS)

Voyant, Cyril; Motte, Fabrice; Fouilloy, Alexis; Notton, Gilles; Paoli, Christophe; Nivet, Marie-Laure

2017-01-01

Integration of unpredictable renewable energy sources into electrical networks intensifies the complexity of the grid management due to their intermittent and unforeseeable nature. Because of the strong increase of solar power generation the prediction of solar yields becomes more and more important. Electrical operators need an estimation of the future production. For nowcasting and short term forecasting, the usual technics based on machine learning need large historical data sets of good quality during the training phase of predictors. However data are not always available and induce an advanced maintenance of meteorological stations, making the method inapplicable for poor instrumented or isolated sites. In this work, we propose intuitive methodologies based on the Kalman filter use (also known as linear quadratic estimation), able to predict a global radiation time series without the need of historical data. The accuracy of these methods is compared to other classical data driven methods, for different horizons of prediction and time steps. The proposed approach shows interesting capabilities allowing to improve quasi-systematically the prediction. For one to 10 h horizons Kalman model performances are competitive in comparison to more sophisticated models such as ANN which require both consistent historical data sets and computational resources. - Highlights: • Solar radiation forecasting with time series formalism. • Trainless approach compared to machine learning methods. • Very simple method dedicated to solar irradiation forecasting with high accuracy.

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

 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

Improving students' meaningful learning on the predictive nature of quantum mechanics

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Rodolfo Alves de Carvalho Neto

2009-03-01

Full Text Available This paper deals with research about teaching quantum mechanics to 3rd year high school students and their meaningful learning of its predictive aspect; it is based on the Master’s dissertation of one of the authors (CARVALHO NETO, 2006. While teaching quantum mechanics, we emphasized its predictive and essentially probabilistic nature, based on Niels Bohr’s complementarity interpretation (BOHR, 1958. In this context, we have discussed the possibility of predicting measurement results in well-defined experimental contexts, even for individual events. Interviews with students reveal that they have used quantum mechanical ideas, suggesting their meaningful learning of the essentially probabilistic predictions of quantum mechanics.

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

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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.

Liquefied natural gas tender crashworthiness in train-to-train collisions

Science.gov (United States)

2016-04-12

This paper focuses on technical information to help support : development of alternative static requirements for the train-to-train : collision scenario. The goal of the static requirements is to : provide the same level of crashworthiness as the dyn...

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

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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.

The pattern and loci of training-induced brain changes in healthy older adults are predicted by the nature of the intervention.

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Sylvie Belleville

Full Text Available There is enormous interest in designing training methods for reducing cognitive decline in healthy older adults. Because it is impaired with aging, multitasking has often been targeted and has been shown to be malleable with appropriate training. Investigating the effects of cognitive training on functional brain activation might provide critical indication regarding the mechanisms that underlie those positive effects, as well as provide models for selecting appropriate training methods. The few studies that have looked at brain correlates of cognitive training indicate a variable pattern and location of brain changes--a result that might relate to differences in training formats. The goal of this study was to measure the neural substrates as a function of whether divided attentional training programs induced the use of alternative processes or whether it relied on repeated practice. Forty-eight older adults were randomly allocated to one of three training programs. In the single repeated training, participants practiced an alphanumeric equation and a visual detection task, each under focused attention. In the divided fixed training, participants practiced combining verification and detection by divided attention, with equal attention allocated to both tasks. In the divided variable training, participants completed the task by divided attention, but were taught to vary the attentional priority allocated to each task. Brain activation was measured with fMRI pre- and post-training while completing each task individually and the two tasks combined. The three training programs resulted in markedly different brain changes. Practice on individual tasks in the single repeated training resulted in reduced brain activation whereas divided variable training resulted in a larger recruitment of the right superior and middle frontal gyrus, a region that has been involved in multitasking. The type of training is a critical factor in determining the pattern of

The Pattern and Loci of Training-Induced Brain Changes in Healthy Older Adults Are Predicted by the Nature of the Intervention

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Belleville, Sylvie; Mellah, Samira; de Boysson, Chloé; Demonet, Jean-Francois; Bier, Bianca

2014-01-01

There is enormous interest in designing training methods for reducing cognitive decline in healthy older adults. Because it is impaired with aging, multitasking has often been targeted and has been shown to be malleable with appropriate training. Investigating the effects of cognitive training on functional brain activation might provide critical indication regarding the mechanisms that underlie those positive effects, as well as provide models for selecting appropriate training methods. The few studies that have looked at brain correlates of cognitive training indicate a variable pattern and location of brain changes - a result that might relate to differences in training formats. The goal of this study was to measure the neural substrates as a function of whether divided attentional training programs induced the use of alternative processes or whether it relied on repeated practice. Forty-eight older adults were randomly allocated to one of three training programs. In the SINGLE REPEATED training, participants practiced an alphanumeric equation and a visual detection task, each under focused attention. In the DIVIDED FIXED training, participants practiced combining verification and detection by divided attention, with equal attention allocated to both tasks. In the DIVIDED VARIABLE training, participants completed the task by divided attention, but were taught to vary the attentional priority allocated to each task. Brain activation was measured with fMRI pre- and post-training while completing each task individually and the two tasks combined. The three training programs resulted in markedly different brain changes. Practice on individual tasks in the SINGLE REPEATED training resulted in reduced brain activation whereas DIVIDED VARIABLE training resulted in a larger recruitment of the right superior and middle frontal gyrus, a region that has been involved in multitasking. The type of training is a critical factor in determining the pattern of brain activation

Blind prediction of natural video quality.

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Saad, Michele A; Bovik, Alan C; Charrier, Christophe

2014-03-01

We propose a blind (no reference or NR) video quality evaluation model that is nondistortion specific. The approach relies on a spatio-temporal model of video scenes in the discrete cosine transform domain, and on a model that characterizes the type of motion occurring in the scenes, to predict video quality. We use the models to define video statistics and perceptual features that are the basis of a video quality assessment (VQA) algorithm that does not require the presence of a pristine video to compare against in order to predict a perceptual quality score. The contributions of this paper are threefold. 1) We propose a spatio-temporal natural scene statistics (NSS) model for videos. 2) We propose a motion model that quantifies motion coherency in video scenes. 3) We show that the proposed NSS and motion coherency models are appropriate for quality assessment of videos, and we utilize them to design a blind VQA algorithm that correlates highly with human judgments of quality. The proposed algorithm, called video BLIINDS, is tested on the LIVE VQA database and on the EPFL-PoliMi video database and shown to perform close to the level of top performing reduced and full reference VQA algorithms.

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

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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.

Prediction about chaotic times series of natural circulation flow under rolling motion

International Nuclear Information System (INIS)

Yuan Can; Cai Qi; Guo Li; Yan Feng

2014-01-01

The paper have proposed a chaotic time series prediction model, which combined phase space reconstruction with support vector machines. The model has been used to predict the coolant volume flow, in which a synchronous parameter optimization method was brought up based on particle swarm optimization algorithm, since the numerical value selection of related parameter was a key factor for the prediction precision. The average relative error of prediction values and actual observation values was l,5% and relative precision was 0.9879. The result indicated that the model could apply for the natural circulation coolant volume flow prediction under rolling motion condition with high accuracy and robustness. (authors)

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...

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.

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

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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.

STUDENT PREDICTION SYSTEM FOR PLACEMENT TRAINING USING FUZZY INFERENCE SYSTEM

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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.

Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO-NNGBM Model

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Xiaoyu Wang

2017-01-01

Full Text Available The accurate and reasonable prediction of natural gas consumption is significant for the government to formulate energy planning. To this end, we use the multiverse optimizer (MVO algorithm to optimize the parameters of the Nash nonlinear grey Bernoulli model (NNGBM (1,1 and propose a hybrid MVO-NNGBM model to predict the natural gas consumption in 30 regions of China. The results indicate that the prediction precision of the hybrid MVO-NNGBM model is better than that of other grey-based models. According to the forecast results, China’s natural gas consumption will grow rapidly over the next five years and reach 354.1 billion cubic meters (bcm by 2020. Moreover, the spatial distribution of natural gas consumption will shift from being supply oriented towards being demand driven and will be mainly concentrated in coastal and developed provinces.

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

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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.

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

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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

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.

 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.

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

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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.

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.

Nature and nurture in the family physician's choice of practice location.

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Orzanco, Maria Gabriela; Lovato, Chris; Bates, Joanna; Slade, Steve; Grand'Maison, Paul; Vanasse, Alain

2011-01-01

An understanding of the contextual, professional, and personal factors that affect choice of practice location for physicians is needed to support successful strategies in addressing geographic maldistribution of physicians. This study compared two categories of predictors of family practice location in non-metropolitan areas among undergraduate medical students: individual characteristics (nature), and the rural program component of their training program (nurture). The study aimed to identify factors that predict the location of practice 2 years post-residency training and determine the predictive value of combining nature and nurture variables using administrative data from two undergraduate medical education programs. Databases were developed from available administrative sources for a retrospective analysis of two undergraduate medical education programs in Canada: Université de Sherbrooke (UdeS) and University of British Columbia (UBC). Both schools have a strong mandate to evaluate the impact of their programs on physician distribution. The dependent variable was location of practice 2 years after completing postgraduate training in family medicine. Independent variables included individual and program characteristics. Separate analyses were conducted for each program using multiple logistic regression. The nature and nurture variables considered in the models explained only 21% to 27% of the variance in the eventual location of practice of family physician graduates. For UdeS, having an address in a rural/small-town environment at application to medical school (OR=2.61, 95% CI: 1.24-6.06) and for UBC, location of high school in a rural/small town (OR=4.03, 95% CI: 1.05-15.41), both increased the chances of practicing in a non-metropolitan area. For UdeS the nurture variable (ie length of clerkship in a non-metropolitan area) was the most significant predictor (OR=1.14, 95% CI: 1.067-1.22). For both medical schools, adding a single nurture variable to the

The Impact Hazard in the Context of Other Natural Hazards and Predictive Science

Science.gov (United States)

Chapman, C. R.

1998-09-01

The hazard due to impact of asteroids and comets has been recognized as analogous, in some ways, to other infrequent but consequential natural hazards (e.g. floods and earthquakes). Yet, until recently, astronomers and space agencies have felt no need to do what their colleagues and analogous agencies must do in order the assess, quantify, and communicate predictions to those with a practical interest in the predictions (e.g. public officials who must assess the threats, prepare for mitigation, etc.). Recent heightened public interest in the impact hazard, combined with increasing numbers of "near misses" (certain to increase as Spaceguard is implemented) requires that astronomers accept the responsibility to place their predictions and assessments in terms that may be appropriately considered. I will report on preliminary results of a multi-year GSA/NCAR study of "Prediction in the Earth Sciences: Use and Misuse in Policy Making" in which I have represented the impact hazard, while others have treated earthquakes, floods, weather, global climate change, nuclear waste disposal, acid rain, etc. The impact hazard presents an end-member example of a natural hazard, helping those dealing with more prosaic issues to learn from an extreme. On the other hand, I bring to the astronomical community some lessons long adopted in other cases: the need to understand the policy purposes of impact predictions, the need to assess potential societal impacts, the requirements to very carefully assess prediction uncertainties, considerations of potential public uses of the predictions, awareness of ethical considerations (e.g. conflicts of interest) that affect predictions and acceptance of predictions, awareness of appropriate means for publicly communicating predictions, and considerations of the international context (especially for a hazard that knows no national boundaries).

Time course of the hemoglobin mass response to natural altitude training in elite endurance cyclists.

Science.gov (United States)

Garvican, L; Martin, D; Quod, M; Stephens, B; Sassi, A; Gore, C

2012-02-01

To determine the time course of hemoglobin mass (Hb(mass)) to natural altitude training, Hb(mass), erythropoietin [EPO], reticulocytes, ferritin and soluble transferrin receptor (sTfR) were measured in 13 elite cyclists during, and 10 days after, 3 weeks of sea level (n=5) or altitude (n=8, 2760 m) training. Mean Hb(mass), with a typical error of ∼2%, increased during the first 11 days at altitude (mean ± standard deviation 2.9 ± 2.0%) and was 3.5 ± 2.5% higher than baseline after 19 days. [EPO] increased 64.2 ± 18.8% after 2 nights at altitude but was not different from baseline after 12 nights. Hb(mass) and [EPO] did not increase in sea level. Reticulocytes (%) were slightly elevated in altitude at Days 5 and 12 (18.9 ± 17.7% and 20.4 ± 25.3%), sTfR was elevated at Day 12 (18.9 ± 15.0%), but both returned to baseline by Day 20. Hb(mass) and [EPO] decreased on descent to sea level while ferritin increased. The mean increase in Hb(mass) observed after 11 days (∼300 h) of altitude training was beyond the measurement error and consitent with the mean increase after 300 h of simulated live high:train low altitude. Our results suggest that in elite cyclists, Hb(mass) increases progressively with 3 weeks of natural altitude exposure, with greater increases expected as exposure persists. © 2010 John Wiley & Sons A/S.

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…

Prediction of natural disasters basing of chrono-and-information field characters

Science.gov (United States)

Sapunov, Valentin

2013-04-01

Living organisms are able to predict some future events particular catastrophic incidents. This is adaptive characters producing by evolution. The more energy produces incident the more possibility to predict one. Wild animals escaped natural hazards including tsunami (e.g. extremal tsunami in Asia December 2004). Living animals are able to predict strong phenomena of obscure nature. For example majority of animals escaped Tungus catastrophe taking place in Siberia at 1908. Wild animals are able to predict nuclear weapon experiences. The obscure characters are not typical for human, but they are fixed under probability 15%. Such were summarized by L.Vasiliev (1961). Effective theory describing such a characters is absent till now. N.Kozyrev (1991) suggested existence of unknown physical field (but gravitation and electro magnetic). The field was named "time" or "chrono". Some characters of the field appeared to be object of physical experiment. Kozyrev suggested specific role of the field for function of living organisms. Transition of biological information throw space (telepathy) and time (proscopy) may be based on characters of such a field. Hence physical chrono-and-information field is under consideration. Animals are more familiar with such a field than human. Evolutionary process experienced with possibility of extremal development of contact with such a field using highest primates. This mode of evolution appeared to stay obscure producing probable species "Wildman" (Bigfoot). Specific adaptive fitches suggest impossibility to study of such a species by usual ecological approaches. The perspective way for study of mysterious phenomena of physic is researches of this field characters.

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.

A semi-supervised learning approach for RNA secondary structure prediction.

Science.gov (United States)

Yonemoto, Haruka; Asai, Kiyoshi; Hamada, Michiaki

2015-08-01

RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. Copyright © 2015 Elsevier Ltd. All rights reserved.

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…

Lasting Effects of Workplace Strength Training for Neck/Shoulder/Arm Pain among Laboratory Technicians: Natural Experiment with 3-Year Follow-Up

Directory of Open Access Journals (Sweden)

Peter Mortensen

2014-01-01

Full Text Available Objectives. This study investigated long-term effects and implementation processes of workplace strength training for musculoskeletal disorders. Methods. 333 and 140 laboratory technicians from private and public sector companies, respectively, replied to a 3-year follow-up questionnaire subsequent to a 1-year randomized controlled trial (RCT with high-intensity strength training for prevention and treatment of neck, shoulder, and arm pain. Being a natural experiment, the two participating companies implemented and modified the initial training program in different ways during the subsequent 2 years after the RCT. Results. At 3-year follow-up the pain reduction in neck, shoulder, elbow, and wrist achieved during the first year was largely maintained at both companies. However, the private sector company was rated significantly better than the public sector company in (1 training adherence, (2 training culture, that is, relatively more employees trained at the workplace and with colleagues, (3 self-reported health changes, and (4 prevention of neck and wrist pain development among initially pain-free employees. Conclusions. This natural experiment shows that strength training can be implemented successfully at different companies during working hours on a long-term basis with lasting effects on pain in neck, shoulder, and arm.

Lasting Effects of Workplace Strength Training for Neck/Shoulder/Arm Pain among Laboratory Technicians: Natural Experiment with 3-Year Follow-Up

Science.gov (United States)

Larsen, Anders I.; Zebis, Mette K.; Pedersen, Mogens T.; Sjøgaard, Gisela; Andersen, Lars L.

2014-01-01

Objectives. This study investigated long-term effects and implementation processes of workplace strength training for musculoskeletal disorders. Methods. 333 and 140 laboratory technicians from private and public sector companies, respectively, replied to a 3-year follow-up questionnaire subsequent to a 1-year randomized controlled trial (RCT) with high-intensity strength training for prevention and treatment of neck, shoulder, and arm pain. Being a natural experiment, the two participating companies implemented and modified the initial training program in different ways during the subsequent 2 years after the RCT. Results. At 3-year follow-up the pain reduction in neck, shoulder, elbow, and wrist achieved during the first year was largely maintained at both companies. However, the private sector company was rated significantly better than the public sector company in (1) training adherence, (2) training culture, that is, relatively more employees trained at the workplace and with colleagues, (3) self-reported health changes, and (4) prevention of neck and wrist pain development among initially pain-free employees. Conclusions. This natural experiment shows that strength training can be implemented successfully at different companies during working hours on a long-term basis with lasting effects on pain in neck, shoulder, and arm. PMID:24734247

Predictive Modelling of Concentration of Dispersed Natural Gas in a Single Room

Directory of Open Access Journals (Sweden)

Abdulfatai JIMOH

2009-07-01

Full Text Available This paper aimed at developing a mathematical model equation to predict the concentration of natural gas in a single room. The model equation was developed by using theoretical method of predictive modelling. The model equation developed is as given in equation 28. The validity of the developed expression was tested through the simulation of experimental results using computer software called MathCAD Professional. Both experimental and simulated results were found to be in close agreement. The statistical analysis carried out through the correlation coefficients for the results of experiment 1, 2, 3 and 4 were found to be 0.9986, 1.0000, 0.9981 and 0.9999 respectively, which imply reasonable close fittings between the experimental and simulated concentrations of dispersed natural gas within the room. Thus, the model equation developed can be considered a good representation of the phenomena that occurred when there is a leakage or accidental release of such gas within the room.

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

Battling bias : Effects of training and training context

NARCIS (Netherlands)

Poos, J.M.; Bosch, K. van den; Janssen, C.P.

2017-01-01

This study investigates whether cognitive bias in judgment and decision making can be reduced by training, and whether the effects are affected by the nature of the training environment. Theory suggests that biases can be overcome by training in critical reflective thinking. In addition, applied

Battling Bias: Effects of Training and Training Context

NARCIS (Netherlands)

Poos, Jackie; van den Bosch, Karel; Janssen, C.P.

2017-01-01

This study investigates whether cognitive bias in judgment and decision making can be reduced by training, and whether the effects are affected by the nature of the training environment. Theory suggests that biases can be overcome by training in critical reflective thinking. In addition, applied

Linear zonal atmospheric prediction for adaptive optics

Science.gov (United States)

McGuire, Patrick C.; Rhoadarmer, Troy A.; Coy, Hanna A.; Angel, J. Roger P.; Lloyd-Hart, Michael

2000-07-01

We compare linear zonal predictors of atmospheric turbulence for adaptive optics. Zonal prediction has the possible advantage of being able to interpret and utilize wind-velocity information from the wavefront sensor better than modal prediction. For simulated open-loop atmospheric data for a 2- meter 16-subaperture AO telescope with 5 millisecond prediction and a lookback of 4 slope-vectors, we find that Widrow-Hoff Delta-Rule training of linear nets and Back- Propagation training of non-linear multilayer neural networks is quite slow, getting stuck on plateaus or in local minima. Recursive Least Squares training of linear predictors is two orders of magnitude faster and it also converges to the solution with global minimum error. We have successfully implemented Amari's Adaptive Natural Gradient Learning (ANGL) technique for a linear zonal predictor, which premultiplies the Delta-Rule gradients with a matrix that orthogonalizes the parameter space and speeds up the training by two orders of magnitude, like the Recursive Least Squares predictor. This shows that the simple Widrow-Hoff Delta-Rule's slow convergence is not a fluke. In the case of bright guidestars, the ANGL, RLS, and standard matrix-inversion least-squares (MILS) algorithms all converge to the same global minimum linear total phase error (approximately 0.18 rad2), which is only approximately 5% higher than the spatial phase error (approximately 0.17 rad2), and is approximately 33% lower than the total 'naive' phase error without prediction (approximately 0.27 rad2). ANGL can, in principle, also be extended to make non-linear neural network training feasible for these large networks, with the potential to lower the predictor error below the linear predictor error. We will soon scale our linear work to the approximately 108-subaperture MMT AO system, both with simulations and real wavefront sensor data from prime focus.

Identification of PPARgamma partial agonists of natural origin (II: in silico prediction in natural extracts with known antidiabetic activity.

Directory of Open Access Journals (Sweden)

Laura Guasch

Full Text Available BACKGROUND: Natural extracts have played an important role in the prevention and treatment of diseases and are important sources for drug discovery. However, to be effectively used in these processes, natural extracts must be characterized through the identification of their active compounds and their modes of action. METHODOLOGY/PRINCIPAL FINDINGS: From an initial set of 29,779 natural products that are annotated with their natural source and using a previously developed virtual screening procedure (carefully validated experimentally, we have predicted as potential peroxisome proliferators-activated receptor gamma (PPARγ partial agonists 12 molecules from 11 extracts known to have antidiabetic activity. Six of these molecules are similar to molecules with described antidiabetic activity but whose mechanism of action is unknown. Therefore, it is plausible that these 12 molecules could be the bioactive molecules responsible, at least in part, for the antidiabetic activity of the extracts containing them. In addition, we have also identified as potential PPARγ partial agonists 10 molecules from 16 plants with undescribed antidiabetic activity but that are related (i.e., they are from the same genus to plants with known antidiabetic properties. None of the 22 molecules that we predict as PPARγ partial agonists show chemical similarity with a group of 211 known PPARγ partial agonists obtained from the literature. CONCLUSIONS/SIGNIFICANCE: Our results provide a new hypothesis about the active molecules of natural extracts with antidiabetic properties and their mode of action. We also suggest plants with undescribed antidiabetic activity that may contain PPARγ partial agonists. These plants represent a new source of potential antidiabetic extracts. Consequently, our work opens the door to the discovery of new antidiabetic extracts and molecules that can be of use, for instance, in the design of new antidiabetic drugs or functional foods focused

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.

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.

Lithuania training needs analysis

International Nuclear Information System (INIS)

1993-12-01

The present assessment of training needs in the Lithuanian natural gas sector is one of a total of four programmes on which Danish Oil and Natural Gas (DONG) and Lithuanian Gas (LG) cooperate. DONG's contribution is financed by the Danish Foreign Ministry. Long-term Objective is enhancement of the natural gas sector in Lithuania for efficient, cost-effective and environmentally responsible energy supply and utilization, in accordance with international standards. Immediate Objective is to establish a basis of competence in the Lithuanian Gas Sector, manage the modernization process effectively, and develop the involved human resources to the required performance level. The strategy for training in the Lithuanian natural gas sector proposed by this report integrates recommendations under three main headings: Six programmes of training and development. Strengthening of the institutional framework and the system for training. Upgrading of training capacity. The six programmes comprise: Legislation and Educational Planning. Management Development. Economy. Marketing and Sales. Supervisory Skills Development. Training Skills Development. Management and Specialists' Workshops on Technology. Following organisation for implementation of recommendations is proposed: Steering Board. Advisory Board. Coordination Group. Project Management Group. Group of Lithuanian Specialists. Group of Advisers. Short-term Consultants/Teachers. Lithuanian Consultants/Teachers. The project is envisaged to be implemented in carefully timed phases allowing for coordination between management workshops, training programmes and revision work regarding the institutional framework for training and the training system. (EG)

Prediction of Corrosion Resistance of Concrete Containing Natural Pozzolan from Compressive Strength

Science.gov (United States)

al-Swaidani, A. M.; Ismat, R.; Diyab, M. E.; Aliyan, S. D.

2015-11-01

A lot of Reinforced Concrete (RC) structures in Syria have suffered from reinforcement corrosion which shortened significantly their service lives. Probably, one of the most effective approaches to make concrete structures more durable and concrete industry on the whole - more sustainable is to substitute pozzolan for a portion of Portland cement (PC). Syria is relatively rich in natural pozzolan. In the study, in order to predict the corrosion resistance from compressive strength, concrete specimens were produced with seven cement types: one plain Portland cement (control) and six natural pozzolan-based cements with replacement levels ranging from 10 to 35%. The development of the compressive strengths of concrete cube specimens with curing time has been investigated. Chloride penetrability has also been evaluated for all concrete mixes after three curing times of 7, 28 and 90 days. The effect on resistance of concrete against damage caused by corrosion of the embedded reinforcing steel has been investigated using an accelerated corrosion test by impressing a constant anodic potential for 7, 28 and 90 days curing. Test results have been statistically analysed and correlation equations relating compressive strength and corrosion performance have been developed. Significant correlations have been noted between the compressive strength and both rapid chloride penetrability and corrosion initiation times. So, this prediction could be reliable in concrete mix design when using natural pozzolan as cement replacement.

SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

Science.gov (United States)

Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

2017-01-01

Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

Technical skill set training in natural orifice transluminal endoscopic surgery: how should we approach it?

LENUS (Irish Health Repository)

Nugent, Emmeline

2011-03-01

The boundaries in minimally invasive techniques are continually being pushed further. Recent years have brought new and exciting changes with the advent of natural orifice transluminal endoscopic surgery. With the evolution of this field of surgery come challenges in the development of new instruments and the actual steps of the procedure. Included in these challenges is the idea of developing a proficiency-based curriculum for training.

Artificial intelligence methods applied for quantitative analysis of natural radioactive sources

International Nuclear Information System (INIS)

Medhat, M.E.

2012-01-01

Highlights: ► Basic description of artificial neural networks. ► Natural gamma ray sources and problem of detections. ► Application of neural network for peak detection and activity determination. - Abstract: Artificial neural network (ANN) represents one of artificial intelligence methods in the field of modeling and uncertainty in different applications. The objective of the proposed work was focused to apply ANN to identify isotopes and to predict uncertainties of their activities of some natural radioactive sources. The method was tested for analyzing gamma-ray spectra emitted from natural radionuclides in soil samples detected by a high-resolution gamma-ray spectrometry based on HPGe (high purity germanium). The principle of the suggested method is described, including, relevant input parameters definition, input data scaling and networks training. It is clear that there is satisfactory agreement between obtained and predicted results using neural network.

Processing LiDAR Data to Predict Natural Hazards

Science.gov (United States)

Fairweather, Ian; Crabtree, Robert; Hager, Stacey

2008-01-01

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Private Training Providers: Their Characteristics and Training Activities. Support Document

Science.gov (United States)

Harris, Roger; Simons, Michele; McCarthy, Carmel

2006-01-01

This document was produced by the authors based on their research for the report, "Private Training Providers: Their Characteristics and Training Activities," [ED495181] and is an added resource for further information. That study examined the nature of the training activity of private registered training organisations (RTOs) offered to…

Lifetime experience with (classic) psychedelics predicts pro-environmental behavior through an increase in nature relatedness.

Science.gov (United States)

Forstmann, Matthias; Sagioglou, Christina

2017-08-01

In a large-scale ( N = 1487) general population online study, we investigated the relationship between past experience with classic psychedelic substances (e.g. LSD, psilocybin, mescaline), nature relatedness, and ecological behavior (e.g. saving water, recycling). Using structural equation modeling we found that experience with classic psychedelics uniquely predicted self-reported engagement in pro-environmental behaviors, and that this relationship was statistically explained by people's degree of self-identification with nature. Our model controlled for experiences with other classes of psychoactive substances (cannabis, dissociatives, empathogens, popular legal drugs) as well as common personality traits that usually predict drug consumption and/or nature relatedness (openness to experience, conscientiousness, conservatism). Although correlational in nature, results suggest that lifetime experience with psychedelics in particular may indeed contribute to people's pro-environmental behavior by changing their self-construal in terms of an incorporation of the natural world, regardless of core personality traits or general propensity to consume mind-altering substances. Thereby, the present research adds to the contemporary literature on the beneficial effects of psychedelic substance use on mental wellbeing, hinting at a novel area for future research investigating their potentially positive effects on a societal level. Limitations of the present research and future directions are discussed.

Prediction during natural language comprehension

NARCIS (Netherlands)

Willems, R.M.; Frank, S.L.; Nijhof, A.D.; Hagoort, P.; Bosch, A.P.J. van den

2016-01-01

The notion of prediction is studied in cognitive neuroscience with increasing intensity. We investigated the neural basis of 2 distinct aspects of word prediction, derived from information theory, during story comprehension. We assessed the effect of entropy of next-word probability distributions as

Larger Lateral Prefrontal Cortex Volume Predicts Better Exercise Adherence Among Older Women: Evidence From Two Exercise Training Studies.

Science.gov (United States)

Best, John R; Chiu, Bryan K; Hall, Peter A; Liu-Ambrose, Teresa

2017-06-01

Recent research has suggested an important role of lateral prefrontal cortex (lPFC) in consistent implementation of positive health behaviors and avoidance of negative health behaviors. We examined whether gray matter volume in the lPFC prospectively predicts exercise class attendance among older women (n = 122) who underwent either a 52-week or 26-week exercise training intervention. Structural magnetic resonance imaging determined gray matter volume at baseline. Independent of intracranial volume, age, education, body composition, mobility, depressive symptoms, and general cognitive functioning, larger lPFC volume predicted greater exercise class attendance (all p values exercise adherence as well as identified other regions, especially in the insula and temporal cortex, that predicted exercise adherence. These findings suggest that sustained engagement in exercise training might rely in part on functions of the lPFC and that lPFC volume might be a reasonable proxy for such functions. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

ACHIEVING THE NATURAL VOICE: THE ANALYSIS OF THE LINKLATER METHOD FROM A TRAINING PERSPECTİVE–

Directory of Open Access Journals (Sweden)

Asli YILMAZ DAVUTOGLU

2015-09-01

Full Text Available In the 20th century, one of the most widespread of the voice training methods that start with the concepts “natural voice” and “the rediscovery of voice” is the Linklater Method. The primary target group of this method is actors. With its exercises designed for the "reconstruction of the body, the voice and the mind", this method aims at utilizing the innate voice capacity. As a multidisciplinary method fostered by many a scientific discipline and Eastern teaching, Linklater method comes with a language that is imbued with sophisticated and metaphorical expressions, scientific terminology and acting jargon, which makes the method prone to false and/or superficial references. The target of the present study is to explicate with a “trainer's perspective” the fundamental concepts and propositions of the Linklater Method, most notably the “natural voice”. Also, the present study aims at analysing the relation between the basic practices of the method and recent scientific data, thus examining the mental substructure these practices are based on and their physical/technical goals. In this direction, the present study involves the adopting of a general framework with respect to the inclinations and scientific sources of voice training in the 20th century that affected Linklater's propositions, a simplified summarisation of the neuro-anatomic process producing the voice, the selection of exercises on which the principles and goals of the method can be seen concretely and the grouping of these exercises under titles pertaining to the four basic steps of voice production. In the conclusion part of the study, it is argued that this method, despite being regarded as “alternative/experimental” when compared to conventional methods in Turkey, is one of the mainstream methods in contemporary voice training and that it is shaped through a multi-purpose system whose aim is not only voice training but also to develop the creativity and

Temporal associations between individual changes in hormones, training motivation and physical performance in elite and non-elite trained men.

Science.gov (United States)

Crewther, B T; Carruthers, J; Kilduff, L P; Sanctuary, C E; Cook, C J

2016-09-01

To advance our understanding of the hormonal contribution to athletic performance, we examined the temporal associations between individual changes in testosterone (T) and/or cortisol (C) concentrations, training motivation and physical performance in elite and non-elite trained men. Two male cohorts classified as elites (n = 12) and non-elites (n = 12) completed five testing sessions over a six-week period. The athletes were tested for salivary T, C, T/C ratio, self-perceived training motivation, countermovement jump (CMJ) height and isometric mid-thigh pull peak force (IMTP PF), after which an actual training workout was performed. The elite men reported higher motivation to train and they produced greater CMJ height overall, whereas the non-elites had higher pooled T levels (p motivation in the elite men only (p = 0.033), but the hormonal and motivation measures did not predict CMJ height or IMTP PF in either group. The monitoring of elite and non-elite men across a short training block revealed differences in T levels, motivation and lower-body power, which may reflect training and competitive factors in each group. Despite having lower T levels, the elite athletes showed better linkage between pre-training T fluctuations and subsequent motivation to train. The nature of the performance tests (i.e. single repetition trials) could partly explain the lack of an association with the hormonal and motivational measures.

A New Approach to Predict the Fish Fillet Shelf-Life in Presence of Natural Preservative Agents

OpenAIRE

Giuffrida, Alessandro; Giarratana, Filippo; Valenti, Davide; Muscolino, Daniele; Parisi, Roberta; Parco, Alessio; Marotta, Stefania; Ziino, Graziella; Panebianco, Antonio

2017-01-01

Three data sets concerning the behaviour of spoilage flora of fillets treated with natural preservative substances (NPS) were used to construct a new kind of mathematical predictive model. This model, unlike other ones, allows expressing the antibacterial effect of the NPS separately from the prediction of the growth rate. This approach, based on the introduction of a parameter into the predictive primary model, produced a good fitting of observed data and allowed characterising quantitativel...

Improvements to executive function during exercise training predict maintenance of physical activity over the following year

Directory of Open Access Journals (Sweden)

John eBest

2014-05-01

Full Text Available Previous studies have shown that exercise training benefits cognitive, neural, and physical health markers in older adults. It is likely that these positive effects will diminish if participants return to sedentary lifestyles following training cessation. Theory posits that that the neurocognitive processes underlying self-regulation, namely executive function (EF, are important to maintaining positive health behaviors. Therefore, we examined whether better EF performance in older women would predict greater adherence to routine physical activity (PA over 1 year following a 12-month resistance exercise training randomized controlled trial. The study sample consisted of 125 community-dwelling women aged 65 to 75 years old. Our primary outcome measure was self-reported PA, as measured by the Physical Activity Scale for the Elderly (PASE, assessed on a monthly basis from month 13 to month 25. Executive function was assessed using the Stroop Test at baseline (month 0 and post-training (month 12. Latent growth curve analyses showed that, on average, PA decreased during the follow-up period but at a decelerating rate. Women who made greater improvements to EF during the training period showed better adherence to PA during the 1-year follow-up period (β = -.36, p .10. Overall, these findings suggest that improving EF plays an important role in whether older women maintain higher levels of PA following exercise training and that this association is only apparent after training when environmental support for PA is low.

Training algorithms evaluation for artificial neural network to temporal prediction of photovoltaic generation

International Nuclear Information System (INIS)

Arantes Monteiro, Raul Vitor; Caixeta Guimarães, Geraldo; Rocio Castillo, Madeleine; Matheus Moura, Fabrício Augusto; Tamashiro, Márcio Augusto

2016-01-01

Current energy policies are encouraging the connection of power generation based on low-polluting technologies, mainly those using renewable sources, to distribution networks. Hence, it becomes increasingly important to understand technical challenges, facing high penetration of PV systems at the grid, especially considering the effects of intermittence of this source on the power quality, reliability and stability of the electric distribution system. This fact can affect the distribution networks on which they are attached causing overvoltage, undervoltage and frequency oscillations. In order to predict these disturbs, artificial neural networks are used. This article aims to analyze 3 training algorithms used in artificial neural networks for temporal prediction of the generated active power thru photovoltaic panels. As a result it was concluded that the algorithm with the best performance among the 3 analyzed was the Levenberg-Marquadrt.

A predictive model of natural gas mixture combustion in internal combustion engines

Directory of Open Access Journals (Sweden)

Henry Espinoza

2007-05-01

Full Text Available This study shows the development of a predictive natural gas mixture combustion model for conventional com-bustion (ignition engines. The model was based on resolving two areas; one having unburned combustion mixture and another having combustion products. Energy and matter conservation equations were solved for each crankshaft turn angle for each area. Nonlinear differential equations for each phase’s energy (considering compression, combustion and expansion were solved by applying the fourth-order Runge-Kutta method. The model also enabled studying different natural gas components’ composition and evaluating combustion in the presence of dry and humid air. Validation results are shown with experimental data, demonstrating the software’s precision and accuracy in the results so produced. The results showed cylinder pressure, unburned and burned mixture temperature, burned mass fraction and combustion reaction heat for the engine being modelled using a natural gas mixture.

Supervisor's role in training programs as a manager of learning program

Directory of Open Access Journals (Sweden)

2011-06-01

Full Text Available According to the training literature, a supervisor's role in training programs has two major elements: supervisor support and supervisor communication. The ability of supervisors to play effective roles in training programs may increase employees' motivation to learn. The nature of this relationship is interesting, but the role of supervisor's role as a predicting variable is less emphasized in a training program models. Therefore, this study was conducted to examine the effect of supervisor's role in training programs on motivation to learn using 152 usable questionnaires gathered from non-academic employees who have worked in a technological based public university, Malaysia. The outcomes of stepwise regression analysis showed that the supervisor support and supervisor communication significantly associated with motivation to learn. Statistically, this result demonstrates that supervisor's role in training programs does act as an important predictor of motivation to learn in the organizational sample. In addition, discussion, implication and conclusion are elaborated.

Predicting dynamic range and intensity discrimination for electrical pulse-train stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

Science.gov (United States)

Xu, Yifang; Collins, Leslie M

2005-06-01

This work investigates dynamic range and intensity discrimination for electrical pulse-train stimuli that are modulated by noise using a stochastic auditory nerve model. Based on a hypothesized monotonic relationship between loudness and the number of spikes elicited by a stimulus, theoretical prediction of the uncomfortable level has previously been determined by comparing spike counts to a fixed threshold, Nucl. However, no specific rule for determining Nucl has been suggested. Our work determines the uncomfortable level based on the excitation pattern of the neural response in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven by a stimulus at an uncomfortable level in a normal ear is related to Nucl at an uncomfortable level of the electrical stimulus. Intensity discrimination limens are predicted using signal detection theory via the probability mass function of the neural response and via experimental simulations. The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, overall intensity coding performance may not improve significantly.

Data Normalization to Accelerate Training for Linear Neural Net to Predict Tropical Cyclone Tracks

Directory of Open Access Journals (Sweden)

Jian Jin

2015-01-01

Full Text Available When pure linear neural network (PLNN is used to predict tropical cyclone tracks (TCTs in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (−1, 1 and (0, 1; with normal distribution method, each variable’s mean and standard deviation pair is set to (0, 1 and (100, 1. We present the following results: (1 data scaled to the similar intervals have similar effects, no matter the use of min.-max. or normal distribution method; (2 mapping data to around 0 gains much faster training speed than mapping them to the intervals far away from 0 or using unnormalized raw data, although all of them can approach the same lower level after certain steps from their training error curves. This could be useful to decide data normalization method when PLNN is used individually.

ACADEMIC TRAINING: Probing nature with high precision; particle traps, laser spectroscopy and optical combs

CERN Multimedia

Françoise Benz

2002-01-01

17, 18, 19 June LECTURE SERIES from 11.00 to 12.00 hrs - Auditorium, bldg. 500 Probing nature with high precision; particle traps, laser spectroscopy and optical combs by G. GABRIELSE / Harvard University, USA Experiments with atomic energy scales probe nature and its symmetries with exquisite precision. Particle traps allow the manipulation of single charged particles for months at a time, allow the most accurate comparison of theory and experiment, and promise to allow better measurement of fundamental quantities like the fine structure constant. Ions and atoms can be probed with lasers that are phase locked to microwave frequency standards via optical combs, thus calibrating optical sources in terms of the official cesium second. A series of three lectures will illustrate what can be measured and discuss key techniques.  ACADEMIC TRAINING Françoise Benz Tel. 73127 francoise.benz@cern.ch

Operational and contractual impacts in E and P offshore during predicted natural hazards

Energy Technology Data Exchange (ETDEWEB)

Benevides, Paulo Roberto Correa de Sa e [PETROBRAS, Rio de Janeiro, RJ (Brazil)

2008-07-01

Generally, when E and P operators using DP (Dynamic Positioning) are advised previously of a possible natural hazard occurrence, usually they consider it like an emergency situation and their main action is oriented only to prepare the first response and use the 'force majeure' argumentation to protect itself from any additional responsibility. When the natural phenomenon actually happens, the expenses due to the losses will be accepted because it was already considered in its budget as 'Losses due to accident' and it will be shared by the partners of the project according to the correspondent contractual terms. This paper describes real cases of the evolution of predictions for natural hazards in offshore basins in Brazil, Western Africa and Gulf of Mexico where PETROBRAS and many other oil companies have used DP operations. It proposes some alternative procedures through the BCM (Business Continuity Management) to manage natural crisis instead of the common use of the traditional 'force majeure' argumentation. (author)

Educational and training aspects of new surgical techniques: experience with the endoscopic–laparoscopic interdisciplinary training entity (ELITE) model in training for a natural orifice translumenal endoscopic surgery (NOTES) approach to appendectomy.

Science.gov (United States)

Gillen, Sonja; Gröne, Jörn; Knödgen, Fritz; Wolf, Petra; Meyer, Michael; Friess, Helmut; Buhr, Heinz-Johannes; Ritz, Jörg-Peter; Feussner, Hubertus; Lehmann, Kai S

2012-08-01

Natural orifice translumenal endoscopic surgery (NOTES) is a new surgical concept that requires training before it is introduced into clinical practice. The endoscopic–laparoscopic interdisciplinary training entity (ELITE) is a training model for NOTES interventions. The latest research has concentrated on new materials for organs with realistic optical and haptic characteristics and the possibility of high-frequency dissection. This study aimed to assess both the ELITE model in a surgical training course and the construct validity of a newly developed NOTES appendectomy scenario. The 70 attendees of the 2010 Practical Course for Visceral Surgery (Warnemuende, Germany) took part in the study and performed a NOTES appendectomy via a transsigmoidal access. The primary end point was the total time required for the appendectomy, including retrieval of the appendix. Subjective evaluation of the model was performed using a questionnaire. Subgroups were analyzed according to laparoscopic and endoscopic experience. The participants with endoscopic or laparoscopic experience completed the task significantly faster than the inexperienced participants (p = 0.009 and 0.019, respectively). Endoscopic experience was the strongest influencing factor, whereas laparoscopic experience had limited impact on the participants with previous endoscopic experience. As shown by the findings, 87.3% of the participants stated that the ELITE model was suitable for the NOTES training scenario, and 88.7% found the newly developed model anatomically realistic. This study was able to establish face and construct validity for the ELITE model with a large group of surgeons. The ELITE model seems to be well suited for the training of NOTES as a new surgical technique in an established gastrointestinal surgery skills course.

A New Approach to Predict the Fish Fillet Shelf-Life in Presence of Natural Preservative Agents.

Science.gov (United States)

Giuffrida, Alessandro; Giarratana, Filippo; Valenti, Davide; Muscolino, Daniele; Parisi, Roberta; Parco, Alessio; Marotta, Stefania; Ziino, Graziella; Panebianco, Antonio

2017-04-13

Three data sets concerning the behaviour of spoilage flora of fillets treated with natural preservative substances (NPS) were used to construct a new kind of mathematical predictive model. This model, unlike other ones, allows expressing the antibacterial effect of the NPS separately from the prediction of the growth rate. This approach, based on the introduction of a parameter into the predictive primary model, produced a good fitting of observed data and allowed characterising quantitatively the increase of shelf-life of fillets.

Confidence scores for prediction models

DEFF Research Database (Denmark)

Gerds, Thomas Alexander; van de Wiel, MA

2011-01-01

In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

Testing a cue outside the training context increases attention to the contexts and impairs performance in human predictive learning.

Science.gov (United States)

Aristizabal, José A; Ramos-Álvarez, Manuel M; Callejas-Aguilera, José E; Rosas, Juan M

2017-12-01

One experiment in human predictive learning explored the impact of a context change on attention to contexts and predictive ratings controlled by the cue. In Context A: cue X was paired with an outcome four times, while cue Y was presented without an outcome four times in Context B:. In both contexts filler cues were presented without the outcome. During the test, target cues X and Y were presented either in the context where they were trained, or in the alternative context. With the context change expectation of the outcome X, expressed as predictive ratings, decreased in the presence of X and increased in the presence of Y. Looking at the contexts, expressed as a percentage of the overall gaze dwell time on a trial, was high across the four training trials, and increased with the context change. Results suggest that the presentation of unexpected information leads to increases in attention to contextual cues. Implications for contextual control of behavior are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Individually - oriented a marching during study of natural-science disciplines - a basis of forming of professional competence of the future teacher of physical training.

Directory of Open Access Journals (Sweden)

Omelchuk O.V.

2010-08-01

Full Text Available Directions of forming of professional competence of the future teachers are construed during study of natural-science disciplines. It is revealed, that in individually - oriented training one of the most efficient forms of preparation training is. Training is considered as the scheduled process of refilling of skills, knowledge, checks of the relation, idea, conduct. It is indicated, that the procedure of training rests on capabilities, tendencies, interests, valuable orientations, subject experience. She ensures intellectual development which affects in educational reachings.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

Science.gov (United States)

Zhang, Xingyu; Kim, Joyce; Patzer, Rachel E; Pitts, Stephen R; Patzer, Aaron; Schrager, Justin D

2017-10-26

To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient

Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array.

Science.gov (United States)

Mueller, Amy V; Hemond, Harold F

2016-05-18

Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of

Traditional and non-traditional approaches to the prediction of natural disasters

Science.gov (United States)

Sapunov, Valentin; Glazyrina, Tatiana

2016-04-01

Since the beginning of the 21st century the number of disasters in the world increased approximately two times. Damage from disasters cost an average of 230 billion dollars per year. Recently, the death toll in the disaster has reached 230,000 - 1 000,000 per year. Along with earthquakes, tsunamis, floods, increased the number of forest and steppe fires. These processes are not fully known global, geophysical and space reasons. Of great importance are perennial not until the end of the study of natural cycles. There is evidence that the state of the planet's surface affect processes in the Earth's core. Understanding the causes and prediction of the tragic events require an integrated effort based on the synthesis of various sciences as well as history which has knowledge about the disasters of the past. Factor that reduces the risk is constant monitoring, including both distant and contact methods. However, its possibility is limited. Firstly, due to the high cost of global, especially space monitoring. Secondly, due to the unpredictability of some processes. In December 2004, the countries of Southeast Asia hit by the tsunami. The death gotten 250 000 people. Animals in this cataclysm appeared to stay safety and advance left the danger zone. Animals are able to predict hazards having no materials predecessors. Participants nuclear tests show - a day before the explosion of the animals escape dangerous zone. This means that animals have the ability to predict the catastrophic events. The most important abiotic factor, the physical nature of which is still not clear is time. One of the scientists, who achieved some success in the study of time, was N.Kozyrev (1908-1983). He devoted his life to the study of the phenomenon of time and attempt to systematize the knowledge of him as a physical substance. Kozyrev in his theoretical calculations and experiments found the new field - the field of time (chrono-information). Through it can instantly and accurately transmit

Modelling techniques for predicting the long term consequences of radiation on natural aquatic populations

International Nuclear Information System (INIS)

Wallis, I.G.

1978-01-01

The purpose of this working paper is to describe modelling techniques for predicting the long term consequences of radiation on natural aquatic populations. Ideally, it would be possible to use aquatic population models: (1) to predict changes in the health and well-being of all aquatic populations as a result of changing the composition, amount and location of radionuclide discharges; (2) to compare the effects of steady, fluctuating and accidental releases of radionuclides; and (3) to evaluate the combined impact of the discharge of radionuclides and other wastes, and natural environmental stresses on aquatic populations. At the onset it should be stated that there is no existing model which can achieve this ideal performance. However, modelling skills and techniques are available to develop useful aquatic population models. This paper discusses the considerations involved in developing these models and briefly describes the various types of population models which have been developed to date

Self-organized natural roads for predicting traffic flow: a sensitivity study

International Nuclear Information System (INIS)

Jiang, Bin; Zhao, Sijian; Yin, Junjun

2008-01-01

In this paper, we extended road-based topological analysis to both nationwide and urban road networks, and concentrated on a sensitivity study with respect to the formation of self-organized natural roads based on the Gestalt principle of good continuity. Both annual average daily traffic (AADT) and global positioning system (GPS) data were used to correlate with a series of ranking metrics including five centrality-based metrics and two PageRank metrics. It was found that there exists a tipping point from segment-based to road-based network topology in terms of correlation between ranking metrics and their traffic. To our great surprise, (1) this correlation is significantly improved if a selfish rather than utopian strategy is adopted in forming the self-organized natural roads, and (2) point-based metrics assigned by summation into individual roads tend to have a much better correlation with traffic flow than line-based metrics. These counter-intuitive surprising findings constitute emergent properties of self-organized natural roads, which are intelligent enough for predicting traffic flow, thus shedding substantial light on the understanding of road networks and their traffic from the perspective of complex networks

A new approach to predict the fish fillet shelf-life in presence of natural preservative agents

Directory of Open Access Journals (Sweden)

Alessandro Giufffrida

2017-06-01

Full Text Available Three data sets concerning the behaviour of spoilage flora of fillets treated with natural preservative substances (NPS were used to construct a new kind of mathematical predictive model. This model, unlike other ones, allows expressing the antibacterial effect of the NPS separately from the prediction of the growth rate. This approach, based on the introduction of a parameter into the predictive primary model, produced a good fitting of observed data and allowed characterising quantitatively the increase of shelf-life of fillets.

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.

Predicting the required number of training samples. [for remotely sensed image data based on covariance matrix estimate quality criterion of normal distribution

Science.gov (United States)

Kalayeh, H. M.; Landgrebe, D. A.

1983-01-01

A criterion which measures the quality of the estimate of the covariance matrix of a multivariate normal distribution is developed. Based on this criterion, the necessary number of training samples is predicted. Experimental results which are used as a guide for determining the number of training samples are included. Previously announced in STAR as N82-28109

Enhanced learning of proportional math through music training and spatial-temporal training.

Science.gov (United States)

Graziano, A B; Peterson, M; Shaw, G L

1999-03-01

It was predicted, based on a mathematical model of the cortex, that early music training would enhance spatial-temporal reasoning. We have demonstrated that preschool children given six months of piano keyboard lessons improved dramatically on spatial-temporal reasoning while children in appropriate control groups did not improve. It was then predicted that the enhanced spatial-temporal reasoning from piano keyboard training could lead to enhanced learning of specific math concepts, in particular proportional math, which is notoriously difficult to teach using the usual language-analytic methods. We report here the development of Spatial-Temporal Math Video Game software designed to teach fractions and proportional math, and its strikingly successful use in a study involving 237 second-grade children (age range six years eight months-eight years five months). Furthermore, as predicted, children given piano keyboard training along with the Math Video Game training scored significantly higher on proportional math and fractions than children given a control training along with the Math Video Game. These results were readily measured using the companion Math Video Game Evaluation Program. The training time necessary for children on the Math Video Game is very short, and they rapidly reach a high level of performance. This suggests that, as predicted, we are tapping into fundamental cortical processes of spatial-temporal reasoning. This spatial-temporal approach is easily generalized to teach other math and science concepts in a complementary manner to traditional language-analytic methods, and at a younger age. The neural mechanisms involved in thinking through fractions and proportional math during training with the Math Video Game might be investigated in EEG coherence studies along with priming by specific music.

Prediction of resource volumes at untested locations using simple local prediction models

Science.gov (United States)

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

Interspecific communication from people to horses (Equus ferus caballus) is influenced by different horsemanship training styles.

Science.gov (United States)

Dorey, Nicole R; Conover, Alicia M; Udell, Monique A R

2014-11-01

The ability of many domesticated animals to follow human pointing gestures to locate hidden food has led to scientific debate on the relative importance of domestication and individual experience on the origins and development of this capacity. To further explore this question, we examined the influence of different prior training histories/methods on the ability of horses (Equus ferus caballus) to follow a momentary distal point. Ten horses previously trained using one of two methods (Parelli™ natural horsemanship or traditional horse training) were tested using a standard object choice task. The results show that neither group of horses was initially able to follow the momentary distal point. However, after more experience with the point, horses previously trained using the Parelli natural horsemanship method learned to follow momentary distal points significantly faster than those previously trained with traditional methods. The poor initial performance of horses on distal pointing tasks, coupled with the finding that prior training history and experimental experience can lead to success on this task, fails to support the predictions of the domestication hypothesis and instead lends support to the two-stage hypothesis. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Analysis of a Moodle-Based Training Program about the Pedagogical Content Knowledge of Evolution Theory and Natural Selection

Science.gov (United States)

Stasinakis, Panagiotis K.; Kalogiannnakis, Michail

2017-01-01

In this study we aim to find out whether a training program for secondary school science teachers which was organized based on the model of Pedagogical Content Knowledge (PCK), could improve their individual PCK for a specific scientific issue. The Evolution Theory (ET) and the Natural Selection (NS) were chosen as the scientific issues of…

A neural network - based algorithm for predicting stone - free status after ESWL therapy.

Science.gov (United States)

Seckiner, Ilker; Seckiner, Serap; Sen, Haluk; Bayrak, Omer; Dogan, Kazim; Erturhan, Sakip

2017-01-01

The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones. Copyright® by the International Brazilian Journal of Urology.

On the best learning algorithm for web services response time prediction

DEFF Research Database (Denmark)

Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

2013-01-01

In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

Prediction of knock limited operating conditions of a natural gas engine

International Nuclear Information System (INIS)

Soylu, Seref

2005-01-01

Computer models of engine processes are valuable tools for predicting and analyzing engine performance and allow exploration of many engine design alternatives in an inexpensive fashion. In the present work, a zero-dimensional, two zone thermodynamic model was used to determine the knock limited operating conditions of a natural gas engine. Experimentally based burning rate models were used for flame initiation and propagation calculations. A knock model was incorporated with the zero-dimensional model. Comparison of the measured and calculated cylinder pressure data indicated that the model is able to match the measured cylinder pressure data with less than 8% error in magnitudes if the computations are started at the experimental spark timing. The knock predictions agreed with the measurements also. With the established knock model, it is possible not only to investigate whether knock is observed with changing operating and design parameters, but also to evaluate their effects on the maximum possible knock intensity

A prediction method of natural gas hydrate formation in deepwater gas well and its application

Directory of Open Access Journals (Sweden)

Yanli Guo

2016-09-01

Full Text Available To prevent the deposition of natural gas hydrate in deepwater gas well, the hydrate formation area in wellbore must be predicted. Herein, by comparing four prediction methods of temperature in pipe with field data and comparing five prediction methods of hydrate formation with experiment data, a method based on OLGA & PVTsim for predicting the hydrate formation area in wellbore was proposed. Meanwhile, The hydrate formation under the conditions of steady production, throttling and shut-in was predicted by using this method based on a well data in the South China Sea. The results indicate that the hydrate formation area decreases with the increase of gas production, inhibitor concentrations and the thickness of insulation materials and increases with the increase of thermal conductivity of insulation materials and shutdown time. Throttling effect causes a plunge in temperature and pressure in wellbore, thus leading to an increase of hydrate formation area.

Ethical dilemmas related to predictions and warnings of impending natural disaster.

Science.gov (United States)

Phua, Kai-Lit; Hue, J W

2013-01-01

Scientists and policy makers issuing predictions and warnings of impending natural disaster are faced with two major challenges, that is, failure to warn and issuing a false alarm. The consequences of failure to warn can be serious for society overall, for example, significant economic losses, heavy infrastructure and environmental damage, large number of human casualties, and social disruption. Failure to warn can also have serious for specific individuals, for example, legal proceedings against disaster research scientists, as in the L'Aquila earthquake affair. The consequences of false alarms may be less serious. Nevertheless, false alarms may violate the principle of nonmaleficence (do no harm), affect individual autonomy (eg, mandatory evacuations), and may result in the "cry wolf" effect. Other ethical issues associated with natural disasters include the promotion of global justice through international predisaster technical assistance and postdisaster aid. Social justice within a particular country is promoted through greater postdisaster aid allocation to the less privileged.

Predicting Attrition in a Military Special Program Training Command

Science.gov (United States)

2016-05-20

made by assessing additional psychological factors. Specifically, motivation (s) to enter the training program (e.g., intrinsic versus extrinsic ...this and other training programs. Motivations to enter the training program could be assessed using a measure such as the Work Extrinsic and...MEDICINE GRADUATE PROGRAMS Graduate Education Office (A 1045), 4301 Jones Bridge Road, Bethesda, MD 20814 APPROVAL OF THE DOCTORAL DISSERTATION IN THE

Recommendations for natural bodybuilding contest preparation: resistance and cardiovascular training.

Science.gov (United States)

Helms, E R; Fitschen, P J; Aragon, A A; Cronin, J; Schoenfeld, B J

2015-03-01

The anabolic effect of resistance training can mitigate muscle loss during contest preparation. In reviewing relevant literature, we recommend a periodized approach be utilized. Block and undulating models show promise. Muscle groups should be trained 2 times weekly or more, although high volume training may benefit from higher frequencies to keep volume at any one session from becoming excessive. Low to high (~3-15) repetitions can be utilized but most repetitions should occur in the 6-12 range using 70-80% of 1 repetition maximum. Roughly 40-70 reps per muscle group per session should be performed, however higher volume may be appropriate for advanced bodybuilders. Traditional rest intervals of 1-3 minutes are adequate, but longer intervals can be used. Tempo should allow muscular control of the load; 1-2 s concentric and 2-3 s eccentric tempos. Training to failure should be limited when performing heavy loads on taxing exercises, and primarily relegated to single-joint exercises and higher repetitions. A core of multi-joint exercises with some single-joint exercises to address specific muscle groups as needed should be used, emphasizing full range of motion and proper form. Cardiovascular training can be used to enhance fat loss. Interference with strength training adaptations increases concomitantly with frequency and duration of cardiovascular training. Thus, the lowest frequency and duration possible while achieving sufficient fat loss should be used. Full-body modalities or cycling may reduce interference. High intensities may as well; however, require more recovery. Fasted cardiovascular training may not have benefits over fed-state and could be detrimental.

Prediction of the diffuse-field transmission loss of interior natural-ventilation openings and silencers.

Science.gov (United States)

Bibby, Chris; Hodgson, Murray

2017-01-01

The work reported here, part of a study on the performance and optimal design of interior natural-ventilation openings and silencers ("ventilators"), discusses the prediction of the acoustical performance of such ventilators, and the factors that affect it. A wave-based numerical approach-the finite-element method (FEM)-is applied. The development of a FEM technique for the prediction of ventilator diffuse-field transmission loss is presented. Model convergence is studied with respect to mesh, frequency-sampling and diffuse-field convergence. The modeling technique is validated by way of predictions and the comparison of them to analytical and experimental results. The transmission-loss performance of crosstalk silencers of four shapes, and the factors that affect it, are predicted and discussed. Performance increases with flow-path length for all silencer types. Adding elbows significantly increases high-frequency transmission loss, but does not increase overall silencer performance which is controlled by low-to-mid-frequency transmission loss.

Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF neural networks

Directory of Open Access Journals (Sweden)

Tatar Afshin

2016-03-01

Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.

The Fatigue Life Prediction of Train Wheel Rims Containing Spherical Inclusions

Science.gov (United States)

Li, Yajie; Chen, Huanguo; Cai, Li; Chen, Pei; Qian, Jiacheng; Wu, Jianwei

2018-03-01

It is a common phenomenon that fatigue crack initiation occurs frequently in the inclusions of wheel rims. Research on the fatigue life of wheel rims with spherical inclusions is of great significance on the reliability of wheels. To find the danger point and working condition of a wheel, the stress state of the wheel rim with spherical inclusions was analyzed using the finite element method. Results revealed that curve conditions are dangerous. The critical plane method, based on the cumulative fatigue damage theory, was used to predict the fatigue life of the wheel rim and whether it contained spherical inclusions or not under curve conditions. It was found that the fatigue life of the wheel rim is significantly shorter when the wheel rim contains spherical inclusions. Analysis of the results can provide a theoretical basis and technical support for train operations and maintenance.

Which way the natural gas price. An attempt to predict the direction of natural gas spot price movements using trader positions

International Nuclear Information System (INIS)

Buchanan, W.K.; Hodges, P.; Theis, J.

2001-01-01

This research provides a method of predicting direction of spot price movements in the natural gas market for the month succeeding from market participants positions in the futures market. Cumby and Modest (Cumby, R.E., Modest, D.M., 1987. Testing for market timing ability: a framework for forecast evaluation. Journal of Financial Economics 19, 169-189) provide the backdrop for analyzing the futures market positions of large hedgers and speculators to arrive at conclusions of market price movements in the spot market. This methodology is suggested as a means for municipalities entering the natural gas market to improve upon their ordering of quantities of gas for the ensuing months in order to take advantage of possibly foreseeable price trends

Natural Analoges as a Check of Predicted Drift Stability at Yucca Mountain, Nevada

International Nuclear Information System (INIS)

J. Stuckless

2006-01-01

Calculations made by the U.S. Department of Energy's Yucca Mountain Project as part of the licensing of a proposed geologic repository (in southwestern Nevada) for the disposal of high-level radioactive waste, predict that emplacement tunnels will remain open with little collapse long after ground support has disintegrated. This conclusion includes the effects of anticipated seismic events. Natural analogues cannot provide a quantitative test of this conclusion, but they can provide a reasonableness test by examining the natural and anthropogenic examples of stability of subterranean openings. Available data from a variety of sources, combined with limited observations by the author, show that natural underground openings tend to resist collapse for millions of years and that anthropogenic subterranean openings have remained open from before recorded history through today. This stability is true even in seismically active areas. In fact, the archaeological record is heavily skewed toward preservation of underground structures relative to those found at the surface

Natural Analoges as a Check of Predicted Drift Stability at Yucca Mountain, Nevada

Energy Technology Data Exchange (ETDEWEB)

J. Stuckless

2006-03-10

Calculations made by the U.S. Department of Energy's Yucca Mountain Project as part of the licensing of a proposed geologic repository (in southwestern Nevada) for the disposal of high-level radioactive waste, predict that emplacement tunnels will remain open with little collapse long after ground support has disintegrated. This conclusion includes the effects of anticipated seismic events. Natural analogues cannot provide a quantitative test of this conclusion, but they can provide a reasonableness test by examining the natural and anthropogenic examples of stability of subterranean openings. Available data from a variety of sources, combined with limited observations by the author, show that natural underground openings tend to resist collapse for millions of years and that anthropogenic subterranean openings have remained open from before recorded history through today. This stability is true even in seismically active areas. In fact, the archaeological record is heavily skewed toward preservation of underground structures relative to those found at the surface.

New studies of the natural convection around a fuel rod of the BME training reactor with PIV/LIF technique

International Nuclear Information System (INIS)

Szijarto, R.; Aszodi, A.; Yamaji, B.

2011-01-01

In this paper the model of a fuel pin of the Training Reactor of Budapest University of Technology and Economics was investigated with Particle Image Velocimetry and Laser Induced Fluorescence measurement methods. An experimental setup was designed, built and optimized to investigate the natural convection around a model of a fuel pin of the Training Reactor. The processes were analysed using an electrically heated rod, which models the geometry of the fuel rods in the Training Reactor. The heated length of the model is the same as the active length of the real fuel rods. The rod is placed in a glass tank with a shape of a square-based prism. An additional cooling system ensures constant flow conditions around the rod. The setup consists of an additional flow channel box, the equivalent diameter of which is equal to the equivalent diameter of the real fuel assembly. Simultaneous measurements of velocity and temperature fields were performed in different vertical positions for both cases of natural convection with and without the flow channel box. The effect of the presence of the channel was analyzed, and a laminarizating influence was observed. The local heat transfer coefficient was calculated for every measurement. The two dimensional measurement techniques gave extensive results, the structure of the hydraulic and thermal boundary layer were fully analyzed. (Authors)

PHMC post-NPH emergency response training

International Nuclear Information System (INIS)

Conrads, T.J.

1997-01-01

This document describes post-Natural Phenomena Hazard (NPH) emergency response training that was provided to two teams of Project Hanford Management Contractors (PHMC) staff that will be used to assess potential structural damage that may occur as a result of a significant natural phenomena event. This training supports recent plans and procedures to use trained staff to inspect structures following an NPH event on the Hanford Site

PHMC post-NPH emergency response training

Energy Technology Data Exchange (ETDEWEB)

Conrads, T.J.

1997-04-08

This document describes post-Natural Phenomena Hazard (NPH) emergency response training that was provided to two teams of Project Hanford Management Contractors (PHMC) staff that will be used to assess potential structural damage that may occur as a result of a significant natural phenomena event. This training supports recent plans and procedures to use trained staff to inspect structures following an NPH event on the Hanford Site.

Back to the Roots: Prediction of Biologically Active Natural Products from Ayurveda Traditional Medicine

DEFF Research Database (Denmark)

Polur, Honey; Joshi, Tejal; Workman, Christopher

2011-01-01

Ayurveda, the traditional Indian medicine is one of the most ancient, yet living medicinal traditions. In the present work, we developed an in silico library of natural products from Ayurveda medicine, coupled with structural information, plant origin and traditional therapeutic use. Following this....... We hereby present a number of examples where the traditional medicinal use of the plant matches with the medicinal use of the drug that is structurally similar to a plant component. With this approach, we have brought to light a number of obscure compounds of natural origin (e.g. kanugin......, we compared their structures with those of drugs from DrugBank and we constructed a structural similarity network. Information on the traditional therapeutic use of the plants was integrated in the network in order to provide further evidence for the predicted biologically active natural compounds...

Prediction During Natural Language Comprehension.

Science.gov (United States)

Willems, Roel M; Frank, Stefan L; Nijhof, Annabel D; Hagoort, Peter; van den Bosch, Antal

2016-06-01

The notion of prediction is studied in cognitive neuroscience with increasing intensity. We investigated the neural basis of 2 distinct aspects of word prediction, derived from information theory, during story comprehension. We assessed the effect of entropy of next-word probability distributions as well as surprisal A computational model determined entropy and surprisal for each word in 3 literary stories. Twenty-four healthy participants listened to the same 3 stories while their brain activation was measured using fMRI. Reversed speech fragments were presented as a control condition. Brain areas sensitive to entropy were left ventral premotor cortex, left middle frontal gyrus, right inferior frontal gyrus, left inferior parietal lobule, and left supplementary motor area. Areas sensitive to surprisal were left inferior temporal sulcus ("visual word form area"), bilateral superior temporal gyrus, right amygdala, bilateral anterior temporal poles, and right inferior frontal sulcus. We conclude that prediction during language comprehension can occur at several levels of processing, including at the level of word form. Our study exemplifies the power of combining computational linguistics with cognitive neuroscience, and additionally underlines the feasibility of studying continuous spoken language materials with fMRI. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Narrowing the scope of failure prediction using targeted fault load injection

Science.gov (United States)

Jordan, Paul L.; Peterson, Gilbert L.; Lin, Alan C.; Mendenhall, Michael J.; Sellers, Andrew J.

2018-05-01

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend the focus of traditional fault injection techniques to predict failure in the Microsoft enterprise authentication service and Apache web server. These new fault loads were successful in creating failure conditions that were identifiable using statistical learning methods, with fewer irrelevant faults being created.

Private Training Providers in Australia: Their Characteristics and Training Activities. A National Vocational Education and Training Research and Evaluation Program Report

Science.gov (United States)

Harris, Roger; Simons, Michele; McCarthy, Carmel

2006-01-01

This study examines the nature of the training activity of private registered training organisations (RTOs) offered to Australian students in 2003, based on data from a national sample of 330 RTOs. The study also provides estimates of the private sector's overall contribution to the total vocational education and training (VET) effort in Australia…

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

Science.gov (United States)

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

Long-term predictions using natural analogues

International Nuclear Information System (INIS)

Ewing, R.C.

1995-01-01

One of the unique and scientifically most challenging aspects of nuclear waste isolation is the extrapolation of short-term laboratory data (hours to years) to the long time periods (10 3 -10 5 years) required by regulatory agencies for performance assessment. The direct validation of these extrapolations is not possible, but methods must be developed to demonstrate compliance with government regulations and to satisfy the lay public that there is a demonstrable and reasonable basis for accepting the long-term extrapolations. Natural systems (e.g., open-quotes natural analoguesclose quotes) provide perhaps the only means of partial open-quotes validation,close quotes as well as data that may be used directly in the models that are used in the extrapolation. Natural systems provide data on very large spatial (nm to km) and temporal (10 3 -10 8 years) scales and in highly complex terranes in which unknown synergisms may affect radionuclide migration. This paper reviews the application (and most importantly, the limitations) of data from natural analogue systems to the open-quotes validationclose quotes of performance assessments

Data and prediction of water content of high pressure nitrogen, methane and natural gas

DEFF Research Database (Denmark)

Folas, Georgios; Froyna, E.W.; Lovland, J.

2007-01-01

New data for the equilibrium water content of nitrogen, methane and one natural gas mixture are presented. The new binary data and existing binary sets were compared to calculated values of dew point temperature using both the CPA (Cubic-Plus-Association) EoS and the GERG-water EoS. CPA is purely...... predictive (i.e. all binary interaction parameters are set equal to 0), while GERG-water uses a temperature dependent interaction parameter fitted to published data. The GERG-water model is proposed as an ISO standard for determining the water content of natural gas. The data sets for nitrogen cover...... conclusion is that GERG-water must be used with caution outside its specified working range. For some selected natural gas mixtures the two models also perform very much alike. The water content of the mixtures decreases with increasing amount of heavier components, and it seems that both models slightly...

Counseling psychology trainees' perceptions of training and commitments to social justice.

Science.gov (United States)

Beer, Amanda M; Spanierman, Lisa B; Greene, Jennifer C; Todd, Nathan R

2012-01-01

This mixed methods study examined social justice commitments of counseling psychology graduate trainees. In the quantitative portion of the study, a national sample of trainees (n = 260) completed a web-based survey assessing their commitments to social justice and related personal and training variables. Results suggested that students desired greater social justice training than what they experienced in their programs. In the qualitative portion, we used a phenomenological approach to expand and elaborate upon quantitative results. A subsample (n = 7) of trainees who identified as strong social justice activists were interviewed regarding their personal, professional, and training experiences. Eleven themes related to participants' meanings of and experiences with social justice emerged within 4 broad categories: nature of social justice, motivation for activism, role of training, and personal and professional integration. Thematic findings as well as descriptive statistics informed the selection and ordering of variables in a hierarchical regression analysis that examined predictors of social justice commitment. Results indicated that trainees' perceptions of training environment significantly predicted their social justice commitment over and above their general activist orientation and spirituality. Findings are discussed collectively, and implications for training and future research are provided. (c) 2012 APA, all rights reserved.

From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing

Directory of Open Access Journals (Sweden)

Juan Andres Laura

2018-03-01

Full Text Available In recent studies Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data Compression is also based on prediction. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in the natural language processing tasks of sentiment analysis and automatic text generation. If this is possible, then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in such tasks. In our journey, a fundamental difference between a Data Compression Algorithm and Recurrent Neural Networks has been discovered.

Predicting human activities in sequences of actions in RGB-D videos

Science.gov (United States)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

The nature and prevalence of injury during CrossFit training.

Science.gov (United States)

Hak, Paul Taro; Hodzovic, Emil; Hickey, Ben

2013-11-22

CrossFit is a constantly varied, high intensity, functional movement strength and conditioning program which has seen a huge growth in popularity around the world since its inception twelve years ago. There has been much criticism as to the potential injuries associated with CrossFit training including rhabdomyolysis and musculoskeletal injuries. However to date no evidence exists in the literature to the injures and rates sustained. The purpose of this study was to determine the injury rates and profiles of CrossFit athletes sustained during routine CrossFit training. An online questionnaire was distributed amongst international CrossFit online forums. Data collected included general demographics, training programs, injury profiles and supplement use. A total of 132 responses were collected with 97 (73.5%) having sustained an injury during CrossFit training. A total of 186 injuries were reported with 9 (7.0%) requiring surgical intervention. An injury rate of 3.1 per 1000 hours trained was calculated. No incidences of rhabdomyolysis were reported. Injury rates with CrossFit training are similar to that reported in the literature for sports such as Olympic weight-lifting, power-lifting and gymnastics and lower than competitive contact sports such as rugby union and rugby league. Shoulder and spine injuries predominate with no incidences of rhabdomyolysis obtained. To our knowledge this is the first paper in the literature detailing the injury rates and profiles with CrossFit participation.

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.

GPA/GPSA/OSU-Okmulgee natural gas compression technician training program

Energy Technology Data Exchange (ETDEWEB)

Doede, S.

1999-07-01

Approximately one year ago, OSU-Okmulgee and the Gas Processors Association began discussions about the possibility of developing a natural Gas Technician Training Program for GPA members. Following a presentation to the Membership and Services Committee, Chairman John Ehlers solicited and obtained the approval of the GPA Executive Committee to sponsor the program. Participation in the program was also made available to GPSA members. The purpose of the program is to upgrade the technical competency and professional level of incoming natural gas compression technicians. It educates students to analytically diagnose, service and maintain gas compression equipment and systems using industry recommended procedures, special tools and service information. It also provides course content, which will enable successful graduates to advance in position after additional experience, and to understand new systems, technologies and components as they are introduced. The two-year Associate-In-Applied Science Degree program includes six successive college semesters. Nearly one-half of the time is designated for technical/academic education at Oklahoma State University-Okmulgee with the balance of time allocated for on-the-job internship experiences at sponsoring GPA/GPSA members. Each block of technical education and general education course work is followed by an immediate work experience time period designated to reinforce the technical and general education. These time periods are approximately seven and one-half weeks in length each. It is essential for the success of the students and the program that the students' education at OSU-Okmulgee and work experiences at GPA/GPSA member facilities be closely aligned for maximum student learning and retention. In addition to technical classes on gas compression equipment and components, the courses offered in math, speech, technical writing, psychology and ethics for example, prepare students to be able to communicate well, get

A Prediction Study of Aluminum Alloy Oxidation of the Fuel Cladding in Jordan Research and Training Reactor

Energy Technology Data Exchange (ETDEWEB)

Tahk, Y. W.; Oh, J. Y.; Lee, B. H.; Seo, C. G.; Chae, H. T.; Yim, J. S. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2010-10-15

U{sub 3}Si{sub 2}-Al dispersion fuel with Al cladding will be used for Jordan Research and Training Reactor (JRTR). Aluminum alloy cladding experiences the oxidation layer growth on the surface during the reactor operation. The formation of oxides on the cladding affects fuel performance by increasing fuel temperature. According to the current JRTR fuel management scheme and operation strategy for 5 MW power, a fresh fuel is discharged after 900 effective full power days (EFPD) with 18 cycles of 50 days loading. For the proper prediction of the aluminum oxide thickness of fuel cladding during the long residence time, a reliable model is needed. In this work, several oxide thickness prediction models are compared with the measured data from in-pile test by RERTR program. Moreover, specific parametric studies and a preliminary prediction of the aluminum alloy oxidation using the latest model are performed for JRTR fuel

Mechanics of train collision

Science.gov (United States)

1976-04-30

A simple and a more detailed mathematical model for the simulation of train collisions are presented. The study presents considerable insight as to the causes and consequences of train motions on impact. Comparison of model predictions with two full ...

Measuring sense of presence and user characteristics to predict effective training in an online simulated virtual environment.

Science.gov (United States)

De Leo, Gianluca; Diggs, Leigh A; Radici, Elena; Mastaglio, Thomas W

2014-02-01

a model that predicts the level of presence based on the user characteristics. To maximize results and minimize costs, only those individuals who, based on their characteristics, are supposed to have a higher sense of presence and less negative effects could be selected for online simulated virtual environment training.

Probe train including a flaw detector and a radiation responsive recording means with alignment means having a natural curved cast

International Nuclear Information System (INIS)

Stone, R.M.

1975-01-01

An inspection system for a multitube steam generator comprising a probe train for insertion in a tube to be inspected is described. The probe train includes, in series, directional probe means, such as an eddy current probe, for indicating the longitudinal and angular location of an irregularity at or in the wall of the tube, and radiation responsive recording means nonrotatable relative to the eddy current probe during operation and in substantially close longitudinal relationship thereto for receiving an image of the irregularity when laterally adjacent thereto; elongated alignment means joined to at least one end of the probe train against rotation relative thereto and insertable in the tube for controlling or determining the angular orientation of the probe train within the tube; means for propelling the probe train longitudinally within the tube; and a source of radiation insertable in another tube of the steam generator to a position therealong laterally adjacent the indicated irregularity for irradiation of the irregularity to project said image on the recording means. The directional probe means may preferably be an eddy current probe and the radiation responsive recording means may preferably be a film bearing cassette probe. The alignment means may be provided by a resilient naturally curved plastic cable, which cable might also be used to propel the probe train. (auth)

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

Science.gov (United States)

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Alexander Technique Training Coupled With an Integrative Model of Behavioral Prediction in Teachers With Low Back Pain.

Science.gov (United States)

Kamalikhah, Tahereh; Morowatisharifabad, Mohammad Ali; Rezaei-Moghaddam, Farid; Ghasemi, Mohammad; Gholami-Fesharaki, Mohammad; Goklani, Salma

2016-09-01

Individuals suffering from chronic low back pain (CLBP) experience major physical, social, and occupational disruptions. Strong evidence confirms the effectiveness of Alexander technique (AT) training for CLBP. The present study applied an integrative model (IM) of behavioral prediction for improvement of AT training. This was a quasi-experimental study of female teachers with nonspecific LBP in southern Tehran in 2014. Group A contained 42 subjects and group B had 35 subjects. In group A, AT lessons were designed based on IM constructs, while in group B, AT lessons only were taught. The validity and reliability of the AT questionnaire were confirmed using content validity (CVR 0.91, CVI 0.96) and Cronbach's α (0.80). The IM constructs of both groups were measured after the completion of training. Statistical analysis used independent and paired samples t-tests and the univariate generalized linear model (GLM). Significant differences were recorded before and after intervention (P < 0.001) for the model constructs of intention, perceived risk, direct attitude, behavioral beliefs, and knowledge in both groups. Direct attitude and behavioral beliefs in group A were higher than in group B after the intervention (P < 0.03). The educational framework provided by IM for AT training improved attitude and behavioral beliefs that can facilitate the adoption of AT behavior and decreased CLBP.

The perception of naturalness correlates with low-level visual features of environmental scenes.

Directory of Open Access Journals (Sweden)

Marc G Berman

Full Text Available Previous research has shown that interacting with natural environments vs. more urban or built environments can have salubrious psychological effects, such as improvements in attention and memory. Even viewing pictures of nature vs. pictures of built environments can produce similar effects. A major question is: What is it about natural environments that produces these benefits? Problematically, there are many differing qualities between natural and urban environments, making it difficult to narrow down the dimensions of nature that may lead to these benefits. In this study, we set out to uncover visual features that related to individuals' perceptions of naturalness in images. We quantified naturalness in two ways: first, implicitly using a multidimensional scaling analysis and second, explicitly with direct naturalness ratings. Features that seemed most related to perceptions of naturalness were related to the density of contrast changes in the scene, the density of straight lines in the scene, the average color saturation in the scene and the average hue diversity in the scene. We then trained a machine-learning algorithm to predict whether a scene was perceived as being natural or not based on these low-level visual features and we could do so with 81% accuracy. As such we were able to reliably predict subjective perceptions of naturalness with objective low-level visual features. Our results can be used in future studies to determine if these features, which are related to naturalness, may also lead to the benefits attained from interacting with nature.

Mathematical models of human paralyzed muscle after long-term training.

Science.gov (United States)

Law, L A Frey; Shields, R K

2007-01-01

Spinal cord injury (SCI) results in major musculoskeletal adaptations, including muscle atrophy, faster contractile properties, increased fatigability, and bone loss. The use of functional electrical stimulation (FES) provides a method to prevent paralyzed muscle adaptations in order to sustain force-generating capacity. Mathematical muscle models may be able to predict optimal activation strategies during FES, however muscle properties further adapt with long-term training. The purpose of this study was to compare the accuracy of three muscle models, one linear and two nonlinear, for predicting paralyzed soleus muscle force after exposure to long-term FES training. Further, we contrasted the findings between the trained and untrained limbs. The three models' parameters were best fit to a single force train in the trained soleus muscle (N=4). Nine additional force trains (test trains) were predicted for each subject using the developed models. Model errors between predicted and experimental force trains were determined, including specific muscle force properties. The mean overall error was greatest for the linear model (15.8%) and least for the nonlinear Hill Huxley type model (7.8%). No significant error differences were observed between the trained versus untrained limbs, although model parameter values were significantly altered with training. This study confirmed that nonlinear models most accurately predict both trained and untrained paralyzed muscle force properties. Moreover, the optimized model parameter values were responsive to the relative physiological state of the paralyzed muscle (trained versus untrained). These findings are relevant for the design and control of neuro-prosthetic devices for those with SCI.

Personal best marathon time and longest training run, not anthropometry, predict performance in recreational 24-hour ultrarunners.

Science.gov (United States)

Knechtle, Beat; Knechtle, Patrizia; Rosemann, Thomas; Lepers, Romuald

2011-08-01

In recent studies, a relationship between both low body fat and low thicknesses of selected skinfolds has been demonstrated for running performance of distances from 100 m to the marathon but not in ultramarathon. We investigated the association of anthropometric and training characteristics with race performance in 63 male recreational ultrarunners in a 24-hour run using bi and multivariate analysis. The athletes achieved an average distance of 146.1 (43.1) km. In the bivariate analysis, body mass (r = -0.25), the sum of 9 skinfolds (r = -0.32), the sum of upper body skinfolds (r = -0.34), body fat percentage (r = -0.32), weekly kilometers ran (r = 0.31), longest training session before the 24-hour run (r = 0.56), and personal best marathon time (r = -0.58) were related to race performance. Stepwise multiple regression showed that both the longest training session before the 24-hour run (p = 0.0013) and the personal best marathon time (p = 0.0015) had the best correlation with race performance. Performance in these 24-hour runners may be predicted (r2 = 0.46) by the following equation: Performance in a 24-hour run, km) = 234.7 + 0.481 (longest training session before the 24-hour run, km) - 0.594 (personal best marathon time, minutes). For practical applications, training variables such as volume and intensity were associated with performance but not anthropometric variables. To achieve maximum kilometers in a 24-hour run, recreational ultrarunners should have a personal best marathon time of ∼3 hours 20 minutes and complete a long training run of ∼60 km before the race, whereas anthropometric characteristics such as low body fat or low skinfold thicknesses showed no association with performance.

Combating Training-Stress Syndromes.

Science.gov (United States)

Voight, Mike

2002-01-01

Addresses the nature and ramifications of various training stress syndromes (overtraining, under-recovery, distress, staleness, and burnout) that can accompany inappropriate training practices, examining the interventions that players and coaches can use to combat these syndromes (including physical, psychological, and performance interventions),…

Predicting Resident Performance from Preresidency Factors: A Systematic Review and Applicability to Neurosurgical Training.

Science.gov (United States)

Zuckerman, Scott L; Kelly, Patrick D; Dewan, Michael C; Morone, Peter J; Yengo-Kahn, Aaron M; Magarik, Jordan A; Baticulon, Ronnie E; Zusman, Edie E; Solomon, Gary S; Wellons, John C

2018-02-01

Neurosurgical educators strive to identify the best applicants, yet formal study of resident selection has proved difficult. We conducted a systematic review to answer the following question: What objective and subjective preresidency factors predict resident success? PubMed, ProQuest, Embase, and the CINAHL databases were queried from 1952 to 2015 for literature reporting the impact of preresidency factors (PRFs) on outcomes of residency success (RS), among neurosurgery and all surgical subspecialties. Due to heterogeneity of specialties and outcomes, a qualitative summary and heat map of significant findings were constructed. From 1489 studies, 21 articles met inclusion criteria, which evaluated 1276 resident applicants across five surgical subspecialties. No neurosurgical studies met the inclusion criteria. Common objective PRFs included standardized testing (76%), medical school performance (48%), and Alpha Omega Alpha (43%). Common subjective PRFs included aggregate rank scores (57%), letters of recommendation (38%), research (33%), interviews (19%), and athletic or musical talent (19%). Outcomes of RS included faculty evaluations, in-training/board exams, chief resident status, and research productivity. Among objective factors, standardized test scores correlated well with in-training/board examinations but poorly correlated with faculty evaluations. Among subjective factors, aggregate rank scores, letters of recommendation, and athletic or musical talent demonstrated moderate correlation with faculty evaluations. Standardized testing most strongly correlated with future examination performance but correlated poorly with faculty evaluations. Moderate predictors of faculty evaluations were aggregate rank scores, letters of recommendation, and athletic or musical talent. The ability to predict success of neurosurgical residents using an evidence-based approach is limited, and few factors have correlated with future resident performance. Given the importance of

A landscape analysis of leadership training in postgraduate medical education training programs at the University of Ottawa

Directory of Open Access Journals (Sweden)

Marlon Danilewitz

2016-10-01

Conclusions: While there is strong recognition of the importance of training future physician leaders, the nature and design of PGME leadership training is highly variable. These data can be used to potentially inform future PGME leadership training curricula.

Electrophysiological correlates of predictive coding of auditory location in the perception of natural audiovisual events.

Science.gov (United States)

Stekelenburg, Jeroen J; Vroomen, Jean

2012-01-01

In many natural audiovisual events (e.g., a clap of the two hands), the visual signal precedes the sound and thus allows observers to predict when, where, and which sound will occur. Previous studies have reported that there are distinct neural correlates of temporal (when) versus phonetic/semantic (which) content on audiovisual integration. Here we examined the effect of visual prediction of auditory location (where) in audiovisual biological motion stimuli by varying the spatial congruency between the auditory and visual parts. Visual stimuli were presented centrally, whereas auditory stimuli were presented either centrally or at 90° azimuth. Typical sub-additive amplitude reductions (AV - V audiovisual interaction was also found at 40-60 ms (P50) in the spatially congruent condition, while no effect of congruency was found on the suppression of the P2. This indicates that visual prediction of auditory location can be coded very early in auditory processing.

Self-organizing maps applied to two-phase flow on natural circulation loop studies

Energy Technology Data Exchange (ETDEWEB)

Castro, Leonardo F.; Cunha, Kelly de P.; Andrade, Delvonei A.; Sabundjian, Gaiane; Torres, Walmir M.; Macedo, Luiz A.; Rocha, Marcelo da S.; Masotti, Paulo H.F.; Mesquita, Roberto N. de, E-mail: rnavarro@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2015-07-01

Two-phase flow of liquid and gas is found in many closed circuits using natural circulation for cooling purposes. Natural circulation phenomenon is important on recent nuclear power plant projects for heat removal on 'loss of pump power' or 'plant shutdown' accidents. The accuracy of heat transfer estimation has been improved based on models that require precise prediction of pattern transitions of flow. Self-Organizing Maps are trained to digital images acquired on natural circulation flow instabilities. This technique will allow the selection of the more important characteristics associated with each flow pattern, enabling a better comprehension of each observed instability. This periodic flow oscillation behavior can be observed thoroughly in this facility due its glass-made tubes transparency. The Natural Circulation Facility (Circuito de Circulacao Natural - CCN) installed at Instituto de Pesquisas Energeticas e Nucleares, IPEN/CNEN, is an experimental circuit designed to provide thermal hydraulic data related to one and two phase flow under natural circulation conditions. (author)

Human factors in training

International Nuclear Information System (INIS)

Dutton, J.W.; Brown, W.R.

1981-01-01

The Human Factors concept is a focused effort directed at those activities which require human involvement. Training is, by its nature, an activity totally dependent on the Human Factor. This paper identifies several concerns significant to training situations and discusses how Human Factor awareness can increase the quality of learning. Psychology in the training arena is applied Human Factors. Training is a method of communication represented by sender, medium, and receiver. Two-thirds of this communications model involves the human element directly

Prediction of a thermodynamic wave train from the monsoon to the Arctic following extreme rainfall events

Science.gov (United States)

Krishnamurti, T. N.; Kumar, Vinay

2017-04-01

This study addresses numerical prediction of atmospheric wave trains that provide a monsoonal link to the Arctic ice melt. The monsoonal link is one of several ways that heat is conveyed to the Arctic region. This study follows a detailed observational study on thermodynamic wave trains that are initiated by extreme rain events of the northern summer south Asian monsoon. These wave trains carry large values of heat content anomalies, heat transports and convergence of flux of heat. These features seem to be important candidates for the rapid melt scenario. This present study addresses numerical simulation of the extreme rains, over India and Pakistan, and the generation of thermodynamic wave trains, simulations of large heat content anomalies, heat transports along pathways and heat flux convergences, potential vorticity and the diabatic generation of potential vorticity. We compare model based simulation of many features such as precipitation, divergence and the divergent wind with those evaluated from the reanalysis fields. We have also examined the snow and ice cover data sets during and after these events. This modeling study supports our recent observational findings on the monsoonal link to the rapid Arctic ice melt of the Canadian Arctic. This numerical modeling suggests ways to interpret some recent episodes of rapid ice melts that may require a well-coordinated field experiment among atmosphere, ocean, ice and snow cover scientists. Such a well-coordinated study would sharpen our understanding of this one component of the ice melt, i.e. the monsoonal link, which appears to be fairly robust.

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.

Statistical analysis in the design of nuclear fuel cells and training of a neural network to predict safety parameters for reactors BWR

International Nuclear Information System (INIS)

Jauregui Ch, V.

2013-01-01

In this work the obtained results for a statistical analysis are shown, with the purpose of studying the performance of the fuel lattice, taking into account the frequency of the pins that were used. For this objective, different statistical distributions were used; one approximately to normal, another type X 2 but in an inverse form and a random distribution. Also, the prediction of some parameters of the nuclear reactor in a fuel reload was made through a neuronal network, which was trained. The statistical analysis was made using the parameters of the fuel lattice, which was generated through three heuristic techniques: Ant Colony Optimization System, Neuronal Networks and a hybrid among Scatter Search and Path Re linking. The behavior of the local power peak factor was revised in the fuel lattice with the use of different frequencies of enrichment uranium pines, using the three techniques mentioned before, in the same way the infinite multiplication factor of neutrons was analyzed (k..), to determine within what range this factor in the reactor is. Taking into account all the information, which was obtained through the statistical analysis, a neuronal network was trained; that will help to predict the behavior of some parameters of the nuclear reactor, considering a fixed fuel reload with their respective control rods pattern. In the same way, the quality of the training was evaluated using different fuel lattices. The neuronal network learned to predict the next parameters: Shutdown Margin (SDM), the pin burn peaks for two different fuel batches, Thermal Limits and the Effective Neutron Multiplication Factor (k eff ). The results show that the fuel lattices in which the frequency, which the inverted form of the X 2 distribution, was used revealed the best values of local power peak factor. Additionally it is shown that the performance of a fuel lattice could be enhanced controlling the frequency of the uranium enrichment rods and the variety of the gadolinium

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)

Levels of naturally occurring gamma radiation measured in British homes and their prediction in particular residences

Energy Technology Data Exchange (ETDEWEB)

Kendall, G.M. [University of Oxford, Cancer Epidemiology Unit, Oxford (United Kingdom); Wakeford, R. [University of Manchester, Centre for Occupational and Environmental Health, Institute of Population Health, Manchester (United Kingdom); Athanson, M. [University of Oxford, Bodleian Library, Oxford (United Kingdom); Vincent, T.J. [University of Oxford, Childhood Cancer Research Group, Oxford (United Kingdom); Carter, E.J. [University of Worcester, Earth Heritage Trust, Geological Records Centre, Henwick Grove, Worcester (United Kingdom); McColl, N.P. [Public Health England, Centre for Radiation, Chemical and Environmental Hazards, Chilton, Didcot, Oxon (United Kingdom); Little, M.P. [National Cancer Institute, DHHS, NIH, Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, Bethesda, MD (United States)

2016-03-15

Gamma radiation from natural sources (including directly ionising cosmic rays) is an important component of background radiation. In the present paper, indoor measurements of naturally occurring gamma rays that were undertaken as part of the UK Childhood Cancer Study are summarised, and it is shown that these are broadly compatible with an earlier UK National Survey. The distribution of indoor gamma-ray dose rates in Great Britain is approximately normal with mean 96 nGy/h and standard deviation 23 nGy/h. Directly ionising cosmic rays contribute about one-third of the total. The expanded dataset allows a more detailed description than previously of indoor gamma-ray exposures and in particular their geographical variation. Various strategies for predicting indoor natural background gamma-ray dose rates were explored. In the first of these, a geostatistical model was fitted, which assumes an underlying geologically determined spatial variation, superimposed on which is a Gaussian stochastic process with Matern correlation structure that models the observed tendency of dose rates in neighbouring houses to correlate. In the second approach, a number of dose-rate interpolation measures were first derived, based on averages over geologically or administratively defined areas or using distance-weighted averages of measurements at nearest-neighbour points. Linear regression was then used to derive an optimal linear combination of these interpolation measures. The predictive performances of the two models were compared via cross-validation, using a randomly selected 70 % of the data to fit the models and the remaining 30 % to test them. The mean square error (MSE) of the linear-regression model was lower than that of the Gaussian-Matern model (MSE 378 and 411, respectively). The predictive performance of the two candidate models was also evaluated via simulation; the OLS model performs significantly better than the Gaussian-Matern model. (orig.)

Levels of naturally occurring gamma radiation measured in British homes and their prediction in particular residences

International Nuclear Information System (INIS)

Kendall, G.M.; Wakeford, R.; Athanson, M.; Vincent, T.J.; Carter, E.J.; McColl, N.P.; Little, M.P.

2016-01-01

Gamma radiation from natural sources (including directly ionising cosmic rays) is an important component of background radiation. In the present paper, indoor measurements of naturally occurring gamma rays that were undertaken as part of the UK Childhood Cancer Study are summarised, and it is shown that these are broadly compatible with an earlier UK National Survey. The distribution of indoor gamma-ray dose rates in Great Britain is approximately normal with mean 96 nGy/h and standard deviation 23 nGy/h. Directly ionising cosmic rays contribute about one-third of the total. The expanded dataset allows a more detailed description than previously of indoor gamma-ray exposures and in particular their geographical variation. Various strategies for predicting indoor natural background gamma-ray dose rates were explored. In the first of these, a geostatistical model was fitted, which assumes an underlying geologically determined spatial variation, superimposed on which is a Gaussian stochastic process with Matern correlation structure that models the observed tendency of dose rates in neighbouring houses to correlate. In the second approach, a number of dose-rate interpolation measures were first derived, based on averages over geologically or administratively defined areas or using distance-weighted averages of measurements at nearest-neighbour points. Linear regression was then used to derive an optimal linear combination of these interpolation measures. The predictive performances of the two models were compared via cross-validation, using a randomly selected 70 % of the data to fit the models and the remaining 30 % to test them. The mean square error (MSE) of the linear-regression model was lower than that of the Gaussian-Matern model (MSE 378 and 411, respectively). The predictive performance of the two candidate models was also evaluated via simulation; the OLS model performs significantly better than the Gaussian-Matern model. (orig.)

Prediction of flow recirculation in a blanket assembly under worst-case natural-convection conditions

International Nuclear Information System (INIS)

Khan, E.U.; Rector, D.R.

1982-01-01

Reactor fuel and blanket assemblies within a Liquid Metal Fast Breeder Reactor (LMFBR) can be subjected to severe radial heat flux gradients. At low-flow conditions, with power-to-flow ratios of nearly the same magnitude as design conditions, buoyancy forces cause flow redistribution to the side of a bundle with the higher heat generation rate. Recirculation of fluid within a rod bundle can occur during a natural convection transient because of the combined effect of flow coastdown and buoyancy-induced redistribution. An important concern is whether recirculation leads to high coolant temperatures. For this reason, the COBRA-WC code was developed with the capability of modeling recirculating flows. Experiments have been conducted in a 2 x 6 rod bundle for flow and power transients to study recirculation in the mixed-convection (forced cooled) and natural-convection regimes. The data base developed was used to validate the recirculation module in the COBRA-WC code. COBRA-WC code calculations were made to predict flow and temperature distributions in a typical LMFBR blanket assembly for the worst-case, natural-circulation transient

Predictability problems of global change as seen through natural systems complexity description. 2. Approach

Directory of Open Access Journals (Sweden)

Vladimir V. Kozoderov

1998-01-01

Full Text Available Developing the general statements of the proposed global change theory, outlined in Part 1 of the publication, Kolmogorov's probability space is used to study properties of information measures (unconditional, joint and conditional entropies, information divergence, mutual information, etc.. Sets of elementary events, the specified algebra of their sub-sets and probability measures for the algebra are composite parts of the space. The information measures are analyzed using the mathematical expectance operator and the adequacy between an additive function of sets and their equivalents in the form of the measures. As a result, explanations are given to multispectral satellite imagery visualization procedures using Markov's chains of random variables represented by pixels of the imagery. The proposed formalism of the information measures application enables to describe the natural targets complexity by syntactically governing probabilities. Asserted as that of signal/noise ratios finding for anomalies of natural processes, the predictability problem is solved by analyses of temporal data sets of related measurements for key regions and their background within contextually coherent structures of natural targets and between particular boundaries of the structures.

Natural language acquisition in large scale neural semantic networks

Science.gov (United States)

Ealey, Douglas

This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

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.

Predictive Models to Estimate Probabilities of Injuries and Adverse Performance Outcomes in U.S. Army Basic Combat Training

Science.gov (United States)

2014-03-01

orofacial injuries.10 These and other efforts have been associated with reduced BCT injuries over time as shown in Figure 111 but injury incidence...to predict first episode of low back pain in Soldiers undergoing combat medic training. Moran et al30 reported an AUG of . 765 for a pragmatic 5...Dugan JL, Robinson ME. Predictors of occurrence and severity of first time low back pain episodes: Findings from a military inception cohort. PLoS

Cleaning up a salt spill : predictive modelling and monitoring natural attenuation to save remedial costs

Energy Technology Data Exchange (ETDEWEB)

Tsang, B.; Shaikh, A.A. [EBA Engineering Consultants Ltd., Calgary, AB (Canada)

2006-07-01

Predictive modelling and monitoring natural attenuation to save remedial costs in cleaning up a salt spill were discussed with reference to a site located in central Alberta, as well as a pipeline break in 2002 from a corroded pipe which resulted in a large spill of produced water and oil. Remedial alternatives and an assessment of the site were presented. This included an electromagnetic survey in 2004, groundwater flow regime, soil and groundwater quality data, vegetation survey, and predictive modelling versus observed water quality. Photos and illustrations of the site from the air were provided. A conceptual salt leaching and transport model was proposed as a solution. Model calculation results were also presented. Last, the presentation discussed some important considerations for predictive modeling and next steps for the site. These included continued monitoring, implementation of a restoration plan and engagement of stakeholders such as Alberta Environment and the site landowner. tabs., figs.

A novel bridge scour monitoring and prediction system

Science.gov (United States)

Valyrakis, Manousos; Michalis, Panagiotis; Zhang, Hanqing

2015-04-01

Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the use of a novel methodology is proposed for the prediction of bridge scour. Specifically, the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation. Training of the system to new bridge geometries and flow conditions can be achieved by

Enhanced backpropagation training algorithm for transient event identification

International Nuclear Information System (INIS)

Vitela, J.; Reifman, J.

1993-01-01

We present an enhanced backpropagation (BP) algorithm for training feedforward neural networks that avoids the undesirable premature saturation of the network output nodes and accelerates the training process even in cases where premature saturation is not present. When the standard BP algorithm is applied to train patterns of nuclear power plant (NPP) transients, the network output nodes often become prematurely saturated causing the already slow rate of convergence of the algorithm to become even slower. When premature saturation occurs, the gradient of the prediction error becomes very small, although the prediction error itself is still large, yielding negligible weight updates and hence no significant decrease in the prediction error until the eventual recovery of the output nodes from saturation. By defining the onset of premature saturation and systematically modifying the gradient of the prediction error at saturation, we developed an enhanced BP algorithm that is compared with the standard BP algorithm in training a network to identify NPP transients

Strength Training: For Overall Fitness

Science.gov (United States)

Healthy Lifestyle Fitness Strength training is an important part of an overall fitness program. Here's what strength training can do for ... is a key component of overall health and fitness for everyone. Lean muscle mass naturally diminishes with ...

33 CFR 127.503 - Training: General.

Science.gov (United States)

2010-07-01

... operations. (5) LNG release response procedures. (6) First aid procedures for— (i) Frostbite; (ii) Burns... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Training: General. 127.503... Waterfront Facilities Handling Liquefied Natural Gas Personnel Training § 127.503 Training: General. The...

Do effects of theoretical training and rewards for energy-efficient behavior persist over time and interact? A natural field experiment on eco-driving in a company fleet

International Nuclear Information System (INIS)

Schall, Dominik L.; Wolf, Menas; Mohnen, Alwine

2016-01-01

Increasing energy efficiency is a cornerstone of policy initiatives to tackle climate change and increase corporate sustainability. Convincing people to drive more fuel-efficiently (“eco-driving”) is often an integral part of these approaches, especially in the transport sector. But there is a lack of studies on the long-term persistence and potential interaction of the effects of incentives and training on energy conservation behavior in general and eco-driving behavior in particular. We address this gap with a twelve months long natural field experiment in a logistics company to analyze the time-dependent and potentially interacting effects of rewards and theoretical training for eco-driving on fuel consumption in a real-world setting. We find an immediate reduction of fuel consumption following the introduction of a non-monetary reward and an attenuation of this effect over time. Theoretical eco-driving training shows no effect, neither short-term nor long-term, highlighting the often neglected necessity to include practical training elements. Contrary to common assumptions, the interaction of incentives and theoretical training does not show an additional reduction effect. Our results demonstrate the difficulty of changing engrained behavior and habits, and underline the need for a careful selection and combination of interventions. Policy implications for public and private actors are discussed. - Highlights: • Natural field experiment on training and incentives for fuel-efficient driving. • Focus on long-term and interaction effects over twelve months. • Immediate reduction effect of non-monetary reward that attenuates over time. • Theoretical eco-driving training shows no effect, neither short-term nor long-term. • Interaction of incentives and training shows no additional reduction effect.

Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning

International Nuclear Information System (INIS)

Ruan, Dan; Keall, Paul

2010-01-01

Accurate real-time prediction of respiratory motion is desirable for effective motion management in radiotherapy for lung tumor targets. Recently, nonparametric methods have been developed and their efficacy in predicting one-dimensional respiratory-type motion has been demonstrated. To exploit the correlation among various coordinates of the moving target, it is natural to extend the 1D method to multidimensional processing. However, the amount of learning data required for such extension grows exponentially with the dimensionality of the problem, a phenomenon known as the 'curse of dimensionality'. In this study, we investigate a multidimensional prediction scheme based on kernel density estimation (KDE) in an augmented covariate-response space. To alleviate the 'curse of dimensionality', we explore the intrinsic lower dimensional manifold structure and utilize principal component analysis (PCA) to construct a proper low-dimensional feature space, where kernel density estimation is feasible with the limited training data. Interestingly, the construction of this lower dimensional representation reveals a useful decomposition of the variations in respiratory motion into the contribution from semiperiodic dynamics and that from the random noise, as it is only sensible to perform prediction with respect to the former. The dimension reduction idea proposed in this work is closely related to feature extraction used in machine learning, particularly support vector machines. This work points out a pathway in processing high-dimensional data with limited training instances, and this principle applies well beyond the problem of target-coordinate-based respiratory-based prediction. A natural extension is prediction based on image intensity directly, which we will investigate in the continuation of this work. We used 159 lung target motion traces obtained with a Synchrony respiratory tracking system. Prediction performance of the low-dimensional feature learning

The Hinton train disaster.

Science.gov (United States)

Smiley, A M

1990-10-01

In February of 1986 a head-on collision occurred between a freight train and a passenger train in western Canada killing 23 people and causing over $30 million of damage. A Commission of Inquiry appointed by the Canadian government concluded that human error was the major reason for the collision. This report discusses the factors contributing to the human error: mainly poor work-rest schedules, the monotonous nature of the train driving task, insufficient information about train movements, and the inadequate backup systems in case of human error.

Long-term prediction of chaotic time series with multi-step prediction horizons by a neural network with Levenberg-Marquardt learning algorithm

International Nuclear Information System (INIS)

Mirzaee, Hossein

2009-01-01

The Levenberg-Marquardt learning algorithm is applied for training a multilayer perception with three hidden layer each with ten neurons in order to carefully map the structure of chaotic time series such as Mackey-Glass time series. First the MLP network is trained with 1000 data, and then it is tested with next 500 data. After that the trained and tested network is applied for long-term prediction of next 120 data which come after test data. The prediction is such a way that, the first inputs to network for prediction are the four last data of test data, then the predicted value is shifted to the regression vector which is the input to the network, then after first four-step of prediction, the input regression vector to network is fully predicted values and in continue, each predicted data is shifted to input vector for subsequent prediction.

Worker reciprocity and employer investment in training

NARCIS (Netherlands)

Leuven, E.; Oosterbeek, H.; Sloof, R.; van Klaveren, C.

2005-01-01

Standard economic theory predicts that firms will not invest in general training and will underinvest in specific training. Empirical evidence, however, indicates that firms do invest in general training of their workers. Evidence from laboratory experiments points to less underinvestment in

Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

International Nuclear Information System (INIS)

Wurzbach, Richard N.

2002-01-01

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

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...

Neural Reactivity to Emotional Stimuli Prospectively Predicts the Impact of a Natural Disaster on Psychiatric Symptoms in Children.

Science.gov (United States)

Kujawa, Autumn; Hajcak, Greg; Danzig, Allison P; Black, Sarah R; Bromet, Evelyn J; Carlson, Gabrielle A; Kotov, Roman; Klein, Daniel N

2016-09-01

Natural disasters expose entire communities to stress and trauma, leading to increased risk for psychiatric symptoms. Yet, the majority of exposed individuals are resilient, highlighting the importance of identifying underlying factors that contribute to outcomes. The current study was part of a larger prospective study of children in Long Island, New York (n = 260). At age 9, children viewed unpleasant and pleasant images while the late positive potential (LPP), an event-related potential component that reflects sustained attention toward salient information, was measured. Following the event-related potential assessment, Hurricane Sandy, the second costliest hurricane in United States history, hit the region. Eight weeks after the hurricane, mothers reported on exposure to hurricane-related stress and children's internalizing and externalizing symptoms. Symptoms were reassessed 8 months after the hurricane. The LPP predicted both internalizing and externalizing symptoms after accounting for prehurricane symptomatology and interacted with stress to predict externalizing symptoms. Among children exposed to higher levels of hurricane-related stress, enhanced neural reactivity to unpleasant images predicted greater externalizing symptoms 8 weeks after the disaster, while greater neural reactivity to pleasant images predicted lower externalizing symptoms. Moreover, interactions between the LPP and stress continued to predict externalizing symptoms 8 months after the hurricane. Results indicate that heightened neural reactivity and attention toward unpleasant information, as measured by the LPP, predispose children to psychiatric symptoms when exposed to higher levels of stress related to natural disasters, while greater reactivity to and processing of pleasant information may be a protective factor. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

motivation and physical performance in elite and non-elite trained men

Directory of Open Access Journals (Sweden)

BT Crewther

2016-08-01

Full Text Available To advance our understanding of the hormonal contribution to athletic performance, we examined the temporal associations between individual changes in testosterone (T and/or cortisol (C concentrations, training motivation and physical performance in elite and non-elite trained men. Two male cohorts classified as elites (n = 12 and non-elites (n = 12 completed five testing sessions over a six-week period. The athletes were tested for salivary T, C, T/C ratio, self-perceived training motivation, countermovement jump (CMJ height and isometric mid-thigh pull peak force (IMTP PF, after which an actual training workout was performed. The elite men reported higher motivation to train and they produced greater CMJ height overall, whereas the non-elites had higher pooled T levels (p < 0.05. No significant group differences in C concentrations, T/C ratio or IMTP PF were found. The individual changes in T levels were positively associated with training motivation in the elite men only (p = 0.033, but the hormonal and motivation measures did not predict CMJ height or IMTP PF in either group. The monitoring of elite and non-elite men across a short training block revealed differences in T levels, motivation and lower-body power, which may reflect training and competitive factors in each group. Despite having lower T levels, the elite athletes showed better linkage between pre-training T fluctuations and subsequent motivation to train. The nature of the performance tests (i.e. single repetition trials could partly explain the lack of an association with the hormonal and motivational measures.

Predictive importance of anthropometric and training data in recreational male Ironman triathletes and marathon runners: comment on the study by Gianoli, et al. (2012).

Science.gov (United States)

Burtscher, Martin; Gatterer, Hannes

2013-04-01

Anthropometric and training data have been reported as statistically significant predictors of race performance in endurance events. However, it is well established that physiological characteristics, i.e., maximal oxygen uptake (VO2max), the use of a high percentage of VO2max during sustained exercise, and work efficiency are predominant predictors of performance in those events. Thus, the essential issue is whether the anthropometric and training data give additional predictive power beyond these other measures.

Sediment bacterial community structures and their predicted functions implied the impacts from natural processes and anthropogenic activities in coastal area.

Science.gov (United States)

Su, Zhiguo; Dai, Tianjiao; Tang, Yushi; Tao, Yile; Huang, Bei; Mu, Qinglin; Wen, Donghui

2018-06-01

Coastal ecosystem structures and functions are changing under natural and anthropogenic influences. In this study, surface sediment samples were collected from disturbed zone (DZ), near estuary zone (NEZ), and far estuary zone (FEZ) of Hangzhou Bay, one of the most seriously polluted bays in China. The bacterial community structures and predicted functions varied significantly in different zones. Firmicutes were found most abundantly in DZ, highlighting the impacts of anthropogenic activities. Sediment total phosphorus was most influential on the bacterial community structures. Predicted by PICRUSt analysis, DZ significantly exceeded FEZ and NEZ in the subcategory of Xenobiotics Biodegradation and Metabolism; and DZ enriched all the nitrate reduction related genes, except nrfA gene. Seawater salinity and inorganic nitrogen, respectively as the representative natural and anthropogenic factor, performed exact-oppositely in nitrogen metabolism functions. The changes of bacterial community compositions and predicted functions provide a new insight into human-induced pollution impacts on coastal ecosystem. Copyright © 2018 Elsevier Ltd. All rights reserved.

AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT

Directory of Open Access Journals (Sweden)

Kottalanka Srikanth

2017-07-01

Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

Learning from nature: Nature-inspired algorithms

DEFF Research Database (Denmark)

Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin

2016-01-01

.), genetic and evolutionary strategies, artificial immune systems etc. Well-known examples of applications include: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based......During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms...

Concept of the aero-train and its aerodynamic stability nature

OpenAIRE

Kohama, Yasuaki P.; Watanabe, Hideo; Kikuchi, Satoshi; Ota, Fukuo; Ito, Takatoshi; 小濱　泰昭; 渡部　英夫; 菊池　聡; 太田　福雄; 伊藤　高敏

2000-01-01

Taking into account the serious greenhouse effect of the earth, drastic proposal which prevents the carbon dioxide emission from transportation system must be done. In Japan, over 20 percent of the carbon dioxide are emitted from transportation. Aero-train is the new zero-emission high speed vehicle, which is being proposed. Wing in ground effect is introduced to obtain highest lift to drag ratio and highest payload ratio. In order to compare the performance of the aero-train at 500 km/h, wit...

Predictive value of the korean academy of family medicine in-training examination for certifying examination.

Science.gov (United States)

Cho, Jung-Jin; Kim, Ji-Yong

2011-09-01

In-training examination (ITE) is a cognitive examination similar to the written test, but it is different from the Clinical Practice Examination of the Korean Academy of Family Medicine (KAFM) Certification Examination (CE). The objective of this is to estimate the positive predictive value of the KAFM-ITE for identifying residents at risk for poor performance on the three types of KAFM-CE. 372 residents who completed the KAFM-CE in 2011 were included. We compared the mean KAFM-CE scores with ITE experience. We evaluated the correlation and the positive predictive value (PPV) of ITE for the multiple choice question (MCQ) scores of 1st written test & 2nd slide examination, the total clinical practice examination scores, and the total sum of 2nd test. 275 out of 372 residents completed ITE. Those who completed ITE had significantly higher MCQ scores of 1st written test than those who did not. The correlation of ITE scores with 1st written MCQ (0.627) was found to be the highest among the other kinds of CE. The PPV of the ITE score for 1st written MCQ scores was 0.672. The PPV of the ITE score ranged from 0.376 to 0.502. The score of the KAFM ITE has acceptable positive predictive value that could be used as a part of comprehensive evaluation system for residents in cognitive field.

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

Science.gov (United States)

Karimi, Davood; Samei, Golnoosh; Kesch, Claudia; Nir, Guy; Salcudean, Septimiu E

2018-05-15

Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.

Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations.

Science.gov (United States)

Zhang, Ao; Wang, Hongwu; Beyene, Yoseph; Semagn, Kassa; Liu, Yubo; Cao, Shiliang; Cui, Zhenhai; Ruan, Yanye; Burgueño, Juan; San Vicente, Felix; Olsen, Michael; Prasanna, Boddupalli M; Crossa, José; Yu, Haiqiu; Zhang, Xuecai

2017-01-01

Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy ( r MG ) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability ( h 2 ), TPS and MD on r MG estimation. Our results showed that: (1) moderate r MG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) r MG increased with an increase in h 2 , TPS and MD, both correlation and variance analyses showed that h 2 is the most important factor and MD is the least important factor on r MG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the r MG values for all the six trait-environment combinations were centered around zero, 49% predictions had r MG values above zero; (4) the trend observed in r MG differed with the trend observed in r MG / h , and h is the square root of heritability of the predicted trait, it indicated that both r MG and r MG / h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations

Directory of Open Access Journals (Sweden)

Ao Zhang

2017-11-01

Full Text Available Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG of the six trait-environment combinations under various levels of training population size (TPS and marker density (MD, and assess the effect of trait heritability (h2, TPS and MD on rMG estimation. Our results showed that: (1 moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2 rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3 predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4 the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

Comparative analysis of modified PMV models and SET models to predict human thermal sensation in naturally ventilated buildings

DEFF Research Database (Denmark)

Gao, Jie; Wang, Yi; Wargocki, Pawel

2015-01-01

In this paper, a comparative analysis was performed on the human thermal sensation estimated by modified predicted mean vote (PMV) models and modified standard effective temperature (SET) models in naturally ventilated buildings; the data were collected in field study. These prediction models were....../s, the expectancy factors for the extended PMV model and the extended SET model were from 0.770 to 0.974 and from 1.330 to 1.363, and the adaptive coefficients for the adaptive PMV model and the adaptive SET model were from 0.029 to 0.167 and from-0.213 to-0.195. In addition, the difference in thermal sensation...... between the measured and predicted values using the modified PMV models exceeded 25%, while the difference between the measured thermal sensation and the predicted thermal sensation using modified SET models was approximately less than 25%. It is concluded that the modified SET models can predict human...

Music training, cognition, and personality.

Science.gov (United States)

Corrigall, Kathleen A; Schellenberg, E Glenn; Misura, Nicole M

2013-01-01

Although most studies that examined associations between music training and cognitive abilities had correlational designs, the prevailing bias is that music training causes improvements in cognition. It is also possible, however, that high-functioning children are more likely than other children to take music lessons, and that they also differ in personality. We asked whether individual differences in cognition and personality predict who takes music lessons and for how long. The participants were 118 adults (Study 1) and 167 10- to 12-year-old children (Study 2). We collected demographic information and measured cognitive ability and the Big Five personality dimensions. As in previous research, cognitive ability was associated with musical involvement even when demographic variables were controlled statistically. Novel findings indicated that personality was associated with musical involvement when demographics and cognitive ability were held constant, and that openness-to-experience was the personality dimension with the best predictive power. These findings reveal that: (1) individual differences influence who takes music lessons and for how long, (2) personality variables are at least as good as cognitive variables at predicting music training, and (3) future correlational studies of links between music training and non-musical ability should account for individual differences in personality.

Music training, cognition, and personality

Directory of Open Access Journals (Sweden)

Kathleen A Corrigall

2013-04-01

Full Text Available Although most studies that examined associations between music training and cognitive abilities had correlational designs, the prevailing bias is that music training causes improvements in cognition. It is also possible, however, that high-functioning children are more likely than other children to take music lessons, and that they also differ in personality. We asked whether individual differences in cognition and personality predict who takes music lessons and for how long. The participants were 118 adults (Study 1 and 167 10- to 12-year-old children (Study 2. We collected demographic information and measured cognitive ability and the Big Five personality dimensions. As in previous research, cognitive ability was associated with musical involvement even when demographic variables were controlled statistically. Novel findings indicated that personality was associated with musical involvement when demographics and cognitive ability were held constant, and that openness-to-experience was the personality dimension with the best predictive power. These findings reveal that: (1 individual differences influence who takes music lessons and for how long, (2 personality variables are at least as good as cognitive variables at predicting music training, and (3 future correlational studies of links between music training and nonmusical ability should account for individual differences in personality.

The Shaping of Managers' Security Objectives through Information Security Awareness Training

Science.gov (United States)

Harris, Mark A.

2010-01-01

Information security research states that corporate security policy and information security training should be socio-technical in nature and that corporations should consider training as a primary method of protecting their information systems. However, information security policies and training are predominately technical in nature. In addition,…

The Effect of Natural or Simulated Altitude Training on High-Intensity Intermittent Running Performance in Team-Sport Athletes: A Meta-Analysis.

Science.gov (United States)

Hamlin, Michael J; Lizamore, Catherine A; Hopkins, Will G

2018-02-01

While adaptation to hypoxia at natural or simulated altitude has long been used with endurance athletes, it has only recently gained popularity for team-sport athletes. To analyse the effect of hypoxic interventions on high-intensity intermittent running performance in team-sport athletes. A systematic literature search of five journal databases was performed. Percent change in performance (distance covered) in the Yo-Yo intermittent recovery test (level 1 and level 2 were used without differentiation) in hypoxic (natural or simulated altitude) and control (sea level or normoxic placebo) groups was meta-analyzed with a mixed model. The modifying effects of study characteristics (type and dose of hypoxic exposure, training duration, post-altitude duration) were estimated with fixed effects, random effects allowed for repeated measurement within studies and residual real differences between studies, and the standard-error weighting factors were derived or imputed via standard deviations of change scores. Effects and their uncertainty were assessed with magnitude-based inference, with a smallest important improvement of 4% estimated via between-athlete standard deviations of performance at baseline. Ten studies qualified for inclusion, but two were excluded owing to small sample size and risk of publication bias. Hypoxic interventions occurred over a period of 7-28 days, and the range of total hypoxic exposure (in effective altitude-hours) was 4.5-33 km h in the intermittent-hypoxia studies and 180-710 km h in the live-high studies. There were 11 control and 15 experimental study-estimates in the final meta-analysis. Training effects were moderate and very likely beneficial in the control groups at 1 week (20 ± 14%, percent estimate, ± 90% confidence limits) and 4-week post-intervention (25 ± 23%). The intermittent and live-high hypoxic groups experienced additional likely beneficial gains at 1 week (13 ± 16%; 13 ± 15%) and 4-week post

The NHERI RAPID Facility: Enabling the Next-Generation of Natural Hazards Reconnaissance

Science.gov (United States)

Wartman, J.; Berman, J.; Olsen, M. J.; Irish, J. L.; Miles, S.; Gurley, K.; Lowes, L.; Bostrom, A.

2017-12-01

The NHERI post-disaster, rapid response research (or "RAPID") facility, headquartered at the University of Washington (UW), is a collaboration between UW, Oregon State University, Virginia Tech, and the University of Florida. The RAPID facility will enable natural hazard researchers to conduct next-generation quick response research through reliable acquisition and community sharing of high-quality, post-disaster data sets that will enable characterization of civil infrastructure performance under natural hazard loads, evaluation of the effectiveness of current and previous design methodologies, understanding of socio-economic dynamics, calibration of computational models used to predict civil infrastructure component and system response, and development of solutions for resilient communities. The facility will provide investigators with the hardware, software and support services needed to collect, process and assess perishable interdisciplinary data following extreme natural hazard events. Support to the natural hazards research community will be provided through training and educational activities, field deployment services, and by promoting public engagement with science and engineering. Specifically, the RAPID facility is undertaking the following strategic activities: (1) acquiring, maintaining, and operating state-of-the-art data collection equipment; (2) developing and supporting mobile applications to support interdisciplinary field reconnaissance; (3) providing advisory services and basic logistics support for research missions; (4) facilitating the systematic archiving, processing and visualization of acquired data in DesignSafe-CI; (5) training a broad user base through workshops and other activities; and (6) engaging the public through citizen science, as well as through community outreach and education. The facility commenced operations in September 2016 and will begin field deployments beginning in September 2018. This poster will provide an overview

Predicting natural catastrophes tsunamis

CERN Multimedia

CERN. Geneva

2005-01-01

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 related milestones. Reconstructing historical tsunamis: eyewitnesses and geological evidence 5. Lessons from the 2004 Indonesian tsunami - Lessons in seismology Lessons in Geology The new technologies Lessons in civ...

Genomic Prediction Within and Across Biparental Families: Means and Variances of Prediction Accuracy and Usefulness of Deterministic Equations

Directory of Open Access Journals (Sweden)

Pascal Schopp

2017-11-01

Full Text Available A major application of genomic prediction (GP in plant breeding is the identification of superior inbred lines within families derived from biparental crosses. When models for various traits were trained within related or unrelated biparental families (BPFs, experimental studies found substantial variation in prediction accuracy (PA, but little is known about the underlying factors. We used SNP marker genotypes of inbred lines from either elite germplasm or landraces of maize (Zea mays L. as parents to generate in silico 300 BPFs of doubled-haploid lines. We analyzed PA within each BPF for 50 simulated polygenic traits, using genomic best linear unbiased prediction (GBLUP models trained with individuals from either full-sib (FSF, half-sib (HSF, or unrelated families (URF for various sizes (Ntrain of the training set and different heritabilities (h2 . In addition, we modified two deterministic equations for forecasting PA to account for inbreeding and genetic variance unexplained by the training set. Averaged across traits, PA was high within FSF (0.41–0.97 with large variation only for Ntrain < 50 and h2 < 0.6. For HSF and URF, PA was on average ∼40–60% lower and varied substantially among different combinations of BPFs used for model training and prediction as well as different traits. As exemplified by HSF results, PA of across-family GP can be very low if causal variants not segregating in the training set account for a sizeable proportion of the genetic variance among predicted individuals. Deterministic equations accurately forecast the PA expected over many traits, yet cannot capture trait-specific deviations. We conclude that model training within BPFs generally yields stable PA, whereas a high level of uncertainty is encountered in across-family GP. Our study shows the extent of variation in PA that must be at least reckoned with in practice and offers a starting point for the design of training sets composed of multiple BPFs.

A novel method for predicting activity of cis-regulatory modules, based on a diverse training set.

Science.gov (United States)

Yang, Wei; Sinha, Saurabh

2017-01-01

With the rapid emergence of technologies for locating cis-regulatory modules (CRMs) genome-wide, the next pressing challenge is to assign precise functions to each CRM, i.e. to determine the spatiotemporal domains or cell-types where it drives expression. A popular approach to this task is to model the typical k-mer composition of a set of CRMs known to drive a common expression pattern, and assign that pattern to other CRMs exhibiting a similar k-mer composition. This approach does not rely on prior knowledge of transcription factors relevant to the CRM or their binding motifs, and is thus more widely applicable than motif-based methods for predicting CRM activity, but is also prone to false positive predictions. We present a novel strategy to improve the above-mentioned approach: to predict if a CRM drives a specific gene expression pattern, assess not only how similar the CRM is to other CRMs with similar activity but also to CRMs with distinct activities. We use a state-of-the-art statistical method to quantify a CRM's sequence similarity to many different training sets of CRMs, and employ a classification algorithm to integrate these similarity scores into a single prediction of the CRM's activity. This strategy is shown to significantly improve CRM activity prediction over current approaches. Our implementation of the new method, called IMMBoost, is freely available as source code, at https://github.com/weiyangedward/IMMBoost CONTACT: sinhas@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression.

Science.gov (United States)

Carrillo, Facundo; Sigman, Mariano; Fernández Slezak, Diego; Ashton, Philip; Fitzgerald, Lily; Stroud, Jack; Nutt, David J; Carhart-Harris, Robin L

2018-04-01

Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not. A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response. Speech analytics and machine learning successfully differentiated depressed patients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision). Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity. The sample size was small and replication is required to strengthen inferences on these results. Copyright © 2018 Elsevier B.V. All rights reserved.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans

Directory of Open Access Journals (Sweden)

Alencar Xavier

2016-08-01

Full Text Available Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr. We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set.

Assessing Predictive Properties of Genome-Wide Selection in Soybeans.

Science.gov (United States)

Xavier, Alencar; Muir, William M; Rainey, Katy Martin

2016-08-09

Many economically important traits in plant breeding have low heritability or are difficult to measure. For these traits, genomic selection has attractive features and may boost genetic gains. Our goal was to evaluate alternative scenarios to implement genomic selection for yield components in soybean (Glycine max L. merr). We used a nested association panel with cross validation to evaluate the impacts of training population size, genotyping density, and prediction model on the accuracy of genomic prediction. Our results indicate that training population size was the factor most relevant to improvement in genome-wide prediction, with greatest improvement observed in training sets up to 2000 individuals. We discuss assumptions that influence the choice of the prediction model. Although alternative models had minor impacts on prediction accuracy, the most robust prediction model was the combination of reproducing kernel Hilbert space regression and BayesB. Higher genotyping density marginally improved accuracy. Our study finds that breeding programs seeking efficient genomic selection in soybeans would best allocate resources by investing in a representative training set. Copyright © 2016 Xavie et al.

Roles of Radon-222 and other natural radionuclides in earthquake prediction

International Nuclear Information System (INIS)

Smith, A.R.; Wollenberg, H.A.; Mosier, D.F.

1980-01-01

The concentration of 222 Rn in subsurface waters is one of the natural parameters being investigated to help develop the capability to predict destructive earthquakes. Since 1966, scientists in several nations have sought to link radon variations with ongoing seismic activity, primarily through the dilatancy model for earthquake occurrences. Within the range of these studies, alpha-, beta-, and gamma-radiation detection techniques have been used in both discrete-sampling and continiuous-monitoring programs. These measured techniques are reviewed in terms of instrumentation adapted to seismic-monitoring purposes. A recent Lawrence Berkeley Laboratory study conducted in central California incorporated discrete sampling of wells in the aftershock area of the 1975 Oroville earthquake and continuous monitoring of water radon in a well on the San Andreas Fault. The results presented show short-term radon variations that may be associated with aftershocks and diurnal changes that may reflect earth tidal forces

Collaborative Science: Human Sensor Networks for Real-time Natural Disaster Prediction

Science.gov (United States)

Halem, M.; Yesha, Y.; Aulov, O.; Martineau, J.; Brown, S.; Conte, T.; CenterHybrid Multicore Productivity Research

2010-12-01

We have implemented a ‘Human Sensor Network’ as a real time collaborative science data observing system by collecting and integrating the vast untapped information potential of digital social media data sources occurring during the oil spill situation arising from the Macondo well in the Gulf of Mexico. We collected, and archived blogs, Twitter status updates (aka tweets), photographs posted to Flicker, and videos posted to YouTube related to the Gulf oil spill and processed the meta data, text, and photos to extract quantitative physical data such as locations and estimates of the severity and dispersion of oil being collected on the beaches and marshes, frequencies of observations of tar ball sightings, correlations of sightings from different media, numbers of dead or distressed animals, trends, etc. These data were then introduced into the NOAA operational Gnome oil spill predictive model as time dependent boundary conditions employing a 2-D variational data assimilation scheme. The three participating institutions employed a distributed cloud computing system for the processing and model executions. In this presentation, we conducted preliminary forecast impact tests of the Gnome model with and without the use of social media data using a 2-D variational data assimilation technique. The 2-D VAR is used to adjust the state variables of the model by recursively minimizing the differences between oil spill predictions reaching locations across the entire coastlines of the Gulf of Mexico and the estimated positions of oil derived from analyzed social media data. Ensemble forecasts will be performed to provide estimates of the rates of oil and surface oil distributions emanating from the Deepwater Horizon. We display the derived predictions from the photos and animations from Flicker, YouTube, and extracted content from tweets and blogs in a dynamic representation on very large tiled walls of LCDs at the UCSD Cal IT2 visualization facility. We describe the

Characterization and prediction of extreme events in turbulence

Science.gov (United States)

Fonda, Enrico; Iyer, Kartik P.; Sreenivasan, Katepalli R.

2017-11-01

Extreme events in Nature such as tornadoes, large floods and strong earthquakes are rare but can have devastating consequences. The predictability of these events is very limited at present. Extreme events in turbulence are the very large events in small scales that are intermittent in character. We examine events in energy dissipation rate and enstrophy which are several tens to hundreds to thousands of times the mean value. To this end we use our DNS database of homogeneous and isotropic turbulence with Taylor Reynolds numbers spanning a decade, computed with different small scale resolutions and different box sizes, and study the predictability of these events using machine learning. We start with an aggressive data augmentation to virtually increase the number of these rare events by two orders of magnitude and train a deep convolutional neural network to predict their occurrence in an independent data set. The goal of the work is to explore whether extreme events can be predicted with greater assurance than can be done by conventional methods (e.g., D.A. Donzis & K.R. Sreenivasan, J. Fluid Mech. 647, 13-26, 2010).

Gender, Families, and Science: Influences on Early Science Training and Career Choices

Science.gov (United States)

Hanson, Sandra L.

This research examines the effects of gender and a number of family experiences on young people's chances of going into postsecondary science training and science occupations in the years immediately following high school. Data came from the nationally representative, longitudinal High School and Beyond survey. Results show that gender plays a significant role in choices involving early science training and occupations - especially training. Amongst young men and women with comparable resources and qualifications, young women are less likely to make the science choice. The family experiences and expectations examined here are not a major factor in understanding gender differences in access to science training and occupations. Although much of the literature describes the domains of science and of family as being at odds, results from this research suggest that family experiences play a rather minimal role in predicting who will enter science training or occupations in the early post-high school years. When family variables do have an effect, they are not always negative and the nature of the effect varies by the time in the life cycle that the family variable is measured, by type of family experience (orientation vs. procreation), by outcome (science major vs. science occupation), and by gender.

Arabic Natural Language Processing System Code Library

Science.gov (United States)

2014-06-01

Adelphi, MD 20783-1197 This technical note provides a brief description of a Java library for Arabic natural language processing ( NLP ) containing code...for training and applying the Arabic NLP system described in the paper "A Cross-Task Flexible Transition Model for Arabic Tokenization, Affix...and also English) natural language processing ( NLP ), containing code for training and applying the Arabic NLP system described in Stephen Tratz’s

Gender discrimination and prediction on the basis of facial metric information.

Science.gov (United States)

Fellous, J M

1997-07-01

Horizontal and vertical facial measurements are statistically independent. Discriminant analysis shows that five of such normalized distances explain over 95% of the gender differences of "training" samples and predict the gender of 90% novel test faces exhibiting various facial expressions. The robustness of the method and its results are assessed. It is argued that these distances (termed fiducial) are compatible with those found experimentally by psychophysical and neurophysiological studies. In consequence, partial explanations for the effects observed in these experiments can be found in the intrinsic statistical nature of the facial stimuli used.

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.

Agroforestry In-Service Training. A Training Aid for Asia & the Pacific Islands (Honiara, Solomon Islands, South Pacific, October 23-29, 1983). Training for Development. Peace Corps Information Collection & Exchange Training Manual No. T-16.

Science.gov (United States)

Fillion, Jacob; Weeks, Julius

The Forestry/Natural Resources Sector in the Office of Training and Program Support of the Peace Corps conducted an agroforestry inservice training workshop in Honiara, Solomon Islands, in 1983. Participants included Peace Corps volunteers and their host country national counterparts from six countries of the Pacific Islands and Asia (Western…

Implications of the difference between true and predicted breeding values for the study of natural selection and micro-evolution

NARCIS (Netherlands)

Postma, E.

2006-01-01

The ability to predict individual breeding values in natural populations with known pedigrees has provided a powerful tool to separate phenotypic values into their genetic and environmental components in a nonexperimental setting. This has allowed sophisticated analyses of selection, as well as

Voltage control on a train system

Science.gov (United States)

Gordon, Susanna P.; Evans, John A.

2004-01-20

The present invention provides methods for preventing low train voltages and managing interference, thereby improving the efficiency, reliability, and passenger comfort associated with commuter trains. An algorithm implementing neural network technology is used to predict low voltages before they occur. Once voltages are predicted, then multiple trains can be controlled to prevent low voltage events. Further, algorithms for managing inference are presented in the present invention. Different types of interference problems are addressed in the present invention such as "Interference During Acceleration", "Interference Near Station Stops", and "Interference During Delay Recovery." Managing such interference avoids unnecessary brake/acceleration cycles during acceleration, immediately before station stops, and after substantial delays. Algorithms are demonstrated to avoid oscillatory brake/acceleration cycles due to interference and to smooth the trajectories of closely following trains. This is achieved by maintaining sufficient following distances to avoid unnecessary braking/accelerating. These methods generate smooth train trajectories, making for a more comfortable ride, and improve train motor reliability by avoiding unnecessary mode-changes between propulsion and braking. These algorithms can also have a favorable impact on traction power system requirements and energy consumption.

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.

Sci-Thur AM: YIS – 05: Prediction of lung tumor motion using a generalized neural network optimized from the average prediction outcome of a group of patients

Energy Technology Data Exchange (ETDEWEB)

Teo, Troy; Alayoubi, Nadia; Bruce, Neil; Pistorius, Stephen [University of Manitoba/ CancerCare Manitoba, University of Manitoba, University of Manitoba, University of Manitoba / CancerCare Manitoba (Canada)

2016-08-15

Purpose: In image-guided adaptive radiotherapy systems, prediction of tumor motion is required to compensate for system latencies. However, due to the non-stationary nature of respiration, it is a challenge to predict the associated tumor motions. In this work, a systematic design of the neural network (NN) using a mixture of online data acquired during the initial period of the tumor trajectory, coupled with a generalized model optimized using a group of patient data (obtained offline) is presented. Methods: The average error surface obtained from seven patients was used to determine the input data size and number of hidden neurons for the generalized NN. To reduce training time, instead of using random weights to initialize learning (method 1), weights inherited from previous training batches (method 2) were used to predict tumor position for each sliding window. Results: The generalized network was established with 35 input data (∼4.66s) and 20 hidden nodes. For a prediction horizon of 650 ms, mean absolute errors of 0.73 mm and 0.59 mm were obtained for method 1 and 2 respectively. An average initial learning period of 8.82 s is obtained. Conclusions: A network with a relatively short initial learning time was achieved. Its accuracy is comparable to previous studies. This network could be used as a plug-and play predictor in which (a) tumor positions can be predicted as soon as treatment begins and (b) the need for pretreatment data and optimization for individual patients can be avoided.

Sci-Thur AM: YIS – 05: Prediction of lung tumor motion using a generalized neural network optimized from the average prediction outcome of a group of patients

International Nuclear Information System (INIS)

Teo, Troy; Alayoubi, Nadia; Bruce, Neil; Pistorius, Stephen

2016-01-01

Purpose: In image-guided adaptive radiotherapy systems, prediction of tumor motion is required to compensate for system latencies. However, due to the non-stationary nature of respiration, it is a challenge to predict the associated tumor motions. In this work, a systematic design of the neural network (NN) using a mixture of online data acquired during the initial period of the tumor trajectory, coupled with a generalized model optimized using a group of patient data (obtained offline) is presented. Methods: The average error surface obtained from seven patients was used to determine the input data size and number of hidden neurons for the generalized NN. To reduce training time, instead of using random weights to initialize learning (method 1), weights inherited from previous training batches (method 2) were used to predict tumor position for each sliding window. Results: The generalized network was established with 35 input data (∼4.66s) and 20 hidden nodes. For a prediction horizon of 650 ms, mean absolute errors of 0.73 mm and 0.59 mm were obtained for method 1 and 2 respectively. An average initial learning period of 8.82 s is obtained. Conclusions: A network with a relatively short initial learning time was achieved. Its accuracy is comparable to previous studies. This network could be used as a plug-and play predictor in which (a) tumor positions can be predicted as soon as treatment begins and (b) the need for pretreatment data and optimization for individual patients can be avoided.

Predictors of Early Termination in a University Counseling Training Clinic

Science.gov (United States)

Lampropoulos, Georgios K.; Schneider, Mercedes K.; Spengler, Paul M.

2009-01-01

Despite the existence of counseling dropout research, there are limited predictive data for counseling in training clinics. Potential predictor variables were investigated in this archival study of 380 client files in a university counseling training clinic. Multinomial logistic regression, predictive discriminant analysis, and classification and…

The Nature and Predictive Value of Mothers’ Beliefs Regarding Infants’ and Toddlers’ TV/Video Viewing: Applying the Integrative Model of Behavioral Prediction

Science.gov (United States)

Vaala, Sarah E.

2014-01-01

Viewing television and video programming has become a normative behavior among US infants and toddlers. Little is understood about parents’ decision-making about the extent of their young children’s viewing, though numerous organizations are interested in reducing time spent viewing among infants and toddlers. Prior research has examined parents’ belief in the educational value of TV/videos for young children and the predictive value of this belief for understanding infant/toddler viewing rates, though other possible salient beliefs remain largely unexplored. This study employs the integrative model of behavioral prediction (Fishbein & Ajzen, 2010) to examine 30 maternal beliefs about infants’ and toddlers’ TV/video viewing which were elicited from a prior sample of mothers. Results indicate that mothers tend to hold more positive than negative beliefs about the outcomes associated with young children’s TV/video viewing, and that the nature of the aggregate set of beliefs is predictive of their general attitudes and intentions to allow their children to view, as well as children’s estimated viewing rates. Analyses also uncover multiple dimensions within the full set of beliefs, which explain more variance in mothers’ attitudes and intentions and children’s viewing than the uni-dimensional index. The theoretical and practical implications of the findings are discussed. PMID:25431537

Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective

Directory of Open Access Journals (Sweden)

Nikolaos Aletras

2016-10-01

Full Text Available Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court’s decisions with a strong accuracy (79% on average. Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis.

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

Electrophysiological correlates of predictive coding of auditory location in the perception of natural audiovisual events

Directory of Open Access Journals (Sweden)

Jeroen eStekelenburg

2012-05-01

Full Text Available In many natural audiovisual events (e.g., a clap of the two hands, the visual signal precedes the sound and thus allows observers to predict when, where, and which sound will occur. Previous studies have already reported that there are distinct neural correlates of temporal (when versus phonetic/semantic (which content on audiovisual integration. Here we examined the effect of visual prediction of auditory location (where in audiovisual biological motion stimuli by varying the spatial congruency between the auditory and visual part of the audiovisual stimulus. Visual stimuli were presented centrally, whereas auditory stimuli were presented either centrally or at 90° azimuth. Typical subadditive amplitude reductions (AV – V < A were found for the auditory N1 and P2 for spatially congruent and incongruent conditions. The new finding is that the N1 suppression was larger for spatially congruent stimuli. A very early audiovisual interaction was also found at 30-50 ms in the spatially congruent condition, while no effect of congruency was found on the suppression of the P2. This indicates that visual prediction of auditory location can be coded very early in auditory processing.

Mental skills training in soccer

DEFF Research Database (Denmark)

Diment, Gregory Michael

2014-01-01

Psychological Skills Training (PST) has been a tool used by sport psychology consultants. However, within soccer many of these programs have been delivered as workshops, homework tasks, or individual consultations with athletes. The aim of the project was to develop an ecological intervention...... by creating a series of drillbased sessions to train psychological skills, and educate coaches about how to implement and integrate PST as a natural part of daily training. The program was delivered to the youth academies in nine Danish professional soccer clubs and consisted of three phases: (a) planning...... of the program, (b) education and designing soccer drills, and (c) delivery of the drills on the soccer pitch. The program was well received by clubs, coaches, and players. With regards to project aims, the intervention was generally considered a success. Coaches reported that the drill-based nature...

Consensus models to predict endocrine disruption for all ...

Science.gov (United States)

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

Science.gov (United States)

Jurtz, Vanessa; Paul, Sinu; Andreatta, Massimo; Marcatili, Paolo; Peters, Bjoern; Nielsen, Morten

2017-11-01

Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway. Therefore, in the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has attracted widespread attention. In the past, predictors of peptide-MHC interactions have primarily been trained on binding affinity data. Recently, an increasing number of MHC-presented peptides identified by mass spectrometry have been reported containing information about peptide-processing steps in the presentation pathway and the length distribution of naturally presented peptides. In this article, we present NetMHCpan-4.0, a method trained on binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increase in predictive performance compared with state-of-the-art methods when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes. Copyright © 2017 by The American Association of Immunologists, Inc.

Training scientist from developing countries

International Nuclear Information System (INIS)

Schultze-Kraft, P.

1987-01-01

The system of the training of specialists at the IAEA training courses, which are organized on interregional, regional and national basis, is presented. The necessity in the training of specialists in the given field, which is expressed by the states asking for assistance, is the main criterion for choosing subjects at the training courses. The IAEA has concentrated its attention on the courses in the following three directions: courses on the planning (expansion of power systems, prediction of needs in electric power); courses on the supervision (project realization, safety and reliability of NPP operation, radiation protection); courses for NPP construction inspectors, site selection, safety assessment. Training of teachers for national personnel is one of the new directions

Predicting Autonomous and Controlled Motivation to Transfer Training

Science.gov (United States)

Gegenfurtner, Andreas; Festner, Dagmar; Gallenberger, Wolfgang; Lehtinen, Erno; Gruber, Hans

2009-01-01

In spite of a broad consensus on the importance of motivation for the transfer of learning from training to the job in work organizations, studies investigating motivation to transfer are limited. This study combines the self-determination theory, the expectancy theory and the theory of planned behaviour to provide a theoretical framework for…

Do Different Training Conditions Facilitate Team Implementation?

DEFF Research Database (Denmark)

Nielsen, Karina; Randall, Raymond; Christensen, Karl B.

2017-01-01

A mixed methods approach was applied to examine the effects of a naturally occurring teamwork intervention supported with training. The first objective was to integrate qualitative process evaluation and quantitative effect evaluation to examine how and why the training influence intervention...... outcomes. The intervention (N = 328) was supplemented with four training conditions (no training, team member training, team leader training, and a combination of training types). The second objective was to examine whether different training conditions support team member training in isolation......, but not in combination, led to positive outcomes. The integrated analysis of qualitative and quantitative data indicated that a number of contextual factors interacted with training experiences and outcomes to influence the success of team intervention....

Experience with simulator training for emergency conditions

International Nuclear Information System (INIS)

1987-12-01

The training of operators by the use of simulators is common to most countries with nuclear power plants. Simulator training programmes are generally well developed, but their value can be limited by the age, type, size and capability of the simulator. Within these limits, most full scope simulators have a capability of training operators for a range of design basis accidents. It is recognized that human performance under accident conditions is difficult to predict or analyse, particularly in the area of severe accidents. These are rare events and by their very nature, unpredictable. Of importance, therefore, is to investigate the training of operators for severe accident conditions, and to examine ways in which simulators may be used in this task. The International Nuclear Safety Advisory Group (INSAG) has reviewed this field and the associated elements of human behaviour. It has recommended that activities are concentrated on this area. Initially it is encouraging the following objectives: i) To train operators for accident conditions including severe accidents and to strongly encourage the development and use of simulators for this purpose; ii) To improve the man-machine interface by the use of computer aids to the operator; iii) To develop human performance requirements for plant operating staff. As part of this work, the IAEA convened a technical committee on 15-19 September 1986 to review the experience with simulator training for emergency conditions, to review simulator modelling for severe accident training, to examine the role of human cognitive behaviour modelling, and to review guidance on accident scenarios. A substantial deviation may be a major fuel failure, a Loss of Coolant Accident (LOCA), etc. Examples of engineered safety features are: an Emergency Core Cooling System (ECCS), and Containment Systems. This report was prepared by the participants during the meeting and reviewed further in a Consultant's Meeting. It also includes papers which were

NRC methods for evaluation of industry training

International Nuclear Information System (INIS)

Morisseau, D.S.; Koontz, J.L.; Persensky, J.J.

1987-01-01

On March 20, 1985, the Nuclear Regulatory Commission published the Policy Statement on Training and Qualification. The Policy Statement endorsed the INPO-managed Training Accreditation Program because it encompasses the five elements of performance-based training. This paper described the multiple methods that the NRC is using to monitor industry efforts to improve training and implement the NRC Policy Statement on Training and Qualification. The results of the evaluation of industry training improvement programs will be reviewed by the Commissioners in April 1987 to determine the nature of continuing NRC policy and programs for ensuring effective training for the US nuclear industry

TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

Energy Technology Data Exchange (ETDEWEB)

Liu, J; Wu, Q.J.; Yin, F; Kirkpatrick, J; Cabrera, A [Duke University Medical Center, Durham, NC (United States); Ge, Y [University of North Carolina at Charlotte, Charlotte, NC (United States)

2014-06-15

Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH

TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

International Nuclear Information System (INIS)

Liu, J; Wu, Q.J.; Yin, F; Kirkpatrick, J; Cabrera, A; Ge, Y

2014-01-01

Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

Science.gov (United States)

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

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Gavin Robertson

2011-01-01

Full Text Available Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs, the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (RMSE5 day not used during ANN training. For BGL predictions of up to 1 hour a RMSE5 day of (±SD 0.15±0.04 mmol/L was observed. For BGL predictions up to 10 hours, a RMSE5  day of (±SD 0.14±0.16 mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.

Using Genetic Distance to Infer the Accuracy of Genomic Prediction.

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Marco Scutari

2016-09-01

Full Text Available The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

Scientific Library Offers New Training Options | Poster

Science.gov (United States)

The Scientific Library is expanding its current training opportunities by offering webinars, allowing employees to take advantage of trainings from the comfort of their own offices. Due to the nature of their work, some employees find it inconvenient to attend in-person training classes; others simply prefer to use their own computers. The Scientific Library has been

Saccadic gain adaptation is predicted by the statistics of natural fluctuations in oculomotor function

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Mark V Albert

2012-12-01

Full Text Available Due to multiple factors such as fatigue, muscle strengthening, and neural plasticity, the responsiveness of the motor apparatus to neural commands changes over time. To enable precise movements the nervous system must adapt to compensate for these changes. Recent models of motor adaptation derive from assumptions about the way the motor apparatus changes. Characterizing these changes is difficult because motor adaptation happens at the same time, masking most of the effects of ongoing changes. Here, we analyze eye movements of monkeys with lesions to the posterior cerebellar vermis that impair adaptation. Their fluctuations better reveal the underlying changes of the motor system over time. When these measured, unadapted changes are used to derive optimal motor adaptation rules the prediction precision significantly improves. Among three models that similarly fit single-day adaptation results, the model that also matches the temporal correlations of the nonadapting saccades most accurately predicts multiple day adaptation. Saccadic gain adaptation is well matched to the natural statistics of fluctuations of the oculomotor plant.

Detection and Prediction of Hail Storms in Satellite Imagery using Deep Learning

Science.gov (United States)

Pullman, M.; Gurung, I.; Ramachandran, R.; Maskey, M.

2017-12-01

Natural hazards, such as damaging hail storms, dramatically disrupt both industry and agriculture, having significant socio-economic impacts in the United States. In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest 2016 weather phenomenon in the United States. The destructive nature and high cost of hail storms has driven research into the development of more accurate hail-prediction algorithms in an effort to mitigate societal impacts. Recently, weather forecasting efforts have turned to deep learning neural networks because neural networks can more effectively model complex, nonlinear, dynamical phenomenon that exist in large datasets through multiple stages of transformation and representation. In an effort to improve hail-prediction techniques, we propose a deep learning technique that leverages satellite imagery to detect and predict the occurrence of hail storms. The technique is applied to satellite imagery from 2006 to 2016 for the contiguous United States and incorporates hail reports obtained from the National Center for Environmental Information Storm Events Database for training and validation purposes. In this presentation, we describe a novel approach to predicting hail via a neural network model that creates a large labeled dataset of hail storms, the accuracy and results of the model, and its applications for improving hail forecasting.

Adaptive value of phenological traits in stressful environments: predictions based on seed production and laboratory natural selection.

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Benjamin Brachi

Full Text Available Phenological traits often show variation within and among natural populations of annual plants. Nevertheless, the adaptive value of post-anthesis traits is seldom tested. In this study, we estimated the adaptive values of pre- and post-anthesis traits in two stressful environments (water stress and interspecific competition, using the selfing annual species Arabidopsis thaliana. By estimating seed production and by performing laboratory natural selection (LNS, we assessed the strength and nature (directional, disruptive and stabilizing of selection acting on phenological traits in A. thaliana under the two tested stress conditions, each with four intensities. Both the type of stress and its intensity affected the strength and nature of selection, as did genetic constraints among phenological traits. Under water stress, both experimental approaches demonstrated directional selection for a shorter life cycle, although bolting time imposes a genetic constraint on the length of the interval between bolting and anthesis. Under interspecific competition, results from the two experimental approaches showed discrepancies. Estimation of seed production predicted directional selection toward early pre-anthesis traits and long post-anthesis periods. In contrast, the LNS approach suggested neutrality for all phenological traits. This study opens questions on adaptation in complex natural environment where many selective pressures act simultaneously.

Market Imperfections and Firm-Sponsored Training

NARCIS (Netherlands)

Picchio, M.; van Ours, J.C.

2010-01-01

Recent human capital theories predict that labor market frictions and product market competition influence firm-sponsored training. Using matched worker-firm data from Dutch manufacturing, our paper empirically assesses the validity of these predictions. We find that a decrease in labor market

How do Epistemological Beliefs Affect Training Motivation?

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Ingrid Molan

2014-05-01

Full Text Available Studies show that human resources development through workplace training is one of the major investments in the workforce in today’s globalized and challenging market. As training motivation influences employees’ preparation for the workplace training, their respond to the programme, their learning outcome, their performance levels, and use of acquired knowledge and skills in their workplace it seems logical to investigate and determine antecedents of training motivation. The aim of this study was to examine the relationship between the concepts of epistemological beliefs, training motivation and the actual participation in the workplace training. We predicted that epistemological beliefs would have an effect on training motivation and actual participation on the workplace training and that there would be a positive relationship between the concepts, meaning that the more sophisticated epistemological beliefs would lead to higher motivation and participation. To test the epistemological beliefs, the Epistemic Belief Inventory (Schraw, Bendixen & Dunkle, 2002 was used and adjusted to the workplace setting. Then the results were compared to employees’ training motivation, which was measured with a questionnaire made by authors of the present study, and employees’ actual number of training hours annually. The results confirmed the relationship between the concepts as well as a significant predicting value of epistemological beliefs on motivation and actual participation. Epistemic Belief Inventory did not yield expected results reported by the authors of the instrument therefore the limitations, possible other interpretations and suggested further exploration are discussed.

Stage-specific predictive models for breast cancer survivability.

Science.gov (United States)

Kate, Rohit J; Nadig, Ramya

2017-01-01

Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Hybrid Corporate Performance Prediction Model Considering Technical Capability

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Joonhyuck Lee

2016-07-01

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

Using squat testing to predict training loads for lower-body exercises in elite karate athletes.

Science.gov (United States)

Wong, Del P; Tan, Erik C H; Chaouachi, Anis; Carling, Christopher; Castagna, Carlo; Bloomfield, Jonathan; Behm, David G

2010-11-01

The purpose of this study was to determine the relationship between squat loads and 2 bilateral and 2 unilateral stepping lower-body exercises in predominantly unilateral movement elite athletes (Karate). Equations to predict loads for lower-body exercises based on the squat load were also determined. Fourteen male elite Karate athletes (age = 22.6 ± 1.2 years) performed 6 repetition maximum (RM) of the following free-weight bilateral exercises: back half squat, deadlift, leg press and unilateral stepping exercises, lunge; and step-up. Results showed that 6RM squat load was significantly (p squat load was a significant predictor for deadlift, leg press, lunge, and step-up (R2 range from 0.57 to 0.85, p squat load (1.12)-16.60 kg, (b) Leg press = squat load (1.66) + 16.10 kg, (c) Lunge = squat load (0.61) + 9.39 kg, and (d) step-up = squat load (0.85)-10.36 kg. Coaches and fitness professionals can use the 6RM squat load as a time effective and accurate method to predict training loads for both bilateral and unilateral lower-body exercises with quadriceps as the prime mover. Load prescriptions for unilateral exercises should take into account the type of athletic population.

Modeling techniques for predicting long-term consequences of the effects of radiation on natural aquatic populations and ecosystems

International Nuclear Information System (INIS)

Van Winkle, W.

1977-01-01

Appropriate modeling techniques already exist for investigating some long-term consequences of the effects of radiation on natural aquatic populations and ecosystems, even if to date these techniques have not been used for this purpose. At the low levels of irradiation estimated to occur in natural aquatic systems, effects are difficult to detect at even the individual level much less the population or ecosystem level where the subtle effects of radiation are likely to be completely overshadowed by the effects of other environmental factors and stresses and the natural variability of the system. The claim that population and ecosystem models can be accurate and reliable predictive tools in assessing any stress has been oversold. Nonetheless, the use of these tools can be useful for learning more about the effects of radioactive releases on aquatic populations and ecosystems

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

Science.gov (United States)

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

2018-05-01

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

Lithuanian Publisher’s Needs for Training

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Arūnas Gudinavičius

2016-12-01

Full Text Available The survey on the training and professional development needs of Lithuanian publishers was designed and conducted. The pilot research showed that publishers in Lithuania have an interest in professional training. According to the results, the need for training among the employees of Lithuanian publishing houses depends on the previous training experience: the more training courses the employee had before, the greater need for training he/she expresses. 82% of publishing house employees from four different fields agreed and strongly agreed on the need for training. Very few employees would like to pay for training by themselves. The identified need for training appears to reflect the nature of the problems encountered in the publishing field in Lithuania: Copyright, Law & Rights is the most wanted training topic among managing directors. The list of preferred topics for training can be used as the grounds for creating a supply of publishing training courses.

Crisis-counselor perceptions of job training, stress, and satisfaction during disaster recovery.

Science.gov (United States)

Bellamy, Nikki D; Wang, Min Qi; McGee, Lori A; Liu, Julie S; Robinson, Maryann E

2018-05-03

The United States Crisis Counseling Assistance and Training Program (CCP; authorized by the Robert T. Stafford Disaster Relief and Emergency Assistance Act, 1974/2013) aims to provide disaster-recovery support to communities following natural or human-caused disasters through outreach. Job satisfaction among the crisis counselors the CCP employs may affect the delivery of outreach services to survivors and their communities. The present study was conducted to gain insight into CCP crisis counselors' experiences with job training and work-related stress as predictors of job satisfaction. Data was collected from 47 CCP service-provider agencies, including 532 completed service-provider feedback surveys to examine the usefulness of the CCP training they had received, the support and supervision provided by program management, the workload and its duration, resources provided, and the stress experienced. Quantitative and qualitative data were examined, and a multiple linear regression was calculated to predict job satisfaction based on training usefulness, job stress, gender, age, race, full- or part-time status, highest level of education achieved, and supervisory position. The overall regression equation was significant, F(8, 341) = 8.428, p job training was rated as useful (p job stress (p job satisfaction. Findings suggest that proper training and management of stress among crisis counselors are necessary for influencing levels of staff job satisfaction. Where self-care and stress management were not adequately emphasized, more stress was reported. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

New Management Practices and Enterprise Training.

Science.gov (United States)

Smith, Andrew; Oczkowski, Eddie; Noble, Charles; Macklin, Robert

The changing nature of the demand for training in Australian enterprises adopting new management practices and the implications of those changes for training providers were examined. More than 3,400 private sector enterprises were surveyed by mail, after which follow-up telephone interviews were conducted with 80 human resource practitioners from…

Positive-Unlabeled Learning for Pupylation Sites Prediction

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Ming Jiang

2016-01-01

Full Text Available Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-consuming and labor-sensitive. With the existing computational methods, the experimentally annotated pupylation sites are used as the positive training set and the remaining nonannotated lysine residues as the negative training set to build classifiers to predict new pupylation sites from the unknown proteins. However, the remaining nonannotated lysine residues may contain pupylation sites which have not been experimentally validated yet. Unlike previous methods, in this study, the experimentally annotated pupylation sites were used as the positive training set whereas the remaining nonannotated lysine residues were used as the unlabeled training set. A novel method named PUL-PUP was proposed to predict pupylation sites by using positive-unlabeled learning technique. Our experimental results indicated that PUL-PUP outperforms the other methods significantly for the prediction of pupylation sites. As an application, PUL-PUP was also used to predict the most likely pupylation sites in nonannotated lysine sites.

Job Matching and On-the-Job Training.

OpenAIRE

Barron, John M; Black, Dan A; Loewenstein, Mark A

1989-01-01

Conventional analysis predicts that workers pay part of their on-the-job training costs by accepting a lower starting wage and subsequently realize a return to this investment in the form of greater wage growth. Missing from the conventional treatment of on-the-job training is a discussion of the process by which heterogeneous worker s are matched to jobs requiring varying amounts of training. This matching process constitutes a key feature of the on-the-job training model that is presented i...

The Prediction of Training Proficiency in Firefighters: A Study of Predictive Validity in Spain

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Alfredo Berges

2018-02-01

Full Text Available The present study provides results of criterion validity in the selection of firefighters in Spain. The predictors were cognitive skills, job knowledge, and physical aptitudes, and the criterion was training proficiency. The process involves 639 candidates, but only 44 complete successfully the selection process. Our results support previous evidence showing that general cognitive ability is the best predictor of training proficiency, with an operational validity of .57. With respect to the other predictors, job knowledge presented an operational validity of .55 and physical tests of .49. In addition, multiple regression analysis showed that cognitive aptitude explains 33% of the variance, but when physical aptitudes are included the explained variance increases to 50%. If we also add job knowledge, explained variance increases to 55%. Our study offers recent results of criterion validity in a barely investigated job, gathered in a country other than the one where prior research had been carried out.

Plutonium in nature; Le plutonium dans la nature

Energy Technology Data Exchange (ETDEWEB)

Madic, C.

1994-12-31

Plutonium in nature comes from natural sources and anthropogenic ones. Plutonium at the earth surface comes principally from anthropogenic sources. It is easily detectable in environment. The plutonium behaviour in environment is complex. It seems necessary for the future to reduce releases in environment, to improve predictive models of plutonium behaviour in geosphere, to precise biological impact of anthropogenic plutonium releases.

Is the preference of natural versus man-made scenes driven by bottom-up processing of the visual features of nature?

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Omid eKardan

2015-04-01

Full Text Available Previous research has shown that viewing images of nature scenes can have a beneficial effect on memory, attention and mood. In this study we aimed to determine whether the preference of natural versus man-made scenes is driven by bottom-up processing of the low-level visual features of nature. We used participants’ ratings of perceived naturalness as well as aesthetic preference for 307 images with varied natural and urban content. We then quantified ten low-level image features for each image (a combination of spatial and color properties. These features were used to predict aesthetic preference in the images, as well as to decompose perceived naturalness to its predictable (modelled by the low-level visual features and non-modelled aspects. Interactions of these separate aspects of naturalness with the time it took to make a preference judgment showed that naturalness based on low-level features related more to preference when the judgment was faster (bottom-up. On the other hand perceived naturalness that was not modelled by low-level features was related more to preference when the judgment was slower. A quadratic discriminant classification analysis showed how relevant each aspect of naturalness (modelled and non-modelled was to predicting preference ratings, as well as the image features on their own. Finally, we compared the effect of color-related and structure-related modelled naturalness, and the remaining unmodelled naturalness in predicting aesthetic preference. In summary bottom-up (color and spatial properties of natural images captured by our features and the non-modelled naturalness are important to aesthetic judgments of natural and man-made scenes, with each predicting unique variance.

Military Ecological Risk Assessment Framework (MERAF) for Assessment of Risks of Military Training and Testing to Natural Resources

Energy Technology Data Exchange (ETDEWEB)

Suter II, G.W.

2003-06-18

The objective of this research is to provide the DoD with a framework based on a systematic, risk-based approach to assess impacts for management of natural resources in an ecosystem context. This risk assessment framework is consistent with, but extends beyond, the EPA's ecological risk assessment framework, and specifically addresses DoD activities and management needs. MERAF is intended to be consistent with existing procedures for environmental assessment and planning with DoD testing and training. The intention is to supplement these procedures rather than creating new procedural requirements. MERAF is suitable for use for training and testing area assessment and management. It does not include human health risks nor does it address specific permitting or compliance requirements, although it may be useful in some of these cases. Use of MERAF fits into the National Environmental Policy Act (NEPA) process by providing a consistent and rigorous way of organizing and conducting the technical analysis for Environmental Impact Statements (EISs) (Sigal 1993; Carpenter 1995; Canter and Sadler 1997). It neither conflicts with, nor replaces, procedural requirements within the NEPA process or document management processes already in place within DoD.

The Effects of Long-Duration Spaceflight on Training Retention and Transfer

Science.gov (United States)

Barshi, Immanuel; Healy, Alice; Dempsey, Donna L.; McGuire, Kerry M.; Landon, Lauren B.

2018-01-01

Training our crew members for long duration, exploration-class missions will have to maximize long-term retention and transfer of the trained skills. The expected duration of the missions, our inability to predict all the possible tasks the crew will be called upon to perform, and the low training-to-mission time ratio require that the training be maximally effective such that the skills acquired during training will be retained and will be transferrable across a wide range of specific tasks that are different from the particular tasks used during training. However, to be able to design training that can achieve these ambitious goals, we must first understand the ways in which long-duration spaceflight affects training retention and transfer. Current theories of training retention and transfer are largely based on experimental studies conducted at university laboratories using undergraduate students as participants. Furthermore, all such studies have been conducted on Earth. We do not know how well the results of these studies predict the performance of crew members. More specifically, we do not know how well the results of these studies predict the performance of crew members in space and especially during long-duration missions. To address this gap in our knowledge, the current on-going study seeks to test the null hypothesis that performance of university undergraduate students on Earth on training retention and transfer tests do in fact predict accurately the performance of crew members during long-duration spaceflights. To test this hypothesis, the study employs a single 16-month long experimental protocol with 3 different participant groups: undergraduate university students, crew members on the ground, and crew members in space. Results from this study will be presented upon its completion. This poster presents results of study trials of the two tasks used in this study: a data entry task and a mapping task. By researching established training principles, by

An efficient training scheme for supermodels

Science.gov (United States)

Schevenhoven, Francine J.; Selten, Frank M.

2017-06-01

Weather and climate models have improved steadily over time as witnessed by objective skill scores, although significant model errors remain. Given these imperfect models, predictions might be improved by combining them dynamically into a so-called supermodel. In this paper a new training scheme to construct such a supermodel is explored using a technique called cross pollination in time (CPT). In the CPT approach the models exchange states during the prediction. The number of possible predictions grows quickly with time, and a strategy to retain only a small number of predictions, called pruning, needs to be developed. The method is explored using low-order dynamical systems and applied to a global atmospheric model. The results indicate that the CPT training is efficient and leads to a supermodel with improved forecast quality as compared to the individual models. Due to its computational efficiency, the technique is suited for application to state-of-the art high-dimensional weather and climate models.

Threats of natural character, factors affecting sustainable development of territories and their prevention

Directory of Open Access Journals (Sweden)

Guskova N.D.

2013-01-01

Full Text Available XXI century is characterized by globalization of socio-economic processes, economic growth, and excessive consumption of natural resources that leads to imbalance in socio-economic systems. Significant threats to the sustainable development of territories are natural and anthropogenic disasters, the extent and severity of which significantly increased in recent decades. They do great damage to economy and environment, often accompanied by loss of human lives. Russia with its broad territory, significant difference in climate conditions, is exposed to the wide range of natural hazards and disasters. The most dangerous are earthquakes, floods, forest fires. About 20 % of the Russian Federation is situated in zones of high risk, which are inhabited by more than 20 million people. Area of flooding as a result of floods can reach over 400 km2. Annually in the forests appear from 100 to 300 thousand of fires on the total area of 1.5 - 2.5 million hectares. The impact of natural disasters on the sustainable development of territory is considered in the article as an example of one of the Russian regions - the Republic of Mordovia. It was analyzed the dynamics of emergencies for the period of 2000 - 2012 years, paid attention to natural emergencies (forest fires and floods. Despite the fact that occurrence of emergency and catastrophic situations of natural character happens spontaneously, size of damage they do largely determined by timeliness and accuracy of the prediction and adoption of adequate preventive measures. In this regard, the article provides recommendations to reduce threats of natural character for the sustainable development of the Republic of Mordovia. They cover a range of activities on monitoring of natural phenomena, protection of the population from emergency situations to minimize potential damage, training of population in the face of natural disasters, development of economic policy in the region and training of personnel in the

A Model to Predict the Steady-State Concentration of Hydroxyl Radicals in the Surface Layer of Natural Waters

International Nuclear Information System (INIS)

Minero, C.; Lauri, V.; Maurino, V.; Pelizzetti, E.; Vione, D.

2007-01-01

A model was developed to predict the steady-state [·OH] in the surface layer of natural waters as a function of nitrate, inorganic carbon (IC) and dissolved organic matter (DOM). The parameter values were studied in the range detected in shallow high-mountain lakes, to which the model results are most relevant. Calculations indicate that [·OH] increases with increasing nitrate and decreasing IC, and conditions are also identified where [·OH] is directly proportional, inversely proportional or independent of DOM. Based on the model results it is possible to predict the half-life time, due to reaction with ·OH, of given dissolved compounds, including organic pollutants, from the water composition data

Dimensionality and predictive validity of the HAM-Nat, a test of natural sciences for medical school admission.

Science.gov (United States)

Hissbach, Johanna C; Klusmann, Dietrich; Hampe, Wolfgang

2011-10-14

Knowledge in natural sciences generally predicts study performance in the first two years of the medical curriculum. In order to reduce delay and dropout in the preclinical years, Hamburg Medical School decided to develop a natural science test (HAM-Nat) for student selection. In the present study, two different approaches to scale construction are presented: a unidimensional scale and a scale composed of three subject specific dimensions. Their psychometric properties and relations to academic success are compared. 334 first year medical students of the 2006 cohort responded to 52 multiple choice items from biology, physics, and chemistry. For the construction of scales we generated two random subsamples, one for development and one for validation. In the development sample, unidimensional item sets were extracted from the item pool by means of weighted least squares (WLS) factor analysis, and subsequently fitted to the Rasch model. In the validation sample, the scales were subjected to confirmatory factor analysis and, again, Rasch modelling. The outcome measure was academic success after two years. Although the correlational structure within the item set is weak, a unidimensional scale could be fitted to the Rasch model. However, psychometric properties of this scale deteriorated in the validation sample. A model with three highly correlated subject specific factors performed better. All summary scales predicted academic success with an odds ratio of about 2.0. Prediction was independent of high school grades and there was a slight tendency for prediction to be better in females than in males. A model separating biology, physics, and chemistry into different Rasch scales seems to be more suitable for item bank development than a unidimensional model, even when these scales are highly correlated and enter into a global score. When such a combination scale is used to select the upper quartile of applicants, the proportion of successful completion of the curriculum

Dimensionality and predictive validity of the HAM-Nat, a test of natural sciences for medical school admission

Directory of Open Access Journals (Sweden)

Hissbach Johanna C

2011-10-01

Full Text Available Abstract Background Knowledge in natural sciences generally predicts study performance in the first two years of the medical curriculum. In order to reduce delay and dropout in the preclinical years, Hamburg Medical School decided to develop a natural science test (HAM-Nat for student selection. In the present study, two different approaches to scale construction are presented: a unidimensional scale and a scale composed of three subject specific dimensions. Their psychometric properties and relations to academic success are compared. Methods 334 first year medical students of the 2006 cohort responded to 52 multiple choice items from biology, physics, and chemistry. For the construction of scales we generated two random subsamples, one for development and one for validation. In the development sample, unidimensional item sets were extracted from the item pool by means of weighted least squares (WLS factor analysis, and subsequently fitted to the Rasch model. In the validation sample, the scales were subjected to confirmatory factor analysis and, again, Rasch modelling. The outcome measure was academic success after two years. Results Although the correlational structure within the item set is weak, a unidimensional scale could be fitted to the Rasch model. However, psychometric properties of this scale deteriorated in the validation sample. A model with three highly correlated subject specific factors performed better. All summary scales predicted academic success with an odds ratio of about 2.0. Prediction was independent of high school grades and there was a slight tendency for prediction to be better in females than in males. Conclusions A model separating biology, physics, and chemistry into different Rasch scales seems to be more suitable for item bank development than a unidimensional model, even when these scales are highly correlated and enter into a global score. When such a combination scale is used to select the upper quartile of

GAMIFICATION AS A STRATEGY FOR TRAINING AND DEVELOPMENT

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Ricardo Di Bartolomeo

2015-12-01

Full Text Available The aim of this research was investigate how gamification can help to generate solutions for Training and Development area, identifying its concepts, how and why apply it in training programs. The research instrument used was the literature review on exiting information about gamification, training and development. The research has an exploratory nature, with the aim of improving the ideas on the training models. The results compared the traditional models of training to training based upon the gamified systems. It concluded that gamified systems show better results than more traditional systems.

Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging.

Science.gov (United States)

Choi, Lark Kwon; You, Jaehee; Bovik, Alan Conrad

2015-11-01

We propose a referenceless perceptual fog density prediction model based on natural scene statistics (NSS) and fog aware statistical features. The proposed model, called Fog Aware Density Evaluator (FADE), predicts the visibility of a foggy scene from a single image without reference to a corresponding fog-free image, without dependence on salient objects in a scene, without side geographical camera information, without estimating a depth-dependent transmission map, and without training on human-rated judgments. FADE only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. Fog aware statistical features that define the perceptual fog density index derive from a space domain NSS model and the observed characteristics of foggy images. FADE not only predicts perceptual fog density for the entire image, but also provides a local fog density index for each patch. The predicted fog density using FADE correlates well with human judgments of fog density taken in a subjective study on a large foggy image database. As applications, FADE not only accurately assesses the performance of defogging algorithms designed to enhance the visibility of foggy images, but also is well suited for image defogging. A new FADE-based referenceless perceptual image defogging, dubbed DEnsity of Fog Assessment-based DEfogger (DEFADE) achieves better results for darker, denser foggy images as well as on standard foggy images than the state of the art defogging methods. A software release of FADE and DEFADE is available online for public use: http://live.ece.utexas.edu/research/fog/index.html.

[THE PRESENT STATE OF EPIZOOTOLOGICAL MONITORING OF THE NATURAL FOCI OF INFECTIONS IN THE RUSSIAN FEDERATION].

Science.gov (United States)

Trankvilevsky, D V; Tsarenko, V A; Zhukov, V I

2016-01-01

The facilities of the Russian Federal Service for Supervision of Consumer Rights Protection and Human Welfare play a leading role in epizootological monitoring. The specialists (zoologists and entomologists) of Hygiene and Epidemiology Centers do basic work in the subjects of the Russian Federation. The data obtained in the participation of different ministries and departments are used to analyze the results of monitoring. The latter is one of the important steps in the management of the epidemic, process in natural focal infections. In recent years, there has been an unjustified reduction in the volume of studies in the natural foci. This negatively affects the reliability of estimates and predictions of the epidemic activity of the natural foci of infections. Ensuring the national, security of the Russian Federation, epidemiological surveillance, and control of its natural foci requires staffing and appropriate professional training in the zoological and entomological subdivisions of the Russian Federal Service for Supervision of Consumer Rights Protection and Human Welfare.

Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron

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Mohammad Subhi Al-batah

2015-01-01

Full Text Available Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP and Cascade Forward Neural Network (CFNN, are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.

Natural hazard management high education: laboratory of hydrologic and hydraulic risk management and applied geomorphology

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Giosa, L.; Margiotta, M. R.; Sdao, F.; Sole, A.; Albano, R.; Cappa, G.; Giammatteo, C.; Pagliuca, R.; Piccolo, G.; Statuto, D.

2009-04-01

The Environmental Engineering Faculty of University of Basilicata have higher-level course for students in the field of natural hazard. The curriculum provides expertise in the field of prediction, prevention and management of earthquake risk, hydrologic-hydraulic risk, and geomorphological risk. These skills will contribute to the training of specialists, as well as having a thorough knowledge of the genesis and the phenomenology of natural risks, know how to interpret, evaluate and monitor the dynamic of environment and of territory. In addition to basic training in the fields of mathematics and physics, the course of study provides specific lessons relating to seismic and structural dynamics of land, environmental and computational hydraulics, hydrology and applied hydrogeology. In particular in this course there are organized two connected examination arguments: Laboratory of hydrologic and hydraulic risk management and Applied geomorphology. These course foresee the development and resolution of natural hazard problems through the study of a real natural disaster. In the last year, the work project has regarded the collapse of two decantation basins of fluorspar, extracted from some mines in Stava Valley, 19 July 1985, northern Italy. During the development of the course, data and event information has been collected, a guided tour to the places of the disaster has been organized, and finally the application of mathematical models to simulate the disaster and analysis of the results has been carried out. The student work has been presented in a public workshop.

Deflection Prediction of No-Fines Lightweight Concrete Wall Using Neural Network Caused Dynamic Loads

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Ridho Bayuaji

2018-04-01

Full Text Available No-fines lightweight concrete wall with horizontal reinforcement refers to an alternative material for wall construction with an aim of improving the wall quality towards horizontal loads. This study is focused on artificial neural network (ANN application to predicting the deflection deformation caused by dynamic loads. The ANN method is able to capture the complex interactions among input/output variables in a system without any knowledge of interaction nature and without any explicit assumption to model form. This paper explains the existing data research, data selection and process of ANN modelling training process and validation. The results of this research show that the deformation can be predicted more accurately, simply and quickly due to the alternating horizontal loads.

Educating the next generation of nature entrepreneurs

Science.gov (United States)

Judith C. Jobse; Loes Witteveen; Judith Santegoets; Daan van der Linde

2015-01-01

With this paper, it is illustrated that a focus on entrepreneurship training in the nature and wilderness sector is relevant for diverse organisations and situations. The first curricula on nature entrepreneurship are currently being developed. In this paper the authors describe a project that focusses on educating the next generation of nature entrepreneurs, reflect...

A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs

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Merlijn Blaauw

2017-12-01

Full Text Available We recently presented a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Nonetheless, compared to modeling waveform directly, ways of effectively handling higher-dimensional outputs, multiple feature streams and regularization become more important with our approach. In this work, we extend our proposed system to include additional components for predicting F0 and phonetic timings from a musical score with lyrics. These expression-related features are learned together with timbrical features from a single set of natural songs. We compare our method to existing statistical parametric, concatenative, and neural network-based approaches using quantitative metrics as well as listening tests.

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

Science.gov (United States)

Salmi, Juha; Koistinen, Olli-Pekka; Glerean, Enrico; Jylänki, Pasi; Vehtari, Aki; Jääskeläinen, Iiro P; Mäkelä, Sasu; Nummenmaa, Lauri; Nummi-Kuisma, Katarina; Nummi, Ilari; Sams, Mikko

2017-08-15

During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

Better estimation of protein-DNA interaction parameters improve prediction of functional sites

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O'Flanagan Ruadhan A

2008-12-01

Full Text Available Abstract Background Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment method designed to produce many such sequences. Results We analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP, and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites. Conclusion We think that training binding site detection

Using Opinions and Knowledge to Identify Natural Groups of Gambling Employees.

Science.gov (United States)

Gray, Heather M; Tom, Matthew A; LaPlante, Debi A; Shaffer, Howard J

2015-12-01

Gaming industry employees are at higher risk than the general population for health conditions including gambling disorder. Responsible gambling training programs, which train employees about gambling and gambling-related problems, might be a point of intervention. However, such programs tend to use a "one-size-fits-all" approach rather than multiple tiers of instruction. We surveyed employees of one Las Vegas casino (n = 217) and one online gambling operator (n = 178) regarding their gambling-related knowledge and opinions prior to responsible gambling training, to examine the presence of natural knowledge groups among recently hired employees. Using k-means cluster analysis, we observed four natural groups within the Las Vegas casino sample and two natural groups within the online operator sample. We describe these natural groups in terms of opinion/knowledge differences as well as distributions of demographic/occupational characteristics. Gender and language spoken at home were correlates of cluster group membership among the sample of Las Vegas casino employees, but we did not identify demographic or occupational correlates of cluster group membership among the online gambling operator employees. Gambling operators should develop more sophisticated training programs that include instruction that targets different natural knowledge groups.

Evaluation method for core thermohydraulics during natural circulation in fast reactors numerical predictions of inter-wrapper flow

International Nuclear Information System (INIS)

Kamide, H.; Kimura, N.; Miyakoshi, H.; Nagasawa, K.

2001-01-01

Decay heat removal using natural circulation is one of the important functions for the safety of fast reactors. As a decay heat removal system, direct reactor auxiliary cooling system has been selected in current designs of fast reactors. In this design, dumped heat exchanger provides cold sodium and it covers the reactor core outlet. The cold sodium can penetrate into the gap region between the subassemblies. This gap flow is referred as inter-wrapper flow (IWF). A numerical estimation method for such natural circulation phenomena in a reactor core has been developed, which models each subassembly as a rectangular duct with gap region between the subassemblies and also the upper plenum in a reactor vessel. This numerical simulation method was verified based on experimental data of a sodium test using 7- subassembly core model and also a water test which simulates IWF using the 1/12 sector model of a reactor core. We applied the estimation method to the natural circulation in a 600 MW class fast reactor. The temperature in the core strongly depended on IWF, flow redistribution in the core, and inter-subassembly heat transfer. It is desired for prediction methods on the natural circulation to simulate these phenomena. (author)

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

Science.gov (United States)

Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

2015-01-01

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

A study on improvement of scaling factor prediction using artificial neural network

International Nuclear Information System (INIS)

Lee, Sang Chul; Hwang, Ki Ha; Kang, Sang Hee; Lee, Kun Jai

2003-01-01

Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed knowledge of the natures and quantities of radionuclides in waste package. Many of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentrations of DTM (Difficult-to Measure) nuclide is decided using the relation of concentrations (Scaling Factor) between Key (Easy-to-Measure) nuclide and DTM nuclide with measured concentrations of Key nuclide. In general, scaling factor is determined by using of log mean average (LMA) and regression. These methods are adequate to apply most corrosion product nuclides. But in case of fission product nuclides and some corrosion product nuclides, the predicted values aren't well matched with the original values. In this study, the models using artificial neural network (ANN) for C-14 and Sr-90 are compared with those using LMA and regression. The assessment of models is executed in the two parts divided by a training part and a validation part. For all of two nuclides in the training part, the predicted values using ANN are well matched with the measured values compared with those using LMA and regression. In the validation part, the accuracy of the predicted values using ANN is better than that using LMA and is similar to or better than that using regression. It is concluded that the predicted values using ANN model are better than those using conventional model in some nuclides and ANN model can be used as the complement of LMA and regression model

Data Driven Economic Model Predictive Control

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Masoud Kheradmandi

2018-04-01

Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

Personality Traits and Training Initiation Process: Intention, Planning, and Action Initiation.

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Laguna, Mariola; Purc, Ewelina

2016-01-01

The article aims at investigating the role of personality traits in relation to training initiation. Training initiation is conceptualized as a goal realization process, and explained using goal theories. There are three stages of the process analyzed: intention to undertake training, plan formulation, and actual training undertaking. Two studies tested the relationships between five personality traits, defined according to the five factor model, and the stages of the goal realization process. In Study 1, which explains training intention and training plans' formulation, 155 employees participated. In Study 2, which was time-lagged with two measurement points, and which explains intention, plans, and training actions undertaken, the data from 176 employees was collected at 3 month intervals. The results of these studies show that personality traits, mainly openness to experience, predict the training initiation process to some degree: intention, plans, and actual action initiation. The findings allow us to provide recommendations for practitioners responsible for human resource development. The assessment of openness to experience in employees helps predict their motivation to participate in training activities. To increase training motivation it is vital to strengthen intentions to undertake training, and to encourage training action planning.

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines

Science.gov (United States)

Nielsen, Nanna Hellum; Jahoor, Ahmed; Jensen, Jens Due; Orabi, Jihad; Cericola, Fabio; Edriss, Vahid; Jensen, Just

2016-01-01

Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines) are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families. PMID:27783639

Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

Directory of Open Access Journals (Sweden)

Nanna Hellum Nielsen

Full Text Available Genomic selection was recently introduced in plant breeding. The objective of this study was to develop genomic prediction for important seed quality parameters in spring barley. The aim was to predict breeding values without expensive phenotyping of large sets of lines. A total number of 309 advanced spring barley lines tested at two locations each with three replicates were phenotyped and each line was genotyped by Illumina iSelect 9Kbarley chip. The population originated from two different breeding sets, which were phenotyped in two different years. Phenotypic measurements considered were: seed size, protein content, protein yield, test weight and ergosterol content. A leave-one-out cross-validation strategy revealed high prediction accuracies ranging between 0.40 and 0.83. Prediction across breeding sets resulted in reduced accuracies compared to the leave-one-out strategy. Furthermore, predicting across full and half-sib-families resulted in reduced prediction accuracies. Additionally, predictions were performed using reduced marker sets and reduced training population sets. In conclusion, using less than 200 lines in the training set can result in low prediction accuracy, and the accuracy will then be highly dependent on the family structure of the selected training set. However, the results also indicate that relatively small training sets (200 lines are sufficient for genomic prediction in commercial barley breeding. In addition, our results indicate a minimum marker set of 1,000 to decrease the risk of low prediction accuracy for some traits or some families.

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

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Xiaochen Zhang

2017-01-01

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

Gender matters: Private sector training in Vietnamese SMEs

DEFF Research Database (Denmark)

Trifkovic, Neda; Bjerge, Benedikte Alkjærsig; Torm, Nina

such training may be in closing the gender wage gap. We use a matched employer–employee panel dataset to assess why firms train and whether formal training affects wage outcomes in Vietnamese SMEs. Training is generally found to be firm-sponsored and specific in nature. We find that training is associated......, firm-sponsored on-the-job training helps close the gender wage gap.......In many developing countries the skill base is a cause of concern with respect to international competition. Firm-provided training is generally seen as an important tool for bridging the skills gap between labour force and private sector demand. Yet little is known about how successful...

Scientific and technical training in the Soviet Union

Science.gov (United States)

Spearman, M. L.

1984-01-01

The Soviet Union recognizes that the foundation of their system depends upon complete dedication of the people to the state through thorough psychological training as well as through military training, and through specialized education in the broad fields of engineering, natural sciences, life sciences, social sciences, and education. An outline of the U.S.S.R. educational system indicates the extent of academic training, coupled with on-the-job and military training, that can produce a highly skilled, dedicated, and matured person. Observations on the coupling of political, economic, and psychological training along with the technical training are made, along with some mention of positive and negative aspects of the training.

Minimum DNBR Prediction Using Artificial Intelligence

Energy Technology Data Exchange (ETDEWEB)

Kim, Dong Su; Kim, Ju Hyun; Na, Man Gyun [Chosun University, Gwangju (Korea, Republic of)

2011-05-15

The minimum DNBR (MDNBR) for prevention of the boiling crisis and the fuel clad melting is very important factor that should be consistently monitored in safety aspects. Artificial intelligence methods have been extensively and successfully applied to nonlinear function approximation such as the problem in question for predicting DNBR values. In this paper, support vector regression (SVR) model and fuzzy neural network (FNN) model are developed to predict the MDNBR using a number of measured signals from the reactor coolant system. Also, two models are trained using a training data set and verified against test data set, which does not include training data. The proposed MDNBR estimation algorithms were verified by using nuclear and thermal data acquired from many numerical simulations of the Yonggwang Nuclear Power Plant Unit 3 (YGN-3)

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.

Mechanisms of children's exposure to nature: Predicting adulthood environmental citizenship and commitment to nature-based activities

Science.gov (United States)

Stanley T. Asah; David N. Bengston; Lynne M. Westphal; Catherine H. Gowan

2017-01-01

Childhood-nature experiences have lifelong effects on environmental citizenship and commitment to nature-based activities. But, it is unclear whether, and to what extent, the different mechanisms through which children and youth experience nature are associated with these outcomes. To test these associations, an online questionnaire assessing mechanisms of childhood...

A dynamic particle filter-support vector regression method for reliability prediction

International Nuclear Information System (INIS)

Wei, Zhao; Tao, Tao; ZhuoShu, Ding; Zio, Enrico

2013-01-01

Support vector regression (SVR) has been applied to time series prediction and some works have demonstrated the feasibility of its use to forecast system reliability. For accuracy of reliability forecasting, the selection of SVR's parameters is important. The existing research works on SVR's parameters selection divide the example dataset into training and test subsets, and tune the parameters on the training data. However, these fixed parameters can lead to poor prediction capabilities if the data of the test subset differ significantly from those of training. Differently, the novel method proposed in this paper uses particle filtering to estimate the SVR model parameters according to the whole measurement sequence up to the last observation instance. By treating the SVR training model as the observation equation of a particle filter, our method allows updating the SVR model parameters dynamically when a new observation comes. Because of the adaptability of the parameters to dynamic data pattern, the new PF–SVR method has superior prediction performance over that of standard SVR. Four application results show that PF–SVR is more robust than SVR to the decrease of the number of training data and the change of initial SVR parameter values. Also, even if there are trends in the test data different from those in the training data, the method can capture the changes, correct the SVR parameters and obtain good predictions. -- Highlights: •A dynamic PF–SVR method is proposed to predict the system reliability. •The method can adjust the SVR parameters according to the change of data. •The method is robust to the size of training data and initial parameter values. •Some cases based on both artificial and real data are studied. •PF–SVR shows superior prediction performance over standard SVR

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.

Natural gas prices and the end of gradual change

International Nuclear Information System (INIS)

Osten, J.A.

1998-01-01

Natural gas price predictions for the years 1998, 1999-2001, 2000-2005 are provided. In general, prices are predicted to decrease with increase in storage. Some other factors that will influence the price of natural gas and, therefore, should receive consideration in price predictions, include growth in demand, natural gas production, deliverability, new pipelines, and the Alberta price basis. tabs., figs

Description of the Nuclear Training Centre

International Nuclear Information System (INIS)

Wagadarikar, V.K.

1974-01-01

The Department of Atomic Energy, Government of India has developed an on-going programme for constructing and operating heavy water moderated, natural uranium fuelled power stations of the CANDU-type. With the view to train personnel required for operation and maintenance of these stations, a Nuclear Training Centre has been set up at the site of the Rajasthan Atomic Power Station. A description of the nuclear training centre with its facilities is given. The training programme for engineers, operators, mechanical, electrical and control maintainers etc. is given in detail, along with the actual syllabi for respective courses. Examples of the typical field check list are provided. (K.B.)

Predicting perceptual quality of images in realistic scenario using deep filter banks

Science.gov (United States)

Zhang, Weixia; Yan, Jia; Hu, Shiyong; Ma, Yang; Deng, Dexiang

2018-03-01

Classical image perceptual quality assessment models usually resort to natural scene statistic methods, which are based on an assumption that certain reliable statistical regularities hold on undistorted images and will be corrupted by introduced distortions. However, these models usually fail to accurately predict degradation severity of images in realistic scenarios since complex, multiple, and interactive authentic distortions usually appear on them. We propose a quality prediction model based on convolutional neural network. Quality-aware features extracted from filter banks of multiple convolutional layers are aggregated into the image representation. Furthermore, an easy-to-implement and effective feature selection strategy is used to further refine the image representation and finally a linear support vector regression model is trained to map image representation into images' subjective perceptual quality scores. The experimental results on benchmark databases present the effectiveness and generalizability of the proposed model.

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.

Multiple-point statistical prediction on fracture networks at Yucca Mountain

International Nuclear Information System (INIS)

Liu, X.Y; Zhang, C.Y.; Liu, Q.S.; Birkholzer, J.T.

2009-01-01

In many underground nuclear waste repository systems, such as at Yucca Mountain, water flow rate and amount of water seepage into the waste emplacement drifts are mainly determined by hydrological properties of fracture network in the surrounding rock mass. Natural fracture network system is not easy to describe, especially with respect to its connectivity which is critically important for simulating the water flow field. In this paper, we introduced a new method for fracture network description and prediction, termed multi-point-statistics (MPS). The process of the MPS method is to record multiple-point statistics concerning the connectivity patterns of a fracture network from a known fracture map, and to reproduce multiple-scale training fracture patterns in a stochastic manner, implicitly and directly. It is applied to fracture data to study flow field behavior at the Yucca Mountain waste repository system. First, the MPS method is used to create a fracture network with an original fracture training image from Yucca Mountain dataset. After we adopt a harmonic and arithmetic average method to upscale the permeability to a coarse grid, THM simulation is carried out to study near-field water flow in the surrounding waste emplacement drifts. Our study shows that connectivity or patterns of fracture networks can be grasped and reconstructed by MPS methods. In theory, it will lead to better prediction of fracture system characteristics and flow behavior. Meanwhile, we can obtain variance from flow field, which gives us a way to quantify model uncertainty even in complicated coupled THM simulations. It indicates that MPS can potentially characterize and reconstruct natural fracture networks in a fractured rock mass with advantages of quantifying connectivity of fracture system and its simulation uncertainty simultaneously.

Can we predict age at natural menopause using ovarian reserve tests or mother's age at menopause? A systematic literature review.

Science.gov (United States)

Depmann, Martine; Broer, Simone L; van der Schouw, Yvonne T; Tehrani, Fahimeh R; Eijkemans, Marinus J; Mol, Ben W; Broekmans, Frank J

2016-02-01

This review aimed to appraise data on prediction of age at natural menopause (ANM) based on antimüllerian hormone (AMH), antral follicle count (AFC), and mother's ANM to evaluate clinical usefulness and to identify directions for further research. We conducted three systematic reviews of the literature to identify studies of menopause prediction based on AMH, AFC, or mother's ANM, corrected for baseline age. Six studies selected in the search for AMH all consistently demonstrated AMH as being capable of predicting ANM (hazard ratio, 5.6-9.2). The sole study reporting on mother's ANM indicated that AMH was capable of predicting ANM (hazard ratio, 9.1-9.3). Two studies provided analyses of AFC and yielded conflicting results, making this marker less strong. AMH is currently the most promising marker for ANM prediction. The predictive capacity of mother's ANM demonstrated in a single study makes this marker a promising contributor to AMH for menopause prediction. Models, however, do not predict the extremes of menopause age very well and have wide prediction interval. These markers clearly need improvement before they can be used for individual prediction of menopause in the clinical setting. Moreover, potential limitations for such use include variations in AMH assays used and a lack of correction for factors or diseases affecting AMH levels or ANM. Future studies should include women of a broad age range (irrespective of cycle regularity) and should base predictions on repeated AMH measurements. Furthermore, currently unknown candidate predictors need to be identified.

Predicting Protein Secondary Structure with Markov Models

DEFF Research Database (Denmark)

Fischer, Paul; Larsen, Simon; Thomsen, Claus

2004-01-01

we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

Identification of novel human dipeptidyl peptidase-IV inhibitors of natural origin (Part II: in silico prediction in antidiabetic extracts.

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Laura Guasch

Full Text Available BACKGROUND: Natural extracts play an important role in traditional medicines for the treatment of diabetes mellitus and are also an essential resource for new drug discovery. Dipeptidyl peptidase IV (DPP-IV inhibitors are potential candidates for the treatment of type 2 diabetes mellitus, and the effectiveness of certain antidiabetic extracts of natural origin could be, at least partially, explained by the inhibition of DPP-IV. METHODOLOGY/PRINCIPAL FINDINGS: Using an initial set of 29,779 natural products that are annotated with their natural source and an experimentally validated virtual screening procedure previously developed in our lab (Guasch et al.; 2012 [1], we have predicted 12 potential DPP-IV inhibitors from 12 different plant extracts that are known to have antidiabetic activity. Seven of these molecules are identical or similar to molecules with described antidiabetic activity (although their role as DPP-IV inhibitors has not been suggested as an explanation for their bioactivity. Therefore, it is plausible that these 12 molecules could be responsible, at least in part, for the antidiabetic activity of these extracts through their inhibitory effect on DPP-IV. In addition, we also identified as potential DPP-IV inhibitors 6 molecules from 6 different plants with no described antidiabetic activity but that share the same genus as plants with known antidiabetic properties. Moreover, none of the 18 molecules that we predicted as DPP-IV inhibitors exhibits chemical similarity with a group of 2,342 known DPP-IV inhibitors. CONCLUSIONS/SIGNIFICANCE: Our study identified 18 potential DPP-IV inhibitors in 18 different plant extracts (12 of these plants have known antidiabetic properties, whereas, for the remaining 6, antidiabetic activity has been reported for other plant species from the same genus. Moreover, none of the 18 molecules exhibits chemical similarity with a large group of known DPP-IV inhibitors.

Fluid intellingence and spatial reasoning as predictors of pilot training performance in the South African Air Force (SAAF

Directory of Open Access Journals (Sweden)

François de Kock

2009-04-01

Full Text Available Pilot selection is a form of high-stakes selection due to the massive costs of training, high trainee ability requirements and costly repercussions of poor selection decisions. This criterion-related validation study investigated the predictive ability of fluid intelligence and spatial reasoning in predicting three criteria of pilot training performance, using an accumulated sample of South African Air Force pilots (N = 108. Hierarchical multiple regression analyses with training grade achieved as criterion were performed for each of the phases of training, namely practical flight training, ground school training, and officers’ formative training. Multiple correlations of 0.35 (p 0.05 and 0.23 (p > 0.05 were obtained for flight, ground school and formative training results, respectively. Spatial ability had incremental validity over fluid intelligence for predicting flight training performance.

Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy

International Nuclear Information System (INIS)

Shiraishi, Satomi; Moore, Kevin L.

2016-01-01

Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = D clin − D pred . The mean (〈δD r 〉), standard deviation (σ δD r ), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (r PTV ) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over r PTV ∈ [ − 6, 30] mm. The average prediction error was less

Strength Training: A Natural Prescription for Staying Healthy and Fit.

Science.gov (United States)

Adams, Raymond, Ed.

2003-01-01

This newsletter highlights the importance of strength training in keeping older adults healthy and fit, explaining how it can forestall declines in strength and muscle mass, along with their attendant negative impact upon other metabolic functions and activities of daily living. Physical inactivity is common throughout the nation. Approximately 11…

Early predictors of need for remediation in the Australian general practice training program: a retrospective cohort study.

Science.gov (United States)

Magin, Parker; Stewart, Rebecca; Turnock, Allison; Tapley, Amanda; Holliday, Elizabeth; Cooling, Nick

2017-10-01

Underperforming trainees requiring remediation may threaten patient safety and are challenging for vocational training programs. Decisions to institute remediation are high-stakes-remediation being resource-intensive and emotionally demanding on trainees. Detection of underperformance requiring remediation is particularly problematic in general (family) practice. We sought to establish early-training assessment instruments predictive of general practice (GP) trainees' subsequently requiring formal remediation. We conducted a retrospective cohort study of trainees from a large Australian regionally-based GP training organization. The outcome factor was requirement for formal remediation. Independent variables were demographic factors and a range of formative assessments conducted immediately prior to or during early-stage training. Analyses employed univariate and multivariate logistic regression of each predictor assessment modality with the outcome, adjusting for potential confounders. Of 248 trainees, 26 (10.5 %) required formal remediation. Performance on the Colleague Feedback Evaluation Tool (entailing feedback from a trainee's clinical colleagues on clinical performance, communication and probity) and External Clinical Teaching Visits (half-day sessions of the trainee's clinical consultations observed directly by an experienced GP), along with non-Australian primary medical qualification, were significantly associated with requiring remediation. There was a non-significant trend for association with performance on the Doctors Interpersonal Skills Questionnaire (patient feedback on interpersonal elements of the consultation). There were no significant associations with entry-selection scores or formative exam or assessment scores. Our finding that 'in vivo' assessments of complex behaviour, but not 'in vitro' knowledge-based assessments, predict need for remediation is consistent with theoretical understanding of the nature of remediation decision-making and

49 CFR 193.2713 - Training: operations and maintenance.

Science.gov (United States)

2010-10-01

... first-aid; and (3) All operating and appropriate supervisory personnel— (i) To understand detailed... 49 Transportation 3 2010-10-01 2010-10-01 false Training: operations and maintenance. 193.2713... LIQUEFIED NATURAL GAS FACILITIES: FEDERAL SAFETY STANDARDS Personnel Qualifications and Training § 193.2713...

Various multistage ensembles for prediction of heating energy consumption

Directory of Open Access Journals (Sweden)

Radisa Jovanovic

2015-04-01

Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.

Abramovo Counterterrorism Training Center

International Nuclear Information System (INIS)

Hayes, Christopher M.; Ross, Larry; Kaldenbach, Karen Yvonne; Estigneev, Yuri; Murievav, Andrey

2011-01-01

The U.S. government has been assisting the Russian Federation (RF) Ministry of Defense (MOD) for many years with nuclear weapons transportation security (NWTS) through the provision of specialized guard escort railcars and cargo railcars with integrated physical security and communication systems, armored transport vehicles, and armored escort vehicles. As a natural continuation of the NWTS program, a partnership has been formed to construct a training center that will provide counterterrorism training to personnel in all branches of the RF MOD. The Abramovo Counterterrorism Training Center (ACTC) is a multinational, multiagency project with funding from Canada, RF and the U.S. Departments of Defense and Energy. ACTC will be a facility where MOD personnel can conduct basic through advanced training in various security measures to protect Category IA material against the threat of terrorist attack. The training will enhance defense-in-depth principles by integrating MOD guard force personnel into the overall physical protection systems and improving their overall response time and neutralization capabilities. The ACTC project includes infrastructure improvements, renovation of existing buildings, construction of new buildings, construction of new training facilities, and provision of training and other equipment. Classroom training will be conducted in a renovated training building. Basic and intermediate training will be conducted on three different security training areas where various obstacles and static training devices will be constructed. The central element of ACTC, where advanced training will be held, is the 'autodrome,' a 3 km road along which various terrorist events can be staged to challenge MOD personnel in realistic and dynamic nuclear weapons transportation scenarios. This paper will address the ACTC project elements and the vision for training development and integrating this training into actual nuclear weapons transportation operations.

Predicting Positive and Negative Relationships in Large Social Networks.

Directory of Open Access Journals (Sweden)

Guan-Nan Wang

Full Text Available In a social network, users hold and express positive and negative attitudes (e.g. support/opposition towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM. Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

Predicting Positive and Negative Relationships in Large Social Networks.

Science.gov (United States)

Wang, Guan-Nan; Gao, Hui; Chen, Lian; Mensah, Dennis N A; Fu, Yan

2015-01-01

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

Generation method of synthetic training data for mobile OCR system

Science.gov (United States)

Chernyshova, Yulia S.; Gayer, Alexander V.; Sheshkus, Alexander V.

2018-04-01

This paper addresses one of the fundamental problems of machine learning - training data acquiring. Obtaining enough natural training data is rather difficult and expensive. In last years usage of synthetic images has become more beneficial as it allows to save human time and also to provide a huge number of images which otherwise would be difficult to obtain. However, for successful learning on artificial dataset one should try to reduce the gap between natural and synthetic data distributions. In this paper we describe an algorithm which allows to create artificial training datasets for OCR systems using russian passport as a case study.

NEW APPROACHES: Toppling trains

Science.gov (United States)

Parry, Malcolm

1998-03-01

This article explains a novel way of approaching centripetal force: theory is used to predict an orbital period at which a toy train will topple from a circular track. The demonstration has proved useful in A-level, GNVQ and undergraduate Physics and Engineering schemes.

Applying Technology to Train Visualization Skills

National Research Council Canada - National Science Library

Nanda, Sanjeeb

2005-01-01

.... Training visualization skills, such as terrain appreciation, is generally difficult and inefficient in the real world with natural representations or in a classroom with analog representations...

Training, Innovation and Business Performance: An Analysis of the Business Longitudinal Survey.

Science.gov (United States)

Dockery, A. Michael

This paper uses the Australian Bureau of Statistics' Business Longitudinal Survey to explore relationships between training, innovation, and firm performance for Australian businesses with less than 200 employees. The longitudinal nature of the data is used to test various hypotheses about the nature of the link between training, business changes,…

Prediction of major pollutants emission in direct injection dual-fuel diesel and natural-gas engines

International Nuclear Information System (INIS)

Pirouzpanah, V.; Kashani, B.O.

2000-01-01

The dual-fuel diesel engine is a conventional diesel engine in which much of the energy released, hence power, comes from the combustion of gaseous fuel such as natural gas. The exhaust emission characteristics of the dual-fuel diesel engine needs further refinements, particularly in terms of reduction of Unburnt Hydrocarbons and Carbon Monoxide (CO) emission, because the concentration of these pollutants are higher than that of the baseline diesel engine. Furthermore, the combustion process in a typical dual-fuel diesel engine tends to be complex, showing combination of the problems encountered both in diesel and spark ignition engines. In this work, a computer code has been modified for simulation of dual-fuel diesel engine combustion process. This model simulates dual-fuel diesel engine combustion by using a Multi-Zone Combustion Model for diesel pilot jet combustion and a conventional spark ignition combustion model for modelling of combustion of premixed gas/air charge. Also, in this model, there are four submodels for prediction of major emission pollutants such as: Unburnt Hydrocarbons, No, Co and soot which are emitted from dual-fuel diesel engine. For prediction of formation and oxidation rates of pollutants, relevant s conventional kinetically-controlled mechanisms and mass balances are used. the model has been verified by experimental data obtained from a heavy-duty truck and bus diesel engines. The comparison shows that, there exist good agreements between the experimental and predicted results from the dual-fuel diesel engine

Predicting Liaison: an Example-Based Approach

NARCIS (Netherlands)

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

2016-01-01

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

Gene Expression Profiling to Predict Clinical Outcome of Breast Cancer: reproducing, analyzing and extending the Nature publication by vhVeer et al

NARCIS (Netherlands)

Li R.; Visser, H.M.

2010-01-01

Chemotherapy and hormonal therapy as adjuvant systemic therapies to inhibit breast cancer recurrence are not necessary for each patient. In Veer's paper "Gene expression profiling predicts clinical outcome of breast cancer" (Nature 2002, PMID: 11823860), they introduced a method based on DNA

Group decision-making techniques for natural resource management applications

Science.gov (United States)

Coughlan, Beth A.K.; Armour, Carl L.

1992-01-01

This report is an introduction to decision analysis and problem-solving techniques for professionals in natural resource management. Although these managers are often called upon to make complex decisions, their training in the natural sciences seldom provides exposure to the decision-making tools developed in management science. Our purpose is to being to fill this gap. We present a general analysis of the pitfalls of group problem solving, and suggestions for improved interactions followed by the specific techniques. Selected techniques are illustrated. The material is easy to understand and apply without previous training or excessive study and is applicable to natural resource management issues.

Implementation of genomic prediction in Lolium perenne (L. breeding populations

Directory of Open Access Journals (Sweden)

Nastasiya F Grinberg

2016-02-01

Full Text Available Perennial ryegrass (Lolium perenne L. is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values (GEBV are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning (ML techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium (LD between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.

More than a Museum: Natural History is Relevant in 21st Century Environmental Science

Science.gov (United States)

Hernandez, R. R.; Murphy-Mariscal, M. L.; Barrows, C. W.

2015-12-01

In the Anthropocene, the relevancy of natural history in environmental science is challenged and marginalized today more than ever. We tested the hypothesis that natural history is relevant to the fields of environmental science and ecology by assessing the values, needs, and decisions related to natural history of graduate students and environmental science professionals across 31 universities and various employers, respectively, in California. Graduate students surveyed (93.3%) agreed that natural history was relevant to science, approximately 70% believed it "essential" for conducting field-based research; however, 54.2% felt inadequately trained to teach a natural history course and would benefit from additional training in natural history (> 80%). Of the 185 professionals surveyed, all felt that natural history was relevant to science and "essential" or "desirable" in their vocation (93%). Our results indicate a disconnect between the value and relevancy of natural history in 21st century ecological science and opportunities for gaining those skills and knowledge through education and training.

A new approach for diagnosing type 1 diabetes in autoantibody-positive individuals based on prediction and natural history.

Science.gov (United States)

Sosenko, Jay M; Skyler, Jay S; DiMeglio, Linda A; Beam, Craig A; Krischer, Jeffrey P; Greenbaum, Carla J; Boulware, David; Rafkin, Lisa E; Matheson, Della; Herold, Kevan C; Mahon, Jeffrey; Palmer, Jerry P

2015-02-01

We assessed whether type 1 diabetes (T1D) can be diagnosed earlier using a new approach based on prediction and natural history in autoantibody-positive individuals. Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Natural History Study (TNNHS) participants were studied. A metabolic index, the T1D Diagnostic Index60 (Index60), was developed from 2-h oral glucose tolerance tests (OGTTs) using the log fasting C-peptide, 60-min C-peptide, and 60-min glucose. OGTTs with Index60 ≥2.00 and 2-h glucose <200 mg/dL (Ind60+Only) were compared with Index60 <2.00 and 2-h glucose ≥200 mg/dL (2hglu+Only) OGTTs as criteria for T1D. Individuals were assessed for C-peptide loss from the first Ind60+Only OGTT to diagnosis. Areas under receiver operating characteristic curves were significantly higher for Index60 than for the 2-h glucose (P < 0.001 for both DPT-1 and the TNNHS). As a diagnostic criterion, sensitivity was higher for Ind60+Only than for 2hglu+Only (0.44 vs. 0.15 in DPT-1; 0.26 vs. 0.17 in the TNNHS) OGTTs. Specificity was somewhat higher for 2hglu+Only OGTTs in DPT-1 (0.97 vs. 0.91) but equivalent in the TNNHS (0.98 for both). Positive and negative predictive values were higher for Ind60+Only OGTTs in both studies. Postchallenge C-peptide levels declined significantly at each OGTT time point from the first Ind60+Only OGTT to the time of standard diagnosis (range -22 to -34% in DPT-1 and -14 to -27% in the TNNHS). C-peptide and glucose patterns differed markedly between Ind60+Only and 2hglu+Only OGTTs. An approach based on prediction and natural history appears to have utility for diagnosing T1D. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

The effectiveness of airline pilot training for abnormal events.

Science.gov (United States)

Casner, Stephen M; Geven, Richard W; Williams, Kent T

2013-06-01

To evaluate the effectiveness of airline pilot training for abnormal in-flight events. Numerous accident reports describe situations in which pilots responded to abnormal events in ways that were different from what they had practiced many times before. One explanation for these missteps is that training and testing for these skills have become a highly predictable routine for pilots who arrive to the training environment well aware of what to expect. Under these circumstances, pilots get plentiful practice in responding to abnormal events but may get little practice in recognizing them and deciding which responses to offer. We presented 18 airline pilots with three abnormal events that are required during periodic training and testing. Pilots were presented with each event under the familiar circumstances used during training and also under less predictable circumstances as they might occur during flight. When presented in the routine ways seen during training, pilots gave appropriate responses and showed little variability. However, when the abnormal events were presented unexpectedly, pilots' responses were less appropriate and showed great variability from pilot to pilot. The results suggest that the training and testing practices used in airline training may result in rote-memorized skills that are specific to the training situation and that offer modest generalizability to other situations. We recommend a more complete treatment of abnormal events that allows pilots to practice recognizing the event and choosing and recalling the appropriate response. The results will aid the improvement of existing airline training practices.

Effects of training and anthropometric factors on marathon and 100 km ultramarathon race performance

Directory of Open Access Journals (Sweden)

Tanda G

2015-04-01

Full Text Available Giovanni Tanda,1 Beat Knechtle2,3 1Polytechnic School, University of Genoa, Genoa, Italy; 2Gesundheitszentrum St Gallen, St Gallen, 3Institute of Primary Care, University of Zurich, Zurich, Switzerland Background: Marathon (42 km and 100 km ultramarathon races are increasing in popularity. The aim of the present study was to investigate the potential associations of anthropometric and training variables with performance in these long-distance running competitions. Methods: Training and anthropometric data from a large cohort of marathoners and 100 km ultramarathoners provided the basis of this work. Correlations between training and anthropometric indices of subjects and race performance were assessed using bivariate and multiple regression analyses. Results: A combination of volume and intensity in training was found to be suitable for prediction of marathon and 100 km ultramarathon race pace. The relative role played by these two variables was different, in that training volume was more important than training pace for the prediction of 100 km ultramarathon performance, while the opposite was found for marathon performance. Anthropometric characteristics in terms of body fat percentage negatively affected 42 km and 100 km race performance. However, when this factor was relatively low (ie, less than 15% body fat, the performance of 42 km and 100 km races could be predicted solely on the basis of training indices. Conclusion: Mean weekly training distance run and mean training pace were key predictor variables for both marathon and 100 km ultramarathon race performance. Predictive correlations for race performance are provided for runners with a relatively low body fat percentage. Keywords: running, performance, training indices, body fat, sports training

A Survey of the Status of Listening Training in Some Fortune 500 Corporations.

Science.gov (United States)

Wolvin, Andrew D.; Coakley, Carolyn Gwynn

1991-01-01

Surveys training directors of Fortune 500 corporations to determine the content and nature of listening training offered to employees. Discusses types of listening instruction, personnel receiving listening training, length of listening training, and backgrounds of listening trainers. (KEH)

A Longitudinal Study of the Effects of Undergraduate Training on Reasoning.

Science.gov (United States)

Lehman, Darrin R.; Nisbett, Richard E.

1990-01-01

Effects of undergraduate training on inductive reasoning and logic were examined. Social science training produced significant effects on statistical and methodological reasoning. Natural science and humanities training produced significant effects on conditional logic reasoning. Results indicate that reasoning is taught and generalizable. (BC)

Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market

International Nuclear Information System (INIS)

Qiu, Mingyue; Song, Yu; Akagi, Fumio

2016-01-01

Accurate prediction of stock market returns is a very challenging task because of the highly nonlinear nature of the financial time series. In this study, we apply an artificial neural network (ANN) that can map any nonlinear function without a prior assumption to predict the return of the Japanese Nikkei 225 index. (1) To improve the effectiveness of prediction algorithms, we propose a new set of input variables for ANN models. (2) To verify the prediction ability of the selected input variables, we predict returns for the Nikkei 225 index using the classical back propagation (BP) learning algorithm. (3) Global search techniques, i.e., a genetic algorithm (GA) and simulated annealing (SA), are employed to improve the prediction accuracy of the ANN and overcome the local convergence problem of the BP algorithm. It is observed through empirical experiments that the selected input variables were effective to predict stock market returns. A hybrid approach based on GA and SA improve prediction accuracy significantly and outperform the traditional BP training algorithm.

Local Dynamics in Trained Recurrent Neural Networks.

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-23

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Local Dynamics in Trained Recurrent Neural Networks

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-01

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Antral Follicle Count Predicts Natural Menopause in a Population-Based Sample: The CARDIA Women’s Study

Science.gov (United States)

Wellons, Melissa F.; Bates, Gordon Wright; Schreiner, Pamela J.; Siscovick, David S.; Sternfeld, Barbara; Lewis, Cora E.

2013-01-01

Objective The timing of menopause is associated with multiple chronic diseases. Tools to predict this milestone have relevance for clinical and research purposes. Among infertile women, a positive relationship exists between antral follicle count (AFC) and response to controlled ovarian hyperstimulation, a marker of ovarian reserve. However, a relationship between AFC and menopause that is age-independent has not been demonstrated. Thus, our objective was to evaluate the relationship between AFC measured in women at ages 34–49 and incident natural menopause over 7-years of follow-up. Methods The Coronary Artery Risk Development in Young Adults (CARDIA) study is a longitudinal community-based study (Chicago, Illinois; Birmingham, Alabama; Minneapolis, Minnesota; and Oakland, California) begun in 1985–1986. In 2002–03, the CARDIA Women’s Study measured FSH levels and performed a transvaginal ultrasound protocol that included AFC (2mm–10mm follicles on both ovaries). Incident natural menopause was assessed by survey in 2005–06 and 2009–10. Results In our sample (n=456), median AFC and FSH were 5 (IQR 2–9) and 7.8 mIU/mL (IQR 5.6–11.0), respectively, at a mean age of 42 (range 34–49) in 2002–03. 101 women reported natural menopause by 2009–10. In Cox models, current smoking, stable menses, FSH>13, and AFC ≤4 were independently associated with incident natural menopause. Compared to AFC >4, those with AFC ≤4 were nearly twice as likely to have undergone menopause over 7-years of follow-up (HR 1.89, 95% CI 1.19–3.02) after adjustment for covariates. Conclusion AFC is independently associated with natural menopause over 7-years of follow-up after controlling for other markers of ovarian aging. PMID:23422869

Predicting Energy Consumption for Potential Effective Use in Hybrid Vehicle Powertrain Management Using Driver Prediction

Science.gov (United States)

Magnuson, Brian

A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The

Effects of 12-Week Endurance Training at Natural Low Altitude on the Blood Redox Homeostasis of Professional Adolescent Athletes: A Quasi-Experimental Field Trial

Directory of Open Access Journals (Sweden)

Tomas K. Tong

2016-01-01

Full Text Available This field study investigated the influences of exposure to natural low altitude on endurance training-induced alterations of redox homeostasis in professional adolescent runners undergoing 12-week off-season conditioning program at an altitude of 1700 m (Alt, by comparison with that of their counterparts completing the program at sea-level (SL. For age-, gender-, and Tanner-stage-matched comparison, 26 runners (n=13 in each group were selected and studied. Following the conditioning program, unaltered serum levels of thiobarbituric acid reactive substances (TBARS, total antioxidant capacity (T-AOC, and superoxide dismutase accompanied with an increase in oxidized glutathione (GSSG and decreases of xanthine oxidase, reduced glutathione (GSH, and GSH/GSSG ratio were observed in both Alt and SL groups. Serum glutathione peroxidase and catalase did not change in SL, whereas these enzymes, respectively, decreased and increased in Alt. Uric acid (UA decreased in SL and increased in Alt. Moreover, the decreases in GSH and GSH/GSSG ratio in Alt were relatively lower compared to those in SL. Further, significant interindividual correlations were found between changes in catalase and TBARS, as well as between UA and T-AOC. These findings suggest that long-term training at natural low altitude is unlikely to cause retained oxidative stress in professional adolescent runners.

Effects of 12-Week Endurance Training at Natural Low Altitude on the Blood Redox Homeostasis of Professional Adolescent Athletes: A Quasi-Experimental Field Trial.

Science.gov (United States)

Tong, Tomas K; Kong, Zhaowei; Lin, Hua; He, Yeheng; Lippi, Giuseppe; Shi, Qingde; Zhang, Haifeng; Nie, Jinlei

2016-01-01

This field study investigated the influences of exposure to natural low altitude on endurance training-induced alterations of redox homeostasis in professional adolescent runners undergoing 12-week off-season conditioning program at an altitude of 1700 m (Alt), by comparison with that of their counterparts completing the program at sea-level (SL). For age-, gender-, and Tanner-stage-matched comparison, 26 runners (n = 13 in each group) were selected and studied. Following the conditioning program, unaltered serum levels of thiobarbituric acid reactive substances (TBARS), total antioxidant capacity (T-AOC), and superoxide dismutase accompanied with an increase in oxidized glutathione (GSSG) and decreases of xanthine oxidase, reduced glutathione (GSH), and GSH/GSSG ratio were observed in both Alt and SL groups. Serum glutathione peroxidase and catalase did not change in SL, whereas these enzymes, respectively, decreased and increased in Alt. Uric acid (UA) decreased in SL and increased in Alt. Moreover, the decreases in GSH and GSH/GSSG ratio in Alt were relatively lower compared to those in SL. Further, significant interindividual correlations were found between changes in catalase and TBARS, as well as between UA and T-AOC. These findings suggest that long-term training at natural low altitude is unlikely to cause retained oxidative stress in professional adolescent runners.

Feasibility of high-intensity training in asthma

DEFF Research Database (Denmark)

Tønnesen, Louise Lindhardt; Sørensen, E D; Hostrup, Morten

2018-01-01

Background: High-intensity interval training is an effective and popular training regime but its feasibility in untrained adults with asthma is insufficiently described. Objective: The randomized controlled trial 'EFFORT Asthma' explored the effects of behavioural interventions including high......-intensity interval training on clinical outcomes in nonobese sedentary adults with asthma. In this article we present a sub analysis of data aiming to evaluate if patients' pre-intervention levels of asthma control, FEV1, airway inflammation and airway hyperresponsiveness (AHR) predicted their training response...... to the high-intensity interval training program, measured as increase in maximal oxygen consumption (VO2max). Design: We used data from the EFFORT Asthma Study. Of the 36 patients randomized to the 8-week exercise intervention consisting of high-intensity training three times per week, 29 patients (45...

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

Science.gov (United States)

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

OpenAIRE

Tajbakhsh, Nima; Shin, Jae Y.; Gurudu, Suryakanth R.; Hurst, R. Todd; Kendall, Christopher B.; Gotway, Michael B.; Liang, Jianming

2017-01-01

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following centr...

Prefrontal activation may predict working-memory training gain in normal aging and mild cognitive impairment.

Science.gov (United States)

Vermeij, Anouk; Kessels, Roy P C; Heskamp, Linda; Simons, Esther M F; Dautzenberg, Paul L J; Claassen, Jurgen A H R

2017-02-01

Cognitive training has been shown to result in improved behavioral performance in normal aging and mild cognitive impairment (MCI), yet little is known about the neural correlates of cognitive plasticity, or about individual differences in responsiveness to cognitive training. In this study, 21 healthy older adults and 14 patients with MCI received five weeks of adaptive computerized working-memory (WM) training. Before and after training, functional Near-Infrared Spectroscopy (fNIRS) was used to assess the hemodynamic response in left and right prefrontal cortex during performance of a verbal n-back task with varying levels of WM load. After training, healthy older adults demonstrated decreased prefrontal activation at high WM load, which may indicate increased processing efficiency. Although MCI patients showed improved behavioral performance at low WM load after training, no evidence was found for training-related changes in prefrontal activation. Whole-group analyses showed that a relatively strong hemodynamic response at low WM load was related to worse behavioral performance, while a relatively strong hemodynamic response at high WM load was related to higher training gain. Therefore, a 'youth-like' prefrontal activation pattern at older age may be associated with better behavioral outcome and cognitive plasticity.

Common variant in OXTR predicts growth in positive emotions from loving-kindness training.

Science.gov (United States)

Isgett, Suzannah F; Algoe, Sara B; Boulton, Aaron J; Way, Baldwin M; Fredrickson, Barbara L

2016-11-01

Ample research suggests that social connection reliably generates positive emotions. Oxytocin, a neuropeptide implicated in social cognition and behavior, is one biological mechanism that may influence an individual's capacity to extract positive emotions from social contexts. Because variation in certain genes may indicate underlying neurobiological differences, we tested whether several SNPs in two genes related to oxytocin signaling would show effects on positive emotions that were context-specific, depending on sociality. For six weeks, a sample of mid-life adults (N=122) participated in either socially-focused loving-kindness training or mindfulness training. During this timespan they reported their positive emotions daily. Five SNPs within OXTR and CD38 were assayed, and each was tested for its individual effect on daily emotions. The hypothesized three-way interaction between time, training type, and genetic variability emerged: Individuals homozygous for the G allele of OXTR rs1042778 experienced gains in daily positive emotions from loving-kindness training, whereas individuals with the T allele did not experience gains in positive emotions with either training. These findings are among the first to show how genetic differences in oxytocin signaling may influence an individual's capacity to experience positive emotions as a result of a socially-focused intervention. Copyright © 2016 Elsevier Ltd. All rights reserved.

Flipped clinical training: a structured training method for undergraduates in complete denture prosthesis.

Science.gov (United States)

K, Anbarasi; K, Kasim Mohamed; Vijayaraghavan, Phagalvarthy; Kandaswamy, Deivanayagam

2016-12-01

To design and implement flipped clinical training for undergraduate dental students in removable complete denture treatment and predict its effectiveness by comparing the assessment results of students trained by flipped and traditional methods. Flipped training was designed by shifting the learning from clinics to learning center (phase I) and by preserving the practice in clinics (phase II). In phase I, student-faculty interactive session was arranged to recap prior knowledge. This is followed by a display of audio synchronized video demonstration of the procedure in a repeatable way and subsequent display of possible errors that may occur in treatment with guidelines to overcome such errors. In phase II, live demonstration of the procedure was given. Students were asked to treat three patients under instructor's supervision. The summative assessment was conducted by applying the same checklist criterion and rubric scoring used for the traditional method. Assessment results of three batches of students trained by flipped method (study group) and three traditionally trained previous batches (control group) were taken for comparison by chi-square test. The sum of traditionally trained three batch students who prepared acceptable dentures (score: 2 and 3) and unacceptable dentures (score: 1) was compared with the same of flipped trained three batch students revealed that the number of students who demonstrated competency by preparing acceptable dentures was higher for flipped training (χ 2 =30.996 with p<0.001). The results reveal the supremacy of flipped training in enhancing students competency and hence recommended for training various clinical procedures.

Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

Science.gov (United States)

Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef

2016-10-01

We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

Using an external surrogate for predictor model training in real-time motion management of lung tumors

Energy Technology Data Exchange (ETDEWEB)

Rottmann, Joerg; Berbeco, Ross [Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 (United States)

2014-12-15

Purpose: Precise prediction of respiratory motion is a prerequisite for real-time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly. Methods: The authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable to overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal–external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases. Results: Similar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum

Linguistic Structure Prediction

CERN Document Server

Smith, Noah A

2011-01-01

A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. W

Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Shiraishi, Satomi; Moore, Kevin L., E-mail: kevinmoore@ucsd.edu [Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California 92093 (United States)

2016-01-15

Purpose: To demonstrate knowledge-based 3D dose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = D{sub clin} − D{sub pred}. The mean (〈δD{sub r}〉), standard deviation (σ{sub δD{sub r}}), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (r{sub PTV}) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over r{sub PTV} ∈ [ − 6, 30] mm. The

Predicting Increased Blood Pressure Using Machine Learning

Science.gov (United States)

Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Soares, Telma de Jesus; dos Reis, Luciana Araujo

2014-01-01

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R 2 (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R 2 (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power. PMID:24669313

Predicting Increased Blood Pressure Using Machine Learning

Directory of Open Access Journals (Sweden)

Hudson Fernandes Golino

2014-01-01

Full Text Available The present study investigates the prediction of increased blood pressure by body mass index (BMI, waist (WC and hip circumference (HC, and waist hip ratio (WHR using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42, misclassification (.19, and the higher pseudo R2 (.43. This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25, misclassification (.16, and the higher pseudo R2 (.46. This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

Predicting increased blood pressure using machine learning.

Science.gov (United States)

Golino, Hudson Fernandes; Amaral, Liliany Souza de Brito; Duarte, Stenio Fernando Pimentel; Gomes, Cristiano Mauro Assis; Soares, Telma de Jesus; Dos Reis, Luciana Araujo; Santos, Joselito

2014-01-01

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning

Science.gov (United States)

Shi, Bibo; Hou, Rui; Mazurowski, Maciej A.; Grimm, Lars J.; Ren, Yinhao; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2018-02-01

Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. Method: In this study, we collected digital mammography magnification views for 140 patients with DCIS at biopsy, 35 of which were subsequently upstaged to invasive cancer. We utilized a deep CNN model that was pre-trained on two natural image data sets (ImageNet and DTD) and one mammographic data set (INbreast) as the feature extractor, hypothesizing that these data sets are increasingly more similar to our target task and will lead to better representations of deep features to describe DCIS lesions. Through a statistical pooling strategy, three sets of deep features were extracted using the CNNs at different levels of convolutional layers from the lesion areas. A logistic regression classifier was then trained to predict which tumors contain occult invasive disease. The generalization performance was assessed and compared using repeated random sub-sampling validation and receiver operating characteristic (ROC) curve analysis. Result: The best performance of deep features was from CNN model pre-trained on INbreast, and the proposed classifier using this set of deep features was able to achieve a median classification performance of ROC-AUC equal to 0.75, which is significantly better (p<=0.05) than the performance of deep features extracted using ImageNet data set (ROCAUC = 0.68). Conclusion: Transfer learning is helpful for learning a better representation of deep features, and improves the prediction of occult invasive disease in DCIS.

Global discriminative learning for higher-accuracy computational gene prediction.

Directory of Open Access Journals (Sweden)

Axel Bernal

2007-03-01

Full Text Available Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

LNG (Liquefied Natural Gas): emerging control; GNL (Gas Natural Liquefeito): controle de emergencia

Energy Technology Data Exchange (ETDEWEB)

Berardinelli, Ricardo Porto; Correa, Kleber Macedo; Moura Filho, Nelson Barboza de; Matos, Jose Eduardo Nogueira de; Fernandez, Carlos Antonio [TRANSPETRO, Rio de Janeiro, RJ (Brazil). Gerencia de Seguranca, Meio Ambiente e Saude

2008-07-01

The operation to Liquefied Natural Gas (LNG) is innovative for the PETROBRAS System. PETROBRAS Transporte - TRANSPETRO will operate two LNG flexible terminals. In accordance with the health, safety and environmental policy - training, education and awareness action plans were formulated by TRANSPETRO to assure the operational safety for the activity. Part of this action plan includes the training of LNG spill control and fire suppression. The training was carried out in 20 hours and divided into two parts: theoretical and practice. In the practice part, 3.000 gallons of LNG were unloaded and the students could verify the behaviour of the LNG and the effectiveness of the resources available for the emergency control. The knowledge was introduced in the company to create specific procedures, local emergency plans and develop internal instructors. (author)

Prediction of soil distribution on two soilscapes in land type Dc17 east of Bloemfontein, South Africa

Directory of Open Access Journals (Sweden)

Mussie G. Zerizghy

2013-11-01

Full Text Available The predictive nature of digital soil mapping makes it a labour- and cost-effective way of facilitating soil surveys. A digital elevation model was used to generate terrain attributes that can be used to infer the distribution of soil associations relative to the topography. Two study areas Gladstone and Potsane in the Free State Province of South Africa were considered. Slope, aspect, contour and plan curvature, topographic wetness index and topographic morphological unit were used to develop a model for predicting soil associations. Discriminant analysis was employed to develop the model. The model was trained on data obtained from Gladstone and validated on data from Gladstone and Potsane. Predicting soil form was unsatisfactory. Prediction done on soil associations, with soils grouped as deep, shallow and valley-bottom soils (criteria closely related to the suitability for in-field rainwater harvesting, achieved acceptable improvement in prediction accuracy. For Gladstone, when analysis was done using equal prior probability, accuracy percentages of 56.9%, 51.5% and 58.3% were found for calibration, cross-validation and areas suited to in-field rainwater harvesting, respectively. With prior probability set in accordance to sample frequency, the accuracy percentages were improved to 83.1%, 80.0% and 94.6%, respectively. In Potsane, the prediction accuracy percentage was low (38.23% with equal prior probability but markedly improved (67.65% when prior probability was similar to sample frequency. These results support the validity of the statement that the predictive nature of digital soil mapping makes it a labour- and cost-effective way of facilitating soil surveys.

Shoulder injuries attributed to resistance training: a brief review.

Science.gov (United States)

Kolber, Morey J; Beekhuizen, Kristina S; Cheng, Ming-Shun S; Hellman, Madeleine A

2010-06-01

The popularity of resistance training (RT) is evident by the more than 45 million Americans who engage in strength training regularly. Although the health and fitness benefits ascribed to RT are generally agreed upon, participation is not without risk. Acute and chronic injuries attributed to RT have been cited in the epidemiological literature among both competitive and recreational participants. The shoulder complex in particular has been alluded to as one of the most prevalent regions of injury. The purpose of this manuscript is to present an overview of documented shoulder injuries among the RT population and where possible discern mechanisms of injury and risk factors. A literature search was conducted in the PUBMED, CINAHL, SPORTDiscus, and OVID databases to identify relevant articles for inclusion using combinations of key words: resistance training, shoulder, bodybuilding, weightlifting, shoulder injury, and shoulder disorder. The results of the review indicated that up to 36% of documented RT-related injuries and disorders occur at the shoulder complex. Trends that increased the likelihood of injury were identified and inclusive of intrinsic risk factors such as joint and muscle imbalances and extrinsic risk factors, namely, that of improper attention to exercise technique. A majority of the available research was retrospective in nature, consisting of surveys and descriptive epidemiological reports. A paucity of research was available to identify predictive variables leading to injury, suggesting the need for future prospective-based investigations.

Addressing Nature Deficit Disorder through Primitive Camping Experiences

Science.gov (United States)

Allen, Kevin; Varner, Keegan; Sallee, Jeff

2011-01-01

Today's youth suffer from Nature Deficit Disorder, a condition that has been connected to ADHD, shortage of creativity, and general lack of knowledge about the outdoors. A team of educators and specialists are addressing this issue with primitive camping. County educators were trained using experiential learning and train-the-trainer techniques.…

Snorkel: Rapid Training Data Creation with Weak Supervision.

Science.gov (United States)

Ratner, Alexander; Bach, Stephen H; Ehrenberg, Henry; Fries, Jason; Wu, Sen; Ré, Christopher

2017-11-01

Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train state-of- the-art models without hand labeling any training data. Instead, users write labeling functions that express arbitrary heuristics, which can have unknown accuracies and correlations. Snorkel denoises their outputs without access to ground truth by incorporating the first end-to-end implementation of our recently proposed machine learning paradigm, data programming. We present a flexible interface layer for writing labeling functions based on our experience over the past year collaborating with companies, agencies, and research labs. In a user study, subject matter experts build models 2.8× faster and increase predictive performance an average 45.5% versus seven hours of hand labeling. We study the modeling tradeoffs in this new setting and propose an optimizer for automating tradeoff decisions that gives up to 1.8× speedup per pipeline execution. In two collaborations, with the U.S. Department of Veterans Affairs and the U.S. Food and Drug Administration, and on four open-source text and image data sets representative of other deployments, Snorkel provides 132% average improvements to predictive performance over prior heuristic approaches and comes within an average 3.60% of the predictive performance of large hand-curated training sets.

Improvements in disruption prediction at ASDEX Upgrade

Energy Technology Data Exchange (ETDEWEB)

Aledda, R., E-mail: raffaele.aledda@diee.unica.it; Cannas, B., E-mail: cannas@diee.unica.it; Fanni, A., E-mail: fanni@diee.unica.it; Pau, A., E-mail: alessandro.pau@diee.unica.it; Sias, G., E-mail: giuliana.sias@diee.unica.it

2015-10-15

Highlights: • A disruption prediction system for AUG, based on a logistic model, is designed. • The length of the disruptive phase is set for each disruption in the training set. • The model is tested on dataset different from that used during the training phase. • The generalization capability and the aging of the model have been tested. • The predictor performance is compared with the locked mode detector. - Abstract: In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption.

Improvements in disruption prediction at ASDEX Upgrade

International Nuclear Information System (INIS)

Aledda, R.; Cannas, B.; Fanni, A.; Pau, A.; Sias, G.

2015-01-01

Highlights: • A disruption prediction system for AUG, based on a logistic model, is designed. • The length of the disruptive phase is set for each disruption in the training set. • The model is tested on dataset different from that used during the training phase. • The generalization capability and the aging of the model have been tested. • The predictor performance is compared with the locked mode detector. - Abstract: In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45 ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45 ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption.

Plutonium in nature

International Nuclear Information System (INIS)

Madic, C.

1994-01-01

Plutonium in nature comes from natural sources and anthropogenic ones. Plutonium at the earth surface comes principally from anthropogenic sources. It is easily detectable in environment. The plutonium behaviour in environment is complex. It seems necessary for the future to reduce releases in environment, to improve predictive models of plutonium behaviour in geosphere, to precise biological impact of anthropogenic plutonium releases

Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population

Directory of Open Access Journals (Sweden)

A. H. Sallam

2015-03-01

Full Text Available Prediction accuracy of genomic selection (GS has been previously evaluated through simulation and cross-validation; however, validation based on progeny performance in a plant breeding program has not been investigated thoroughly. We evaluated several prediction models in a dynamic barley breeding population comprised of 647 six-row lines using four traits differing in genetic architecture and 1536 single nucleotide polymorphism (SNP markers. The breeding lines were divided into six sets designated as one parent set and five consecutive progeny sets comprised of representative samples of breeding lines over a 5-yr period. We used these data sets to investigate the effect of model and training population composition on prediction accuracy over time. We found little difference in prediction accuracy among the models confirming prior studies that found the simplest model, random regression best linear unbiased prediction (RR-BLUP, to be accurate across a range of situations. In general, we found that using the parent set was sufficient to predict progeny sets with little to no gain in accuracy from generating larger training populations by combining the parent set with subsequent progeny sets. The prediction accuracy ranged from 0.03 to 0.99 across the four traits and five progeny sets. We explored characteristics of the training and validation populations (marker allele frequency, population structure, and linkage disequilibrium, LD as well as characteristics of the trait (genetic architecture and heritability, . Fixation of markers associated with a trait over time was most clearly associated with reduced prediction accuracy for the mycotoxin trait DON. Higher trait in the training population and simpler trait architecture were associated with greater prediction accuracy.

Reaching for the stars: The story of astronaut training and the lunar landing

Science.gov (United States)

Goldstein, Stanley H.

1987-01-01

The training for the Mercury, Gemini, and Apollo programs is described. The form and function of training and the historical background which shaped the nature of that training are reviewed. For the three programs, the astronaut selection, the meeting of training requirements, and program management are addressed.

Thermodynamic DFT analysis of natural gas.

Science.gov (United States)

Neto, Abel F G; Huda, Muhammad N; Marques, Francisco C; Borges, Rosivaldo S; Neto, Antonio M J C

2017-08-01

Density functional theory was performed for thermodynamic predictions on natural gas, whose B3LYP/6-311++G(d,p), B3LYP/6-31+G(d), CBS-QB3, G3, and G4 methods were applied. Additionally, we carried out thermodynamic predictions using G3/G4 averaged. The calculations were performed for each major component of seven kinds of natural gas and to their respective air + natural gas mixtures at a thermal equilibrium between room temperature and the initial temperature of a combustion chamber during the injection stage. The following thermodynamic properties were obtained: internal energy, enthalpy, Gibbs free energy and entropy, which enabled us to investigate the thermal resistance of fuels. Also, we estimated an important parameter, namely, the specific heat ratio of each natural gas; this allowed us to compare the results with the empirical functions of these parameters, where the B3LYP/6-311++G(d,p) and G3/G4 methods showed better agreements. In addition, relevant information on the thermal and mechanic resistance of natural gases were investigated, as well as the standard thermodynamic properties for the combustion of natural gas. Thus, we show that density functional theory can be useful for predicting the thermodynamic properties of natural gas, enabling the production of more efficient compositions for the investigated fuels. Graphical abstract Investigation of the thermodynamic properties of natural gas through the canonical ensemble model and the density functional theory.

Age, training, and previous experience predict race performance in long-distance inline skaters, not anthropometry.

Science.gov (United States)

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

2012-02-01

The association of characteristics of anthropometry, training, and previous experience with race time in 84 recreational, long-distance, inline skaters at the longest inline marathon in Europe (111 km), the Inline One-eleven in Switzerland, was investigated to identify predictor variables for performance. Age, duration per training unit, and personal best time were the only three variables related to race time in a multiple regression, while none of the 16 anthropometric variables were related. Anthropometric characteristics seem to be of no importance for a fast race time in a long-distance inline skating race in contrast to training volume and previous experience, when controlled with covariates. Improving performance in a long-distance inline skating race might be related to a high training volume and previous race experience. Also, doing such a race requires a parallel psychological effort, mental stamina, focus, and persistence. This may be reflected in the preparation and training for the event. Future studies should investigate what motivates these athletes to train and compete.

Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters

International Nuclear Information System (INIS)

Hao, Ke; Sham, Pak C; Poon, Ronnie TP; Luk, John M; Lee, Nikki PY; Mao, Mao; Zhang, Chunsheng; Ferguson, Mark D; Lamb, John; Dai, Hongyue; Ng, Irene O

2009-01-01

Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new

Prediction of beta-turns with learning machines.

Science.gov (United States)

Cai, Yu-Dong; Liu, Xiao-Jun; Li, Yi-Xue; Xu, Xue-biao; Chou, Kuo-Chen

2003-05-01

The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.

The Effect of Project-Based History and Nature of Science Practices on the Change of Nature of Scientific Knowledge

Science.gov (United States)

Çibik, Ayse Sert

2016-01-01

The aim of this study is to compare the change of pre-service science teachers' views about the nature of scientific knowledge through Project-Based History and Nature of Science training and Conventional Method. The sample of the study consists of two groups of 3rd grade undergraduate students attending teacher preparation program of science…

A time series based sequence prediction algorithm to detect activities of daily living in smart home.

Science.gov (United States)

Marufuzzaman, M; Reaz, M B I; Ali, M A M; Rahman, L F

2015-01-01

The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included. The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge. The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED. Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.

Acute post-exercise change in blood pressure and exercise training response in patients with coronary artery disease

Directory of Open Access Journals (Sweden)

Antti M Kiviniemi

2015-01-01

Full Text Available We tested the hypothesis that acute post-exercise change in blood pressure (BP may predict exercise training responses in BP in patients with coronary artery disease (CAD. Patients with CAD (n=116, age 62±5 years, 85 men underwent BP assessments at rest and during 10-min recovery following a symptom-limited exercise test before and after the 6-month training intervention (one strength and 3-4 aerobic moderate-intensity exercises weekly. Post-exercise change in systolic BP (SBP was calculated by subtracting resting SBP from lowest post-exercise SBP. The training-induced change in resting SBP was -2±13 mmHg (p=0.064, ranging from -42 to 35 mmHg. Larger post-exercise decrease in SBP and baseline resting SBP predicted a larger training-induced decrement in SBP (β=0.46 and β=-0.44, respectively, p<0.001 for both. Acute post-exercise decrease in SBP provided additive value to baseline resting SBP in the prediction of training-induced change in resting SBP (R squared from 0.20 to 0.26, p=0.002. After further adjustments for other potential confounders (sex, age, baseline body mass index, realized training load, post-exercise decrease in SBP still predicted the training response in resting SBP (β=0.26, p=0.015. Acute post-exercise change in SBP was associated with training-induced change in resting SBP in patients with CAD, providing significant predictive information beyond baseline resting SBP.

SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria.

Science.gov (United States)

Chevrette, Marc G; Aicheler, Fabian; Kohlbacher, Oliver; Currie, Cameron R; Medema, Marnix H

2017-10-15

Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool. SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3). chevrette@wisc.edu or marnix.medema@wur.nl. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Salutogenetic approach to professional training of future teachers

Directory of Open Access Journals (Sweden)

Ionova O.M.

2015-02-01

Full Text Available Purpose: disclosure of a nature and characteristics of the Movement for the renewal of adult education (New Adult Learning Movement - NALM as salutogenetic approach to the training of future teachers. Results: to analyze the nature and characteristics of salutogenetic approach to training of future teachers, which is based on anthroposophical methodological foundations and practically realized in the world as Movement for the renewal of adult education. Described the theoretical basis and direction of training (academic training, learning experiences, inner spiritual development, that allow to activate the internal intention of the person, arouse will of students to learn throughout life and contributes to the healthy development of the whole structure of the individual. Conclusions: were reported health saving forms and methods of education of future teachers: the organization of health-improving educational space, development of integrated programs (integration of educational elements - lectures, discussions, group work, project work, art classes, social exercises, etc., the rhythmic organization of educational process taking into account the human biorhythms, work with the biography of a man, pedagogical diagnostics and etc.

Prediction of Asphalt Creep Compliance Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Zofka A.

2012-06-01

Full Text Available Creep compliance of the hot-mix asphalt (HMA is a primary input of the pavement thermal cracking prediction model in the recently developed Mechanistic-Empirical Pavement Design Guide (M-EPDG in the US. The HMA creep compliance is typically determined from the Indirect Tension (IDT tests and requires complex experimental setup. On the other hand, creep compliance of asphalt binders is determined from a relatively simple three- point bending test performed in the Bending Beam Rheometer (BBR device. This paper discusses a process of training an Artificial Neural Network (ANN to correlate the creep compliance values obtained from the IDT with those from an innovative approach of testing HMA beams in the BBR. In addition, ANNs are also trained to predict HMA creep compliance from the creep compliance of asphalt binder and vice versa using the BBR setup. All trained ANNs exhibited a very high correlation of 97 to 99 percent between predicted and measured values. The binder creep compliance curves built on the ANN-predicted values also exhibited good correlation with those obtained from laboratory experiments. However, the simulation of trained ANNs on the independent dataset produced a significant deviation from the expected values which was most likely caused by the differences in material composition, such as aggregate type and gradation, presence of recycled additives, and binder type.

Stimulating Interest in Natural Sciences and Training Observation Skills: The UAP Observations Reporting Scheme

Science.gov (United States)

Ailleris, P.

2012-04-01

how to record as accurately as possible a UAP event, in order to facilitate future identification and study. Lastly, one of the project's objectives is also to collect reports of trained observers (astronomers) of apparently inexplicable events for further analysis. Certainly, whenever there are unexplained observations there is the possibility that scientists could learn something new by studying these events. During this presentation, we will provide an overview of the project, present the website's extensive and well illustrated list of misidentifications, describe how people can further check details, develop their knowledge (e.g. satellite paths, stars/planets charts, characteristics of meteors, pictures of sprites, clouds classification) and enhance their observation skills. In order to show the relevance of the project, a short illustrated list of UAP cases received by the project will be featured, both explained and inexplicable. Finally, we will explore potential plans for strengthening the visibility and usefulness of the project, while requesting feedback from the community of atmospheric and natural sciences' researchers. (1) www.uapreporting.org (*): Disclaimer: Work undertaken as personal work; not endorsed as research activity by ESA.

Evaluation of the Predictive Capabilities of a Phenomenological Combustion Model for Natural Gas SI Engine

Directory of Open Access Journals (Sweden)

Toman Rastislav

2017-12-01

Full Text Available The current study evaluates the predictive capabilities of a new phenomenological combustion model, available as a part of the GT-Suite software package. It is comprised of two main sub-models: 0D model of in-cylinder flow and turbulence, and turbulent SI combustion model. The 0D in-cylinder flow model (EngCylFlow uses a combined K-k-ε kinetic energy cascade approach to predict the evolution of the in-cylinder charge motion and turbulence, where K and k are the mean and turbulent kinetic energies, and ε is the turbulent dissipation rate. The subsequent turbulent combustion model (EngCylCombSITurb gives the in-cylinder burn rate; based on the calculation of flame speeds and flame kernel development. This phenomenological approach reduces significantly the overall computational effort compared to the 3D-CFD, thus allowing the computation of full engine operating map and the vehicle driving cycles. Model was calibrated using a full map measurement from a turbocharged natural gas SI engine, with swirl intake ports. Sensitivity studies on different calibration methods, and laminar flame speed sub-models were conducted. Validation process for both the calibration and sensitivity studies was concerning the in-cylinder pressure traces and burn rates for several engine operation points achieving good overall results.

Prenatal Stress due to a Natural Disaster Predicts Adiposity in Childhood: The Iowa Flood Study

Directory of Open Access Journals (Sweden)

Kelsey N. Dancause

2015-01-01

Full Text Available Prenatal stress can affect lifelong physical growth, including increased obesity risk. However, human studies remain limited. Natural disasters provide models of independent stressors unrelated to confounding maternal characteristics. We assessed degree of objective hardship and subjective distress in women pregnant during severe flooding. At ages 2.5 and 4 years we assessed body mass index (BMI, subscapular plus triceps skinfolds (SS + TR, an index of total adiposity, and SS : TR ratio (an index of central adiposity in their children (n=106. Hierarchical regressions controlled first for several potential confounds. Controlling for these, flood exposure during early gestation predicted greater BMI increase from age 2.5 to 4, as well as total adiposity at 2.5. Greater maternal hardship and distress due to the floods, as well as other nonflood life events during pregnancy, independently predicted greater increase in total adiposity between 2.5 and 4 years. These results support the hypothesis that prenatal stress increases adiposity beginning in childhood and suggest that early gestation is a sensitive period. Results further highlight the additive effects of maternal objective and subjective stress, life events, and depression, emphasizing the importance of continued studies on multiple, detailed measures of maternal mental health and experience in pregnancy and child growth.

A natureza argumentativa dos processos inferenciais preditivos na compreensão textual The argumentative nature of the predictive inferencial processes of the textual comprehension

Directory of Open Access Journals (Sweden)

Tícia Cassiany Ferro Cavalcante

2012-04-01

Full Text Available No presente estudo, propõe-se que a inferência de predição é um processo cognitivo/discursivo de natureza eminentemente argumentativa. Partindo desta proposta teórica, o estudo empírico relatado teve como objetivo investigar, de forma processual, a produção de inferências preditivas por parte de sete estudantes universitários no curso de uma leitura individualmente realizada (compreensão online. A perspectiva metodológica adotada foi a do estudo de casos em série, em cada um dos quais o participante foi solicitado a responder, em voz audível, a questões formuladas à medida que lia uma crônica. A análise de dados realizada, de natureza notadamente qualitativa e processual, evidencia a natureza essencialmente argumentativa das inferências preditivas produzidas.The present study argues that a prediction inference is a cognitive/discursive process fundamentally of argumentative nature. From this theoretical premise, this empirical study investigated, in a procedural manner, the production of predictive inferences by seven university students, using individual readings (online comprehension. The methodology used was that of case studies in series, in each of which the participant was asked to answer, aloud, questions formulated while he/she was reading a story. The analysis of the data, notably of qualitative and procedural nature, makes evident the essentially argumentative nature of the predictive inferences produced.

Entrepreneurship training in Ghana

DEFF Research Database (Denmark)

Schmidt, Leila Kæmsgaard Pagh

2017-01-01

Abstract Due to the very high youth unemployment in Northern Ghana, there is a huge need for enterprising skills among young people. A natural focus in recent years has therefore been entrepreneurship training, focusing on training young Ghanaians to start-up businesses. Unfortunately, the young...... is that adding a focus on the young entrepreneur’s means, attitude and enterprising behaviour skills to the existing focus on starting up businesses, will increase the value of the Entrepreneurship training and support the overcoming of constraints. The paper build on a design-based research project...... in collaboration with the local NGO YEfL. Based on relevant theory, a qualitative field research in Northern Ghana and a quantitative baseline survey a new Entrepreneurship Model has been designed. The new model was tested in autumn 2016 at three Entrepreneurship boot camps in Northern Ghana. The study has...

Models of Integrated Training in Psychiatry and Child and Adolescent Psychiatry

Science.gov (United States)

Sexson, Sandra B.; Thomas, Christopher R.; Pope, Kayla

2008-01-01

Objective: Previous studies indicate declining interest in child and adolescent psychiatry (CAP) as a career choice during psychiatry residency training. Programs have developed integrated training in psychiatry and CAP as a means to address the workforce shortage in CAP, but little is known about the number or nature of these training tracks.…

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

Directory of Open Access Journals (Sweden)

Akhuetie Jane

2009-07-01

years and USMLE step I & step II clinical skills scores were 85 (IQR: 80–88 & 82 (IQR: 79–87 respectively. The median aggregate CBE scores during training were: PG1 5.8 (IQR: 5.6–6.3; PG2 6.3 (IQR 6–6.8 & PG3 6.7 (IQR: 6.7 – 7.1. 25% of our residents scored consistently above US national median ITE scores in all 3 years of training and 16% pursued a fellowship. Younger residents had higher aggregate annual CBE score than the program median (p Conclusion Background IMG features namely, age and USMLE scores predict performance evaluation and in-training examination scores during residency training. In addition enhanced research activities during residency training could facilitate fellowship goals among interested IMGs.

Training of cosmonauts and astronauts

Science.gov (United States)

Gurovskiy, N. N.; Link, M. M.

1975-01-01

The biomedical and preflight training of spacecraft crews is discussed based on a survey of scientific and technical literature in the U.S. and U.S.S.R. Experience gained from high velocity and high altitude aircraft flights, predictions of human reactions and theoretical models of human adaptation to the new environment of space, and actual spaceflight experience provided scientists and specialists with data from which the state of human health in space could be predicted and life support measures developed.

Resistance training and predicted risk of coronary heart disease in ...

African Journals Online (AJOL)

The purpose of this study was to determine the impact of resistance training, designed to prevent the development of coronary heart disease (CHD) based on the Framingham Risk Assessment (FRA) score. Twenty-five healthy sedentary men with low CHD risk were assigned to participate in a 16-week (three days per week) ...

Approaches to training in companies

Directory of Open Access Journals (Sweden)

Magnoler Patrizia

2016-12-01

Full Text Available The need to address generational change and the challenges of a global market in terms of maintaining productivity require small and medium enterprises, mainly of an artisanal nature, to rethink training. The challenges mainly concern production capacity, which is increasingly problematic given that demand does not allow for long-term schedules and enhancement of human resources. There are many tensions and just as many needs for improvement, and training is therefore the space in which to collect and rework in order to restore a new perspective of sustainable and quality change.

Learning receptive fields using predictive feedback.

Science.gov (United States)

Jehee, Janneke F M; Rothkopf, Constantin; Beck, Jeffrey M; Ballard, Dana H

2006-01-01

Previously, it was suggested that feedback connections from higher- to lower-level areas carry predictions of lower-level neural activities, whereas feedforward connections carry the residual error between the predictions and the actual lower-level activities [Rao, R.P.N., Ballard, D.H., 1999. Nature Neuroscience 2, 79-87.]. A computational model implementing the hypothesis learned simple cell receptive fields when exposed to natural images. Here, we use predictive feedback to explain tuning properties in medial superior temporal area (MST). We implement the hypothesis using a new, biologically plausible, algorithm based on matching pursuit, which retains all the features of the previous implementation, including its ability to efficiently encode input. When presented with natural images, the model developed receptive field properties as found in primary visual cortex. In addition, when exposed to visual motion input resulting from movements through space, the model learned receptive field properties resembling those in MST. These results corroborate the idea that predictive feedback is a general principle used by the visual system to efficiently encode natural input.

Prediction of half-marathon race time in recreational female and male runners.

Science.gov (United States)

Knechtle, Beat; Barandun, Ursula; Knechtle, Patrizia; Zingg, Matthias A; Rosemann, Thomas; Rüst, Christoph A

2014-01-01

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were the best predictor variables for half-marathon race times in both women and men. The aim of the present study was to improve the existing equations to predict half-marathon race time in a larger sample of male and female half-marathoners by using percent body fat and running speed during training sessions as predictor variables. In a sample of 147 men and 83 women, multiple linear regression analysis including percent body fat and running speed during training units as independent variables and race time as dependent variable were performed and an equation was evolved to predict half-marathon race time. For men, half-marathon race time might be predicted by the equation (r(2) = 0.42, adjusted r(2) = 0.41, SE = 13.3) half-marathon race time (min) = 142.7 + 1.158 × percent body fat (%) - 5.223 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.71, p marathon race time might be predicted by the equation (r(2) = 0.68, adjusted r(2) = 0.68, SE = 9.8) race time (min) = 168.7 + 1.077 × percent body fat (%) - 7.556 × running speed during training (km/h). The predicted race time correlated highly significantly (r = 0.89, p < 0.0001) to the achieved race time. The coefficients of determination of the models were slightly higher than for the existing equations. Future studies might include physiological variables to increase the coefficients of determination of the