WorldWideScience

Sample records for network training set

  1. Neural network for regression problems with reduced training sets.

    Science.gov (United States)

    Bataineh, Mohammad; Marler, Timothy

    2017-11-01

    Although they are powerful and successful in many applications, artificial neural networks (ANNs) typically do not perform well with complex problems that have a limited number of training cases. Often, collecting additional training data may not be feasible or may be costly. Thus, this work presents a new radial-basis network (RBN) design that overcomes the limitations of using ANNs to accurately model regression problems with minimal training data. This new design involves a multi-stage training process that couples an orthogonal least squares (OLS) technique with gradient-based optimization. New termination criteria are also introduced to improve accuracy. In addition, the algorithms are designed to require minimal heuristic parameters, thus improving ease of use and consistency in performance. The proposed approach is tested with experimental and practical regression problems, and the results are compared with those from typical network models. The results show that the new design demonstrates improved accuracy with reduced dependence on the amount of training data. As demonstrated, this new ANN provides a platform for approximating potentially slow but high-fidelity computational models, and thus fostering inter-model connectivity and multi-scale modeling. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

    Science.gov (United States)

    Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

    1990-01-01

    Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

  3. Training Recurrent Networks

    DEFF Research Database (Denmark)

    Pedersen, Morten With

    1997-01-01

    Training recurrent networks is generally believed to be a difficult task. Excessive training times and lack of convergence to an acceptable solution are frequently reported. In this paper we seek to explain the reason for this from a numerical point of view and show how to avoid problems when...

  4. Training brain networks and states.

    Science.gov (United States)

    Tang, Yi-Yuan; Posner, Michael I

    2014-07-01

    Brain training refers to practices that alter the brain in a way that improves cognition, and performance in domains beyond those involved in the training. We argue that brain training includes network training through repetitive practice that exercises specific brain networks and state training, which changes the brain state in a way that influences many networks. This opinion article considers two widely used methods - working memory training (WMT) and meditation training (MT) - to demonstrate the similarities and differences between network and state training. These two forms of training involve different areas of the brain and different forms of generalization. We propose a distinction between network and state training methods to improve understanding of the most effective brain training. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Influence of the Training Set Value on the Quality of the Neural Network to Identify Selected Moulding Sand Properties

    National Research Council Canada - National Science Library

    J. Jakubski; St. M. Dobosz; K. Major-Gabryś

    2013-01-01

    .... This paper presents the next part of the study on usefulness of artificial neural networks to support rebonding of green moulding sand, using chosen properties of moulding sands, which can be determined fast...

  6. Minimal cut sets in biochemical reaction networks

    National Research Council Canada - National Science Library

    Klamt, Steffen; Gilles, Ernst Dieter

    2004-01-01

    .... We introduce the concept of minimal cut sets for biochemical networks. A minimal cut set (MCS) is a minimal (irreducible) set of reactions in the network whose inactivation will definitely lead to a failure in certain network functions...

  7. Positive train control shared network.

    Science.gov (United States)

    2015-05-01

    The Interoperable Train Control (ITC) Positive : Train Control (PTC) Shared Network (IPSN) : project investigated anticipated industry benefits : and the level of support for the development of : a hosted technological platform for PTC : messaging ac...

  8. Confidence sets for network structure

    CERN Document Server

    Airoldi, Edoardo M; Wolfe, Patrick J

    2011-01-01

    Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that hold with respect to these underlying Bernoulli parameters as a function of any given partition of network nodes, enabling us to assess estimates of 'residual' network structure, that is, structure that cannot be explained by known covariates and thus cannot be easily verified by manual inspection. We demonstrate the proposed methodology by analyzing student friendship networks from the National Longitudinal Survey of Adolescent Health that include race, gender, and school year as covariates. We employ a stochastic expectation-maximization algorithm to fit a logistic regression model that includes these explanatory variables as well as a latent stochastic blockmodel component and additional node-specific...

  9. 1-Set vs. 3-set resistance training: a crossover study.

    Science.gov (United States)

    Humburg, Hartmut; Baars, Hartmut; Schröder, Jan; Reer, Rüdiger; Braumann, Klaus-Michael

    2007-05-01

    This crossover study was conducted to investigate the effects of a 1-set and 3-set strength training program. The subjects were untrained men and women who were randomly signed into 1 of 3 groups: 10 subjects trained during the first 9 weeks (training period 1) with 1 set and 8-12 repetitions per set. After the break (9 weeks), they trained with 3 sets and 8-12 repetitions in training period 2. Twelve subjects started with the 3-set program and continued with the 1-set regime after the break. The control group (n = 7) did not train. The subjects were tested on 1 repetition maximum (1RM) for the biceps curl, leg press (unilateral: left and right), and bench press. Analysis of the data was done in a sampled manner for each strength training program (1-set and 3-set). The 1-set (n = 22) and 3-set (n = 22) programs led to significantly (p < 0.05) improved 1RM performances in every exercise. The relative improvements (%) for the 1RM were significantly higher during the 3-set program for the biceps curl and the bench press compared with the 1-set program. The control group exhibited no changes in any of the tested parameters over the course of this study. The design of this study allowed insight into the effects of different strength training volume without any genetical variations. The same subjects improved their 1RM during the 3-set program by 2.3 kg (biceps curl; corresponding effect size = 0.24), 8.9 kg (leg press right; 0.30), 10.9 kg (leg press left; 0.28), and 2.5 kg (bench press; 0.09) more than during the 1-set program. Depending on the goals of each trainee, these differences between the effects of different strength training volumes indicate that it may be worth spending more time on working out with a 3-set strength training regime.

  10. Network Attack Reference Data Set

    Science.gov (United States)

    2004-12-01

    fingerprinting tools include QueSO [10] (literally translates to “what OS”) and nmap [11], however there are a number of additional tools available for...Network Attack Reference Data Set J. McKenna and J. Treurniet Defence R&D Canada √ Ottawa TECHNICAL...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources

  11. Networking Education and Teacher Training

    NARCIS (Netherlands)

    Ton Koenraad; John Parnell

    2006-01-01

    This paper reports on the EU-project 'Professionally Networking Education and Teacher Training' (PRONETT). The key objective of the PRONETT project (2001-2004) is to develop a regional and cross national learning community of pre- and in-service teachers and teacher educators supported by webbased

  12. Settings in Social Networks : a Measurement Model

    NARCIS (Netherlands)

    Schweinberger, Michael; Snijders, Tom A.B.

    2003-01-01

    A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive

  13. Settings in social networks : A measurement model

    NARCIS (Netherlands)

    Schweinberger, M; Snijders, TAB

    2003-01-01

    A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive

  14. Training Effectiveness Evaluation of the Squad Engagement Training System (SETS)

    Science.gov (United States)

    1990-06-01

    Myron P. Viner "CAE-Link Corp. - , TiPO ?1990 June 1990 Approvud fcr public releas’u. distribution is unil~iutcd- 1 <.. --•( , . . ....-. ... a. .,,w n...SETS software . SETS, however, does provide the unique opportunity for realistic * OPFOR contact within the RC home-station training environment, and the

  15. Routing Trains Through Railway Junctions: A New Set Packing Approach

    DEFF Research Database (Denmark)

    Lusby, Richard; Larsen, Jesper; Ryan, David

    The problem of routing trains through railway junctions is an integral part of railway operations. Large junctions are highly interconnected networks of track where multiple railway lines meet, intersect, and split. The number of possible routings makes this a very complicated problem. Here we show...... how the problem can be formulated as a set packing model. To exploit the structure of the problem we present a solution procedure which entails solving the dual of this formulation through the dynamic addition of violated cuts (primal variables). A discussion of the variable (train path) generation...

  16. Generating route choice sets with operation information on metro networks

    Directory of Open Access Journals (Sweden)

    Wei Zhu

    2016-06-01

    Full Text Available In recent years, the metro system has advanced into an efficient transport system and become the mainstay of urban passenger transport in many mega-cities. Passenger flow is the foundation of making and coordinating operation plans for the metro system, and therefore, a variety of studies were conducted on transit assignment models. Nevertheless route choice sets of passengers also play a paramount role in flow estimation and demand prediction. This paper first discusses the main route constraints of which the train schedule is the most important, that distinguish rail networks from road networks. Then, a two-step approach to generate route choice set in a metro network is proposed. Particularly, the improved approach introduces a route filtering with train operational information based on the conventional method. An initial numerical test shows that the proposed approach gives more reasonable route choice sets for scheduled metro networks, and, consequently, obtains more accurate results from passenger flow assignment. Recommendations for possible opportunities to apply this approach to metro operations are also provided, including its integration into a metro passenger flow assignment and simulation system in practice to help metro authorities provide more precise guidance information for passengers to travel.

  17. Training Results and Information Network

    Data.gov (United States)

    US Agency for International Development — TraiNet is USAID's official training data management system that is accessed from a web browser and the entry point for data about training programs and participants...

  18. Parenclitic networks' representation of data sets

    CERN Document Server

    Zanin, Massimiliano; Carbajosa, Jesus Vicente; Sousa, Pedro; Papo, David; Menasalvas, Ernestina; Boccaletti, Stefano

    2013-01-01

    Of the different ways of representing a multi-unit system, the one afforded by networks is among the most elegant and general. Endowing a system with a network representation requires defining nodes and links connecting them. Often physical or virtual relationships between the elements of the system, e.g. anatomic brain fibres or hyper-links between the pages of a web site, constrain the way a link is defined. When such relationships are not clearly apparent, yet functional links can be built as long as time evolving variables are associated to each node, as e.g. the time evolution of a stock price, or of brain activity in a given region. We propose a third, novel, method which allows treating collections of isolated, possibly heterogeneous, scalars, e.g. sets of biomedical tests, as networked systems. The method builds a network where each node represents a feature, while each pairing quantifies the deviation between those two features and the corresponding typical relationship between them within a studied ...

  19. Assessment of a national network: the case of the French teacher training colleges' health education network.

    Science.gov (United States)

    Guével, Marie-Renée; Jourdan, Didier

    2009-06-01

    The French teacher training colleges' health education (HE) network was set up in 2005 to encourage the inclusion of HE in courses for primary and secondary school teachers. A systematic process of monitoring the activity and the impact of this initiative was implemented. This analysis was systematically compared with the perceptions of teaching staff involved in the network. This paper assesses the network after 2 years using documents produced and interviews with 24 coordinators. Twenty-nine teacher training colleges out of a total of 31 are involved in the network. The network has helped to create links between teacher training colleges, extend HE training and encourage partnerships with other public health organizations. By 2007, HE was included in courses offered by 19 teacher training colleges as opposed to only 3 in 2005. This study not only showed the positive impact of the network but also revealed issues in its management and presented new challenges to ensure the effectiveness of the network. The network has succeeded in attracting and training trainers who were already providing or were interested in HE. Reaching other trainers who are not familiar with HE remains a challenge for the future.

  20. Limited Effects of Set Shifting Training in Healthy Older Adults

    Directory of Open Access Journals (Sweden)

    Petra Grönholm-Nyman

    2017-03-01

    Full Text Available Our ability to flexibly shift between tasks or task sets declines in older age. As this decline may have adverse effects on everyday life of elderly people, it is of interest to study whether set shifting ability can be trained, and if training effects generalize to other cognitive tasks. Here, we report a randomized controlled trial where healthy older adults trained set shifting with three different set shifting tasks. The training group (n = 17 performed adaptive set shifting training for 5 weeks with three training sessions a week (45 min/session, while the active control group (n = 16 played three different computer games for the same period. Both groups underwent extensive pre- and post-testing and a 1-year follow-up. Compared to the controls, the training group showed significant improvements on the trained tasks. Evidence for near transfer in the training group was very limited, as it was seen only on overall accuracy on an untrained computerized set shifting task. No far transfer to other cognitive functions was observed. One year later, the training group was still better on the trained tasks but the single near transfer effect had vanished. The results suggest that computerized set shifting training in the elderly shows long-lasting effects on the trained tasks but very little benefit in terms of generalization.

  1. Optimizing Training Set Construction for Video Semantic Classification

    Directory of Open Access Journals (Sweden)

    Xiuqing Wu

    2007-12-01

    Full Text Available We exploit the criteria to optimize training set construction for the large-scale video semantic classification. Due to the large gap between low-level features and higher-level semantics, as well as the high diversity of video data, it is difficult to represent the prototypes of semantic concepts by a training set of limited size. In video semantic classification, most of the learning-based approaches require a large training set to achieve good generalization capacity, in which large amounts of labor-intensive manual labeling are ineluctable. However, it is observed that the generalization capacity of a classifier highly depends on the geometrical distribution of the training data rather than the size. We argue that a training set which includes most temporal and spatial distribution information of the whole data will achieve a good performance even if the size of training set is limited. In order to capture the geometrical distribution characteristics of a given video collection, we propose four metrics for constructing/selecting an optimal training set, including salience, temporal dispersiveness, spatial dispersiveness, and diversity. Furthermore, based on these metrics, we propose a set of optimization rules to capture the most distribution information of the whole data using a training set with a given size. Experimental results demonstrate these rules are effective for training set construction in video semantic classification, and significantly outperform random training set selection.

  2. News Conference: Bloodhound races into history Competition: School launches weather balloon Course: Update weekends inspire teachers Conference: Finland hosts GIREP conference Astronomy: AstroSchools sets up schools network to share astronomy knowledge Teaching: Delegates praise science events in Wales Resources: ELI goes from strength to strength International: South Sudan teachers receive training Workshop: Delegates experience universality

    Science.gov (United States)

    2011-11-01

    Conference: Bloodhound races into history Competition: School launches weather balloon Course: Update weekends inspire teachers Conference: Finland hosts GIREP conference Astronomy: AstroSchools sets up schools network to share astronomy knowledge Teaching: Delegates praise science events in Wales Resources: ELI goes from strength to strength International: South Sudan teachers receive training Workshop: Delegates experience universality

  3. Deliberate Practice of Creativity Training Set Series - A Creativity Training Material for Education (work in progress)

    DEFF Research Database (Denmark)

    Byrge, Christian

    2018-01-01

    Five training sets including 450 unique thinking direction cards and 120 exercise cards. Designed for Educational Purposes.......Five training sets including 450 unique thinking direction cards and 120 exercise cards. Designed for Educational Purposes....

  4. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  5. Solar Training Network and Solar Ready Vets

    Energy Technology Data Exchange (ETDEWEB)

    Dalstrom, Tenley Ann

    2016-09-14

    In 2016, the White House announced the Solar Ready Vets program, funded under DOE's SunShot initiative would be administered by The Solar Foundation to connect transitioning military personnel to solar training and employment as they separate from service. This presentation is geared to informing and recruiting employer partners for the Solar Ready Vets program, and the Solar Training Network. It describes the programs, and the benefits to employers that choose to connect to the programs.

  6. NATO Education and Training Network

    Science.gov (United States)

    2012-02-01

    F-22 1.9.2.10 NETN Service Manager ..................................................................................................... F...Version Description Pitch Actors MSG-068 CGF Pitch Booster 1.2 Private Simulation Network Overlay Pitch NETN Service Manager MSG-068 Test tool for NETN...IEEE 1516-2010 1 Pitch NETN Service Manager Service Manager NETN Service Manager IEEE 1516-2010 1 Pitch Recorder Pitch Recorder Recorder IEEE 1516

  7. Challenges in Food Scientist Training in a global setting

    Directory of Open Access Journals (Sweden)

    Andreas Höhl

    2012-10-01

    Full Text Available Normal 0 21 false false false EN-GB X-NONE X-NONE Education and training were an integral part of the MoniQA Network of Excellence. Embedded in the "Spreading of excellence programme", Work Package 9 (Joint education programmes and training tools was responsible for establishing a joint training programme for food safety and quality within and beyond the network. So-called `MoniQA Food Scientist Training' (MoniQA FST was offered to provide technical knowledge on different levels and research management skills as well. Training needs for different regions as well as for different target groups (scientists, industry personnel, authorities had to be considered as well as developing strong collaboration links between network partners and related projects. Beside face-to-face workshops e-learning modules have been developed and web seminars were organized. In order to achieve high quality training, a quality assurance concept has been implemented. It turned out that these types of training are of high value in terms of bringing together scientists from different regions and cultures of the globe, involving highly qualified trainers as basis for a sustainable network in the future.

  8. Modelling electric trains energy consumption using Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez Fernandez, P.; Garcia Roman, C.; Insa Franco, R.

    2016-07-01

    Nowadays there is an evident concern regarding the efficiency and sustainability of the transport sector due to both the threat of climate change and the current financial crisis. This concern explains the growth of railways over the last years as they present an inherent efficiency compared to other transport means. However, in order to further expand their role, it is necessary to optimise their energy consumption so as to increase their competitiveness. Improving railways energy efficiency requires both reliable data and modelling tools that will allow the study of different variables and alternatives. With this need in mind, this paper presents the development of consumption models based on neural networks that calculate the energy consumption of electric trains. These networks have been trained based on an extensive set of consumption data measured in line 1 of the Valencia Metro Network. Once trained, the neural networks provide a reliable estimation of the vehicles consumption along a specific route when fed with input data such as train speed, acceleration or track longitudinal slope. These networks represent a useful modelling tool that may allow a deeper study of railway lines in terms of energy expenditure with the objective of reducing the costs and environmental impact associated to railways. (Author)

  9. Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

    Science.gov (United States)

    Amit, Guy; Ben-Ari, Rami; Hadad, Omer; Monovich, Einat; Granot, Noa; Hashoul, Sharbell

    2017-03-01

    Diagnostic interpretation of breast MRI studies requires meticulous work and a high level of expertise. Computerized algorithms can assist radiologists by automatically characterizing the detected lesions. Deep learning approaches have shown promising results in natural image classification, but their applicability to medical imaging is limited by the shortage of large annotated training sets. In this work, we address automatic classification of breast MRI lesions using two different deep learning approaches. We propose a novel image representation for dynamic contrast enhanced (DCE) breast MRI lesions, which combines the morphological and kinetics information in a single multi-channel image. We compare two classification approaches for discriminating between benign and malignant lesions: training a designated convolutional neural network and using a pre-trained deep network to extract features for a shallow classifier. The domain-specific trained network provided higher classification accuracy, compared to the pre-trained model, with an area under the ROC curve of 0.91 versus 0.81, and an accuracy of 0.83 versus 0.71. Similar accuracy was achieved in classifying benign lesions, malignant lesions, and normal tissue images. The trained network was able to improve accuracy by using the multi-channel image representation, and was more robust to reductions in the size of the training set. A small-size convolutional neural network can learn to accurately classify findings in medical images using only a few hundred images from a few dozen patients. With sufficient data augmentation, such a network can be trained to outperform a pre-trained out-of-domain classifier. Developing domain-specific deep-learning models for medical imaging can facilitate technological advancements in computer-aided diagnosis.

  10. Negative Interpersonal Interactions in Student Training Settings

    Science.gov (United States)

    Ferris, Patricia A.; Kline, Theresa J. B.

    2009-01-01

    Studies demonstrate that negative interpersonal interaction(s) (NII) such as bullying are frequent and harmful to individuals in workplace and higher education student settings. Nevertheless, it is unclear whether the degree of perceived severity of NII varies by the status of the actor. The present study explored the moderating effect of actor…

  11. Securing Mobile Networks in an Operational Setting

    Science.gov (United States)

    Ivancic, William D.; Stewart, David H.; Bell, Terry L.; Paulsen, Phillip E.; Shell, Dan

    2004-01-01

    This paper describes a network demonstration and three month field trial of mobile networking using mobile-IPv4. The network was implemented as part of the US Coast Guard operational network which is a ".mil" network and requires stringent levels of security. The initial demonstrations took place in November 2002 and a three month field trial took place from July through September of 2003. The mobile network utilized encryptors capable of NSA-approved Type 1 algorithms, mobile router from Cisco Systems and 802.11 and satellite wireless links. This paper also describes a conceptual architecture for wide-scale deployment of secure mobile networking in operational environments where both private and public infrastructure is used. Additional issues presented include link costs, placement of encryptors and running routing protocols over layer-3 encryption devices.

  12. Global Optimization for Transport Network Expansion and Signal Setting

    Directory of Open Access Journals (Sweden)

    Haoxiang Liu

    2015-01-01

    Full Text Available This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two problems simultaneously. In this study, a combined network capacity expansion and signal setting model with consideration of vehicle queuing on approaching legs of intersection is developed to consider their mutual interactions so that best transport network performance can be guaranteed. We formulate the model as a bilevel program and design an approximated global optimization solution method based on mixed-integer linearization approach to solve the problem, which is inherently nnonlinear and nonconvex. Numerical experiments are conducted to demonstrate the model application and the efficiency of solution algorithm.

  13. Adaptive training of feedforward neural networks by Kalman filtering

    Energy Technology Data Exchange (ETDEWEB)

    Ciftcioglu, Oe. [Istanbul Technical Univ. (Turkey). Dept. of Electrical Engineering; Tuerkcan, E. [Netherlands Energy Research Foundation (ECN), Petten (Netherlands)

    1995-02-01

    Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.).

  14. Effectiveness of Intensive Physician Training in Upfront Agenda Setting

    National Research Council Canada - National Science Library

    Brock, Douglas M; Mauksch, Larry B; Witteborn, Saskia; Hummel, Jeffery; Nagasawa, Pamela; Robins, Lynne S

    2011-01-01

    ... are raised.We hypothesized that when physicians were trained to use collaborative upfront agenda setting, visits would be no longer, more concerns would be identified, fewer concerns would surface late...

  15. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  16. The extraction of information and knowledge from trained neural networks.

    Science.gov (United States)

    Livingstone, David J; Browne, Antony; Crichton, Raymond; Hudson, Brian D; Whitley, David C; Ford, Martyn G

    2008-01-01

    In the past, neural networks were viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed that extract comprehensible representations from trained neural networks, enabling them to be used for data mining and knowledge discovery, that is, the discovery and explanation of previously unknown relationships present in data. This chapter reviews existing algorithms for extracting comprehensible representations from neural networks and outlines research to generalize and extend the capabilities of one of these algorithms, TREPAN. This algorithm has been generalized for application to bioinformatics data sets, including the prediction of splice junctions in human DNA sequences, and cheminformatics. The results generated on these data sets are compared with those generated by a conventional data mining technique (C5) and appropriate conclusions are drawn.

  17. EHV network operation, maintenance, organization and training

    Energy Technology Data Exchange (ETDEWEB)

    Gravier, J.P. [Electricite de France (EDF), 75 - Paris (France)

    1994-12-31

    The service interruptions of electricity have an ever increasing social and industrial impact, it is thus fundamental to operate the network to its best level of performances. To face these changing conditions, Electricite de France has consequently adapted its strategy to improve its organization for maintenance and operation, clarify the operation procedures and give further training to the staff. This work presents the above mentioned issues. (author) 2 figs.

  18. Novel maximum-margin training algorithms for supervised neural networks.

    Science.gov (United States)

    Ludwig, Oswaldo; Nunes, Urbano

    2010-06-01

    This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by

  19. Effectiveness of intensive physician training in upfront agenda setting.

    Science.gov (United States)

    Brock, Douglas M; Mauksch, Larry B; Witteborn, Saskia; Hummel, Jeffery; Nagasawa, Pamela; Robins, Lynne S

    2011-11-01

    Patients want all their concerns heard, but physicians fear losing control of time and interrupt patients before all concerns are raised. We hypothesized that when physicians were trained to use collaborative upfront agenda setting, visits would be no longer, more concerns would be identified, fewer concerns would surface late in the visit, and patients would report greater satisfaction and improved functional status. Post-only randomized controlled trial using qualitative and quantitative methods. Six months after training (March 2004-March 2005) physician-patient encounters in two large primary care organizations were audio taped and patients (1460) and physicians (48) were surveyed. Experimental physicians received training in upfront agenda setting through the Establishing Focus Protocol, including two hours of training and two hours of coaching per week for four consecutive weeks. Outcomes included agenda setting behaviors demonstrated during the early, middle, and late encounter phases, visit length, number of raised concerns, patient and physician satisfaction, trust and functional status. Experimental physicians were more likely to make additional elicitations (p agenda completion in the early phase of the encounter (p agenda setting did not increase visit length or the number of problems addressed per visit but may reduce the likelihood of "oh by the way" concerns surfacing late in the encounter. However, upfront agenda setting is not sufficient to enhance patient satisfaction, trust or functional status. Training focused on physicians instead of teams and without regular reinforcement may have limited impact in changing visit content and time use.

  20. Exploring empowerment in settings: mapping distributions of network power.

    Science.gov (United States)

    Neal, Jennifer Watling

    2014-06-01

    This paper brings together two trends in the empowerment literature-understanding empowerment in settings and understanding empowerment as relational-by examining what makes settings empowering from a social network perspective. Specifically, extending Neal and Neal's (Am J Community Psychol 48(3/4):157-167, 2011) conception of network power, an empowering setting is defined as one in which (1) actors have existing relationships that allow for the exchange of resources and (2) the distribution of network power among actors in the setting is roughly equal. The paper includes a description of how researchers can examine distributions of network power in settings. Next, this process is illustrated in both an abstract example and using empirical data on early adolescents' peer relationships in urban classrooms. Finally, implications for theory, methods, and intervention related to understanding empowering settings are explored.

  1. A comparison of Landsat point and rectangular field training sets for land-use classification

    Science.gov (United States)

    Tom, C. H.; Miller, L. D.

    1984-01-01

    Rectangular training fields of homogeneous spectroreflectance are commonly used in supervised pattern recognition efforts. Trial image classification with manually selected training sets gives irregular and misleading results due to statistical bias. A self-verifying, grid-sampled training point approach is proposed as a more statistically valid feature extraction technique. A systematic pixel sampling network of every ninth row and ninth column efficiently replaced the full image scene with smaller statistical vectors which preserved the necessary characteristics for classification. The composite second- and third-order average classification accuracy of 50.1 percent for 331,776 pixels in the full image substantially agreed with the 51 percent value predicted by the grid-sampled, 4,100-point training set.

  2. Utilizing Maximal Independent Sets as Dominating Sets in Scale-Free Networks

    Science.gov (United States)

    Derzsy, N.; Molnar, F., Jr.; Szymanski, B. K.; Korniss, G.

    Dominating sets provide key solution to various critical problems in networked systems, such as detecting, monitoring, or controlling the behavior of nodes. Motivated by graph theory literature [Erdos, Israel J. Math. 4, 233 (1966)], we studied maximal independent sets (MIS) as dominating sets in scale-free networks. We investigated the scaling behavior of the size of MIS in artificial scale-free networks with respect to multiple topological properties (size, average degree, power-law exponent, assortativity), evaluated its resilience to network damage resulting from random failure or targeted attack [Molnar et al., Sci. Rep. 5, 8321 (2015)], and compared its efficiency to previously proposed dominating set selection strategies. We showed that, despite its small set size, MIS provides very high resilience against network damage. Using extensive numerical analysis on both synthetic and real-world (social, biological, technological) network samples, we demonstrate that our method effectively satisfies four essential requirements of dominating sets for their practical applicability on large-scale real-world systems: 1.) small set size, 2.) minimal network information required for their construction scheme, 3.) fast and easy computational implementation, and 4.) resiliency to network damage. Supported by DARPA, DTRA, and NSF.

  3. Picture this: Managed change and resistance in business network settings

    DEFF Research Database (Denmark)

    Kragh, Hanne; Andersen, Poul Houman

    2009-01-01

    This paper discusses change management in networks. The literature on business networks tends to downplay the role of managerial initiative in network change. The change management literature addresses such initiative, but with its single-firm perspective it overlooks the interdependence of network...... actors. In exploring the void between these two streams of literature, we deploy the concept of network pictures to discuss managed change in network settings. We analyze a change project from the furniture industry and address the consequences of attempting to manage change activities in a network...... context characterized by limited managerial authority over these activities. Our analysis suggests that change efforts unfold as a negotiated process during which the change project is re-negotiated to fit the multiple actor constituencies. The degree of overlap in the co-existing network pictures...

  4. Artificial Neural Network with Hardware Training and Hardware Refresh

    Science.gov (United States)

    Duong, Tuan A. (Inventor)

    2003-01-01

    A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

  5. Physicists set new record for network data transfer

    CERN Multimedia

    2007-01-01

    "An international team of physicists, computer scientists, and network engineers joined forces to set new records for sustained data transfer between storage systems durint the SuperComputing 2006 (SC06) Bandwidth Challenge (BWC). (3 pages)

  6. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    Science.gov (United States)

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  7. Dominating sets and ego-centered decompositions in social networks

    Science.gov (United States)

    Boudourides, M. A.; Lenis, S. T.

    2016-09-01

    Our aim here is to address the problem of decomposing a whole network into a minimal number of ego-centered subnetworks. For this purpose, the network egos are picked out as the members of a minimum dominating set of the network. However, to find such an efficient dominating ego-centered construction, we need to be able to detect all the minimum dominating sets and to compare all the corresponding dominating ego-centered decompositions of the network. To find all the minimum dominating sets of the network, we are developing a computational heuristic, which is based on the partition of the set of nodes of a graph into three subsets, the always dominant vertices, the possible dominant vertices and the never dominant vertices, when the domination number of the network is known. To compare the ensuing dominating ego-centered decompositions of the network, we are introducing a number of structural measures that count the number of nodes and links inside and across the ego-centered subnetworks. Furthermore, we are applying the techniques of graph domination and ego-centered decomposition for six empirical social networks.

  8. A generalized LSTM-like training algorithm for second-order recurrent neural networks.

    Science.gov (United States)

    Monner, Derek; Reggia, James A

    2012-01-01

    The long short term memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM's original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting its applicability to a small set of network architectures. Here we introduce the generalized long short-term memory(LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

  9. Introduction to the EC's Marie Curie Initial Training Network (MC-ITN) project: Particle Training Network for European Radiotherapy (PARTNER).

    Science.gov (United States)

    Dosanjh, Manjit; Magrin, Giulio

    2013-07-01

    PARTNER (Particle Training Network for European Radiotherapy) is a project funded by the European Commission's Marie Curie-ITN funding scheme through the ENLIGHT Platform for 5.6 million Euro. PARTNER has brought together academic institutes, research centres and leading European companies, focusing in particular on a specialized radiotherapy (RT) called hadron therapy (HT), interchangeably referred to as particle therapy (PT). The ultimate goal of HT is to deliver more effective treatment to cancer patients leading to major improvement in the health of citizens. In Europe, several hundred million Euro have been invested, since the beginning of this century, in PT. In this decade, the use of HT is rapidly growing across Europe, and there is an urgent need for qualified researchers from a range of disciplines to work on its translational research. In response to this need, the European community of HT, and in particular 10 leading academic institutes, research centres, companies and small and medium-sized enterprises, joined together to form the PARTNER consortium. All partners have international reputations in the diverse but complementary fields associated with PT: clinical, radiobiological and technological. Thus the network incorporates a unique set of competencies, expertise, infrastructures and training possibilities. This paper describes the status and needs of PT research in Europe, the importance of and challenges associated with the creation of a training network, the objectives, the initial results, and the expected long-term benefits of the PARTNER initiative.

  10. Training product unit neural networks with genetic algorithms

    Science.gov (United States)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  11. Positive train control interoperability and networking research : final report.

    Science.gov (United States)

    2015-12-01

    This document describes the initial development of an ITC PTC Shared Network (IPSN), a hosted : environment to support the distribution, configuration management, and IT governance of Interoperable : Train Control (ITC) Positive Train Control (PTC) s...

  12. Distributed algorithm to train neural networks using the Map Reduce paradigm

    Directory of Open Access Journals (Sweden)

    Cristian Mihai BARCA

    2017-07-01

    Full Text Available With rapid development of powerful computer systems during past decade, parallel and distributed processing becomes a significant resource for fast neural network training, even for real-time processing. Different parallel computing based methods have been proposed in recent years for the development of system performance. The two main methods are to distribute the patterns that are used for training - training set level parallelism, or to distribute the computation performed by the neural network - neural network level parallelism. In the present research work we have focused on the first method.

  13. Parallelization of Neural Network Training for NLP with Hogwild!

    Directory of Open Access Journals (Sweden)

    Deyringer Valentin

    2017-10-01

    Full Text Available Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.

  14. Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions

    DEFF Research Database (Denmark)

    Schmidt, R.; Waschina, S.; Boettger-Schmidt, D.

    2015-01-01

    by inherent inconsistencies and gaps. RESULTS: Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass...

  15. Effect of Single Setting versus Multiple Setting Training on Learning to Shop in a Department Store.

    Science.gov (United States)

    Westling, David L.; And Others

    1990-01-01

    Fifteen students, age 13-21, with moderate to profound mental retardation received shopping skills training in either 1 or 3 department stores. A study of operational behaviors, social behaviors, number of settings in which criterion performance was achieved, and number of sessions required to achieve criterion found no significant differences…

  16. Designing Serious Games for getting transferable skills in training settings

    Directory of Open Access Journals (Sweden)

    Félix Buendía-García

    2014-02-01

    Full Text Available Nowadays, serious games are present in almost every educational context. The current work deals with the design of serious games oriented towards getting transferable skills in different kinds of training settings. These games can be a valuable way of engaging citizens and workers in the learning process by means of metaphors or similar mechanisms close to their user experience. They also contain an encouragement factor to uptake generic job competencies. An approach is proposed to develop this type of game by mixing traditional design steps with an instructional strategy to provide structured learning bites in training settings. Several game prototypes have been developed to test this approach in the context of courses for public employees. The obtained outcomes reveal the wider possibilities of serious games as educational resources, as well as the use of game achievements to evaluate the acquisition of transferable skills.

  17. One-set resistance training elevates energy expenditure for 72 h similar to three sets

    OpenAIRE

    Heden, Timothy; Lox, Curt; Rose, Paul; Reid, Steven; Kirk, Erik P.

    2010-01-01

    To compare the effects of an acute one versus three-set full body resistance training (RT) bout in eight overweight (mean ± SD, BMI = 25.6 ± 1.5 kg m−2) young (21.0 ± 1.5 years) adults on resting energy expenditure (REE) measured on four consecutive mornings following each protocol. Participants performed a single one-set or three-set whole body (10 exercises, 10 repetition maximum) RT bout following the American College of Sports Medicine (ACSM) guidelines for RT. REE and respiratory exchang...

  18. Behaviour in O of the Neural Networks Training Cost

    DEFF Research Database (Denmark)

    Goutte, Cyril

    1998-01-01

    We study the behaviour in zero of the derivatives of the cost function used when training non-linear neural networks. It is shown that a fair number offirst, second and higher order derivatives vanish in zero, validating the belief that 0 is a peculiar and potentially harmful location....... These calculations arerelated to practical and theoretical aspects of neural networks training....

  19. Endoscopic skills training in a simulated clinical setting.

    Science.gov (United States)

    Fisher, Leon; Ormonde, Donald G; Riley, Richard H; Laurence, Bernard H

    2010-08-01

    We describe a simulation and scenario-based model of training in gastrointestinal endoscopic hemostasis, which combines acquisition of procedural and problem-solving skills in a close to reality simulated clinical setting. Two day courses in endoscopic hemostasis were conducted at the Clinical Training and Education Centre, the University of Western Australia, Perth, Australia. In total, 23 trainees were enrolled. The Erlangen Endo-Trainer simulator, porcine specimens of esophagus, stomach, and duodenum with a range of simulated bleeding sources, a separate catheter and a pump to simulate massive bleeding, and a full arm model with injectable veins were used. The SimMan monitor and software package were used to simulate hemodynamic parameters and electrocardiogram. Faculty members adjusted the rate of bleeding and vital parameters. The exercise was video recorded. On the first day, the group underwent simulator training in techniques of endoscopic hemostasis. On the second day, participants were scenario-based trained in full management of a "bleeding patient," which included resuscitation, sedation, endoscopy, and hemostasis, acting as leaders in teams of three. The course was evaluated by participants using a standardized questionnaire. A complex clinical setting of acute gastrointestinal bleeding was recreated with a high degree of realism. All participants reported that the simulated clinical scenario was a positive learning experience, helpful in managing complications and performing complex problem-solving tasks in a dynamic environment. Scenario and simulation-based training in endoscopic hemostasis may provide an opportunity to improve procedural skills and acquire practical experience in managing this medical emergency, which requires the ability to process, integrate, and adequately and quickly respond to complex information in unexpected conditions working as a team leader.

  20. Designing application software in wide area network settings

    Science.gov (United States)

    Makpangou, Mesaac; Birman, Ken

    1990-01-01

    Progress in methodologies for developing robust local area network software has not been matched by similar results for wide area settings. The design of application software spanning multiple local area environments is examined. For important classes of applications, simple design techniques are presented that yield fault tolerant wide area programs. An implementation of these techniques as a set of tools for use within the ISIS system is described.

  1. Minimum steering node set of complex networks and its applications to biomolecular networks.

    Science.gov (United States)

    Wu, Lin; Li, Min; Wang, Jianxin; Wu, Fang-Xiang

    2016-06-01

    Many systems of interests in practices can be represented as complex networks. For biological systems, biomolecules do not perform their functions alone but interact with each other to form so-called biomolecular networks. A system is said to be controllable if it can be steered from any initial state to any other final state in finite time. The network controllability has become essential to study the dynamics of the networks and understand the importance of individual nodes in the networks. Some interesting biological phenomena have been discovered in terms of the structural controllability of biomolecular networks. Most of current studies investigate the structural controllability of networks in notion of the minimum driver node sets (MDSs). In this study, the authors analyse the network structural controllability in notion of the minimum steering node sets (MSSs). They first develop a graph-theoretic algorithm to identify the MSS for a given network and then apply it to several biomolecular networks. Application results show that biomolecules identified in the MSSs play essential roles in corresponding biological processes. Furthermore, the application results indicate that the MSSs can reflect the network dynamics and node importance in controlling the networks better than the MDSs.

  2. Circuity analyses of HSR network and high-speed train paths in China

    Science.gov (United States)

    Zhao, Shuo; Huang, Jie; Shan, Xinghua

    2017-01-01

    Circuity, defined as the ratio of the shortest network distance to the Euclidean distance between one origin–destination (O-D) pair, can be adopted as a helpful evaluation method of indirect degrees of train paths. In this paper, the maximum circuity of the paths of operated trains is set to be the threshold value of the circuity of high-speed train paths. For the shortest paths of any node pairs, if their circuity is not higher than the threshold value, the paths can be regarded as the reasonable paths. With the consideration of a certain relative or absolute error, we cluster the reasonable paths on the basis of their inclusion relationship and the center path of each class represents a passenger transit corridor. We take the high-speed rail (HSR) network in China at the end of 2014 as an example, and obtain 51 passenger transit corridors, which are alternative sets of train paths. Furthermore, we analyze the circuity distribution of paths of all node pairs in the network. We find that the high circuity of train paths can be decreased with the construction of a high-speed railway line, which indicates that the structure of the HSR network in China tends to be more complete and the HSR network can make the Chinese railway network more efficient. PMID:28945757

  3. Circuity analyses of HSR network and high-speed train paths in China.

    Science.gov (United States)

    Hu, Xinlei; Zhao, Shuo; Shi, Feng; Huang, Jie; Shan, Xinghua

    2017-01-01

    Circuity, defined as the ratio of the shortest network distance to the Euclidean distance between one origin-destination (O-D) pair, can be adopted as a helpful evaluation method of indirect degrees of train paths. In this paper, the maximum circuity of the paths of operated trains is set to be the threshold value of the circuity of high-speed train paths. For the shortest paths of any node pairs, if their circuity is not higher than the threshold value, the paths can be regarded as the reasonable paths. With the consideration of a certain relative or absolute error, we cluster the reasonable paths on the basis of their inclusion relationship and the center path of each class represents a passenger transit corridor. We take the high-speed rail (HSR) network in China at the end of 2014 as an example, and obtain 51 passenger transit corridors, which are alternative sets of train paths. Furthermore, we analyze the circuity distribution of paths of all node pairs in the network. We find that the high circuity of train paths can be decreased with the construction of a high-speed railway line, which indicates that the structure of the HSR network in China tends to be more complete and the HSR network can make the Chinese railway network more efficient.

  4. [Training to management of violence in hospital setting].

    Science.gov (United States)

    Bataille, B; Mora, M; Blasquez, S; Moussot, P-E; Silva, S; Cocquet, P

    2013-03-01

    Evaluate the typology of violence in hospital setting, study the psychophysiological state of care givers dealing with the aggression and provide appropriate training. Single centre, observational. A first anonymous questionnaire was given to a sample of emergency and intensive care providers in Narbonne Hospital. The parameters studied included: demographics data, the Trait Anxiety Inventory test, the typology of aggressions, and the psycho-physiological state of subjects dealing with the aggression. Robert Paturel, an instructor of French Special Forces (Recherche-Assistance-Intervention-Dissuasion [RAID]), has provided training for the management of violence. A second questionnaire assessed satisfaction for proposed formation. Forty-one questionnaires were returned. The rates of verbal and physical violence touching care givers were respectively 97 % and 41 % (median of 7years [1-36] experience on the job). Eighty-five percent of care givers wanted training in psychology of conflict and 93 % wanted a formation with a self-defense aspect. The first reason of violence was drugs and alcohol abuse. The "tunnel effect" during stress was identified in 34 % of care givers, and 20 % were unaware of its nature. Twenty-one percent of care givers spontaneously adopting a safe distance of more than 1m during a conflict had been physically assaulted versus 63 % for those staying less than 1m (P=0.03). The proposed formation, including psychology of conflict and self-defense, was satisfactory to all care givers who participated (median score 9/10 [7-10]). The verbal and physical violence affecting emergency departments is a common phenomenon warranting appropriate training. The proposed formation included the comprehension of the conflict causality, self-defense and self-control. Copyright © 2013 Société française d’anesthésie et de réanimation (Sfar). Published by Elsevier SAS. All rights reserved.

  5. One-set resistance training elevates energy expenditure for 72 h similar to three sets.

    Science.gov (United States)

    Heden, Timothy; Lox, Curt; Rose, Paul; Reid, Steven; Kirk, Erik P

    2011-03-01

    To compare the effects of an acute one versus three-set full body resistance training (RT) bout in eight overweight (mean ± SD, BMI = 25.6 ± 1.5 kg m(-2)) young (21.0 ± 1.5 years) adults on resting energy expenditure (REE) measured on four consecutive mornings following each protocol. Participants performed a single one-set or three-set whole body (10 exercises, 10 repetition maximum) RT bout following the American College of Sports Medicine (ACSM) guidelines for RT. REE and respiratory exchange ratio (RER) by indirect calorimetry were measured at baseline and at 24, 48, and 72 h after the RT bout. Participants performed each protocol in randomized, counterbalanced order separated by 7 days. There was no difference between protocols for REE or RER. However, REE was significantly (p ACSM guidelines for RT and requiring only ~15 min to complete was as effective as a three-set RT bout (~35 min to complete) in elevating REE for up to 72 h post RT in overweight college males, a group at high risk of developing obesity. The one-set RT protocol may provide an attractive alternative to either aerobic exercise or multiple-set RT programs for weight management in young adults, due to the minimal time commitment and the elevation in REE post RT bout.

  6. Social network analysis in healthcare settings: a systematic scoping review.

    Science.gov (United States)

    Chambers, Duncan; Wilson, Paul; Thompson, Carl; Harden, Melissa

    2012-01-01

    Social network analysis (SNA) has been widely used across a range of disciplines but is most commonly applied to help improve the effectiveness and efficiency of decision making processes in commercial organisations. We are utilising SNA to inform the development and implementation of tailored behaviour-change interventions to improve the uptake of evidence into practice in the English National Health Service. To inform this work, we conducted a systematic scoping review to identify and evaluate the use of SNA as part of an intervention to support the implementation of change in healthcare settings. We searched ten bibliographic databases to October 2011. We also searched reference lists, hand searched selected journals and websites, and contacted experts in the field. To be eligible for the review, studies had to describe and report the results of an SNA performed with healthcare professionals (e.g. doctors, nurses, pharmacists, radiographers etc.) and others involved in their professional social networks. We included 52 completed studies, reported in 62 publications. Almost all of the studies were limited to cross sectional descriptions of networks; only one involved using the results of the SNA as part of an intervention to change practice. We found very little evidence for the potential of SNA being realised in healthcare settings. However, it seems unlikely that networks are less important in healthcare than other settings. Future research should seek to go beyond the merely descriptive to implement and evaluate SNA-based interventions.

  7. Radar Training Facility Local Area Network -

    Data.gov (United States)

    Department of Transportation — The RTF LAN system provides a progressive training environment for initial and refresher radar training qualification for new and re-hired FAA employees. Its purpose...

  8. Training strategy for convolutional neural networks in pedestrian gender classification

    Science.gov (United States)

    Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

    2017-06-01

    In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

  9. Digital intelligent booster for DCC miniature train networks

    Science.gov (United States)

    Ursu, M. P.; Condruz, D. A.

    2017-08-01

    Modern miniature trains are now driven by means of the DCC (Digital Command and Control) system, which allows the human operator or a personal computer to launch commands to each individual train or even to control different features of the same train. The digital command station encodes these commands and sends them to the trains by means of electrical pulses via the rails of the railway network. Due to the development of the miniature railway network, it may happen that the power requirement of the increasing number of digital locomotives, carriages and accessories exceeds the nominal output power of the digital command station. This digital intelligent booster relieves the digital command station from powering the entire railway network all by itself, and it automatically handles the multiple powered sections of the network. This electronic device is also able to detect and process short-circuits and overload conditions, without the intervention of the digital command station.

  10. Network Analysis of Clinical Placement of Athletic Training Students

    National Research Council Canada - National Science Library

    M G Miller; C Harvatt; K Hirsch; W R Holcomb

    2017-01-01

    An abstract of a study by Miller et al determining communication aspects using social network analysis for on-campus and off campus clinical placement sites of undergraduate athletic training students is presented...

  11. SET UP OF THE NEW AUTOMATIC HYDROMETEOROLOGICAL NETWORK IN HUNGARY

    Directory of Open Access Journals (Sweden)

    J. NAGy

    2013-03-01

    Full Text Available The Hungarian Meteorological Service (OMSZ and General Directorate of Water Management (OVF in Hungary run conventional precipitation measurement networks consisting of at least 1000 stations. OMSZ automated its synoptic and climatological network in 90’s and now more than 100 automatic stations give data every 1-10 minutes via GPRS channel. In 2007 the experts from both institutions determined the requirements of a common network. The predecessor in title of OVF is general Directorate for Water and Environment gave a project proposal in 2008 for establishment of a new hydrometeorological network based on common aims for meteorology and hydrology. The new hydrometeorological network was set up in 2012 financed by KEOP project. This network has got 141 weighing precipitation gauges, 118 temperature - humidity sensors and 25 soil moisture and soil temperature instruments. Near by Tisza-Lake two wind sensors have been installed. The network is operated by OMSZ and OVF together. OVF and its institutions maintain the stations itself and support the electricity. OMSZ operates data collection and transmission, maintaines and calibrates the sensors. Using precipitation data of enhanced network the radar precipitation field quality may be more precise, which are input of run-off model. Thereby the time allowance may be increased in flood-control events. Based on soil moisture and temperature water balance in soil may be modelled and forecast can be produced in different conditions. It is very important task in drought and inland water conditions. Considering OMSZ investment project in which new Doppler dual polarisation radar and 14 disdrometers will be installed, the precipitation estimation may be improved since 2015.

  12. Mapping, Awareness, And Virtualization Network Administrator Training Tool Virtualization Module

    Science.gov (United States)

    2016-03-01

    AND VIRTUALIZATION NETWORK ADMINISTRATOR TRAINING TOOL VIRTUALIZATION MODULE by Erik W. Berndt March 2016 Thesis Advisor: John Gibson...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE MAPPING, AWARENESS, AND VIRTUALIZATION NETWORK ADMINISTRATOR TRAINING TOOL... VIRTUALIZATION MODULE 5. FUNDING NUMBERS 6. AUTHOR(S) Erik W. Berndt 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School

  13. Recurrent neural networks training with stable bounding ellipsoid algorithm.

    Science.gov (United States)

    Yu, Wen; de Jesús Rubio, José

    2009-06-01

    Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

  14. AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET

    Energy Technology Data Exchange (ETDEWEB)

    Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step and therefore do not require ground-truthed lesion training sets which are time consuming to create, difficult to obtain, and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.

  15. The Effect of Employer Networks on Workplace Innovation and Training.

    Science.gov (United States)

    Erickson, Christopher L.; Jacoby, Sanford M.

    2003-01-01

    Multivariate analyses of data from the California Workplace Survey suggested that managers' participation in networks, especially professional and community organizations and internal networks, positively influenced the probability and intensity of adoption of high-performance work practices and training. Multiple affiliations increased the…

  16. Transfer of Training: Adding Insight through Social Network Analysis

    Science.gov (United States)

    Van den Bossche, Piet; Segers, Mien

    2013-01-01

    This article reviews studies which apply a social network perspective to examine transfer of training. The theory behind social networks focuses on the interpersonal mechanisms and social structures that exist among interacting units such as people within an organization. A premise of this perspective is that individual's behaviors and outcomes…

  17. Unsupervised pre-training for fully convolutional neural networks

    NARCIS (Netherlands)

    Wiehman, Stiaan; Kroon, Steve; Villiers, De Hendrik

    2017-01-01

    Unsupervised pre-Training of neural networks has been shown to act as a regularization technique, improving performance and reducing model variance. Recently, fully convolutional networks (FCNs) have shown state-of-The-Art results on various semantic segmentation tasks. Unfortunately, there is no

  18. Asia-Pacific Research and Training Network on Trade (ARTNET ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    The Asia-Pacific Research and Training Network on Trade (ARTNET) was established in 2004 to enhance the capacity of researchers and research institutions to deliver timely, demand-driven, trade-related research to policymakers in the region. During the first phase of support (102568), the Network produced a number of ...

  19. Educational Networking as Key Factor of Specialist Training in Universities

    Science.gov (United States)

    Safargaliev, Ernst Raisovich; Vinogradov, Vladislav Lvovich

    2015-01-01

    The paper considers the problems of science and education space and network formation between business and education. The productive form of integration between the parties is revealed. The authors address employment as an evaluation criterion for networking between university and business. Special emphasis is on active training methods as a way…

  20. Efficient VLSI Architecture for Training Radial Basis Function Networks

    Science.gov (United States)

    Fan, Zhe-Cheng; Hwang, Wen-Jyi

    2013-01-01

    This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired. PMID:23519346

  1. Accelerating deep neural network training with inconsistent stochastic gradient descent.

    Science.gov (United States)

    Wang, Linnan; Yang, Yi; Min, Renqiang; Chakradhar, Srimat

    2017-09-01

    Stochastic Gradient Descent (SGD) updates Convolutional Neural Network (CNN) with a noisy gradient computed from a random batch, and each batch evenly updates the network once in an epoch. This model applies the same training effort to each batch, but it overlooks the fact that the gradient variance, induced by Sampling Bias and Intrinsic Image Difference, renders different training dynamics on batches. In this paper, we develop a new training strategy for SGD, referred to as Inconsistent Stochastic Gradient Descent (ISGD) to address this problem. The core concept of ISGD is the inconsistent training, which dynamically adjusts the training effort w.r.t the loss. ISGD models the training as a stochastic process that gradually reduces down the mean of batch's loss, and it utilizes a dynamic upper control limit to identify a large loss batch on the fly. ISGD stays on the identified batch to accelerate the training with additional gradient updates, and it also has a constraint to penalize drastic parameter changes. ISGD is straightforward, computationally efficient and without requiring auxiliary memories. A series of empirical evaluations on real world datasets and networks demonstrate the promising performance of inconsistent training. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties.

    Science.gov (United States)

    Browning, Nicholas J; Ramakrishnan, Raghunathan; von Lilienfeld, O Anatole; Roethlisberger, Ursula

    2017-04-06

    The training of molecular models of quantum mechanical properties based on statistical machine learning requires large data sets which exemplify the map from chemical structure to molecular property. Intelligent a priori selection of training examples is often difficult or impossible to achieve, as prior knowledge may be unavailable. Ordinarily representative selection of training molecules from such data sets is achieved through random sampling. We use genetic algorithms for the optimization of training set composition consisting of tens of thousands of small organic molecules. The resulting machine learning models are considerably more accurate: in the limit of small training sets, mean absolute errors for out-of-sample predictions are reduced by up to ∼75%. We discuss and present optimized training sets consisting of 10 molecular classes for all molecular properties studied. We show that these classes can be used to design improved training sets for the generation of machine learning models of the same properties in similar but unrelated molecular sets.

  3. Off-training-set error for the Gibbs and the Bayes optimal generalizers

    Energy Technology Data Exchange (ETDEWEB)

    Grossman, T.; Knill, E. [Los Alamos National Lab., NM (United States); Wolpert, D. [The Santa Fe Institute, Santa Fe, NM (United States)

    1995-01-03

    In this paper we analyze the average off-training-set behavior of the Bayes-optimal and Gibbs learning algorithms. We do this by exploiting the concept of refinement, which concerns the relationship between probability distributions. For non-uniform sampling distributions the expected off training-set error for both learning algorithms can rise with, training set size. However we show in this paper that for uniform sampling and either algorithm, the expected error is a non-increasing function of training set size. For uniform sampling distributions, we also characterize the priors for which the expected error of the Bayes-optimal algorithm stays constant. In addition we show that when the target function is fixed, expected off-training-set error can increase with training set size if and only if the expected error averaged over all targets decreases with training set size. Our results hold for arbitrary noise and arbitrary loss functions.

  4. Identifying a set of influential spreaders in complex networks.

    Science.gov (United States)

    Zhang, Jian-Xiong; Chen, Duan-Bing; Dong, Qiang; Zhao, Zhi-Dan

    2016-06-14

    Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What's more, VoteRank has superior computational efficiency.

  5. Routing trains through railway junctions: A new set-packing approach

    DEFF Research Database (Denmark)

    Lusby, Richard Martin; Larsen, Jesper; Ryan, David

    2011-01-01

    The problem of routing trains through railway junctions is an integral part of railway operations. Large junctions are highly interconnected networks of track where multiple railway lines merge, intersect, and split. The number of possible routings makes this a very complicated problem. We show how...... the problem can be formulated as a set-packing model with a resource-based constraint system. We prove that this formulation is tighter than the conventional node-packing model, and develop a branch-and-price algorithm that exploits the structure of the set-packing model. A discussion of the variable...... generation phase, as well as a pricing routine in which these variables are represented by tree structures, is also described. Computational experiments on 25 random timetables show this to be an efficient approach. © 2011 INFORMS....

  6. Physicists set new record for network data transfer

    CERN Multimedia

    2006-01-01

    "An internatinal team of physicists, computer scientists, and network engineers led by the California Institute of Technology, CERN and the University of Michigan and partners at the University of Florida and Vanderbilt, as well as participants from Brazil (Rio de Janeiro State University, UERJ, and the State Universities of Sao Paulo, USP and UNESP) and Korea (Kyungpook National University, KISTI) joined forces to set new records for sustained data transfer between storage systems during the SuperComputing 2006 (SC06) Bandwidth Challenge (BWC)." (2 pages)

  7. The Murrumbidgee soil moisture monitoring network data set

    Science.gov (United States)

    Smith, A. B.; Walker, J. P.; Western, A. W.; Young, R. I.; Ellett, K. M.; Pipunic, R. C.; Grayson, R. B.; Siriwardena, L.; Chiew, F. H. S.; Richter, H.

    2012-07-01

    This paper describes a soil moisture data set from the 82,000 km2 Murrumbidgee River Catchment in southern New South Wales, Australia. Data have been archived from the Murrumbidgee Soil Moisture Monitoring Network (MSMMN) since its inception in September 2001. The Murrumbidgee Catchment represents a range of conditions typical of much of temperate Australia, with climate ranging from semiarid to humid and land use including dry land and irrigated agriculture, remnant native vegetation, and urban areas. There are a total of 38 soil moisture-monitoring sites across the Murrumbidgee Catchment, with a concentration of sites in three subareas. The data set is composed of 0-5 (or 0-8), 0-30, 30-60, and 60-90 cm average soil moisture, soil temperature, precipitation, and other land surface model forcing at all sites, together with other ancillary data. These data are available on the World Wide Web at http://www.oznet.org.au.

  8. Role of physical and mental training in brain network configuration.

    Science.gov (United States)

    Foster, Philip P

    2015-01-01

    It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of "energy cost-driven small-world network disorder" with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be

  9. Brief Mental Training Reorganizes Large-Scale Brain Networks.

    Science.gov (United States)

    Tang, Yi-Yuan; Tang, Yan; Tang, Rongxiang; Lewis-Peacock, Jarrod A

    2017-01-01

    Emerging evidences have shown that one form of mental training-mindfulness meditation, can improve attention, emotion regulation and cognitive performance through changing brain activity and structural connectivity. However, whether and how the short-term mindfulness meditation alters large-scale brain networks are not well understood. Here, we applied a novel data-driven technique, the multivariate pattern analysis (MVPA) to resting-state fMRI (rsfMRI) data to identify changes in brain activity patterns and assess the neural mechanisms induced by a brief mindfulness training-integrative body-mind training (IBMT), which was previously reported in our series of randomized studies. Whole brain rsfMRI was performed on an undergraduate group who received 2 weeks of IBMT with 30 min per session (5 h training in total). Classifiers were trained on measures of functional connectivity in this fMRI data, and they were able to reliably differentiate (with 72% accuracy) patterns of connectivity from before vs. after the IBMT training. After training, an increase in positive functional connections (60 connections) were detected, primarily involving bilateral superior/middle occipital gyrus, bilateral frontale operculum, bilateral superior temporal gyrus, right superior temporal pole, bilateral insula, caudate and cerebellum. These results suggest that brief mental training alters the functional connectivity of large-scale brain networks at rest that may involve a portion of the neural circuitry supporting attention, cognitive and affective processing, awareness and sensory integration and reward processing.

  10. Reformulated radial basis neural networks trained by gradient descent.

    Science.gov (United States)

    Karayiannis, N B

    1999-01-01

    This paper presents an axiomatic approach for constructing radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, including those employing Gaussian RBF's. The form of the RBF's is determined by a generator function. New RBF models can be developed according to the proposed approach by selecting generator functions other than exponential ones, which lead to Gaussian RBF's. This paper also proposes a supervised learning algorithm based on gradient descent for training reformulated RBF neural networks constructed using the proposed approach. A sensitivity analysis of the proposed algorithm relates the properties of RBF's with the convergence of gradient descent learning. Experiments involving a variety of reformulated RBF networks generated by linear and exponential generator functions indicate that gradient descent learning is simple, easily implementable, and produces RBF networks that perform considerably better than conventional RBF models trained by existing algorithms.

  11. Virtual setting for training in interpreting mammography images

    Science.gov (United States)

    Pezzuol, J. L.; Abreu, F. D. L.; Silva, S. M.; Tendolini, A.; Bissaco, M. A. Se; Rodrigues, S. C. M.

    2017-03-01

    This work presents a web system for the training of students or residents (users) interested in the detection of breast density in mammography images. The system consists of a breast imaging database with breast density types classified and demarcated by the specialist (tutor) or online database. The planning was based on ISO / IEC 12207. Through the browser (desktop or notebook), the user will visualize the breast images and in them will realize the markings of the density region and even classify them per the BI-RADS protocol. After marking, this will be compared to the gold standard already existing in the image base, and then the system will inform if the area demarcation has been set or not. The shape of this marking is similar to the paint brush. The evaluation was based on ISO / IEC 1926 or 25010: 2011 by 3 software development specialists and 3 in mammary radiology, evaluating usability, configuration, performance and System interface through the Likert scale-based questionnaire. Where they have totally agreed on usability, configuration, performance and partially on the interface. And as a good thing: the system is able to be accessed anywhere and at any time, the hit or error response is in real time, it can be used in the educational area, the limit of the amount of images will depend on the size of the computer memory, At the end the system sends the results achieved by e-mail to the user, reproduction of the system on any type of screen, complementation of the system with other types of breast structures. Negative points are the need for internet.

  12. Aplication of artificial neural network model in aviation specialist training

    Directory of Open Access Journals (Sweden)

    Висиль Миколайович Казак

    2016-02-01

    Full Text Available This paper reviews the application of artificial neural network (ANN model in aviation specialist training. The ANN model is based on the dependence of residual knowledge of subjects of study on their individual abilities. The residual knowledge is the skills acquired by the subject before he is going for an occupation.  The presented ANN model gives the possibility to predict the level of professional training of the specialists with high accuracy

  13. Role of physical and mental training in brain network configuration

    Directory of Open Access Journals (Sweden)

    Philip P. Foster

    2015-06-01

    Full Text Available Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of energy cost-driven small-world network disorder as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement produces a reconfiguration of brain networks into greater small-worldness. Creation of synaptic connections in a macro-network, and, at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF. The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.. However, mental training, meditation or virtual reality (films, games require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com. molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g. amyotrophic lateral sclerosis, traumatism also achieve successful cognitive enhancement albeit they may only elicit mental practice

  14. Mining big data sets of plankton images: a zero-shot learning approach to retrieve labels without training data

    Science.gov (United States)

    Orenstein, E. C.; Morgado, P. M.; Peacock, E.; Sosik, H. M.; Jaffe, J. S.

    2016-02-01

    Technological advances in instrumentation and computing have allowed oceanographers to develop imaging systems capable of collecting extremely large data sets. With the advent of in situ plankton imaging systems, scientists must now commonly deal with "big data" sets containing tens of millions of samples spanning hundreds of classes, making manual classification untenable. Automated annotation methods are now considered to be the bottleneck between collection and interpretation. Typically, such classifiers learn to approximate a function that predicts a predefined set of classes for which a considerable amount of labeled training data is available. The requirement that the training data span all the classes of concern is problematic for plankton imaging systems since they sample such diverse, rapidly changing populations. These data sets may contain relatively rare, sparsely distributed, taxa that will not have associated training data; a classifier trained on a limited set of classes will miss these samples. The computer vision community, leveraging advances in Convolutional Neural Networks (CNNs), has recently attempted to tackle such problems using "zero-shot" object categorization methods. Under a zero-shot framework, a classifier is trained to map samples onto a set of attributes rather than a class label. These attributes can include visual and non-visual information such as what an organism is made out of, where it is distributed globally, or how it reproduces. A second stage classifier is then used to extrapolate a class. In this work, we demonstrate a zero-shot classifier, implemented with a CNN, to retrieve out-of-training-set labels from images. This method is applied to data from two continuously imaging, moored instruments: the Scripps Plankton Camera System (SPCS) and the Imaging FlowCytobot (IFCB). Results from simulated deployment scenarios indicate zero-shot classifiers could be successful at recovering samples of rare taxa in image sets. This

  15. SKYNET: an efficient and robust neural network training tool for machine learning in astronomy

    Science.gov (United States)

    Graff, Philip; Feroz, Farhan; Hobson, Michael P.; Lasenby, Anthony

    2014-06-01

    We present the first public release of our generic neural network training algorithm, called SKYNET. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for use in a wide range of supervised and unsupervised learning applications, such as regression, classification, density estimation, clustering and dimensionality reduction. SKYNET uses a `pre-training' method to obtain a set of network parameters that has empirically been shown to be close to a good solution, followed by further optimization using a regularized variant of Newton's method, where the level of regularization is determined and adjusted automatically; the latter uses second-order derivative information to improve convergence, but without the need to evaluate or store the full Hessian matrix, by using a fast approximate method to calculate Hessian-vector products. This combination of methods allows for the training of complicated networks that are difficult to optimize using standard backpropagation techniques. SKYNET employs convergence criteria that naturally prevent overfitting, and also includes a fast algorithm for estimating the accuracy of network outputs. The utility and flexibility of SKYNET are demonstrated by application to a number of toy problems, and to astronomical problems focusing on the recovery of structure from blurred and noisy images, the identification of gamma-ray bursters, and the compression and denoising of galaxy images. The SKYNET software, which is implemented in standard ANSI C and fully parallelized using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.

  16. The Effect of Training Data Set Composition on the Performance of a Neural Image Caption Generator

    Science.gov (United States)

    2017-09-01

    ARL-TR-8124 ● SEP 2017 US Army Research Laboratory The Effect of Training Data Set Composition on the Performance of a Neural...Laboratory The Effect of Training Data Set Composition on the Performance of a Neural Image Caption Generator by Abigail Wilson Montgomery Blair...REPORT TYPE Technical Report 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE The Effect of Training Data Set Composition on the Performance of a

  17. Producing a Set of Models for the Iron Homeostasis Network

    Directory of Open Access Journals (Sweden)

    Nicolas Mobilia

    2013-08-01

    Full Text Available This paper presents a method for modeling biological systems which combines formal techniques on intervals, numerical simulations and satisfaction of Signal Temporal Logic (STL formulas. The main modeling challenge addressed by this approach is the large uncertainty in the values of the parameters due to the experimental difficulties of getting accurate biological data. This method considers intervals for each parameter and a formal description of the expected behavior of the model. In a first step, it produces reduced intervals of possible parameter values. Then by performing a systematic search in these intervals, it defines sets of parameter values used in the next step. This procedure aims at finding a sub-space where the model robustly behaves as expected. We apply this method to the modeling of the cellular iron homeostasis network in erythroid progenitors. The produced model describes explicitly the regulation mechanism which acts at the translational level.

  18. Experience in Strategic Networking to Promote Palliative Care in a Clinical Academic Setting in India

    Science.gov (United States)

    Nair, Shoba; Tarey, SD; Barathi, B; Mary, Thiophin Regina; Mathew, Lovely; Daniel, Sudha Pauline

    2016-01-01

    Background: Palliative care in low and middle-income countries is a new discipline, responding to a greater patient need, than in high-income countries. By its very nature, palliative as a specialty has to network with other specialties to provide quality care to patients. For any medical discipline to grow as a specialty, it should be well established in the teaching medical institutions of that country. Data show that palliative care is more likely to establish and grow in an academic health care institution. It is a necessity that multiple networking strategies are adopted to reach this goal. Objectives: (1) To describe a strategic approach to palliative care service development and integration into clinical academic setting. (2) To present the change in metrics to evaluate progress. Design and Setting: This is a descriptive study wherein, the different strategies that are adopted by the Department of Palliative Medicine for networking in an academic health care institution and outside the institution are scrutinized. Measurement: The impact of this networking was assessed, one, at the level of academics and the other, at the level of service. The number of people who attended various training programs conducted by the department and the number of patients who availed palliative care service over the years were assessed. Results: Ten different strategies were identified that helped with networking of palliative care in the institution. During this time, the referrals to the department increased both for malignant diseases (52–395) and nonmalignant diseases (5–353) from 2000 to 2013. The academic sessions conducted by the department for undergraduates also saw an increase in the number of hours from 6 to 12, apart from the increase in a number of courses conducted by the department for doctors and nurses. Conclusion: Networking is an essential strategy for the establishment of a relatively new medical discipline like palliative care in a developing and

  19. Game-Based Training Effectiveness Evaluation in an Operational Setting

    Science.gov (United States)

    2010-09-01

    814-828. Tannenbaum, S. I., & Yukl , G. (1992). Training and development in work organizations . In M. R. Rosenweig, & L. W. Porter (Eds.), Annual...7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Aptima, Inc. 12 Gill Street, Suite 1400 Woburn, MA 01801 8. PERFORMING ORGANIZATION REPORT...outcomes. Both the level of unit preparation for the training and the level of unit leadership involvement during exercise management positively

  20. PARTNER: A Marie Curie Initial Training Network for hadron therapy

    CERN Multimedia

    CERN BULLETIN; Nathalie Hospital; Manuela Cirilli

    2011-01-01

    PARTNER is a 4-year Marie Curie Training project funded by the European Commission with 5.6 million Euros aimed at the creation of the next generation of experts. Ten academic institutes and research centres and two leading companies are participating in PARTNER, that is coordinated by CERN, forming a unique multidisciplinary and multinational European network.

  1. Bayesian model ensembling using meta-trained recurrent neural networks

    NARCIS (Netherlands)

    Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.

    2017-01-01

    In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian

  2. Asia-Pacific Research and Training Network on Trade (ARTNET ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Small and Medium Enterprises (SME) Adjustments to Information Technology (IT) in Trade Facilitation: The South Korean Experience. Documents. Asia - Pacific Research and Training Network on Trade (ARTNeT) newsletter, volume 6, issue 1 / October 2009 - January 2010. Documents. Asia - Pacific Research and ...

  3. MO-DE-BRA-04: The CREATE Medical Physics Research Training Network: Training of New Generation Innovators

    Energy Technology Data Exchange (ETDEWEB)

    Seuntjens, J; Collins, L; Devic, S; El Naqa, I; Nadeau, J; Reader, A [McGill University, Montreal, QC (Canada); Beaulieu, L; Despres, P [Centre Hospitalier Univ de Quebec, Quebec, QC (Canada); Pike, B [University of Calgary, Calgary, Alberta (Canada)

    2015-06-15

    Purpose: Over the past century, physicists have played a major role in transforming scientific discovery into everyday clinical applications. However, with the increasingly stringent requirements to regulate medical physics as a health profession, the role of physicists as scientists and innovators has become at serious risk of erosion. These challenges trigger the need for a new, revolutionized training program at the graduate level that respects scientific rigor, attention for medical physics-relevant developments in basic sciences, innovation and entrepreneurship. Methods: A grant proposal was funded by the Collaborative REsearch and Training Experience program (CREATE) of the Natural Sciences and Engineering Research Council (NSERC) of Canada. This enabled the creation of the Medical Physics Research Training Network (MPRTN) around two CAMPEP-accredited medical physics programs. Members of the network consist of medical device companies, government (research and regulatory) and academia. The MPRTN/CREATE program proposes a curriculum with three main themes: (1) radiation physics, (2) imaging & image processing and (3) radiation response, outcomes and modeling. Results: The MPRTN was created mid 2013 (mprtn.com) and features (1) four new basic Ph.D. courses; (2) industry participation in research projects; (3) formal job-readiness training with involvement of guest faculty from academia, government and industry. MPRTN activities since 2013 include 22 conferences; 7 workshops and 4 exchange travels. Three patents were filed or issued, nine awards/best papers were won. Fifteen journal publications were accepted/published, 102 conference abstracts. There are now 13 industry partners. Conclusion: A medical physics research training network has been set up with the goal to harness graduate student’s job-readiness for industry, government and academia in addition to the conventional clinical role. Two years after inception, significant successes have been booked

  4. Mnemonic Training Reshapes Brain Networks to Support Superior Memory.

    Science.gov (United States)

    Dresler, Martin; Shirer, William R; Konrad, Boris N; Müller, Nils C J; Wagner, Isabella C; Fernández, Guillén; Czisch, Michael; Greicius, Michael D

    2017-03-08

    Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world's most successful memory athletes and matched controls with fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that, in a group of naive controls, functional connectivity changes induced by 6 weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain's functional network organization to enable superior memory performance. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.

    Science.gov (United States)

    Kalderstam, Jonas; Edén, Patrik; Bendahl, Pär-Ola; Strand, Carina; Fernö, Mårten; Ohlsson, Mattias

    2013-06-01

    The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). We have found empirical evidence that ensembles of ANN models can be

  6. THE ROLE OF NETWORKS IN PERMANENT TRAINING

    Directory of Open Access Journals (Sweden)

    Geanina Colan

    2016-03-01

    Full Text Available In order to decide on an education necesity one has to answer the question “What and who do we organize a certain educational process or system for and why do we organize it in a certain way and not otherwize?”. In order to decide on an education necessity one must also answer the question “What social problems does the educational process or system we devised solve?”. The problem of lifelong in-service training is important for supporting the workforce change, being the main instrument it uses in order to adjust to the new requirements, this enabling its nobility among differerent fields of activity.

  7. European training network on full-parallax imaging (Conference Presentation)

    Science.gov (United States)

    Martínez-Corral, Manuel; Saavedra, Genaro

    2017-05-01

    Current displays are far from truly recreating visual reality. This requires a full-parallax display that can reproduce radiance field emanated from the real scenes. The develop-ment of such technology will require a new generation of researchers trained both in the physics, and in the biology of human vision. The European Training Network on Full-Parallax Imaging (ETN-FPI) aims at developing this new generation. Under H2020 funding ETN-FPI brings together 8 beneficiaries and 8 partner organizations from five EU countries with the aim of training 15 talented pre-doctoral students to become future research leaders in this area. In this contribution we will explain the main objectives of the network, and specifically the advances obtained at the University of Valencia.

  8. Special features in managing European Training Networks (ETN)

    Science.gov (United States)

    Henkel, Daniela; Eisenhauer, Anton; Drossou-Berendes, Alexandra

    2016-04-01

    The Marie Skłodowska-Curie European Training Networks (ETN) within Horizon 2020, the EU Framework Programme for Research and Innovation, aim to train a new generation of creative and innovative early-stage researchers with focus on both scientific excellence and researchers' career development extending the traditional academic research training, and providing researchers with tools to develop scientific expertise and transferable skills needed to establish career perspectives in academia as well as non-academia. This profile is different from what we know from "typical" collaborative projects, and project consortia face specific challenges with regard to international recruitment, joint organization of network activities, financial regulations, etc. The poster will give an overview of the main ETN features emphasizing special requirements and needs, and identifying main challenges, which may rise.

  9. Parallel Evolutionary Optimization for Neuromorphic Network Training

    Energy Technology Data Exchange (ETDEWEB)

    Schuman, Catherine D [ORNL; Disney, Adam [University of Tennessee (UT); Singh, Susheela [North Carolina State University (NCSU), Raleigh; Bruer, Grant [University of Tennessee (UT); Mitchell, John Parker [University of Tennessee (UT); Klibisz, Aleksander [University of Tennessee (UT); Plank, James [University of Tennessee (UT)

    2016-01-01

    One of the key impediments to the success of current neuromorphic computing architectures is the issue of how best to program them. Evolutionary optimization (EO) is one promising programming technique; in particular, its wide applicability makes it especially attractive for neuromorphic architectures, which can have many different characteristics. In this paper, we explore different facets of EO on a spiking neuromorphic computing model called DANNA. We focus on the performance of EO in the design of our DANNA simulator, and on how to structure EO on both multicore and massively parallel computing systems. We evaluate how our parallel methods impact the performance of EO on Titan, the U.S.'s largest open science supercomputer, and BOB, a Beowulf-style cluster of Raspberry Pi's. We also focus on how to improve the EO by evaluating commonality in higher performing neural networks, and present the result of a study that evaluates the EO performed by Titan.

  10. Neural network training as a dissipative process.

    Science.gov (United States)

    Gori, Marco; Maggini, Marco; Rossi, Alessandro

    2016-09-01

    This paper analyzes the practical issues and reports some results on a theory in which learning is modeled as a continuous temporal process driven by laws describing the interactions of intelligent agents with their own environment. The classic regularization framework is paired with the idea of temporal manifolds by introducing the principle of least cognitive action, which is inspired by the related principle of mechanics. The introduction of the counterparts of the kinetic and potential energy leads to an interpretation of learning as a dissipative process. As an example, we apply the theory to supervised learning in neural networks and show that the corresponding Euler-Lagrange differential equations can be connected to the classic gradient descent algorithm on the supervised pairs. We give preliminary experiments to confirm the soundness of the theory. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

    Science.gov (United States)

    Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

    2016-03-01

    Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate

  12. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm.

    Science.gov (United States)

    Wu, Haizhou; Zhou, Yongquan; Luo, Qifang; Basset, Mohamed Abdel

    2016-01-01

    Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

  13. Training Platoon Leader Adaptive Thinking Skills in a Classroom Setting

    Science.gov (United States)

    2011-06-01

    constraints and projected student throughput patterns. Selected findings from this research were presented at the 26th Annual Society for Industrial and...2006). Videogame -based training success: The impact of trainee characteristics - Year 2 (Technical Report 1188). Arlington, VA: U. S

  14. Electronic collaboration in dermatology resident training through social networking.

    Science.gov (United States)

    Meeks, Natalie M; McGuire, April L; Carroll, Bryan T

    2017-04-01

    The use of online educational resources and professional social networking sites is increasing. The field of dermatology is currently under-utilizing online social networking as a means of professional collaboration and sharing of training materials. In this study, we sought to assess the current structure of and satisfaction with dermatology resident education and gauge interest for a professional social networking site for educational collaboration. Two surveys-one for residents and one for faculty-were electronically distributed via the American Society for Dermatologic Surgery and Association of Professors of Dermatology (APD) listserves. The surveys confirmed that there is interest among dermatology residents and faculty in a dermatology professional networking site with the goal to enhance educational collaboration.

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

    Science.gov (United States)

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

    2016-05-01

    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 central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

  16. Experience in Strategic Networking to Promote Palliative Care in a Clinical Academic Setting in India.

    Science.gov (United States)

    Nair, Shoba; Tarey, S D; Barathi, B; Mary, Thiophin Regina; Mathew, Lovely; Daniel, Sudha Pauline

    2016-01-01

    Palliative care in low and middle-income countries is a new discipline, responding to a greater patient need, than in high-income countries. By its very nature, palliative as a specialty has to network with other specialties to provide quality care to patients. For any medical discipline to grow as a specialty, it should be well established in the teaching medical institutions of that country. Data show that palliative care is more likely to establish and grow in an academic health care institution. It is a necessity that multiple networking strategies are adopted to reach this goal. (1) To describe a strategic approach to palliative care service development and integration into clinical academic setting. (2) To present the change in metrics to evaluate progress. This is a descriptive study wherein, the different strategies that are adopted by the Department of Palliative Medicine for networking in an academic health care institution and outside the institution are scrutinized. The impact of this networking was assessed, one, at the level of academics and the other, at the level of service. The number of people who attended various training programs conducted by the department and the number of patients who availed palliative care service over the years were assessed. Ten different strategies were identified that helped with networking of palliative care in the institution. During this time, the referrals to the department increased both for malignant diseases (52-395) and nonmalignant diseases (5-353) from 2000 to 2013. The academic sessions conducted by the department for undergraduates also saw an increase in the number of hours from 6 to 12, apart from the increase in a number of courses conducted by the department for doctors and nurses. Networking is an essential strategy for the establishment of a relatively new medical discipline like palliative care in a developing and populous country like India, where the service is disproportionate to the demands.

  17. Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers

    DEFF Research Database (Denmark)

    Schneider, Maria V.; Walter, Peter; Blatter, Marie-Claude

    2012-01-01

    to the development of ‘high-throughput biology’, the need for training in the field of bioinformatics, in particular, is seeing a resurgence: it has been defined as a key priority by many Institutions and research programmes and is now an important component of many grant proposals. Nevertheless, when it comes...... and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review...

  18. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  19. Implementation and Outcomes of a Collaborative Multi-Center Network Aimed at Web-Based Cognitive Training – COGWEB Network

    Science.gov (United States)

    Pais, Joana; Ruano, Luis; Mateus, Cátia; Colunas, Márcio; Alves, Ivânia; Barreto, Rui; Conde, Eduardo; Sousa, Andreia; Araújo, Isabel; Bento, Virgílio; Coutinho, Paula; Rocha, Nelson

    2014-01-01

    Background Cognitive care for the most prevalent neurologic and psychiatric conditions will only improve through the implementation of new sustainable approaches. Innovative cognitive training methodologies and collaborative professional networks are necessary evolutions in the mental health sector. Objective The objective of the study was to describe the implementation process and early outcomes of a nationwide multi-organizational network supported on a Web-based cognitive training system (COGWEB). Methods The setting for network implementation was the Portuguese mental health system and the hospital-, academic-, community-based institutions and professionals providing cognitive training. The network started in August 2012, with 16 centers, and was monitored until September 2013 (inclusions were open). After onsite training, all were allowed to use COGWEB in their clinical or research activities. For supervision and maintenance were implemented newsletters, questionnaires, visits and webinars. The following outcomes were prospectively measured: (1) number, (2) type, (3) time to start, and (4) activity state of centers; age, gender, level of education, and medical diagnosis of patients enrolled. Results The network included 68 professionals from 41 centers, (33/41) 80% clinical, (8/41) 19% nonclinical. A total of 298 patients received cognitive training; 45.3% (n=135) female, mean age 54.4 years (SD 18.7), mean educational level 9.8 years (SD 4.8). The number enrolled each month increased significantly (r=0.6; P=.031). At 12 months, 205 remained on treatment. The major causes of cognitive impairment were: (1) neurodegenerative (115/298, 38.6%), (2) structural brain lesions (63/298, 21.1%), (3) autoimmune (40/298, 13.4%), (4) schizophrenia (30/298, 10.1%), and (5) others (50/298, 16.8%). The comparison of the patient profiles, promoter versus all other clinical centers, showed significant increases in the diversity of causes and spectrums of ages and education

  20. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    Directory of Open Access Journals (Sweden)

    H Francis Song

    2016-02-01

    Full Text Available The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle, which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural

  1. Permanent Set of Cross-Linking Networks: Comparison of Theory with Molecular Dynamics Simulations

    DEFF Research Database (Denmark)

    Rottach, Dana R.; Curro, John G.; Budzien, Joanne

    2006-01-01

    The permanent set of cross-linking networks is studied by molecular dynamics. The uniaxial stress for a bead-spring polymer network is investigated as a function of strain and cross-link density history, where cross-links are introduced in unstrained and strained networks. The permanent set...... is found from the strain of the network after it returns to the state-of-ease where the stress is zero. The permanent set simulations are compared with theory using the independent network hypothesis, together with the various theoretical rubber elasticity theories: affine, phantom, constrained junction...

  2. Deconstructing myths, building alliances: a networking model to enhance tobacco control in hospital mental health settings

    Directory of Open Access Journals (Sweden)

    Montse Ballbè

    2016-09-01

    Full Text Available Life expectancy for people with severe mental disorders is up to 25 years less in comparison to the general population, mainly due to diseases caused or worsened by smoking. However, smoking is usually a neglected issue in mental healthcare settings. The aim of this article is to describe a strategy to improve tobacco control in the hospital mental healthcare services of Catalonia (Spain. To bridge this gap, the Catalan Network of Smoke-free Hospitals launched a nationwide bottom-up strategy in Catalonia in 2007. The strategy relied on the creation of a working group of key professionals from various hospitals —the early adopters— based on Rogers’ theory of the Diffusion of Innovations. In 2016, the working group is composed of professionals from 17 hospitals (70.8% of all hospitals in the region with mental health inpatient units. Since 2007, tobacco control has improved in different areas such as increasing mental health professionals’ awareness of smoking, training professionals on smoking cessation interventions and achieving good compliance with the national smoking ban. The working group has produced and disseminated various materials, including clinical practice and best practice guidelines, implemented smoking cessation programmes and organised seminars and training sessions on smoking cessation measures in patients with mental illnesses. The next challenge is to ensure effective follow-up for smoking cessation after discharge. While some areas of tobacco control within these services still require significant improvement, the aforementioned initiative promotes successful tobacco control in these settings.

  3. Mesoscopic structures reveal the network between the layers of multiplex data sets

    Science.gov (United States)

    Iacovacci, Jacopo; Wu, Zhihao; Bianconi, Ginestra

    2015-10-01

    Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

  4. Sufficient Condition for the Existence of the Compact Set in the RBF Neural Network Control.

    Science.gov (United States)

    Zhu, Jiaming; Cao, Zhiqiang; Zhang, Tianping; Yang, Yuequan; Yi, Yang

    2017-06-20

    In this brief, sufficient conditions are proposed for the existence of the compact sets in the neural network controls. First, we point out that the existence of the compact set in a classical neural network control scheme is unsolved and its result is incomplete. Next, as a simple case, we derive the sufficient condition of the existence of the compact set for the neural network control of first-order systems. Finally, we propose the sufficient condition of the existence of the compact set for the neural-network-based backstepping control of high-order nonlinear systems. The theoretic result is illustrated through a simulation example.

  5. STACCATO: a novel solution to supernova photometric classification with biased training sets

    Science.gov (United States)

    Revsbech, E. A.; Trotta, R.; van Dyk, D. A.

    2018-01-01

    We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. - a diffusion map combined with a random forest classifier - to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the 'gold standard' of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe.

  6. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

  7. Polar narcosis: Designing a suitable training set for QSAR studies.

    Science.gov (United States)

    Ramos, E U; Vaes, W H; Verhaar, H J; Hermens, J L

    1997-01-01

    Substituted phenols, anilines, pyridines and mononitrobenzenes can be classified as polar narcotics. These chemicals differ from non-polar narcotic compounds not only in their toxic potency (normalized by log K(ow)), but also in their Fish Acute Toxicity Syndrome profiles, together suggesting a different mode of action. For 97 polar narcotics, which are not ionized under physiological conditions, 11 physico-chemical and quantum-chemical descriptors were calculated. Using principal component analysis, 91% of the total variance in this descriptor space could be explained by three principal components which were subsequently used as factors in a statistical design. Eleven compounds were selected based on a two-level full factorial design including three compounds near the center of the chemical domain (a 2(3)+3 design). QSARs were developed for both the design set and the whole set of 63 polar narcotics for which guppy and/or fathead minnow data were available in the literature. Both QSARs, based on partial least squares regression (3 latent variables), resulted in good models (R(2)=0.96 and Q(2)=0.82; R(2)=0.86 and Q(2)=0.83 respectively) and provided similar pseudo-regression coefficients. In addition, the model based on the design chemicals was able to predict the toxicity of the 63 compounds (R(2) =0.85). Models show that acute fish toxicity is determined by hydrophobicity, HOMO-LUMO energy gap and hydrogen-bond acceptor capacity.

  8. Communication skills training in dementia care: a systematic review of effectiveness, training content, and didactic methods in different care settings.

    Science.gov (United States)

    Eggenberger, Eva; Heimerl, Katharina; Bennett, Michael I

    2013-03-01

    Caring for and caring about people with dementia require specific communication skills. Healthcare professionals and family caregivers usually receive little training to enable them to meet the communicative needs of people with dementia. This review identifies existent interventions to enhance communication in dementia care in various care settings. We searched MEDLINE, AMED, EMBASE, PsychINFO, CINAHL, The Cochrane Library, Gerolit, and Web of Science for scientific articles reporting interventions in both English and German. An intervention was defined as communication skills training by means of face-to-face interaction with the aim of improving basic communicative skills. Both professional and family caregivers were included. The effectiveness of such training was analyzed. Different types of training were defined. Didactic methods, training content, and additional organizational features were qualitatively examined. This review included 12 trials totaling 831 persons with dementia, 519 professional caregivers, and 162 family caregivers. Most studies were carried out in the USA, the UK, and Germany. Eight studies took place in nursing homes; four studies were located in a home-care setting. No studies could be found in an acute-care setting. We provide a list of basic communicative principles for good communication in dementia care. Didactic methods included lectures, hands-on training, group discussions, and role-play. This review shows that communication skills training in dementia care significantly improves the quality of life and wellbeing of people with dementia and increases positive interactions in various care settings. Communication skills training shows significant impact on professional and family caregivers' communication skills, competencies, and knowledge. Additional organizational features improve the sustainability of communication interventions.

  9. Train Stop Scheduling in a High-Speed Rail Network by Utilizing a Two-Stage Approach

    Directory of Open Access Journals (Sweden)

    Huiling Fu

    2012-01-01

    Full Text Available Among the most commonly used methods of scheduling train stops are practical experience and various “one-step” optimal models. These methods face problems of direct transferability and computational complexity when considering a large-scale high-speed rail (HSR network such as the one in China. This paper introduces a two-stage approach for train stop scheduling with a goal of efficiently organizing passenger traffic into a rational train stop pattern combination while retaining features of regularity, connectivity, and rapidity (RCR. Based on a three-level station classification definition, a mixed integer programming model and a train operating tactics descriptive model along with the computing algorithm are developed and presented for the two stages. A real-world numerical example is presented using the Chinese HSR network as the setting. The performance of the train stop schedule and the applicability of the proposed approach are evaluated from the perspective of maintaining RCR.

  10. An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning

    Directory of Open Access Journals (Sweden)

    Esmond Mok

    2013-09-01

    Full Text Available Ubiquitous positioning provides continuous positional information in both indoor and outdoor environments for a wide spectrum of location based service (LBS applications. With the rapid development of the low-cost and high speed data communication, Wi-Fi networks in many metropolitan cities, strength of signals propagated from the Wi-Fi access points (APs namely received signal strength (RSS have been cleverly adopted for indoor positioning. In this paper, a Wi-Fi positioning algorithm based on neural network modeling of Wi-Fi signal patterns is proposed. This algorithm is based on the correlation between the initial parameter setting for neural network training and output of the mean square error to obtain better modeling of the nonlinear highly complex Wi-Fi signal power propagation surface. The test results show that this neural network based data processing algorithm can significantly improve the neural network training surface to achieve the highest possible accuracy of the Wi-Fi fingerprinting positioning method.

  11. Classifying Single-Trial EEG during Motor Imagery with a Small Training Set

    OpenAIRE

    Wang, Yijun

    2013-01-01

    Before the operation of a motor imagery based brain-computer interface (BCI) adopting machine learning techniques, a cumbersome training procedure is unavoidable. The development of a practical BCI posed the challenge of classifying single-trial EEG with a small training set. In this letter, we addressed this problem by employing a series of signal processing and machine learning approaches to alleviate overfitting and obtained test accuracy similar to training accuracy on the datasets from B...

  12. Financial Resources for Conducting Athletic Training Programs in the Collegiate and High School Settings

    OpenAIRE

    Rankin, James M.

    1992-01-01

    The distribution of resources to athletic training programs varies greatly, depending on the size and scope of the athletic program. No research has been found that assesses the differences in dollars allocated within various athletic training settings or assesses whether the different program levels allocate similar proportions of their resources to like categories of expenditures. In this study, I assessed the financial resources available to athletic training programs at major football NCA...

  13. The Train Driver Recovery Problem - a Set Partitioning Based Model and Solution Method

    DEFF Research Database (Denmark)

    Rezanova, Natalia Jurjevna; Ryan, David

    The need to recover a train driver schedule occurs during major disruptions in the daily railway operations. Using data from the train driver schedule of the Danish passenger railway operator DSB S-tog A/S, a solution method to the Train Driver Recovery Problem (TDRP) is developed. The TDRP...... is formulated as a set partitioning problem. The LP relaxation of the set partitioning formulation of the TDRP possesses strong integer properties. The proposed model is therefore solved via the LP relaxation and Branch & Price. Starting with a small set of drivers and train tasks assigned to the drivers within...... a certain time period, the LP relaxation of the set partitioning model is solved with column generation. If a feasible solution is not found, further drivers are gradually added to the problem or the optimization time period is increased. Fractions are resolved with a constraint branching strategy using...

  14. Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images.

    Science.gov (United States)

    van Grinsven, Mark J J P; van Ginneken, Bram; Hoyng, Carel B; Theelen, Thomas; Sanchez, Clara I

    2016-05-01

    Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance-on par with two human experts-was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set.

  15. A Study on the Practical Carrying Capacity of Large High-Speed Railway Stations considering Train Set Utilization

    Directory of Open Access Journals (Sweden)

    Bin Guo

    2016-01-01

    Full Text Available Methods for solving the carrying capacity problem for High-Speed Railways (HSRs have received increasing attention in the literature in the last few years. As important nodes in the High-Speed Railway (HSR network, large stations are usually the carrying capacity bottlenecks of the entire network due to the presence of multiple connections in different directions and the complexity of train operations at these stations. This paper focuses on solving the station carrying capacity problem and considers train set utilization constraints, which are important influencing factors that have rarely been studied by previous researchers. An integer linear programming model is built, and the CPLEX v12.2 software is used to solve the model. The proposed approach is tested on a real-world case study of the Beijing South Railway Station (BS, which is one of the busiest and most complex stations in China. Studies of the impacts of different train set utilization constraints on the practical station carrying capacity are carried out, and some suggestions are then presented for enhancing the practical carrying capacity. Contrast tests indicate that both the efficiency of the solving process and the quality of the solution show huge breakthroughs compared with the heuristic approach.

  16. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

    Science.gov (United States)

    Wang, Xiao-Jing

    2016-01-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

  17. Performance of a visuomotor walking task in an augmented reality training setting

    NARCIS (Netherlands)

    Haarman, Juliet A.M.; Choi, Julia T.; Buurke, Jaap H.; Rietman, Johan S.; Reenalda, Jasper

    2017-01-01

    Visual cues can be used to train walking patterns. Here, we studied the performance and learning capacities of healthy subjects executing a high-precision visuomotor walking task, in an augmented reality training set-up. A beamer was used to project visual stepping targets on the walking surface of

  18. Modelling expected train passenger delays on large scale railway networks

    DEFF Research Database (Denmark)

    Landex, Alex; Nielsen, Otto Anker

    2006-01-01

    Forecasts of regularity for railway systems have traditionally – if at all – been computed for trains, not for passengers. Relatively recently it has become possible to model and evaluate the actual passenger delays by a passenger regularity model for the operation already carried out. First the ...... and compare future scenarios. In this way it is possible to estimate the network effects of the passengers and to identify critical stations or sections in the railway network for further investigation or optimization.......Forecasts of regularity for railway systems have traditionally – if at all – been computed for trains, not for passengers. Relatively recently it has become possible to model and evaluate the actual passenger delays by a passenger regularity model for the operation already carried out. First...... the paper describes the passenger regularity model used to calculate passenger delays of the Copenhagen suburban rail network the previous day. Secondly, the paper describes how it is possible to estimate future passenger delays by combining the passenger regularity model with railway simulation software...

  19. Transitioning Pharmacogenomics into the Clinical Setting: Training Future Pharmacists.

    Science.gov (United States)

    Frick, Amber; Benton, Cristina S; Scolaro, Kelly L; McLaughlin, Jacqueline E; Bradley, Courtney L; Suzuki, Oscar T; Wang, Nan; Wiltshire, Tim

    2016-01-01

    Pharmacogenomics, once hailed as a futuristic approach to pharmacotherapy, has transitioned to clinical implementation. Although logistic and economic limitations to clinical pharmacogenomics are being superseded by external measures such as preemptive genotyping, implementation by clinicians has met resistance, partly due to a lack of education. Pharmacists, with extensive training in pharmacology and pharmacotherapy and accessibility to patients, are ideally suited to champion clinical pharmacogenomics. This study aimed to analyze the outcomes of an innovative pharmacogenomic teaching approach. Second-year student pharmacists enrolled in a required, 15-week pharmaceutical care lab course in 2015 completed educational activities including lectures and small group work focusing on practical pharmacogenomics. Reflecting the current landscape of direct-to-consumer (DTC) genomic testing, students were offered 23andMe genotyping. Students completed surveys regarding their attitudes and confidence on pharmacogenomics prior to and following the educational intervention. Paired pre- and post-intervention responses were analyzed with McNemar's test for binary comparisons and the Wilcoxon signed-rank test for Likert items. Responses between genotyped and non-genotyped students were analyzed with Fisher's exact test for binary comparisons and the Mann-Whitney U-test for Likert items. Responses were analyzed for all student pharmacists who voluntarily completed the pre-intervention survey (N = 121, 83% response) and for student pharmacists who completed both pre- and post-intervention surveys (N = 39, 27% response). Of those who completed both pre- and post-intervention surveys, 59% obtained genotyping. Student pharmacists demonstrated a significant increase in their knowledge of pharmacogenomic resources (17.9 vs. 56.4%, p < 0.0001) and confidence in applying pharmacogenomic information to manage patients' drug therapy (28.2 vs. 48.7%, p = 0.01), particularly if the student

  20. Transitioning pharmacogenomics into the clinical setting: training future pharmacists

    Directory of Open Access Journals (Sweden)

    Amber Frick

    2016-08-01

    Full Text Available Pharmacogenomics, once hailed as a futuristic approach to pharmacotherapy, has transitioned to clinical implementation. Although logistic and economic limitations to clinical pharmacogenomics are being superseded by external measures such as preemptive genotyping, implementation by clinicians has met resistance, partly due to a lack of education. Pharmacists, with extensive training in pharmacology and pharmacotherapy and accessibility to patients, are ideally suited to champion clinical pharmacogenomics. This study aimed to analyze the outcomes of an innovative pharmacogenomic teaching approach.Second-year student pharmacists enrolled in a required, 15-week pharmaceutical care lab course in 2015 completed educational activities including lectures and small group work focusing on practical pharmacogenomics. Reflecting the current landscape of direct-to-consumer genomic testing, students were offered 23andMe genotyping. Students completed surveys regarding their attitudes and confidence on pharmacogenomics prior to and following the educational intervention. Paired pre- and post-intervention responses were analyzed with McNemar’s test for binary comparisons and the Wilcoxon signed-rank test for Likert items. Responses between genotyped and non-genotyped students were analyzed with Fisher’s exact test for binary comparisons and the Mann-Whitney U-test for Likert items.Responses were analyzed for all student pharmacists who voluntarily completed the pre-intervention survey (N=121, 83% response and for student pharmacists who completed both pre- and post-intervention surveys (N=39, 27% response. Of those who completed both pre- and post-intervention surveys, 59% obtained genotyping. Student pharmacists demonstrated a significant increase in their knowledge of pharmacogenomic resources (17.9% vs. 56.4%, p<0.0001 and confidence in applying pharmacogenomic information to manage patients’ drug therapy (28.2% vs. 48.7%, p=0.01, particularly if the

  1. A Web Based Educational Programming Logic Controller Training Set Based on Vocational High School Students' Demands

    Directory of Open Access Journals (Sweden)

    Abdullah Alper Efe

    2018-01-01

    Full Text Available The purpose of this study was to design and develop aProgramming Logic Controller Training Set according to vocational high school students’ educational needs. In this regard, by using the properties of distance education the proposed system supported “hands-on” PLC programming laboratory exercises in industrial automation area. The system allowed students to access and control the PLC training set remotely. For this purpose, researcher designed a web site to facilitate students’ interactivity and support PLC programming. In the training set, Induction Motor, Frequency Converter and Encoder tripart controlled by Siemens Simatic S7-200 PLC controller by the help of SIMATIC Step 7 Programming Software were used to make the system more effective and efficient. Moreover, training set included an IP camera system allowing to monitor devices and pilot application. By working with this novel remote accessible training set, students and researchers recieved a chance to inhere self paced learning experiences. Also, The PLC training set offered an effective learning enviroenment for distance education, which is based on presenting the content on the web and opening it to the online users and provided a safe and economical solution for multiple users in a workplace to enhance the quality of education with less overall cost.

  2. Monitoring training response in young Friesian dressage horses using two different standardised exercise tests (SETs).

    Science.gov (United States)

    de Bruijn, Cornelis Marinus; Houterman, Willem; Ploeg, Margreet; Ducro, Bart; Boshuizen, Berit; Goethals, Klaartje; Verdegaal, Elisabeth-Lidwien; Delesalle, Catherine

    2017-02-14

    Most Friesian horses reach their anaerobic threshold during a standardized exercise test (SET) which requires lower intensity exercise than daily routine training. to study strengths and weaknesses of an alternative SET-protocol. Two different SETs (SETA and SETB) were applied during a 2 month training period of 9 young Friesian dressage horses. SETB alternated short episodes of canter with trot and walk, lacking long episodes of cantering, as applied in SETA. Following parameters were monitored: blood lactic acid (BLA) after cantering, average heart rate (HR) in trot and maximum HR in canter. HR and BLA of SETA and SETB were analyzed using a paired two-sided T-test and Spearman Correlation-coefficient (p* horses showed a significant training response based upon longitudinal follow-up of BLA. Horses with the lowest fitness at start, displayed the largest training response. BLA was significantly lower in week 8 compared to week 0, in both SETA and SETB. A significantly decreased BLA level after cantering was noticeable in week 6 in SETA, whereas in SETB only as of week 8. In SETA a very strong correlation for BLA and average HR at trot was found throughout the entire training period, not for canter. Young Friesian horses do reach their anaerobic threshold during a SET which requires lower intensity than daily routine training. Therefore close monitoring throughout training is warranted. Longitudinal follow up of BLA and not of HR is suitable to assess training response. In the current study, horses that started with the lowest fitness level, showed the largest training response. During training monitoring HR in trot rather than in canter is advised. SETB is best suited as a template for daily training in the aerobic window.

  3. On Distributed Deep Network for Processing Large-Scale Sets of Complex Data

    OpenAIRE

    Chen, D

    2016-01-01

    Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with hundreds of parameters using distributed CPU cores. We have developed Bagging-Down SGD algorithm to solve the distributing problems. Bagging-Down SGD introduces the parameter server adding on the several model replicas, and separates the updating and the training computing to ...

  4. Training candidate selection for effective out-of-set rejection in robust open-set language identification.

    Science.gov (United States)

    Zhang, Qian; Hansen, John H L

    2018-01-01

    Research in open-set language identification (LID) generally focuses on in-set language modeling versus out-of-set (OOS) language rejection. However, unknown/OOS language rejection is essential for effective speech and language pre-processing. To address this, an approach for OOS language selection is proposed. Using probe OOS data, three effective OOS candidate selection methods are developed for universal OOS language coverage. The selected OOS candidates are expected to reflect the entire OOS language space for the state-of-the-art i-vector LID system followed by a Gaussian back-end. Two front-end feature selection strategies are proposed: (i) unsupervised k-means clustering and (ii) complementary candidate selection. Also, (iii) general candidate selection is proposed according to language relationship explored at the score level. All methods are evaluated on a large-scale corpus (LRE-09) containing 40 languages. The proposed selection methods reduce OOS training data diversity by 86% while achieving performance similar to closed-set using all probe OOS for training. The proposed methods also show clear benefits versus random candidate selection (i.e., the proposed solutions achieve sustained performance while employing a minimum number of effective OOS language candidates). To the best of our knowledge, this is the first major effort on effective OOS language selection and enhancement for improved OOS rejection in open-set LID.

  5. EduCamp Colombia: Social Networked Learning for Teacher Training

    Directory of Open Access Journals (Sweden)

    Diego Ernesto Leal Fonseca

    2011-03-01

    Full Text Available This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked learning ideas. The experience opens new perspectives for the design of technology training workshops and for the development of lifelong learning experiences.

  6. [Training of institutional research networks as a strategy of improvement].

    Science.gov (United States)

    Galván-Plata, María Eugenia; Almeida-Gutiérrez, Eduardo; Salamanca-Gómez, Fabio Abdel

    2017-01-01

    The Instituto Mexicano del Seguro Social (IMSS) through the Coordinación de Investigación en Salud (Health Research Council) has promoted a strong link between the generation of scientific knowledge and the clinical care through the program Redes Institucionales de Investigación (Institutional Research Network Program), whose main aim is to promote and generate collaborative research between clinical, basic, epidemiologic, educational, economic and health services researchers, seeking direct benefits for patients, as well as to generate a positive impact on institutional processes. All of these research lines have focused on high-priority health issues in Mexico. The IMSS internal structure, as well as the sufficient health services coverage, allows the integration of researchers at the three levels of health care into these networks. A few years after their creation, these networks have already generated significant results, and these are currently applied in the institutional regulations in diseases that represent a high burden to health care. Two examples are the National Health Care Program for Patients with Acute Myocardial Infarction "Código Infarto", and the Early Detection Program on Chronic Kidney Disease; another result is the generation of multiple scientific publications, and the promotion of training of human resources in research from the same members of our Research Networks. There is no doubt that the Coordinación de Investigación en Salud advances steadily implementing the translational research, which will keep being fruitful to the benefit of our patients, and of our own institution.

  7. Network Training for a Boy with Learning Disabilities and Behaviours That Challenge

    Science.gov (United States)

    Cooper, Kate; McElwee, Jennifer

    2016-01-01

    Background: Network Training is an intervention that draws upon systemic ideas and behavioural principles to promote positive change in networks of support for people defined as having a learning disability. To date, there are no published case studies looking at the outcomes of Network Training. Materials and Methods: This study aimed to…

  8. Neuromuscular and Blood Lactate Responses to Squat Power Training with Different Rest Intervals Between Sets

    Directory of Open Access Journals (Sweden)

    André Martorelli, Martim Bottaro, Amilton Vieira, Valdinar Rocha-Júnior, Eduardo Cadore, Jonato Prestes, Dale Wagner, Saulo Martorelli

    2015-06-01

    Full Text Available Studies investigating the effect of rest interval length (RI between sets on neuromuscular performance and metabolic response during power training are scarce. Therefore, the purpose of this study was to compare maximal power output, muscular activity and blood lactate concentration following 1, 2 or 3 minutes RI between sets during a squat power training protocol. Twelve resistance-trained men (22.7 ± 3.2 years; 1.79 ± 0.08 cm; 81.8 ± 11.3 kg performed 6 sets of 6 repetitions of squat exercise at 60% of their 1 repetition maximum. Peak and average power were obtained for each repetition and set using a linear position transducer. Muscular activity and blood lactate were measured pre and post-exercise session. There was no significant difference between RI on peak power and average power. However, peak power decreased 5.6%, 1.9%, and 5.9% after 6 sets using 1, 2 and 3 minutes of RI, respectively. Average power also decreased 10.5% (1 min, 2.6% (2 min, and 4.3% (3 min after 6 sets. Blood lactate increased similarly during the three training sessions (1-min: 5.5 mMol, 2-min: 4.3 mMol, and 3-min: 4.0 mMol and no significant changes were observed in the muscle activity after multiple sets, independent of RI length (pooled ES for 1-min: 0.47, 2-min: 0.65, and 3-min: 1.39. From a practical point of view, the results suggest that 1 to 2 minute of RI between sets during squat exercise may be sufficient to recover power output in a designed power training protocol. However, if training duration is malleable, we recommend 2 min of RI for optimal recovery and power output maintenance during the subsequent exercise sets.

  9. Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems

    Science.gov (United States)

    Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

    2016-01-01

    The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance–performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system. PMID:27598390

  10. Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems.

    Science.gov (United States)

    Chang, Kuei-Hu; Chang, Yung-Chia; Chain, Kai; Chung, Hsiang-Yu

    2016-01-01

    The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance-performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.

  11. An Analysis of Training Focused on Improving SMART Goal Setting for Specific Employee Groups

    Science.gov (United States)

    Worden, Jeannie M.

    2014-01-01

    This quantitative study examined the proficiency of employee SMART goal setting following the intervention of employee SMART goal setting training. Current challenges in higher education substantiate the need for employees to align their performance with the mission, vision, and strategic directions of the organization. A performance management…

  12. Combined Ozone Retrieval From METOP Sensors Using META-Training Of Deep Neural Networks

    Science.gov (United States)

    Felder, Martin; Sehnke, Frank; Kaifel, Anton

    2013-12-01

    The newest installment of our well-proven Neural Net- work Ozone Retrieval System (NNORSY) combines the METOP sensors GOME-2 and IASI with cloud information from AVHRR. Through the use of advanced meta- learning techniques like automatic feature selection and automatic architecture search applied to a set of deep neural networks, having at least two or three hidden layers, we have been able to avoid many technical issues normally encountered during the construction of such a joint retrieval system. This has been made possible by harnessing the processing power of modern consumer graphics cards with high performance graphic processors (GPU), which decreases training times by about two orders of magnitude. The system was trained on data from 2009 and 2010, including target ozone profiles from ozone sondes, ACE- FTS and MLS-AURA. To make maximum use of tropospheric information in the spectra, the data were partitioned into several sets of different cloud fraction ranges with the GOME-2 FOV, on which specialized retrieval networks are being trained. For the final ozone retrieval processing the different specialized networks are combined. The resulting retrieval system is very stable and does not show any systematic dependence on solar zenith angle, scan angle or sensor degradation. We present several sensitivity studies with regard to cloud fraction and target sensor type, as well as the performance in several latitude bands and with respect to independent validation stations. A visual cross-comparison against high-resolution ozone profiles from the KNMI EUMETSAT Ozone SAF product has also been performed and shows some distinctive features which we will briefly discuss. Overall, we demonstrate that a complex retrieval system can now be constructed with a minimum of ma- chine learning knowledge, using automated algorithms for many design decisions previously requiring expert knowledge. Provided sufficient training data and computation power of GPUs is available, the

  13. How to Train Safe Drivers: Setting Up and Evaluating a Fatigue Training Program

    Directory of Open Access Journals (Sweden)

    Adamos Giannis

    2015-02-01

    Full Text Available Fatigue is considered as a serious risk driving behavior, causing road accidents, which in many cases involve fatalities and severe injuries. According to CARE database statistics, professional drivers are indicated as a high-risk group to be involved in a fatigue-related accident. Acknowledging these statistics, a training program on driving fatigue was organized, aiming at raising awareness of professional drivers of a leading company in building materials, in Greece. Selected experimental methods were used for collecting data before and after the training program, which allowed monitoring and assessing the potential behavioural changes. A questionnaire survey was conducted before the program implementation to 162 drivers of the company, while two months after the program, the same drivers replied to a second questionnaire. Impact assessment of the program relied on statistical analysis of the responses. Results showed the degree of penetration of the training program in the professional drivers' behavior towards safe driving.

  14. Design of a local area network and a wide area network to connect the US Navy's training organization

    OpenAIRE

    Hill, Kevin Carlos

    1994-01-01

    US Navy training commands use a local area and a wide area network known as the Versatile Training System II (VTS). VTS furnishes word processing, electronic mail, and data base functions, all of which can be transferred throughout the network. Enabling this rather old system is a mainframe at each training site with user terminals dispersed throughout the command. The system was installed and is maintained by civilian contractors. VTS does not have the capabilities to ...

  15. Case Study: Does training of private networks of Family Planning clinicians in urban Pakistan affect service utilization?

    Directory of Open Access Journals (Sweden)

    Qureshi Asma M

    2010-11-01

    Full Text Available Abstract Background To determine whether training of providers participating in franchise clinic networks is associated with increased Family Planning service use among low-income urban families in Pakistan. Methods The study uses 2001 survey data consisting of interviews with 1113 clinical and non-clinical providers working in public and private hospitals/clinics. Data analysis excludes non-clinical providers reducing sample size to 822. Variables for the analysis are divided into client volume, and training in family planning. Regression models are used to compute the association between training and service use in franchise versus private non-franchise clinics. Results In franchise clinic networks, staff are 6.5 times more likely to receive family planning training (P = 0.00 relative to private non-franchises. Service use was significantly associated with training (P = 0.00, franchise affiliation (P = 0.01, providers' years of family planning experience (P = 0.02 and the number of trained staff working at government owned clinics (P = 0.00. In this setting, nurses are significantly less likely to receive training compared to doctors (P = 0.00. Conclusions These findings suggest that franchises recruit and train various cadres of health workers and training maybe associated with increased service use through improvement in quality of services.

  16. Effects of single vs. multiple-set short-term strength training in elderly women.

    Science.gov (United States)

    Radaelli, Regis; Wilhelm, Eurico N; Botton, Cíntia E; Rech, Anderson; Bottaro, Martim; Brown, Lee E; Pinto, Ronei S

    2014-01-01

    The strength training has been shown to be effective for attenuating the age-related physiological decline. However, the adequate volume of strength training volume adequate to promote improvements, mainly during the initial period of training, still remains controversial. Thus, the purpose of this study was to compare the effects of a short-term strength training program with single or multiple sets in elderly women. Maximal dynamic (1-RM) and isometric strength, muscle activation, muscle thickness (MT), and muscle quality (MQ = 1-RM and MT quadriceps quotient) of the knee extensors were assessed. Subjects were randomly assigned into one of two groups: single set (SS; n = 14) that performed one set per exercise or multiple sets (MS; n = 13) that performed three-sets per exercise, twice weekly for 6 weeks. Following training, there were significant increases (p ≤ 0.05) in knee extension 1-RM (16.1 ± 12 % for SS group and 21.7 ± 7.7 % for MS group), in all MT (p ≤ 0.05; vastus lateralis, rectus femoris, vastus medialis, and vastus intermedius), and in MQ (p ≤ 0.05); 15.0 ± 12.2 % for SS group and 12.6 ± 7.2 % for MS group), with no differences between groups. These results suggest that during the initial stages of strength training, single- and multiple-set training demonstrate similar capacity for increasing dynamic strength, MT, and MQ of the knee extensors in elderly women.

  17. Treatment challenges in and outside a network setting: Head and neck cancers.

    Science.gov (United States)

    Orlandi, Ester; Alfieri, Salvatore; Simon, Christian; Trama, Annalisa; Licitra, Lisa

    2018-02-14

    Head and neck cancer (HNC) is a rare disease that can affect different sites and is characterized by variable incidence and 5-year survival rates across Europe. Multiple factors need to be considered when choosing the most appropriate treatment for HNC patients, such as age, comorbidities, social issues, and especially whether to prefer surgery or radiation-based protocols. Given the complexity of this scenario, the creation of a highly specialized multidisciplinary team is recommended to guarantee the best oncological outcome and prevent or adequately treat any adverse effect. Data from literature suggest that the multidisciplinary team-based approach is beneficial for HNC patients and lead to improved survival rates. This result is likely due to improved diagnostic and staging accuracy, a more efficacious therapeutic approach and enhanced communication across disciplines. Despite the benefit of MTD, it must be noted that this approach requires considerable time, effort and financial resources and is usually more frequent in highly organized and high-volume centers. Literature data on clinical research suggest that patients treated in high-accrual centers report better treatment outcomes compared to patients treated in low-volume centers, where a lower radiotherapy-compliance and worst overall survival have been reported. There is general agreement that treatment of rare cancers such as HNC should be concentrated in high volume, specialized and multidisciplinary centers. In order to achieve this goal, the creation of international collaboration network is fundamental. The European Reference Networks for example aim to create an international virtual advisory board, whose objectives are the exchange of expertise, training, clinical collaboration and the reduction of disparities and enhancement of rationalize migration across Europe. The purpose of our work is to review all aspects and challenges in and outside this network setting planned for the management of HNC

  18. Single- and multiple-set resistance training improves skeletal and respiratory muscle strength in elderly women.

    Science.gov (United States)

    Abrahin, Odilon; Rodrigues, Rejane P; Nascimento, Vanderson C; Da Silva-Grigoletto, Marzo E; Sousa, Evitom C; Marçal, Anderson C

    2014-01-01

    Aging involves a progressive reduction of respiratory muscle strength as well as muscle strength. Compare the effects of resistance training volume on the maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP), functional performance, and muscle strength in elderly women. Thirty elderly women were randomly assigned to a group performing either single sets (1-SET) or three sets (3-SET) of exercises. The sit-to-stand test, MIP, MEP, and muscle strength were assessed before and after 24 training sessions. Progressive resistance training was performed two times per week for a total of 8-12 repetitions, using the main muscle groups of the upper and lower limbs. The main results showed that the participants significantly increased their MEP (Ptraining sessions, muscle strength also significantly increased (Ptraining programs increased MIP, MEP, muscle strength, and sit-to-stand test performance in elderly women after 24 sessions of training. In conclusion, our results suggested that elderly women who are not in the habit of physical activity may start with single-set resistance training programs as a short-term strategy for the maintenance of health.

  19. The Study of Maglev Train Control and Diagnosis Networks Based on Role Automation Decentralization

    National Research Council Canada - National Science Library

    LIU, Zhigang; WANG, Qi; TAN, Yongdong

    2008-01-01

    The control and diagnosis networks in Maglev Train are the most important parts. In the paper, the control and diagnosis network structures are discussed, and the disadvantages of them are described and analyzed...

  20. Deconstructing myths, building alliances: a networking model to enhance tobacco control in hospital mental health settings.

    Science.gov (United States)

    Ballbè, Montse; Gual, Antoni; Nieva, Gemma; Saltó, Esteve; Fernández, Esteve

    2016-01-01

    Life expectancy for people with severe mental disorders is up to 25 years less in comparison to the general population, mainly due to diseases caused or worsened by smoking. However, smoking is usually a neglected issue in mental healthcare settings. The aim of this article is to describe a strategy to improve tobacco control in the hospital mental healthcare services of Catalonia (Spain). To bridge this gap, the Catalan Network of Smoke-free Hospitals launched a nationwide bottom-up strategy in Catalonia in 2007. The strategy relied on the creation of a working group of key professionals from various hospitals -the early adopters- based on Rogers' theory of the Diffusion of Innovations. In 2016, the working group is composed of professionals from 17 hospitals (70.8% of all hospitals in the region with mental health inpatient units). Since 2007, tobacco control has improved in different areas such as increasing mental health professionals' awareness of smoking, training professionals on smoking cessation interventions and achieving good compliance with the national smoking ban. The working group has produced and disseminated various materials, including clinical practice and best practice guidelines, implemented smoking cessation programmes and organised seminars and training sessions on smoking cessation measures in patients with mental illnesses. The next challenge is to ensure effective follow-up for smoking cessation after discharge. While some areas of tobacco control within these services still require significant improvement, the aforementioned initiative promotes successful tobacco control in these settings. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  1. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming.

    Science.gov (United States)

    Guziolowski, Carito; Videla, Santiago; Eduati, Federica; Thiele, Sven; Cokelaer, Thomas; Siegel, Anne; Saez-Rodriguez, Julio

    2013-09-15

    Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. Supplementary materials are available at Bioinformatics online. santiago.videla@irisa.fr.

  2. Effect of exercise order on the number of repeats and training volume in the tri-set training method

    Directory of Open Access Journals (Sweden)

    Waynne Ferreira de Faria

    2016-05-01

    Full Text Available DOI: http://dx.doi.org/10.5007/1980-0037.2016v18n2p187   Although the tri-set system is widely adopted by athletes and experienced weight training practitioners aimed at optimizing the metabolic overload, there are still few works in literature on the effect of exercise order manipulation on this training system. Therefore, this work was aimed at investigating the effect of exercise order manipulation on the number of repeats and training volume using the tri-set system for lower limbs. This is a randomized cross-over design study. The experimental group consisted of 14 healthy men (23.53 ± 5.40 years; 24.51 ± 2.96 kg/m2. Subjects were submitted to two experimental sessions at different exercise order for lower limbs: Sequence A: squat on guided bar, leg press 45° and bilateral leg extension; sequence B: bilateral leg extension, leg press 45° and squat on guided bar. Three sets to volitional fatigue in all exercises were performed, with intensity of 75% 1RM. Superiority for sequence B in the total number of repeats (70.14 ± 13 vs 60.93 ± 7.94 repeats, p = 0.004 and total training volume (9129.64 ± 2830.05 vs 8238.29 ± 2354.20 kg, p = 0.014 was observed. Based on the above, the performance of single-joint exercises before multi-joint exercises in the tri-set system adopted for lower limbs induced higher number of repeats and total training volume.

  3. Issue Obtrusiveness and the Agenda-Setting Effects of National Network News.

    Science.gov (United States)

    Demers, David Pearce; And Others

    1989-01-01

    Examines effects of issue obtrusiveness on network news agenda-setting. Tests two competing models: (1) obtrusive contingency (agenda-setting effects decrease as personal experience with issues increase); and (2) cognitive-priming contingency (agenda-setting effects increase as obtrusiveness increases). Finds no support for obtrusive contingency…

  4. Effectiveness of Teacher-Child Interaction Training (TCIT) in a preschool setting.

    Science.gov (United States)

    Lyon, Aaron R; Gershenson, Rachel A; Farahmand, Farahnaz K; Thaxter, Peter J; Behling, Steven; Budd, Karen S

    2009-11-01

    This research addressed the need for trained child care staff to support optimal early social-emotional development in urban, low-income, ethnic minority children. We evaluated effectiveness of Teacher-Child Interaction Training (TCIT), an approach adapted from Eyberg's Parent-Child Interaction Therapy (PCIT). TCIT focuses on increasing preschool teachers' positive attention skills and consistent discipline in order to enhance children's psychosocial functioning and prevent mental health problems. A total of 12 teachers participated in small-group workshop sessions with in vivo coaching on their use of skills in the classroom. A multiple-baseline design across four classrooms (3 teachers each) evaluated effects of training on teacher behaviors during weekly classroom observations. Findings indicated systematic increases in trained skills during intervention, and consumer evaluations showed that the training was rated positively. Our results suggest that TCIT is a promising approach for enhancing positive teacher-child interactions in a preschool setting and should receive further investigation.

  5. Effect of ultrasound training of physicians working in the prehospital setting

    DEFF Research Database (Denmark)

    Krogh, Charlotte Loumann; Steinmetz, Jacob; Rudolph, Søren Steemann

    2016-01-01

    BACKGROUND: Advances in technology have made ultrasound (US) devices smaller and portable, hence accessible for prehospital care providers. This study aims to evaluate the effect of a four-hour, hands-on US training course for physicians working in the prehospital setting. The primary outcome...... measure was US performance assessed by the total score in a modified version of the Objective Structured Assessment of Ultrasound Skills scale (mOSAUS). METHODS: Prehospital physicians participated in a four-hour US course consisting of both hands-on training and e-learning including a pre- and a post......-learning test. Prior to the hands-on training a pre-training test was applied comprising of five videos in which the participants should identify pathology and a five-minute US examination of a healthy volunteer portraying to be a shocked patient after a blunt torso trauma. Following the pre-training test...

  6. HIV/AIDS research in correctional settings: perspectives on training needs from researchers and IRB members.

    Science.gov (United States)

    Kondo, Karli K; Johnson, Mark E; Ironside, Erica F; Brems, Christiane; Eldridge, Gloria D

    2014-12-01

    Being disproportionately represented by individuals living with HIV/AIDS, correctional facilities are an important venue for potentially invaluable HIV/AIDS epidemiological and intervention research. However, unique ethical, regulatory, and environmental challenges exist in these settings that have limited the amount and scope of research. We surveyed 760 HIV/AIDS researchers, and IRB chairs, members, and prisoner representatives to identify areas in which additional training might ameliorate these challenges. Most commonly identified training needs related to federal regulations, ethics (confidentiality, protection for participants/researchers, coercion, privacy, informed consent, and general ethics), and issues specific to the environment (culture of the correctional setting; general knowledge of correctional systems; and correctional environments, policies, and procedures). Bolstering availability of training on the challenges of conducting HIV/AIDS research in correctional settings is a crucial step toward increasing research that will yield significant benefits to incarcerated individuals and society as a whole.

  7. Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method

    Science.gov (United States)

    Liu, J.; Lan, T.; Qin, H.

    2017-10-01

    Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class-imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class-imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class-balanced effect of Time-Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry-INTerferometry (POINT) system.

  8. Dynamics of pneumococcal acquisition and carriage in young adults during training in confined settings in Israel.

    Directory of Open Access Journals (Sweden)

    Hagai Levine

    Full Text Available BACKGROUND: Outbreaks and sporadic cases of pneumococcal illness occur among young adults in confined settings. Our aim was to characterize pneumococcal acquisition and carriage among healthy young adults in Israel during military training in confined settings. METHODS: During the years 2007-2008, an observational longitudinal study was conducted in three cohorts of healthy soldiers, during a 7-month basic training period. Epidemiological data, oropharyngeal and nasopharyngeal cultures were sampled on 5 occasions: before and 3, 6, 12 and 24 weeks after start of training. Samples were processed within 2-18 hours. Relatedness of isolates was investigated by capsular typing of all isolates and pulsed-field gel electrophoresis to determine acquisition and transmission. Carriage and acquisition patterns were analyzed and multivariable logistic regression analysis was performed to assess the impact of time on acquisition after mixing, controlling for other covariates. RESULTS: Pneumococci were recovered on 202 of 1872 visits among 742 individuals, including 40 different serotypes. Mean carriage prevalence increased in all visits following training initiation. Acquisition during training was high, as 36.9% of individuals acquired pneumococci at least once during training, and for almost one fourth of the whole population this occurred during the first 6 weeks. Significant clustering was noted. Sharing drinking glass/bottle was found to be a significant and common risk factor for pneumococcal acquisition. CONCLUSIONS: Pneumococcal acquisition is highly frequent when young adults live in close contact in confined settings, especially early after mixing.

  9. Reinforcement and backpropagation training for an optical neural network using self-lensing effects.

    Science.gov (United States)

    Cruz-Cabrera, A A; Yang, M; Cui, G; Behrman, E C; Steck, J E; Skinner, S R

    2000-01-01

    The optical bench training of an optical feedforward neural network, developed by the authors, is presented. The network uses an optical nonlinear material for neuron processing and a trainable applied optical pattern as the network weights. The nonlinear material, with the applied weight pattern, modulates the phase front of a forward propagating information beam by dynamically altering the index of refraction profile of the material. To verify that the network can be trained in real time, six logic gates were trained using a reinforcement training paradigm. More importantly, to demonstrate optical backpropagation, three gates were trained via optical error backpropagation. The output error is optically backpropagated, detected with a CCD camera, and the weight pattern is updated and stored on a computer. The obtained results lay the ground work for the implementation of multilayer neural networks that are trained using optical error backpropagation and are able to solve more complex problems.

  10. Measuring social networks for medical research in lower-income settings.

    Science.gov (United States)

    Kelly, Laura; Patel, Shivani A; Narayan, K M Venkat; Prabhakaran, Dorairaj; Cunningham, Solveig A

    2014-01-01

    Social networks are believed to affect health-related behaviors and health. Data to examine the links between social relationships and health in low- and middle-income country settings are limited. We provide guidance for introducing an instrument to collect social network data as part of epidemiological surveys, drawing on experience in urban India. We describe development and fielding of an instrument to collect social network information relevant to health behaviors among adults participating in a large, population-based study of non-communicable diseases in Delhi, India. We discuss basic characteristics of social networks relevant to health including network size, health behaviors of network partners (i.e., network exposures), network homogeneity, network diversity, strength of ties, and multiplexity. Data on these characteristics can be collected using a short instrument of 11 items asked about up to 5 network members and 3 items about the network generally, administered in approximately 20 minutes. We found high willingness to respond to questions about social networks (97% response). Respondents identified an average of 3.8 network members, most often relatives (80% of network ties), particularly blood relationships. Ninety-one percent of respondents reported that their primary contacts for discussing health concerns were relatives. Among all listed ties, 91% of most frequent snack partners and 64% of exercise partners in the last two weeks were relatives. These results demonstrate that family relationships are the crux of social networks in some settings, including among adults in urban India. Collecting basic information about social networks can be feasibly and effectively done within ongoing epidemiological studies.

  11. Refining ensembles of predicted gene regulatory networks based on characteristic interaction sets.

    Directory of Open Access Journals (Sweden)

    Lukas Windhager

    Full Text Available Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate

  12. Measuring Social Networks for Medical Research in Lower-Income Settings

    Science.gov (United States)

    Kelly, Laura; Patel, Shivani A.; Narayan, K. M. Venkat; Prabhakaran, Dorairaj; Cunningham, Solveig A.

    2014-01-01

    Social networks are believed to affect health-related behaviors and health. Data to examine the links between social relationships and health in low- and middle-income country settings are limited. We provide guidance for introducing an instrument to collect social network data as part of epidemiological surveys, drawing on experience in urban India. We describe development and fielding of an instrument to collect social network information relevant to health behaviors among adults participating in a large, population-based study of non-communicable diseases in Delhi, India. We discuss basic characteristics of social networks relevant to health including network size, health behaviors of network partners (i.e., network exposures), network homogeneity, network diversity, strength of ties, and multiplexity. Data on these characteristics can be collected using a short instrument of 11 items asked about up to 5 network members and 3 items about the network generally, administered in approximately 20 minutes. We found high willingness to respond to questions about social networks (97% response). Respondents identified an average of 3.8 network members, most often relatives (80% of network ties), particularly blood relationships. Ninety-one percent of respondents reported that their primary contacts for discussing health concerns were relatives. Among all listed ties, 91% of most frequent snack partners and 64% of exercise partners in the last two weeks were relatives. These results demonstrate that family relationships are the crux of social networks in some settings, including among adults in urban India. Collecting basic information about social networks can be feasibly and effectively done within ongoing epidemiological studies. PMID:25153127

  13. Refining Ensembles of Predicted Gene Regulatory Networks Based on Characteristic Interaction Sets

    Science.gov (United States)

    Windhager, Lukas; Zierer, Jonas; Küffner, Robert

    2014-01-01

    Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate predictions for a gene

  14. Effectiveness of behavioral skills training on staff performance in a job training setting for high-functioning adolescents with autism spectrum disorders

    NARCIS (Netherlands)

    Palmen, A.M.J.W.; Didden, H.C.M.; Korzilius, H.P.L.M.

    2010-01-01

    Few studies have focused on improving staff performance in naturalistic training settings for high-functioning adolescents with autism spectrum disorders. Behavioral skills training, consisting of group instruction and supervisory feedback, was used to improve staff performance on (a) providing

  15. Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks.

    Science.gov (United States)

    Thompson, Todd W; Waskom, Michael L; Gabrieli, John D E

    2016-04-01

    Working memory is central to human cognition, and intensive cognitive training has been shown to expand working memory capacity in a given domain. It remains unknown, however, how the neural systems that support working memory are altered through intensive training to enable the expansion of working memory capacity. We used fMRI to measure plasticity in activations associated with complex working memory before and after 20 days of training. Healthy young adults were randomly assigned to train on either a dual n-back working memory task or a demanding visuospatial attention task. Training resulted in substantial and task-specific expansion of dual n-back abilities accompanied by changes in the relationship between working memory load and activation. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with working memory load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.

  16. Kiwi: a tool for integration and visualization of network topology and gene-set analysis.

    Science.gov (United States)

    Väremo, Leif; Gatto, Francesco; Nielsen, Jens

    2014-12-11

    The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures. Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network. Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.

  17. Design and regularization of neural networks: the optimal use of a validation set

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai; Svarer, Claus

    1996-01-01

    We derive novel algorithms for estimation of regularization parameters and for optimization of neural net architectures based on a validation set. Regularisation parameters are estimated using an iterative gradient descent scheme. Architecture optimization is performed by approximative...... combinatorial search among the relevant subsets of an initial neural network architecture by employing a validation set based optimal brain damage/surgeon (OBD/OBS) or a mean field combinatorial optimization approach. Numerical results with linear models and feed-forward neural networks demonstrate...

  18. Training the Next Generation of Psychotraumatologists: A New Collaborative Network for Training and Excellence in Psychotraumatology (CONTEXT)

    DEFF Research Database (Denmark)

    Vallieres, Frederique; Hyland, Philip; Murphy, Jamie

    2017-01-01

    In this paper we present a description of a new, Horizon2020, Marie Curie Sklodowska Action funded, research and training program called CONTEXT, or the ‘COllaborative Network for Training and EXcellence in psychoTraumatology’. The three objectives of the program are put forward, each of which...

  19. The collaborative African genomics network training program: A trainee perspective on training the next generation of African scientists

    Science.gov (United States)

    The Collaborative African Genomics Network (CAfGEN) aims to establish sustainable genomics research programs in Botswana and Uganda through long-term training of PhD students from these countries at Baylor College of Medicine. Here, we present an overview of the CAfGEN PhD training program alongside...

  20. Sport-Specific Outdoor Rehabilitation In A Group Setting: Do The Intentions Match Actual Training Load?

    Science.gov (United States)

    de Bruijn, Jeroen; van der Worp, Henk; Korte, Mark; de Vries, Astrid A J; Nijland, Rick; Brink, Michel M S

    2017-03-02

    Previous research has shown a weak relationship between intended and actual training load in various sports. Due to variety in group and content, this relationship is expected to be even weaker during group rehabilitation. The goal of our study was to examine the relationship between intended and actual training load during sport-specific rehabilitation in a group setting. Observational study Setting: We performed this study during three outdoor rehabilitation sessions. Nine amateur soccer players recovering from lower limb injury participated in our study (age 22 ± 3 y, height 179 ± 9 cm, body mass 75 ± 13 kg). We collected physiotherapists' ratings of intended exertion (RIE) and players' ratings of perceived exertion (RPE). Furthermore, Zephyr Bioharness 3 equipped with GPS-trackers provided heart rate and distance data. We computed heart rate-based training loads using Edwards' method and a modified TRIMP. Overall, we found weak correlations (N = 42) between RIE and RPE (r = 0.35), Edwards' (r = 0.34), TRIMPMOD (r = 0.07) and distance (r = 0.26). In general, the physiotherapists tended to underestimate training loads. To check whether the intended training loads are met, it is thus recommended to monitor training loads during rehabilitation.

  1. Expanding delivery system research in public health settings: lessons from practice-based research networks.

    Science.gov (United States)

    Mays, Glen P; Hogg, Rachel A

    2012-11-01

    Delivery system research to identify how best to organize, finance, and implement health improvement strategies has focused heavily on clinical practice settings, with relatively little attention paid to public health settings-where research is made more difficult by wide heterogeneity in settings and limited sources of existing data and measures. This study examines the approaches used by public health practice-based research networks (PBRNs) to expand delivery system research and evidence-based practice in public health settings. Practice-based research networks employ quasi-experimental research designs, natural experiments, and mixed-method analytic techniques to evaluate how community partnerships, economic shocks, and policy changes impact delivery processes in public health settings. In addition, network analysis methods are used to assess patterns of interaction between practitioners and researchers within PBRNs to produce and apply research findings. Findings from individual PBRN studies elucidate the roles of information exchange, community resources, and leadership and decision-making structures in shaping implementation outcomes in public health delivery. Network analysis of PBRNs reveals broad engagement of both practitioners and researchers in scientific inquiry, with practitioners in the periphery of these networks reporting particularly large benefits from research participation. Public health PBRNs provide effective mechanisms for implementing delivery system research and engaging practitioners in the process. These networks also hold promise for accelerating the translation and application of research findings into public health settings.

  2. Social Networking in School Psychology Training Programs: A Survey of Faculty and Graduate Students

    Science.gov (United States)

    Pham, Andy V.; Goforth, Anisa N.; Segool, Natasha; Burt, Isaac

    2014-01-01

    The increasing use of social networking sites has become an emerging focus in school psychology training, policy, and research. The purpose of the current study is to present data from a survey on social networking among faculty and graduate students in school psychology training programs. A total of 110 faculty and 112 graduate students in school…

  3. Introduction to the EC's Marie Curie Initial Training Network (MC-ITN) project: Particle Training Network for European Radiotherapy (PARTNER)

    CERN Document Server

    Dosanjh, Manjit

    2013-01-01

    PARTNER (Particle Training Network for European Radiotherapy) is a project funded by the European Commission’s Marie Curie-ITN funding scheme through the ENLIGHT Platform for 5.6 million Euro. PARTNER has brought together academic institutes, research centres and leading European companies, focusing in particular on a specialized radiotherapy (RT) called hadron therapy (HT), interchangeably referred to as particle therapy (PT). The ultimate goal of HT is to deliver more effective treatment to cancer patients leading to major improvement in the health of citizens. In Europe, several hundred million Euro have been invested, since the beginning of this century, in PT. In this decade, the use of HT is rapidly growing across Europe, and there is an urgent need for qualified researchers from a range of disciplines to work on its translational research. In response to this need, the European community of HT, and in particular 10 leading academic institutes, research centres, companies and small and medium-sized en...

  4. The Train Driver Recovery Problem - a Set Partitioning Based Model and Solution Method

    DEFF Research Database (Denmark)

    Rezanova, Natalia Jurjevna; Ryan, David

    2010-01-01

    partitioning problem. We define a disruption neighbourhood by identifying a small set of drivers and train tasks directly affected by the disruption. Based on the disruption neighbourhood, the TDRP model is formed and solved. If the TDRP solution provides a feasible recovery for the drivers within......The need to recover a train driver schedule occurs during major disruptions in the daily railway operations. Based on data from the Danish passenger railway operator DSB S-tog A/S, a solution method to the train driver recovery problem (TDRP) is developed. The TDRP is formulated as a set...... the disruption neighbourhood, we consider that the problem is solved. However, if a feasible solution is not found, the disruption neighbourhood is expanded by adding further drivers or increasing the recovery time period. Fractional solutions to the LP relaxation of the TDRP are resolved with a constraint...

  5. Multidisciplinary Team Training in a Simulation Setting for Acute Obstetric Emergencies A Systematic Review

    NARCIS (Netherlands)

    Merién, A. E. R.; van de Ven, J.; Mol, B. W.; Houterman, S.; Oei, S. G.

    2010-01-01

    OBJECTIVE: To perform a systematic review of the literature on the effectiveness of multidisciplinary teamwork training in a simulation setting for the reduction of medical adverse outcomes in obstetric emergency situations. DATA SOURCES: We searched Medline, Embase, and the Cochrane Library from

  6. Evaluating Question, Persuade, Refer (QPR) Suicide Prevention Training in a College Setting

    Science.gov (United States)

    Mitchell, Sharon L.; Kader, Mahrin; Darrow, Sherri A.; Haggerty, Melinda Z.; Keating, Niki L.

    2013-01-01

    This study assesses short-term and long-term learning outcomes of Question, Persuade, Refer (QPR) suicide prevention training in a college setting. Two hundred seventy-three participants completed pretest, posttest, and follow-up surveys regarding suicide prevention knowledge, attitudes, and skills. Results indicated: (a) increases in suicide…

  7. Evaluation of farmed cod products by a trained sensory panel and consumers in different test settings

    NARCIS (Netherlands)

    Sveinsdottir, K.; Martinsdottir, E.; Thorsdottir, F.; Schelvis-Smit, A.A.M.; Kole, A.; Thorsdottir, I.

    2010-01-01

    Sensory characteristics of farmed cod exposed to low or conventional stress levels prior to slaughter were evaluated by a trained sensory panel. Consumers in two different settings, central location test (CLT) and home-use test (HUT), also tasted the products and rated them according to overall

  8. Impact of Play Therapy on Parent-Child Relationship Stress at a Mental Health Training Setting

    Science.gov (United States)

    Ray, Dee C.

    2008-01-01

    This study investigated the impact of Child-Centred Play Therapy (CCPT)/Non-Directive Play Therapy on parent-child relationship stress using archival data from 202 child clients divided into clinical behavioural groups over 3-74 sessions in a mental health training setting. Results demonstrated significant differences between pre and post testing…

  9. City and County Solar PV Training Program, Module 1: Goal Setting and Clarification

    Energy Technology Data Exchange (ETDEWEB)

    McLaren, Joyce A [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2018-02-12

    This module will help attendees understand nuances between different types of renewable energy goals, the importance of terminology when setting and announcing goals, the value of formally clarifying priorities, and how priorities may impact procurement options. It is the first training in a series intended to help municipal staff procure solar PV for their land and buildings.

  10. Effects of Crew Resource Management Training on Medical Errors in a Simulated Prehospital Setting

    Science.gov (United States)

    Carhart, Elliot D.

    2012-01-01

    This applied dissertation investigated the effect of crew resource management (CRM) training on medical errors in a simulated prehospital setting. Specific areas addressed by this program included situational awareness, decision making, task management, teamwork, and communication. This study is believed to be the first investigation of CRM…

  11. The Effects of Relaxation Training Using Wrist Temperature as Biofeedback in an Educational Setting.

    Science.gov (United States)

    Matthews, Doris B.; Casteel, Jim Frank

    To examine the feasibility and effects of implementing relaxation training with a heterogeneous group of secondary school students in the classroom setting, and to determine the validity and reliability of using wrist temperature as a biofeedback method, 532 seventh grade students, divided into experimental and control groups, participated in a…

  12. Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

    Directory of Open Access Journals (Sweden)

    TIMCHENKO, L.

    2012-11-01

    Full Text Available Propositions necessary for development of parallel-hierarchical (PH network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.

  13. Introduction to the EC's Marie Curie Initial Training Network Project: The European Training Network in Digital Medical Imaging for Radiotherapy (ENTERVISION).

    Science.gov (United States)

    Dosanjh, Manjit; Cirilli, Manuela; Navin, Sparsh

    2015-01-01

    Between 2011 and 2015, the ENTERVISION Marie Curie Initial Training Network has been training 15 young researchers from a variety of backgrounds on topics ranging from in-beam Positron Emission Tomography or Single Particle Tomography techniques, to adaptive treatment planning, optical imaging, Monte Carlo simulations and biological phantom design. This article covers the main research activities, as well as the training scheme implemented by the participating institutes, which included academia, research, and industry.

  14. Training set optimization and classifier performance in a top-down diabetic retinopathy screening system

    Science.gov (United States)

    Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.

    2013-03-01

    Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.

  15. Performance of a visuomotor walking task in an augmented reality training setting.

    Science.gov (United States)

    Haarman, Juliet A M; Choi, Julia T; Buurke, Jaap H; Rietman, Johan S; Reenalda, Jasper

    2017-12-01

    Visual cues can be used to train walking patterns. Here, we studied the performance and learning capacities of healthy subjects executing a high-precision visuomotor walking task, in an augmented reality training set-up. A beamer was used to project visual stepping targets on the walking surface of an instrumented treadmill. Two speeds were used to manipulate task difficulty. All participants (n = 20) had to change their step length to hit visual stepping targets with a specific part of their foot, while walking on a treadmill over seven consecutive training blocks, each block composed of 100 stepping targets. Distance between stepping targets was varied between short, medium and long steps. Training blocks could either be composed of random stepping targets (no fixed sequence was present in the distance between the stepping targets) or sequenced stepping targets (repeating fixed sequence was present). Random training blocks were used to measure non-specific learning and sequenced training blocks were used to measure sequence-specific learning. Primary outcome measures were performance (% of correct hits), and learning effects (increase in performance over the training blocks: both sequence-specific and non-specific). Secondary outcome measures were the performance and stepping-error in relation to the step length (distance between stepping target). Subjects were able to score 76% and 54% at first try for lower speed (2.3 km/h) and higher speed (3.3 km/h) trials, respectively. Performance scores did not increase over the course of the trials, nor did the subjects show the ability to learn a sequenced walking task. Subjects were better able to hit targets while increasing their step length, compared to shortening it. In conclusion, augmented reality training by use of the current set-up was intuitive for the user. Suboptimal feedback presentation might have limited the learning effects of the subjects. Copyright © 2017 Elsevier B.V. All rights reserved.

  16. Age, sex, and setting factors and labor force in athletic training.

    Science.gov (United States)

    Kahanov, Leamor; Eberman, Lindsey E

    2011-01-01

    Occupation or occupational setting shifts might be occurring in the athletic training profession, and differences between sexes might exist; however, little evidence exists to confirm this supposition. To evaluate trends in male and female athletic training employment patterns in terms of age and occupational setting. Cross-sectional study. We requested demographic data from the National Athletic Trainers' Association (October 27, 2009) and obtained frequency totals of members by sex across the occupational life span by occupational setting. Our sample included 18 571 athletic trainers employed in the 3 largest classifications of occupational settings within the profession: college or university, clinical, and secondary school. We calculated frequencies and percentages to determine demographic and descriptive data. We analyzed the data using an analysis of variance to identify the differences between sexes across age and setting. We observed trends in occupational setting and sex across ages 22 to 67 years. We identified differences between sexes across the ages 22 to 67 years (F(1,18569) = 110818.080, P beginning around age 28 years and an increase in male athletic trainers in the secondary school setting beginning around their middle to late 40s. We observed differences at the intercept between setting and sex (F(1,18569) = 63529.344, P ages (F(1,18569) = 23566787.642, P ages in addition to an overall decrease in the workforce among all professionals. A marked decline in female athletic trainers occurred at age 28 years, yet the male population increased at the secondary school level, suggesting a setting shift. Burnout, fatigue, pay scale, and a misunderstanding of professional culture and job duties might influence the exodus or shift in athletic training.

  17. Beyond the Cut Set Bound: Uncertainty Computations in Network Coding with Correlated Sources

    CERN Document Server

    Gohari, Amin Aminzadeh; Jaggi, Sidharth

    2011-01-01

    Cut-set bounds on achievable rates for network communication protocols are not in general tight. In this paper we introduce a new technique for proving converses for the problem of transmission of correlated sources in networks, that results in bounds that are tighter than the corresponding cut-set bounds. The technique works as follows: on one hand we show that if the communication problem is solvable, the uncertainty of certain random variables in the network with respect to imaginary parties that have partial knowledge of the sources must satisfy some constraints that depend on the network architecture. On the other hand, the same uncertainties have to satisfy constraints that only depend on the joint distribution of the sources. Matching these two leads to restrictions on the statistical joint distribution of the sources in communication problems that are solvable over a given network architecture.

  18. Non-Linear State Estimation Using Pre-Trained Neural Networks

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

    2010-01-01

    effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve...... other parametric methods around regions with strong non-linearities by including them inside the network....

  19. Purely sequence trained neural networks for ASR based on lattice free MMI (Author’s Manuscript)

    Science.gov (United States)

    2016-09-08

    Purely sequence-trained neural networks for ASR based on lattice-free MMI Daniel Povey1,2, Vijayaditya Peddinti1, Daniel Galvez3, Pegah Ghahrmani1...we describe a method to perform sequence- discriminative training of neural network acoustic models with- out the need for frame-level cross-entropy... neural network outputs at one third the standard frame rate. These changes en- able us to perform the computation for the forward-backward algorithm

  20. Mindful Parenting Training in Child Psychiatric Settings: Heightened Parental Mindfulness Reduces Parents' and Children's Psychopathology.

    Science.gov (United States)

    Meppelink, Renée; de Bruin, Esther I; Wanders-Mulder, Femy H; Vennik, Corinne J; Bögels, Susan M

    Mindful parenting training is an application of mindfulness-based interventions that allows parents to perceive their children with unbiased and open attention without prejudgment and become more attentive and less reactive in their parenting. This study examined the effectiveness of mindful parenting training in a clinical setting on child and parental psychopathology and of mindfulness as a predictor of these outcomes. Seventy parents of 70 children (mean age = 8.7) who were referred to a mental health care clinic because of their children's psychopathology participated in an 8-week mindful parenting training. Parents completed questionnaires at pre-test, post-test and 8-week follow-up. A significant decrease was found in children's and parents' psychopathology and a significant increase in mindful parenting and in general mindful awareness. Improvement in general mindful awareness, but not mindful parenting, was found to predict a reduction in parental psychopathology, whereas improvement in mindful parenting, but not general mindful awareness, predicted the reduction of child psychopathology. This study adds to the emerging body of evidence indicating that mindful parenting training is effective for parents themselves and, indirectly, for their children suffering from psychopathology. As parents' increased mindful parenting, but not increased general mindfulness, is found to predict child psychopathology, mindful parenting training rather than general mindfulness training appears to be the training of choice. However, RCTs comparing mindful parenting to general mindfulness training and to parent management training are needed in order to shed more light on the effects of mindful parenting and mechanisms of change.

  1. Probabilistic neural network with homogeneity testing in recognition of discrete patterns set.

    Science.gov (United States)

    Savchenko, A V

    2013-10-01

    The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Copyright © 2013 Elsevier Ltd. All rights reserved.

  2. The Lateralization of Intrinsic Networks in the Aging Brain Implicates the Effects of Cognitive Training.

    Science.gov (United States)

    Luo, Cheng; Zhang, Xingxing; Cao, Xinyi; Gan, Yulong; Li, Ting; Cheng, Yan; Cao, Weifang; Jiang, Lijuan; Yao, Dezhong; Li, Chunbo

    2016-01-01

    Lateralization of function is an important organization of the human brain. The distribution of intrinsic networks in the resting brain is strongly related to cognitive function, gender and age. In this study, a longitudinal design with 1 year's duration was used to evaluate the cognitive training effects on the lateralization of intrinsic networks among healthy older adults. The subjects were divided into two groups randomly: one with multi-domain cognitive training over 3 months and the other as a wait-list control group. Resting state fMRI data were acquired before training and 1 year after training. We analyzed the functional lateralization in 10 common resting state fMRI networks. We observed statically significant training effects on the lateralization of two important RSNs related to high-level cognition: right- and left- frontoparietal networks (FPNs). The lateralization of the left-FPN was retained especially well in the training group but decreased in the control group. The increased lateralization with aging was observed in the cerebellum network (CereN), in which the lateralization was significantly increased in the control group, although the same change tendency was observed in the training group. These findings indicate that the lateralization of the high-level cognitive intrinsic networks is sensitive to multi-domain cognitive training. This study provides neuroimaging evidence to support the hypothesis that cognitive training should have an advantage in preventing cognitive decline in healthy older adults.

  3. Internal-state analysis in layered artificial neural network trained to categorize lung sounds

    NARCIS (Netherlands)

    Oud, M

    2002-01-01

    In regular use of artificial neural networks, only input and output states of the network are known to the user. Weight and bias values can be extracted but are difficult to interpret. We analyzed internal states of networks trained to map asthmatic lung sound spectra onto lung function parameters.

  4. Monitoring of Students' Interaction in Online Learning Settings by Structural Network Analysis and Indicators.

    Science.gov (United States)

    Ammenwerth, Elske; Hackl, Werner O

    2017-01-01

    Learning as a constructive process works best in interaction with other learners. Support of social interaction processes is a particular challenge within online learning settings due to the spatial and temporal distribution of participants. It should thus be carefully monitored. We present structural network analysis and related indicators to analyse and visualize interaction patterns of participants in online learning settings. We validate this approach in two online courses and show how the visualization helps to monitor interaction and to identify activity profiles of learners. Structural network analysis is a feasible approach for an analysis of the intensity and direction of interaction in online learning settings.

  5. Organization of the state space of a simple recurrent network before and after training on recursive linguistic structures.

    Science.gov (United States)

    Cernanský, Michal; Makula, Matej; Benusková, Lubica

    2007-03-01

    Recurrent neural networks are often employed in the cognitive science community to process symbol sequences that represent various natural language structures. The aim is to study possible neural mechanisms of language processing and aid in development of artificial language processing systems. We used data sets containing recursive linguistic structures and trained the Elman simple recurrent network (SRN) for the next-symbol prediction task. Concentrating on neuron activation clusters in the recurrent layer of SRN we investigate the network state space organization before and after training. Given a SRN and a training stream, we construct predictive models, called neural prediction machines, that directly employ the state space dynamics of the network. We demonstrate two important properties of representations of recursive symbol series in the SRN. First, the clusters of recurrent activations emerging before training are meaningful and correspond to Markov prediction contexts. We show that prediction states that naturally arise in the SRN initialized with small random weights approximately correspond to states of Variable Memory Length Markov Models (VLMM) based on individual symbols (i.e. words). Second, we demonstrate that during training, the SRN reorganizes its state space according to word categories and their grammatical subcategories, and the next-symbol prediction is again based on the VLMM strategy. However, after training, the prediction is based on word categories and their grammatical subcategories rather than individual words. Our conclusion holds for small depths of recursions that are comparable to human performances. The methods of SRN training and analysis of its state space introduced in this paper are of a general nature and can be used for investigation of processing of any other symbol time series by means of SRN.

  6. Comparative study of computational methods to detect the correlated reaction sets in biochemical networks.

    Science.gov (United States)

    Xi, Yanping; Chen, Yi-Ping Phoebe; Qian, Chen; Wang, Fei

    2011-03-01

    Correlated reaction sets (Co-Sets) are mathematically defined modules in biochemical reaction networks which facilitate the study of biological processes by decomposing complex reaction networks into conceptually simple units. According to the degree of association, Co-Sets can be classified into three types: perfect, partial and directional. Five approaches have been developed to calculate Co-Sets, including network-based pathway analysis, Monte Carlo sampling, linear optimization, enzyme subsets and hard-coupled reaction sets. However, differences in design and implementation of these methods lead to discrepancies in the resulted Co-Sets as well as in their use in biotechnology which need careful interpretation. In this paper, we provide a comparative study of the methods for Co-Sets computing in detail from four aspects: (i) sensitivity, (ii) completeness and soundness, (iii) flexibility and (iv) scalability. By applying them to Escherichia coli core metabolic network, the differences and relationships among these methods are clearly articulated which may be useful for potential users.

  7. GMES Initial Operations - Network for Earth Observation Research Training (GIONET)

    Science.gov (United States)

    Nicolas-Perea, V.; Balzter, H.

    2012-12-01

    GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. GIONET is a partnership of leading Universities, research institutes and private companies from across Europe aiming to cultivate a community of early stage researchers in the areas of optical and radar remote sensing skilled for the emerging GMES land monitoring services during the GMES Initial Operations period (2011-2013) and beyond. GIONET is expected to satisfy the demand for highly skilled researchers and provide personnel for operational phase of the GMES and monitoring and emergency services. It will achieve this by: -Providing postgraduate training in Earth Observation Science that exposes students to different research disciplines and complementary skills, providing work experiences in the private and academic sectors, and leading to a recognized qualification (Doctorate). -Enabling access to first class training in both fundamental and applied research skills to early-stage researchers at world-class academic centers and market leaders in the private sector. -Building on the experience from previous GMES research and development projects in the land monitoring and emergency information services. The training program through supervised research focuses on 14 research topics (each carried out by an Early Stage Researchers based in one of the partner organization) divided in 5 main areas: Forest monitoring: Global biomass information systems Forest Monitoring of the Congo Basin using Synthetic Aperture radar (SAR) Multi-concept Earth Observation Capabilities for Biomass Mapping and Change Detection: Synergy of Multi-temporal and Multi-frequency Interferometric Radar and Optical Satellite Data Land cover and change: Multi-scale Remote Sensing Synergy for Land Process Studies: from field Spectrometry to Airborne Hyperspectral and

  8. GIONET (GMES Initial Operations Network for Earth Observation Research Training)

    Science.gov (United States)

    Nicolas, V.; Balzter, H.

    2013-12-01

    GMES Initial Operations - Network for Earth Observation Research Training (GIONET) is a Marie Curie funded project that aims to establish the first of a kind European Centre of Excellence for Earth Observation Research Training. Copernicus (previously known as GMES (Global Monitoring for Environment and Security) is a joint undertaking of the European Space Agency and the European Commission. It develops fully operational Earth Observation monitoring services for a community of end users from the public and private sector. The first services that are considered fully operational are the land monitoring and emergency monitoring core services. In GIONET, 14 early stage researchers are being trained at PhD level in understanding the complex physical processes that determine how electromagnetic radiation interacts with the atmosphere and the land surface ultimately form the signal received by a satellite. In order to achieve this, the researchers are based in industry and universities across Europe, as well as receiving the best technical training and scientific education. The training programme through supervised research focuses on 14 research topics. Each topic is carried out by an Early Stage Researcher based in one of the partner organisations and is expected to lead to a PhD degree. The 14 topics are grouped in 5 research themes: Forest monitoring Land cover and change Coastal zone and freshwater monitoring Geohazards and emergency response Climate adaptation and emergency response The methods developed and used in GIONET are as diverse as its research topics. GIONET has already held two summer schools; one at Friedrich Schiller University in Jena (Germany), on 'New operational radar satellite applications: Introduction to SAR, Interferometry and Polarimetry for Land Surface Mapping'. The 2nd summer school took place last September at the University of Leicester (UK )on 'Remote sensing of land cover and forest in GMES'. The next Summer School in September 2013

  9. Ranking the whole MEDLINE database according to a large training set using text indexing

    Directory of Open Access Journals (Sweden)

    Andrade Miguel A

    2005-03-01

    Full Text Available Abstract Background The MEDLINE database contains over 12 million references to scientific literature, with about 3/4 of recent articles including an abstract of the publication. Retrieval of entries using queries with keywords is useful for human users that need to obtain small selections. However, particular analyses of the literature or database developments may need the complete ranking of all the references in the MEDLINE database as to their relevance to a topic of interest. This report describes a method that does this ranking using the differences in word content between MEDLINE entries related to a topic and the whole of MEDLINE, in a computational time appropriate for an article search query engine. Results We tested the capabilities of our system to retrieve MEDLINE references which are relevant to the subject of stem cells. We took advantage of the existing annotation of references with terms from the MeSH hierarchical vocabulary (Medical Subject Headings, developed at the National Library of Medicine. A training set of 81,416 references was constructed by selecting entries annotated with the MeSH term stem cells or some child in its sub tree. Frequencies of all nouns, verbs, and adjectives in the training set were computed and the ratios of word frequencies in the training set to those in the entire MEDLINE were used to score references. Self-consistency of the algorithm, benchmarked with a test set containing the training set and an equal number of references randomly selected from MEDLINE was better using nouns (79% than adjectives (73% or verbs (70%. The evaluation of the system with 6,923 references not used for training, containing 204 articles relevant to stem cells according to a human expert, indicated a recall of 65% for a precision of 65%. Conclusion This strategy appears to be useful for predicting the relevance of MEDLINE references to a given concept. The method is simple and can be used with any user-defined training

  10. Ranking the whole MEDLINE database according to a large training set using text indexing

    Science.gov (United States)

    Suomela, Brian P; Andrade, Miguel A

    2005-01-01

    Background The MEDLINE database contains over 12 million references to scientific literature, with about 3/4 of recent articles including an abstract of the publication. Retrieval of entries using queries with keywords is useful for human users that need to obtain small selections. However, particular analyses of the literature or database developments may need the complete ranking of all the references in the MEDLINE database as to their relevance to a topic of interest. This report describes a method that does this ranking using the differences in word content between MEDLINE entries related to a topic and the whole of MEDLINE, in a computational time appropriate for an article search query engine. Results We tested the capabilities of our system to retrieve MEDLINE references which are relevant to the subject of stem cells. We took advantage of the existing annotation of references with terms from the MeSH hierarchical vocabulary (Medical Subject Headings, developed at the National Library of Medicine). A training set of 81,416 references was constructed by selecting entries annotated with the MeSH term stem cells or some child in its sub tree. Frequencies of all nouns, verbs, and adjectives in the training set were computed and the ratios of word frequencies in the training set to those in the entire MEDLINE were used to score references. Self-consistency of the algorithm, benchmarked with a test set containing the training set and an equal number of references randomly selected from MEDLINE was better using nouns (79%) than adjectives (73%) or verbs (70%). The evaluation of the system with 6,923 references not used for training, containing 204 articles relevant to stem cells according to a human expert, indicated a recall of 65% for a precision of 65%. Conclusion This strategy appears to be useful for predicting the relevance of MEDLINE references to a given concept. The method is simple and can be used with any user-defined training set. Choice of the part of

  11. Computer vision based method and system for online measurement of geometric parameters of train wheel sets.

    Science.gov (United States)

    Zhang, Zhi-Feng; Gao, Zhan; Liu, Yuan-Yuan; Jiang, Feng-Chun; Yang, Yan-Li; Ren, Yu-Fen; Yang, Hong-Jun; Yang, Kun; Zhang, Xiao-Dong

    2012-01-01

    Train wheel sets must be periodically inspected for possible or actual premature failures and it is very significant to record the wear history for the full life of utilization of wheel sets. This means that an online measuring system could be of great benefit to overall process control. An online non-contact method for measuring a wheel set's geometric parameters based on the opto-electronic measuring technique is presented in this paper. A charge coupled device (CCD) camera with a selected optical lens and a frame grabber was used to capture the image of the light profile of the wheel set illuminated by a linear laser. The analogue signals of the image were transformed into corresponding digital grey level values. The 'mapping function method' is used to transform an image pixel coordinate to a space coordinate. The images of wheel sets were captured when the train passed through the measuring system. The rim inside thickness and flange thickness were measured and analyzed. The spatial resolution of the whole image capturing system is about 0.33 mm. Theoretic and experimental results show that the online measurement system based on computer vision can meet wheel set measurement requirements.

  12. Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Ryan B., E-mail: randerson@astro.cornell.edu [Cornell University Department of Astronomy, 406 Space Sciences Building, Ithaca, NY 14853 (United States); Bell, James F., E-mail: Jim.Bell@asu.edu [Arizona State University School of Earth and Space Exploration, Bldg.: INTDS-A, Room: 115B, Box 871404, Tempe, AZ 85287 (United States); Wiens, Roger C., E-mail: rwiens@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States); Morris, Richard V., E-mail: richard.v.morris@nasa.gov [NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX 77058 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States)

    2012-04-15

    We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO{sub 2} at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by {approx} 3 wt.%. The statistical significance of these improvements was {approx} 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and

  13. Volume Load and Neuromuscular Fatigue During an Acute Bout of Agonist-Antagonist Paired-Set vs. Traditional-Set Training.

    Science.gov (United States)

    Paz, Gabriel A; Robbins, Daniel W; de Oliveira, Carlos G; Bottaro, Martim; Miranda, Humberto

    2017-10-01

    The purpose of this study was to investigate the acute effects of performing paired-set (PS) vs. traditional-set (TS) training over 3 consecutive sets, on volume load and electromyographic fatigue parameters of the latissimus dorsi, biceps brachii, pectoralis major, and triceps brachii muscles. Fifteen trained men performed 2 testing protocols (TS and PS) using 10 repetition maximum loads. The TS protocol consisted of 3 sets of bench press (BP) followed by 3 sets of wide-grip seated row (SR). The PS consisted of 3 sets of BP and 3 sets of SR performed in an alternating manner. Volume load was calculated as load × repetitions. The electromyographic signal, time (CRMS) and frequency (Cf5) domain, parameters were recorded during SR. Under the PS protocol, sets of SR were performed immediately after the sets of BP. A 2-minute rest interval between the completion of the set of SR and the subsequent set of BP was implemented (e.g., between PSs). Under the TS protocol, 2-minute rest intervals were implemented between all sets. BP and SR volume loads decreased significantly from set 1 to set 2 and from set 2 to set 3 under both conditions. Volume load was greater for all sets of both exercises under PS as compared with TS. Muscle fatigue indices were greater under PS as compared with TS. In general, these results indicate that as compared with TS, PS produced a greater training volume in less time and may induce greater fatigue and thereby provide an enhanced training stimulus.

  14. Computer Vision Based Method and System for Online Measurement of Geometric Parameters of Train Wheel Sets

    Directory of Open Access Journals (Sweden)

    Hong-Jun Yang

    2011-12-01

    Full Text Available Train wheel sets must be periodically inspected for possible or actual premature failures and it is very significant to record the wear history for the full life of utilization of wheel sets. This means that an online measuring system could be of great benefit to overall process control. An online non-contact method for measuring a wheel set’s geometric parameters based on the opto-electronic measuring technique is presented in this paper. A charge coupled device (CCD camera with a selected optical lens and a frame grabber was used to capture the image of the light profile of the wheel set illuminated by a linear laser. The analogue signals of the image were transformed into corresponding digital grey level values. The ‘mapping function method’ is used to transform an image pixel coordinate to a space coordinate. The images of wheel sets were captured when the train passed through the measuring system. The rim inside thickness and flange thickness were measured and analyzed. The spatial resolution of the whole image capturing system is about 0.33 mm. Theoretic and experimental results show that the online measurement system based on computer vision can meet wheel set measurement requirements.

  15. Moving Large Data Sets Over High-Performance Long Distance Networks

    Energy Technology Data Exchange (ETDEWEB)

    Hodson, Stephen W [ORNL; Poole, Stephen W [ORNL; Ruwart, Thomas [ORNL; Settlemyer, Bradley W [ORNL

    2011-04-01

    In this project we look at the performance characteristics of three tools used to move large data sets over dedicated long distance networking infrastructure. Although performance studies of wide area networks have been a frequent topic of interest, performance analyses have tended to focus on network latency characteristics and peak throughput using network traffic generators. In this study we instead perform an end-to-end long distance networking analysis that includes reading large data sets from a source file system and committing large data sets to a destination file system. An evaluation of end-to-end data movement is also an evaluation of the system configurations employed and the tools used to move the data. For this paper, we have built several storage platforms and connected them with a high performance long distance network configuration. We use these systems to analyze the capabilities of three data movement tools: BBcp, GridFTP, and XDD. Our studies demonstrate that existing data movement tools do not provide efficient performance levels or exercise the storage devices in their highest performance modes. We describe the device information required to achieve high levels of I/O performance and discuss how this data is applicable in use cases beyond data movement performance.

  16. Fine Registration of Kilo-Station Networks - a Modern Procedure for Terrestrial Laser Scanning Data Sets

    Science.gov (United States)

    Hullo, J.-F.

    2016-06-01

    We propose a complete methodology for the fine registration and referencing of kilo-station networks of terrestrial laser scanner data currently used for many valuable purposes such as 3D as-built reconstruction of Building Information Models (BIM) or industrial asbuilt mock-ups. This comprehensive target-based process aims to achieve the global tolerance below a few centimetres across a 3D network including more than 1,000 laser stations spread over 10 floors. This procedure is particularly valuable for 3D networks of indoor congested environments. In situ, the use of terrestrial laser scanners, the layout of the targets and the set-up of a topographic control network should comply with the expert methods specific to surveyors. Using parametric and reduced Gauss-Helmert models, the network is expressed as a set of functional constraints with a related stochastic model. During the post-processing phase inspired by geodesy methods, a robust cost function is minimised. At the scale of such a data set, the complexity of the 3D network is beyond comprehension. The surveyor, even an expert, must be supported, in his analysis, by digital and visual indicators. In addition to the standard indicators used for the adjustment methods, including Baarda's reliability, we introduce spectral analysis tools of graph theory for identifying different types of errors or a lack of robustness of the system as well as in fine documenting the quality of the registration.

  17. FINE REGISTRATION OF KILO-STATION NETWORKS - A MODERN PROCEDURE FOR TERRESTRIAL LASER SCANNING DATA SETS

    Directory of Open Access Journals (Sweden)

    J.-F. Hullo

    2016-06-01

    Full Text Available We propose a complete methodology for the fine registration and referencing of kilo-station networks of terrestrial laser scanner data currently used for many valuable purposes such as 3D as-built reconstruction of Building Information Models (BIM or industrial asbuilt mock-ups. This comprehensive target-based process aims to achieve the global tolerance below a few centimetres across a 3D network including more than 1,000 laser stations spread over 10 floors. This procedure is particularly valuable for 3D networks of indoor congested environments. In situ, the use of terrestrial laser scanners, the layout of the targets and the set-up of a topographic control network should comply with the expert methods specific to surveyors. Using parametric and reduced Gauss-Helmert models, the network is expressed as a set of functional constraints with a related stochastic model. During the post-processing phase inspired by geodesy methods, a robust cost function is minimised. At the scale of such a data set, the complexity of the 3D network is beyond comprehension. The surveyor, even an expert, must be supported, in his analysis, by digital and visual indicators. In addition to the standard indicators used for the adjustment methods, including Baarda’s reliability, we introduce spectral analysis tools of graph theory for identifying different types of errors or a lack of robustness of the system as well as in fine documenting the quality of the registration.

  18. A real-time control method-based simulation for high-speed trains on large-scale rail network

    Science.gov (United States)

    Liu, Yutong; Cao, Chengxuan; Zhou, Yaling; Feng, Ziyan

    In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.

  19. A managed clinical network for cardiac services: set-up, operation and impact on patient care

    Directory of Open Access Journals (Sweden)

    Karen E. Hamilton

    2005-09-01

    Full Text Available Purpose: To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. Methods: This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Results: Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and ‘bed down’. Its primary “modus operand” was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were

  20. A managed clinical network for cardiac services: set-up, operation and impact on patient care

    Science.gov (United States)

    E StC Hamilton, Karen; M Sullivan, Frank; T Donnan, Peter; Taylor, Rex; Ikenwilo, Divine; Scott, Anthony; Baker, Chris; Wyke, Sally

    2005-01-01

    Abstract Purpose To investigate the set up and operation of a Managed Clinical Network for cardiac services and assess its impact on patient care. Methods This single case study used process evaluation with observational before and after comparison of indicators of quality of care and costs. The study was conducted in Dumfries and Galloway, Scotland and used a three-level framework. Process evaluation of the network set-up and operation through a documentary review of minutes; guidelines and protocols; transcripts of fourteen semi-structured interviews with health service personnel including senior managers, general practitioners, nurses, cardiologists and members of the public. Outcome evaluation of the impact of the network through interrupted time series analysis of clinical data of 202 patients aged less than 76 years admitted to hospital with a confirmed myocardial infarction one-year pre and one-year post, the establishment of the network. The main outcome measures were differences between indicators of quality of care targeted by network protocols. Economic evaluation of the transaction costs of the set-up and operation of the network and the resource costs of the clinical care of the 202 myocardial infarction patients from the time of hospital admission to 6 months post discharge through interrupted time series analysis. The outcome measure was different in National Health Service resource use. Results Despite early difficulties, the network was successful in bringing together clinicians, patients and managers to redesign services, exhibiting most features of good network management. The role of the energetic lead clinician was crucial, but the network took time to develop and ‘bed down’. Its primary “modus operand” was the development of a myocardial infarction pathway and associated protocols. Of sixteen clinical care indicators, two improved significantly following the launch of the network and nine showed improvements, which were not

  1. Balance between noise and information flow maximizes set complexity of network dynamics.

    Directory of Open Access Journals (Sweden)

    Tuomo Mäki-Marttunen

    Full Text Available Boolean networks have been used as a discrete model for several biological systems, including metabolic and genetic regulatory networks. Due to their simplicity they offer a firm foundation for generic studies of physical systems. In this work we show, using a measure of context-dependent information, set complexity, that prior to reaching an attractor, random Boolean networks pass through a transient state characterized by high complexity. We justify this finding with a use of another measure of complexity, namely, the statistical complexity. We show that the networks can be tuned to the regime of maximal complexity by adding a suitable amount of noise to the deterministic Boolean dynamics. In fact, we show that for networks with Poisson degree distributions, all networks ranging from subcritical to slightly supercritical can be tuned with noise to reach maximal set complexity in their dynamics. For networks with a fixed number of inputs this is true for near-to-critical networks. This increase in complexity is obtained at the expense of disruption in information flow. For a large ensemble of networks showing maximal complexity, there exists a balance between noise and contracting dynamics in the state space. In networks that are close to critical the intrinsic noise required for the tuning is smaller and thus also has the smallest effect in terms of the information processing in the system. Our results suggest that the maximization of complexity near to the state transition might be a more general phenomenon in physical systems, and that noise present in a system may in fact be useful in retaining the system in a state with high information content.

  2. Risk Assessment of Distribution Network Based on Random set Theory and Sensitivity Analysis

    Science.gov (United States)

    Zhang, Sh; Bai, C. X.; Liang, J.; Jiao, L.; Hou, Z.; Liu, B. Zh

    2017-05-01

    Considering the complexity and uncertainty of operating information in distribution network, this paper introduces the use of random set for risk assessment. The proposed method is based on the operating conditions defined in the random set framework to obtain the upper and lower cumulative probability functions of risk indices. Moreover, the sensitivity of risk indices can effectually reflect information about system reliability and operating conditions, and by use of these information the bottlenecks that suppress system reliability can be found. The analysis about a typical radial distribution network shows that the proposed method is reasonable and effective.

  3. A practical model for the train-set utilization: The case of Beijing-Tianjin passenger dedicated line in China.

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    Full Text Available As a sustainable transportation mode, high-speed railway (HSR has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA, the utilization efficiency of train-sets can be increased from 43.4% (ACA to 46.9% (Two-Stage, and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved.

  4. A practical model for the train-set utilization: The case of Beijing-Tianjin passenger dedicated line in China.

    Science.gov (United States)

    Zhou, Yu; Zhou, Leishan; Wang, Yun; Li, Xiaomeng; Yang, Zhuo

    2017-01-01

    As a sustainable transportation mode, high-speed railway (HSR) has become an efficient way to meet the huge travel demand. However, due to the high acquisition and maintenance cost, it is impossible to build enough infrastructure and purchase enough train-sets. Great efforts are required to improve the transport capability of HSR. The utilization efficiency of train-sets (carrying tools of HSR) is one of the most important factors of the transport capacity of HSR. In order to enhance the utilization efficiency of the train-sets, this paper proposed a train-set circulation optimization model to minimize the total connection time. An innovative two-stage approach which contains segments generation and segments combination was designed to solve this model. In order to verify the feasibility of the proposed approach, an experiment was carried out in the Beijing-Tianjin passenger dedicated line, to fulfill a 174 trips train diagram. The model results showed that compared with the traditional Ant Colony Algorithm (ACA), the utilization efficiency of train-sets can be increased from 43.4% (ACA) to 46.9% (Two-Stage), and 1 train-set can be saved up to fulfill the same transportation tasks. The approach proposed in the study is faster and more stable than the traditional ones, by using which, the HSR staff can draw up the train-sets circulation plan more quickly and the utilization efficiency of the HSR system is also improved.

  5. Optimizing Intermodal Train Schedules with a Design Balanced Network Design Model

    DEFF Research Database (Denmark)

    Pedersen, Michael Berliner; Crainic, Teodor Gabriel

    We present a modeling approach for optimizing intermodal trains schedules based on an infrastructure divided into time-dependent train paths. The formulation can be generalized to a capacitated multi commodity network design model with additional design balance constraints. We present a Tabu Sear...... based metaheuristic to solve large instances of the generalized problem and compare its results on standard network design problems to those obtained using the solver XpressMP....

  6. Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets

    Directory of Open Access Journals (Sweden)

    Loreto M. Valenzuela

    2016-01-01

    Full Text Available Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R2>0.78 for test set but very low accuracy on cross-validation sets (less than 19% of experimental points within experimental error. Instead, using consolidated parameters like equilibrium water uptake a good model is obtained (R2=0.78 for test set, with accurate predictions for 50% of tested polymers. The semiempirical model was applied to the 56-polymer library of L-tyrosine-derived polyarylates, identifying groups of polymers that are likely to satisfy design criteria for water uptake. This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria.

  7. A randomized trial of group parent training: reducing child conduct problems in real-world settings.

    Science.gov (United States)

    Kjøbli, John; Hukkelberg, Silje; Ogden, Terje

    2013-03-01

    Group-based Parent Management Training, the Oregon model (PMTO, 12 sessions) was delivered by the regular staff of municipal child and family services. PMTO is based on social interaction learning theory and promotes positive parenting skills in parents of children with conduct problems. This study examined the effectiveness of the group-based training intervention in real world settings both immediately following and six months after termination of the intervention. One hundred thirty-seven children (3-12 years) and their parents participated in this study. The families were randomly assigned to group-based training or a comparison group. Data were collected from parents and teachers. The caregiver assessments of parenting practices and child conduct problems and caregiver and teacher reported social competence revealed immediate and significant intervention effects. Short- and long-term beneficial effects were reported from parents, although no follow-up effects were evident on teacher reports. These effectiveness findings and the potential for increasing the number of families served to support the further dissemination and implementation of group-based parent training. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Innovative strategies for transforming internal medicine residency training in resource-limited settings: the Mozambique experience.

    Science.gov (United States)

    Mocumbi, Ana Olga; Carrilho, Carla; Aronoff-Spencer, Eliah; Funzamo, Carlos; Patel, Sam; Preziosi, Michael; Lederer, Philip; Tilghman, Winston; Benson, Constance A; Badaró, Roberto; Nguenha, A; Schooley, Robert T; Noormahomed, Emília V

    2014-08-01

    With approximately 4 physicians per 100,000 inhabitants, Mozambique faces one of the most severe health care provider shortages in Sub-Saharan Africa. The lack of sufficient well-trained medical school faculty is one of Mozambique's major barrier to producing new physicians annually. A partnership between the Universidade Eduardo Mondlane and the University of California, San Diego, has addressed this challenge with support from the Medical Education Partnership Initiative. After an initial needs assessment involving questionnaires and focus groups of residents, and working with key members from the Ministry of Health, the Medical Council, and Maputo Central Hospital, a set of interventions was designed. The hospital's internal medicine residency program was chosen as the focus for the plan. Interventions included curriculum design, new teaching methodologies, investment in an informatics infrastructure for access to digital references, building capacity to support clinical research, and providing financial incentives to retain junior faculty. The number of candidates entering the internal medicine residency program has increased, and detailed monitoring and evaluation is measuring the impact of these changes on the quality of training. These changes are expected to improve the long-term quality of postgraduate training in general through dissemination to other departments. They also have the potential to facilitate equitable distribution of specialists nationwide by expanding postgraduate training to other hospitals and universities.

  9. Peer Network Composition of Acculturated and Ethnoculturally-Affiliated Adolescents in a Multicultural Setting.

    Science.gov (United States)

    Maharaj, Sherry I.; Connolly, Jennifer A.

    1994-01-01

    A study investigated the ethnocultural composition of the peer networks of 896 suburban high school students. Results indicated that gender mix, setting mix, and frequency of contact significantly differed across homogenous, integrated, and heterogeneous peer structures, demonstrating the mitigating impact of environmental factors on the interplay…

  10. How Do Social Networks Influence Learning Outcomes? A Case Study in an Industrial Setting

    Science.gov (United States)

    Maglajlic, Seid; Helic, Denis

    2012-01-01

    and Purpose: The purpose of this research is to shed light on the impact of implicit social networks to the learning outcome of e-learning participants in an industrial setting. Design/methodology/approach: The paper presents a theoretical framework that allows the authors to measure correlation coefficients between the different affiliations that…

  11. A Game Theoretic Approach for Modeling Privacy Settings of an Online Social Network

    Directory of Open Access Journals (Sweden)

    Jundong Chen

    2014-05-01

    Full Text Available Users of online social networks often adjust their privacy settings to control how much information on their profiles is accessible to other users of the networks. While a variety of factors have been shown to affect the privacy strategies of these users, very little work has been done in analyzing how these factors influence each other and collectively contribute towards the users’ privacy strategies. In this paper, we analyze the influence of attribute importance, benefit, risk and network topology on the users’ attribute disclosure behavior by introducing a weighted evolutionary game model. Results show that: irrespective of risk, users aremore likely to reveal theirmost important attributes than their least important attributes; when the users’ range of influence is increased, the risk factor plays a smaller role in attribute disclosure; the network topology exhibits a considerable effect on the privacy in an environment with risk.

  12. Toilet training children with autism and developmental delays: an effective program for school settings.

    Science.gov (United States)

    Cocchiola, Michael A; Martino, Gayle M; Dwyer, Lisa J; Demezzo, Kelly

    2012-01-01

    Current research literature on toilet training for children with autism or developmental delays focuses on smaller case studies, typically with concentrated clinical support. Limited research exists to support an effective school-based program to teach toileting skills implemented by public school staff. We describe an intervention program to toilet train 5 children with autism or developmental delays who demonstrated no prior success in the home or school setting. Intervention focused on (a) removal of diapers during school hours, (b) scheduled time intervals for bathroom visits, (c) a maximum of 3 min sitting on the toilet, (d) reinforcers delivered immediately contingent on urination in the toilet, and (e) gradually increased time intervals between bathroom visits as each participant met mastery during the preceding, shorter time interval. The program was effective across all 5 cases in a community-based elementary school. Paraprofessional staff implemented the program with minimal clinical oversight.

  13. A set packing inspired method for real-time junction train routing

    DEFF Research Database (Denmark)

    Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

    2013-01-01

    Efficiently coordinating the often large number of interdependent, timetabled train movements on a railway junction, while satisfying a number of operational requirements, is one of the most important problems faced by a railway company. The most critical variant of the problem arises on a daily...... basis at major railway junctions where disruptions to rail traffic make the planned schedule/routing infeasible and rolling stock planners are forced to re-schedule/re-route trains in order to recover feasibility. The dynamic nature of the problem means that good solutions must be obtained quickly....... In this paper we describe a set packing inspired formulation of this problem and develop a branch-and-price based solution approach. A real life test instance arising in Germany and supplied by the major German railway company, Deutsche Bahn, indicates the efficiency of the proposed approach by confirming...

  14. A Set Packing Inspired Method for Real-Time Junction Train Routing

    DEFF Research Database (Denmark)

    Lusby, Richard Martin; Larsen, Jesper; Ehrgott, Matthias

    Efficiently coordinating the often large number of interdependent, timetabled train movements on a railway junction, while satisfying a number of operational requirements, is one of the most important problems faced by a railway company. The most critical variant of the problem arises on a daily...... basis at major railway junctions where disruptions to rail traffi c make the planned schedule/routing infeasible and rolling stock planners are forced to reschedule/re-route trains in order to recover feasibility. The dynamic nature of the problem means that good solutions must be obtained quickly....... In this paper we describe a set packing inspired formulation of this problem and develop a branch-and-price based solution approach. A real life test instance arising in Germany and supplied by the major German railway company, Deutsche Bahn, indicates the efficiency of the proposed approach by confirming...

  15. Twelve tips on how to set up postgraduate training via remote clinical supervision

    DEFF Research Database (Denmark)

    Wearne, Susan; Dornan, Tim; Teunissen, Pim W.

    2013-01-01

    Doctors-in-training can now be supervised remotely by specialist clinicians using information and communication technology. This provides an intermediate stage of professional development between on-site supervision and independent medical practice. Remote supervision could increase training...... with resilience, insight into their strengths and weaknesses, capacity to self-monitor and correct, and willingness to seek help. These doctors benefit from remote supervisors who facilitate their learning, monitor their well-being, and support them holistically. Educational organisations need to oversee remote...... placements and match the right registrar, to the right placement with the right supervisor. We outline in our twelve tips how to set up remote supervision in order to maximise the educational benefits and minimise the risks....

  16. Vulnerability analysis for airport networks based on fuzzy soft sets: From the structural and functional perspective

    Directory of Open Access Journals (Sweden)

    Li Shanmei

    2015-06-01

    Full Text Available Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports, as well as to avoid unnecessary disturbances in the planning of flight schedules. In this paper, the evaluation methods of airport importance and network efficiency are established. Firstly, the evaluation indices of airport importance are proposed from both the topological and functional perspectives. The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network. Secondly, an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports, which can fuse together importance indices over different time intervals. Thirdly, an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method. Finally, empirical studies using real traffic data of US and China’s airport networks show that the evaluation method proposed in this paper is the most accurate. The vulnerability of US and China’s airport networks is compared. The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.

  17. A New Training Method for Analyzable Structured Neural Network and Application of Daily Peak Load Forecasting

    Science.gov (United States)

    Iizaka, Tatsuya; Matsui, Tetsuro; Fukuyama, Yoshikazu

    This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors. The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.

  18. Energy cost of the ACSM single-set resistance training protocol.

    Science.gov (United States)

    Phillips, Wayne T; Ziuraitis, Joana R

    2003-05-01

    The purpose of this study was (a) to assess the energy cost and intensity of a single-set resistance training (RT) protocol conducted according to the recent ACSM guidelines and (b) to compare obtained values to those recently reported as eliciting health benefits via endurance-based physical activity (PA). Twelve subjects, mean age 26.7 +/- 3.8 years, performed 1 set of a 15 repetition maximum (15 RM) for each of 8 RT exercises. Metabolic data were collected via a portable calorimetric system. Training intensity in metabolic equivalents (METS) was 3.9 +/- 0.4 for men and 4.2 +/- 0.6 for women (not significant). Total energy was 135.20 +/- 16.6 kcal for men and 81.7 +/- 11.1 kcal for women (p ACSM single-set, 8-exercise RT protocol is a feasible alternative for achieving moderate-intensity (3-6 METS) PA, but it is not sufficient to achieve a moderate amount (150-200 kcal) of PA.

  19. Timetable-based simulation method for choice set generation in large-scale public transport networks

    DEFF Research Database (Denmark)

    Rasmussen, Thomas Kjær; Anderson, Marie Karen; Nielsen, Otto Anker

    2016-01-01

    The composition and size of the choice sets are a key for the correct estimation of and prediction by route choice models. While existing literature has posed a great deal of attention towards the generation of path choice sets for private transport problems, the same does not apply to public...... transport problems. This study proposes a timetable-based simulation method for generating path choice sets in a multimodal public transport network. Moreover, this study illustrates the feasibility of its implementation by applying the method to reproduce 5131 real-life trips in the Greater Copenhagen Area...

  20. Strength and Body Composition Changes in Recreationally Strength-Trained Individuals: Comparison of One versus Three Sets Resistance-Training Programmes

    Directory of Open Access Journals (Sweden)

    J. S. Baker

    2013-01-01

    Full Text Available Purpose. The purpose of this study was to determine the effects of increasing the volume of weight-training from one to three sets upon body composition and muscular strength. Methods. Sixteen male weight-trainers volunteered to act as subjects and were randomly assigned to one of two training groups. Supervised weight-training targeting the upper body was conducted three times per week for eight weeks using one set (n=8 or three sets (n=8 of six repetitions to fatigue. Subjects were measured before and after the training intervention for (1 strength performance (N and kg and (2 adiposity (sum of seven skinfold thicknesses in mm. Results. Both training groups improved significantly (20.7% in terms of muscular strength (P0.05. Significant decreases were also observed for skinfold measures in the one set group (P<0.05. Conclusions. One set of high intensity resistance training was as effective as three sets for increasing the strength of muscle groups in the upper body. The one set protocol also produced significantly greater decreases in adiposity.

  1. Navigating Social Networking and Social Media in School Psychology: Ethical and Professional Considerations in Training Programs

    Science.gov (United States)

    Pham, Andy V.

    2014-01-01

    Social networking and social media have undoubtedly proliferated within the past decade, allowing widespread communication and dissemination of user-generated content and information. Some psychology graduate programs, including school psychology, have started to embrace social networking and media for instructional and training purposes; however,…

  2. Multiobjective training of artificial neural networks for rainfall-runoff modeling

    NARCIS (Netherlands)

    De Vos, N.J.; Rientjes, T.H.M.

    2008-01-01

    This paper presents results on the application of various optimization algorithms for the training of artificial neural network rainfall-runoff models. Multilayered feed-forward networks for forecasting discharge from two mesoscale catchments in different climatic regions have been developed for

  3. Get SET: aligning anatomy demonstrator programmes with Surgical Education and Training selection criteria.

    Science.gov (United States)

    Rhodes, Danielle; Fogg, Quentin A; Lazarus, Michelle D

    2017-12-21

    Prevocational doctors aspiring to surgical careers are commonly recruited as anatomy demonstrators for undergraduate and graduate medical programmes. Entry into Surgical Education and Training (SET) is highly competitive and a unique opportunity exists to align anatomy demonstrator programmes with the selection criteria and core competencies of SET programmes. This study used a qualitative approach to (i) determine what criteria applicants for SET are assessed on and (ii) identify criteria that could be aligned with and enhanced by an anatomy demonstrator programme. The selection guidelines of all nine surgical specialties for the 2017 intake of SET trainees were analysed using qualitative content analysis methodology. The Royal Australasian College of Surgeons adopted a holistic approach to trainee selection that assessed both discipline-specific and discipline-independent skills. Qualitative content analysis identified eight categories of key selection criteria: medical expertise, scholarly activity, professional identity, interpersonal skills, integrity, self-management, insight and self-awareness and community involvement. The structured curriculum vitae was heavily weighted towards discipline-specific skills, such as medical expertise and scholarly activity. Insufficient information was available to determine the weighting of selection criteria assessed by the structured referee reports or interviews. Anatomy demonstrator programmes provide prevocational doctors with unique opportunities to develop surgical skills and competencies in a non-clinical setting. Constructively aligned anatomy demonstrator programmes may be particularly beneficial for prevocational doctors seeking to improve their anatomical knowledge, teaching skills or scholarly activity. © 2017 Royal Australasian College of Surgeons.

  4. Restorative justice training in intercultural settings in Serbia, and the contribution of the arts

    Directory of Open Access Journals (Sweden)

    Liebmann Marian

    2016-01-01

    Full Text Available This paper describes restorative justice training courses the author delivered in Serbia and Montenegro in the period 2003-2006, set in the context of the post-conflict situation, and reflects on the intercultural elements added to this course. The author also makes reference to recent work on hate crime and restorative justice in the UK as an extreme example of intercultural conflict. The final two sections discuss the potential of the arts in providing an extra (non-verbal tool in this work, using as examples two courses the author ran in Serbia.

  5. The collaborative African genomics network training program: a trainee perspective on training the next generation of African scientists.

    Science.gov (United States)

    Mlotshwa, Busisiwe C; Mwesigwa, Savannah; Mboowa, Gerald; Williams, Lesedi; Retshabile, Gaone; Kekitiinwa, Adeodata; Wayengera, Misaki; Kyobe, Samuel; Brown, Chester W; Hanchard, Neil A; Mardon, Graeme; Joloba, Moses; Anabwani, Gabriel; Mpoloka, Sununguko W

    2017-07-01

    The Collaborative African Genomics Network (CAfGEN) aims to establish sustainable genomics research programs in Botswana and Uganda through long-term training of PhD students from these countries at Baylor College of Medicine. Here, we present an overview of the CAfGEN PhD training program alongside trainees' perspectives on their involvement. Historically, collaborations between high-income countries (HICs) and low- and middle-income countries (LMICs), or North-South collaborations, have been criticized for the lack of a mutually beneficial distribution of resources and research findings, often undermining LMICs. CAfGEN plans to address this imbalance in the genomics field through a program of technology and expertise transfer to the participating LMICs. An overview of the training program is presented. Trainees from the CAfGEN project summarized their experiences, looking specifically at the training model, benefits of the program, challenges encountered relating to the cultural transition, and program outcomes after the first 2 years. Collaborative training programs like CAfGEN will not only help establish sustainable long-term research initiatives in LMICs but also foster stronger North-South and South-South networks. The CAfGEN model offers a framework for the development of training programs aimed at genomics education for those for whom genomics is not their "first language." Genet Med advance online publication 06 April 2017.

  6. South Carolina Area Health Education Consortium Disaster Preparedness and Response Training Network: an emerging partner in preparedness training.

    Science.gov (United States)

    Kennedy, Beth; Carson, Deborah Stier; Garr, David

    2009-03-01

    The South Carolina Area Health Education Consortium (SC AHEC) was funded in 2003 to train healthcare professionals in disaster preparedness and response. During the 5 years of funding, its Disaster Preparedness and Response Training Network evolved from disaster awareness training to competency-based instruction and performance assessment. With funding from the assistant secretary for preparedness and response (ASPR), a project with implications for national dissemination was developed to evaluate 2 aspects of preparedness training for community-based healthcare professionals. The SC AHEC designed disaster preparedness curricula and lesson plans, using a consensus-building technique, and then (1) distributed sample curricula and resources through the national Area Health Education Center system to assess an approach for providing preparedness training and (2) delivered a standardized preparedness curriculum to key influential thought leaders from 4 states to evaluate the effectiveness and acceptability of the curriculum. As a result of this project, the SC AHEC recommends that preparedness training for community-based practitioners needs to be concise and professionally relevant. It should be integrated into existing healthcare professions education programs and continuing education offerings. The project also demonstrated that although AHECs may be interested and well suited to incorporate preparedness training as part of their mission, more work needs to be done if they are to assume a prominent role in disaster preparedness training.

  7. Network-Based Coordination of Civil-Service Training: Lessons from the Case of Estonia

    Directory of Open Access Journals (Sweden)

    Metsma Merilin

    2017-06-01

    Full Text Available The focus of this article is on the coordination of civil-service training in a decentralized civil-service system. The Estonian case is studied. The article investigates network-based coordination, analyzes the power sources of the central coordinator and discusses the opportunities and limitations of creating coherence through network-type cooperation. The article concludes that the key power sources for the central coordinator are financial, human and technical resources paired with knowledge, leadership and commitment. The case study shows that, in a decentralized civil service system, a common understanding on training and development can be fostered by intense collaboration through networks.

  8. Regional cross national networks for education and training in health

    DEFF Research Database (Denmark)

    Nøhr, Christian; Bygholm, Ann; Hejlesen, Ole

    The paper argues that the education activities in health informatics should be established in net-works covering regions with comparable health care systems involving one or more comparable countries.......The paper argues that the education activities in health informatics should be established in net-works covering regions with comparable health care systems involving one or more comparable countries....

  9. Functional brain network modularity predicts response to cognitive training after brain injury.

    Science.gov (United States)

    Arnemann, Katelyn L; Chen, Anthony J-W; Novakovic-Agopian, Tatjana; Gratton, Caterina; Nomura, Emi M; D'Esposito, Mark

    2015-04-14

    We tested the value of measuring modularity, a graph theory metric indexing the relative extent of integration and segregation of distributed functional brain networks, for predicting individual differences in response to cognitive training in patients with brain injury. Patients with acquired brain injury (n = 11) participated in 5 weeks of cognitive training and a comparison condition (brief education) in a crossover intervention study design. We quantified the measure of functional brain network organization, modularity, from functional connectivity networks during a state of tonic attention regulation measured during fMRI scanning before the intervention conditions. We examined the relationship of baseline modularity with pre- to posttraining changes in neuropsychological measures of attention and executive control. The modularity of brain network organization at baseline predicted improvement in attention and executive function after cognitive training, but not after the comparison intervention. Individuals with higher baseline modularity exhibited greater improvements with cognitive training, suggesting that a more modular baseline network state may contribute to greater adaptation in response to cognitive training. Brain network properties such as modularity provide valuable information for understanding mechanisms that influence rehabilitation of cognitive function after brain injury, and may contribute to the discovery of clinically relevant biomarkers that could guide rehabilitation efforts. © 2015 American Academy of Neurology.

  10. Pinning Synchronization of Linear Complex Coupling Synchronous Generators Network of Hydroelectric Generating Set

    Directory of Open Access Journals (Sweden)

    Xuefei Wu

    2014-01-01

    Full Text Available A novel linear complex system for hydroturbine-generator sets in multimachine power systems is suggested in this paper and synchronization of the power-grid networks is studied. The advanced graph theory and stability theory are combined to solve the problem. Here we derive a sufficient condition under which the synchronous state of power-grid networks is stable in disturbance attenuation. Finally, numerical simulations are provided to illustrate the effectiveness of the results by the IEEE 39 bus system.

  11. Training a Network of Electronic Neurons for Control of a Mobile Robot

    Science.gov (United States)

    Vromen, T. G. M.; Steur, E.; Nijmeijer, H.

    An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input-output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.

  12. A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

    Directory of Open Access Journals (Sweden)

    Xiaobo Nie

    2016-01-01

    Full Text Available Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with.

  13. Design and characterization of chemical space networks for different compound data sets.

    Science.gov (United States)

    Zwierzyna, Magdalena; Vogt, Martin; Maggiora, Gerald M; Bajorath, Jürgen

    2015-02-01

    Chemical Space Networks (CSNs) are generated for different compound data sets on the basis of pairwise similarity relationships. Such networks are thought to complement and further extend traditional coordinate-based views of chemical space. Our proof-of-concept study focuses on CSNs based upon fingerprint similarity relationships calculated using the conventional Tanimoto similarity metric. The resulting CSNs are characterized with statistical measures from network science and compared in different ways. We show that the homophily principle, which is widely considered in the context of social networks, is a major determinant of the topology of CSNs of bioactive compounds, designed as threshold networks, typically giving rise to community structures. Many properties of CSNs are influenced by numerical features of the conventional Tanimoto similarity metric and largely dominated by the edge density of the networks, which depends on chosen similarity threshold values. However, properties of different CSNs with constant edge density can be directly compared, revealing systematic differences between CSNs generated from randomly collected or bioactive compounds.

  14. The role of training in IBA implementation beyond primary health care settings in the UK.

    Science.gov (United States)

    Thom, Betsy; Herring, Rachel; Bayley, Mariana

    2016-09-02

    There has been a considerable drive to encourage a wide range of professional groups to incorporate alcohol screening (or identification) and brief advice (IBA) into their everyday practice. This article aims to examine the role of training in promoting IBA delivery in contexts outside primary care and other health settings. The data are drawn mainly from a structured online survey supplemented by illustrative material from nine qualitative interviews and insights from an expert workshop. Findings support the results from other research that issues relating to role relevance and role security continue to act as barriers to professional change. Furthermore, issues of organisational commitment and organisational barriers are insufficiently addressed in strategy to promote wider use of IBA. The article concludes that development of appropriate training for alcohol IBA needs to take account of the role of IBA within a complex interactive system of related services and help seeking pathways and consider how training can contribute to changing both professional attitudes and behaviours and organisational approaches to implementing and sustaining IBA in everyday professional practice.

  15. Maximizing the Lifetime of Wireless Sensor Networks Using Multiple Sets of Rendezvous

    Directory of Open Access Journals (Sweden)

    Bo Li

    2015-01-01

    Full Text Available In wireless sensor networks (WSNs, there is a “crowded center effect” where the energy of nodes located near a data sink drains much faster than other nodes resulting in a short network lifetime. To mitigate the “crowded center effect,” rendezvous points (RPs are used to gather data from other nodes. In order to prolong the lifetime of WSN further, we propose using multiple sets of RPs in turn to average the energy consumption of the RPs. The problem is how to select the multiple sets of RPs and how long to use each set of RPs. An optimal algorithm and a heuristic algorithm are proposed to address this problem. The optimal algorithm is highly complex and only suitable for small scale WSN. The performance of the proposed algorithms is evaluated through simulations. The simulation results indicate that the heuristic algorithm approaches the optimal one and that using multiple RP sets can significantly prolong network lifetime.

  16. Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets

    Science.gov (United States)

    Yager, Ronald R.

    that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8-10] to provide a bridge between the human analyst and the formal model of the network.

  17. Protein carbonyls are acutely elevated following single set anaerobic exercise in resistance trained men.

    Science.gov (United States)

    Bloomer, Richard J; Fry, Andrew C; Falvo, Michael J; Moore, Christopher A

    2007-12-01

    The purpose of this investigation was to determine if a single set of strenuous squat exercise would result in an acute oxidative stress, as demonstrated previously by a single sprint. Thirteen resistance trained men performed one set of 15 repetitions of barbell squats using 70% of one repetition maximum and a 30 s maximal cycle sprint on two different occasions. The total work performed was calculated for each exercise bout. Heart rate, perceived exertion, blood lactate, protein carbonyls, 8-hydroxydeoxyguanosine, and malondialdehyde were measured before and within 1 min following exercise. No differences were noted between the squat and sprint tests for total work, heart rate or perceived exertion. An exercise test by time interaction was evident for blood lactate with values greater following sprinting compared to squatting (P=0.0005). Postexercise protein carbonyls were not different between exercise tests but were elevated above rest (P=0.04) by 111% and 74% following sprinting and squatting, respectively, while 8-hydroxydeoxyguanosine and malondialdehyde were relatively unaffected by either exercise test. These data indicate that a single bout of strenuous squatting and sprinting performed by resistance trained men results in elevated protein carbonyls, while having little impact on 8-hydroxydeoxyguanosine or malondialdehyde during the immediate postexercise period.

  18. Kinematics and Kinetics of Multiple Sets Using Lifting Straps During Deadlift Training.

    Science.gov (United States)

    Coswig, Victor S; Machado Freitas, Diogo Felipe; Gentil, Paulo; Fukuda, David H; Del Vecchio, Fabrício Boscolo

    2015-12-01

    The deadlift is a fundamental exercise used in the development of whole body strength and a common element in resistance training programs for all levels. However, many practitioners report the fatigue of forearm muscles and possibly a lack of grip strength as obstacles to exercise performance, which may lead to the use of ergogenic aids, such as lifting straps. The objective of this study was to evaluate kinematic variables during the execution of multiple sets of deadlift with (WS) and without (NS) lifting straps. Eleven subjects (25 ± 3.3 years) with an average of 4 ± 2.6 years of resistance training experience were enrolled in the study. After the 1 repetition maximum (1RM) test WS and NS, subjects performed separate trials of 3 sets to failure at 90% of 1RM in a counterbalanced fashion. With straps resulted in lower speed (0 to -25%) (-3 to -10%) and greater force (20-28%) and duration (concentric phase: 0-13%) when compared with NS. Therefore, it is concluded that the use of straps directly influences exercise performance that requires manual grip strength, increasing the amount of work performed by the target muscles.

  19. Selection of appropriate training and validation set chemicals for modelling dermal permeability by U-optimal design.

    Science.gov (United States)

    Xu, G; Hughes-Oliver, J M; Brooks, J D; Yeatts, J L; Baynes, R E

    2013-01-01

    Quantitative structure-activity relationship (QSAR) models are being used increasingly in skin permeation studies. The main idea of QSAR modelling is to quantify the relationship between biological activities and chemical properties, and thus to predict the activity of chemical solutes. As a key step, the selection of a representative and structurally diverse training set is critical to the prediction power of a QSAR model. Early QSAR models selected training sets in a subjective way and solutes in the training set were relatively homogenous. More recently, statistical methods such as D-optimal design or space-filling design have been applied but such methods are not always ideal. This paper describes a comprehensive procedure to select training sets from a large candidate set of 4534 solutes. A newly proposed 'Baynes' rule', which is a modification of Lipinski's 'rule of five', was used to screen out solutes that were not qualified for the study. U-optimality was used as the selection criterion. A principal component analysis showed that the selected training set was representative of the chemical space. Gas chromatograph amenability was verified. A model built using the training set was shown to have greater predictive power than a model built using a previous dataset [1].

  20. Fast training of neural networks for load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Agosta, J.M.; Nielsen, N.R.; Andeen, G. [SRI International, Menlo Park, CA (United States)

    1996-10-01

    Predicting load demand (e.g., demand for electric power) in a data-rich environment is basically a regression problem. To be successful, however, any regression technique must take into account the nonlinear nature of the problem. Numerous nonlinear regression methods have become practical, with the availability of more powerful computers. Perhaps the best known of these methods are techniques that have been popularized under the name of neural networks, and the most common of these is the back-propagation neural network (BPNN). This paper explains the advantage of a different nonlinear regression method known as the probabilistic neural network (PNN).

  1. Errorless learning for training individuals with schizophrenia at a community mental health setting providing work experience.

    Science.gov (United States)

    Kern, Robert S; Liberman, Robert P; Becker, Deborah R; Drake, Robert E; Sugar, Catherine A; Green, Michael F

    2009-07-01

    The effects of errorless learning (EL) on work performance, tenure, and personal well-being were compared with conventional job training in a community mental health fellowship club offering 12-week time-limited work experience. Participants were 40 clinically stable schizophrenia and schizoaffective disorder outpatients randomly assigned to EL vs conventional instruction (CI) at a thrift-type clothing store. EL participants received training on how to perform their assigned job tasks based on principles of EL, such as error reduction and automation of task performance. CI participants received training common to other community-based entry-level jobs that included verbal instruction, a visual demonstration, independent practice, and corrective feedback. Participants were scheduled to work 2 hours per week for 12 weeks. For both groups, job training occurred during the first 2 weeks at the worksite. Work performance (assessed using the Work Behavior Inventory, WBI) and personal well-being (self-esteem, job satisfaction, and work stress) were assessed at weeks 2, 4, and 12. Job tenure was defined as the number of weeks on the job or total number of hours worked prior to quitting or study end. The EL group performed better than the CI group on the Work Quality Scale from the WBI, and the group differences were relatively consistent over time. Results from the survival analyses of job tenure revealed a non-significant trend favoring EL. There were no group differences on self-esteem, job satisfaction, or work stress. The findings provide modest support for the extensions of EL to community settings for enhancing work performance.

  2. Effect of training algorithms on neural networks aided pavement ...

    African Journals Online (AJOL)

    user

    pavement modeling results for training the NN aided inverse analysis is .... Also, the Asphalt Institute's Thickness Design Manual MS-1 recommends ERi as ..... http://www.infrastructurereportcard.org/sites/default/files/RC2009_full_report.pdf ...

  3. How does investment in research training affect the development of research networks and collaborations?

    Science.gov (United States)

    Paina, Ligia; Ssengooba, Freddie; Waswa, Douglas; M'imunya, James M; Bennett, Sara

    2013-05-20

    Whether and how research training programs contribute to research network development is underexplored. The Fogarty International Center (FIC) has supported overseas research training programs for over two decades. FIC programs could provide an entry point in the development of research networks and collaborations. We examine whether FIC's investment in research training contributed to the development of networks and collaborations in two countries with longstanding FIC investments - Uganda and Kenya - and the factors which facilitated this process. As part of two case studies at Uganda's Makerere University and Kenya's University of Nairobi, we conducted 53 semi-structured in-depth interviews and nine focus group discussions. To expand on our case study findings, we conducted a focused bibliometric analysis on two purposively selected topic areas to examine scientific productivity and used online network illustration tools to examine the resulting network structures. FIC support made important contributions to network development. Respondents from both Uganda and Kenya confirmed that FIC programs consistently provided trainees with networking skills and exposure to research collaborations, primarily within the institutions implementing FIC programs. In both countries, networks struggled with inclusiveness, particularly in HIV/AIDS research. Ugandan respondents perceived their networks to be more cohesive than Kenyan respondents did. Network cohesiveness was positively correlated with the magnitude and longevity of FIC's programs. Support from FIC grants to local and regional research network development and networking opportunities, such as conferences, was rare. Synergies between FIC programs and research grants helped to solidify and maintain research collaborations. Networks developed where FIC's programs focused on a particular institution, there was a critical mass of trainees with similar interests, and investments for network development were available from

  4. Sex and Employment-Setting Differences in Work-Family Conflict in Athletic Training.

    Science.gov (United States)

    Mazerolle, Stephanie M; Eason, Christianne M; Pitney, William A; Mueller, Megan N

    2015-09-01

    Work-family conflict (WFC) has received much attention in athletic training, yet several factors related to this phenomenon have not been examined, specifically a practitioner's sex, occupational setting, willingness to leave the profession, and willingness to use work-leave benefits. To examine how sex and occupational differences in athletic training affect WFC and to examine willingness to leave the profession and use work-leave benefits. Cross-sectional study. Multiple occupational settings, including clinic/outreach, education, collegiate, industrial, professional sports, secondary school, and sales. A total of 246 athletic trainers (ATs) (men = 110, women = 136) participated. Of these, 61.4% (n = 151) were between 20 and 39 years old. Participants responded to a previously validated and reliable WFC instrument. We created and validated a 3-item instrument that assessed willingness to use work-leave benefits, which demonstrated good internal consistency (Cronbach α = 0.88), as well as a single question about willingness to leave the profession. The mean (± SD) WFC score was 16.88 ± 4.4 (range = 5 [least amount of conflict] to 25 [highest amount of conflict]). Men scored 17.01 ± 4.5, and women scored 16.76 ± 4.36, indicating above-average WFC. We observed no difference between men and women based on conflict scores (t244 = 0.492, P = .95) or their willingness to leave the profession (t244 = -1.27, P = .21). We noted differences among ATs in different practice settings (F8,245 = 5.015, P school settings had higher reported WFC scores. A negative relationship existed between WFC score and comfort using work-leave benefits (2-tailed r = -0.533, P < .001). Comfort with using work-leave benefits was different among practice settings (F8,245 = 3.01, P = .003). The ATs employed in traditional practice settings reported higher levels of WFC. Male and female ATs had comparable experiences of WFC and willingness to leave the profession.

  5. An R implementation of a Recurrent Neural Network Trained by Extended Kalman Filter

    Directory of Open Access Journals (Sweden)

    Bogdan Oancea

    2016-06-01

    Full Text Available Nowadays there are several techniques used for forecasting with different performances and accuracies. One of the most performant techniques for time series prediction is neural networks. The accuracy of the predictions greatly depends on the network architecture and training method. In this paper we describe an R implementation of a recurrent neural network trained by the Extended Kalman Filter. For the implementation of the network we used the Matrix package that allows efficient vector-matrix and matrix-matrix operations. We tested the performance of our R implementation comparing it with a pure C++ implementation and we showed that R can achieve about 75% of the C++ programs. Considering the other advantages of R, our results recommend R as a serious alternative to classical programming languages for high performance implementations of neural networks.

  6. Epidemiologic comparison of injured high school basketball athletes reporting to emergency departments and the athletic training setting

    National Research Council Canada - National Science Library

    Fletcher, Erica N; McKenzie, Lara B; Comstock, R Dawn

    2014-01-01

    .... To compare patterns of athletes with basketball-related injuries presenting to US emergency departments from 2005 through 2010 and the high school athletic training setting from the 2005-2011 seasons...

  7. Weighted complex network analysis of the Beijing subway system: Train and passenger flows

    Science.gov (United States)

    Feng, Jia; Li, Xiamiao; Mao, Baohua; Xu, Qi; Bai, Yun

    2017-05-01

    In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences.

  8. A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks

    Science.gov (United States)

    De Paola, Francesco; Galdiero, Enzo; Giugni, Maurizio

    2016-05-01

    This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.

  9. Networked Intermedia Agenda Setting: The Geography of a Hyperlinked Scandinavian News Ecology

    DEFF Research Database (Denmark)

    Sjøvaag, Helle; Stavelin, Eirik; Karlsson, Michael

    How does agenda setting work within the hyperlinked Scandinavian news ecology? This paper investigates intermedia agenda setting within and between the local, regional, national and supra-national levels in Sweden, Denmark and Norway; analyses the center/periphery dimensions of hyperlink connecti......How does agenda setting work within the hyperlinked Scandinavian news ecology? This paper investigates intermedia agenda setting within and between the local, regional, national and supra-national levels in Sweden, Denmark and Norway; analyses the center/periphery dimensions of hyperlink...... between news agendas in the three countries, and c) the connectedness enabled by size, resources and central location in the Scandinavian hyperlinked information structure. The network analysis provides new insights into the relationship between centralized political structures, media ownership dispersal...

  10. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

    Science.gov (United States)

    Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-30

    Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks.

  11. German MedicalTeachingNetwork (MDN) implementing national standards for teacher training.

    Science.gov (United States)

    Lammerding-Koeppel, M; Ebert, T; Goerlitz, A; Karsten, G; Nounla, C; Schmidt, S; Stosch, C; Dieter, P

    2016-01-01

    An increasing demand for proof of professionalism in higher education strives for quality assurance (QA) and improvement in medical education. A wide range of teacher trainings is available to medical staff in Germany. Cross-institutional approval of individual certificates is usually a difficult and time consuming task for institutions. In case of non-acceptance it may hinder medical teachers in their professional mobility. The faculties of medicine aimed to develop a comprehensive national framework, to promote standards for formal faculty development programmes across institutions and to foster professionalization of medical teaching. Addressing the above challenges in a joint approach, the faculties set up the national MedicalTeacherNetwork (MDN). Great importance is attributed to work out nationally concerted standards for faculty development and an agreed-upon quality control process across Germany. Medical teachers benefit from these advantages due to portability of faculty development credentials from one faculty of medicine to another within the MDN system. The report outlines the process of setting up the MDN and the national faculty development programme in Germany. Success factors, strengths and limitations are discussed from an institutional, individual and general perspective. Faculties engaged in similar developments might be encouraged to transfer the MDN concept to their countries.

  12. Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

    Science.gov (United States)

    Orhan, A Emin; Ma, Wei Ji

    2017-07-26

    Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

  13. Helping mothers survive bleeding after birth: an evaluation of simulation-based training in a low-resource setting.

    Science.gov (United States)

    Nelissen, Ellen; Ersdal, Hege; Ostergaard, Doris; Mduma, Estomih; Broerse, Jacqueline; Evjen-Olsen, Bjørg; van Roosmalen, Jos; Stekelenburg, Jelle

    2014-03-01

    To evaluate "Helping Mothers Survive Bleeding After Birth" (HMS BAB) simulation-based training in a low-resource setting. Educational intervention study. Rural referral hospital in Northern Tanzania. Clinicians, nurse-midwives, medical attendants, and ambulance drivers involved in maternity care. In March 2012, health care workers were trained in HMS BAB, a half-day simulation-based training, using a train-the-trainer model. The training focused on basic delivery care, active management of third stage of labor, and treatment of postpartum hemorrhage, including bimanual uterine compression. Evaluation questionnaires provided information on course perception. Knowledge, skills, and confidence of facilitators and learners were tested before and after training. Four master trainers trained eight local facilitators, who subsequently trained 89 learners. After training, all facilitators passed the knowledge test, but pass rates for the skills test were low (29% pass rate for basic delivery and 0% pass rate for management of postpartum hemorrhage). Evaluation revealed that HMS BAB training was considered acceptable and feasible, although more time should be allocated for training, and teaching materials should be translated into the local language. Knowledge, skills, and confidence of learners increased significantly immediately after training. However, overall pass rates for skills tests of learners after training were low (3% pass rate for basic delivery and management of postpartum hemorrhage). The HMS BAB simulation-based training has potential to contribute to education of health care providers. We recommend a full day of training and validation of the facilitators to improve the training. © 2013 Nordic Federation of Societies of Obstetrics and Gynecology.

  14. Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D images.

    Science.gov (United States)

    Brosch, Tom; Tam, Roger

    2015-01-01

    Deep learning has traditionally been computationally expensive, and advances in training methods have been the prerequisite for improving its efficiency in order to expand its application to a variety of image classification problems. In this letter, we address the problem of efficient training of convolutional deep belief networks by learning the weights in the frequency domain, which eliminates the time-consuming calculation of convolutions. An essential consideration in the design of the algorithm is to minimize the number of transformations to and from frequency space. We have evaluated the running time improvements using two standard benchmark data sets, showing a speed-up of up to 8 times on 2D images and up to 200 times on 3D volumes. Our training algorithm makes training of convolutional deep belief networks on 3D medical images with a resolution of up to 128×128×128 voxels practical, which opens new directions for using deep learning for medical image analysis.

  15. Effects of training strategies implemented in a complex videogame on functional connectivity of attentional networks.

    Science.gov (United States)

    Voss, Michelle W; Prakash, Ruchika Shaurya; Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

    2012-01-02

    We used the Space Fortress videogame, originally developed by cognitive psychologists to study skill acquisition, as a platform to examine learning-induced plasticity of interacting brain networks. Novice videogame players learned Space Fortress using one of two training strategies: (a) focus on all aspects of the game during learning (fixed priority), or (b) focus on improving separate game components in the context of the whole game (variable priority). Participants were scanned during game play using functional magnetic resonance imaging (fMRI), both before and after 20 h of training. As expected, variable priority training enhanced learning, particularly for individuals who initially performed poorly. Functional connectivity analysis revealed changes in brain network interaction reflective of more flexible skill learning and retrieval with variable priority training, compared to procedural learning and skill implementation with fixed priority training. These results provide the first evidence for differences in the interaction of large-scale brain networks when learning with different training strategies. Our approach and findings also provide a foundation for exploring the brain plasticity involved in transfer of trained abilities to novel real-world tasks such as driving, sport, or neurorehabilitation. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

    Science.gov (United States)

    Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli

    2013-03-01

    Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.

  17. Probing meaningfulness of oscillatory EEG components with bootstrapping, label noise and reduced training sets.

    Science.gov (United States)

    Castaño-Candamil, Sebastián; Meinel, Andreas; Dähne, Sven; Tangermann, Michael

    2015-01-01

    As oscillatory components of the Electroencephalogram (EEG) and other electrophysiological signals may co-modulate in power with a target variable of interest (e.g. reaction time), data-driven supervised methods have been developed to automatically identify such components based on labeled example trials. Under conditions of challenging signal-to-noise ratio, high-dimensional data and small training sets, however, these methods may overfit to meaningless solutions. Examples are spatial filtering methods like Common Spatial Patterns (CSP) and Source Power Comodulation (SPoC). It is difficult for the practitioner to tell apart meaningful from arbitrary, random components. We propose three approaches to probe the robustness of extracted oscillatory components and show their application to both, simulated and EEG data recorded during a visually cued hand motor reaction time task.

  18. A Realistic Coverage Model with Backup Set Computation for Wireless Video Sensor Network

    Directory of Open Access Journals (Sweden)

    Vijay S Ukani

    2015-08-01

    Full Text Available Wireless Video Sensor Network (WVSN are gaining increasing popularity due availability of low cost CMOS camera and miniaturization of hardware. For many applications it is difficult to have pre-engineered deployment of video camera sensors which leads to redundancy. Due to sectored coverage and random deployment, it becomes challenging to model video sensor coverage to identify redundancy and suppress redundant video transmission. Several efforts have been made to model coverage redundancy considering 2-dimensional coverage. Field of View (FoV of the camera sensor is in 3-dimensions, thus it is very difficult to model the coverage and identify overlap area for realistic camera. 3-dimensional coverage is largely an unexplored problem. In this paper, a realistic 3-dimensional pyramid camera coverage is assumed and backup set of nodes are computed. Backup set of a node is a set of video sensor nodes which collectively covers coverage area of the node under consideration. The approach presented in the paper identifies minimal sized set of backup nodes which can be used to adaptively duty cycle the video capture and transmission. The result shows that number of nodes required to remain active to cover the sensor field is reduced and in turn average energy consumption of the network also reduces.

  19. The influence of trained peer tutoring on tutors' motivation and performance in a French boxing setting.

    Science.gov (United States)

    Legrain, Pascal; D'Arripe-Longueville, Fabienne; Gernigon, Christophe

    2003-07-01

    The aim of this study was to examine the potential motivational and behavioural benefits of two peer tutoring programmes for tutors in a sport setting. Differences between the sexes were also explored. Thirty two college-age males and females, all novices on a French boxing task, were assigned to a 2 x 2 [sex x training type: physical practice associated with trained peer tutoring (TPT) vs physical practice associated with untrained peer tutoring (UPT)] factorial design. All participants were given six French boxing lessons of 2 h each. The TPT programme included structured methods to prepare the participants to fulfil their role of tutors, whereas the UPT programme did not. The results demonstrated that the TPT programme resulted in higher scores for coaching skills. Furthermore, interaction effects revealed that the TPT programme yielded better offensive outcomes for males and better defensive outcomes for females. Although the UPT participants expressed higher self-efficacy, no differences emerged for intrinsic motivation and causal attributions. Finally, male tutors displayed higher self-efficacy and offensive outcomes than female tutors. The results are discussed in the light of previous findings in the educational and sport psychology literature.

  20. Twelve tips on how to set up postgraduate training via remote clinical supervision.

    Science.gov (United States)

    Wearne, Susan; Dornan, Tim; Teunissen, Pim W; Skinner, Timothy

    2013-11-01

    Doctors-in-training can now be supervised remotely by specialist clinicians using information and communication technology. This provides an intermediate stage of professional development between on-site supervision and independent medical practice. Remote supervision could increase training capacity, particularly in underserved areas and ensure doctors are willing and able to practice where they are needed once qualified. Remotely supervised doctors learn via virtual autonomy in clinical decision making and working at the limits of their abilities. It suits experienced registrars with resilience, insight into their strengths and weaknesses, capacity to self-monitor and correct, and willingness to seek help. These doctors benefit from remote supervisors who facilitate their learning, monitor their well-being, and support them holistically. Educational organisations need to oversee remote placements and match the right registrar, to the right placement with the right supervisor. We outline in our twelve tips how to set up remote supervision in order to maximise the educational benefits and minimise the risks.

  1. Clinicians' perspectives on cognitive therapy in community mental health settings: implications for training and implementation.

    Science.gov (United States)

    Stirman, Shannon Wiltsey; Gutiérrez-Colina, Ana; Toder, Katherine; Esposito, Gregory; Barg, Frances; Castro, Frank; Beck, Aaron T; Crits-Christoph, Paul

    2013-07-01

    Policymakers are investing significant resources in large-scale training and implementation programs for evidence-based psychological treatments (EBPTs) in public mental health systems. However, relatively little research has been conducted to understand factors that may influence the success of efforts to implement EBPTs for adult consumers of mental health services. In a formative investigation during the development of a program to implement cognitive therapy (CT) in a community mental health system, we surveyed and interviewed clinicians and clinical administrators to identify potential influences on CT implementation within their agencies. Four primary themes were identified. Two related to attitudes towards CT: (1) ability to address client needs and issues that are perceived as most central to their presenting problems, and (2) reluctance to fully implement CT. Two themes were relevant to context: (1) agency-level barriers, specifically workload and productivity concerns and reactions to change, and (2) agency-level facilitators, specifically, treatment planning requirements and openness to training. These findings provide information that can be used to develop strategies to facilitate the implementation of CT interventions for clients being treated in public-sector settings.

  2. Mobile learning for HIV/AIDS healthcare worker training in resource-limited settings.

    Science.gov (United States)

    Zolfo, Maria; Iglesias, David; Kiyan, Carlos; Echevarria, Juan; Fucay, Luis; Llacsahuanga, Ellar; de Waard, Inge; Suàrez, Victor; Llaque, Walter Castillo; Lynen, Lutgarde

    2010-09-08

    We present an innovative approach to healthcare worker (HCW) training using mobile phones as a personal learning environment.Twenty physicians used individual Smartphones (Nokia N95 and iPhone), each equipped with a portable solar charger. Doctors worked in urban and peri-urban HIV/AIDS clinics in Peru, where almost 70% of the nation's HIV patients in need are on treatment. A set of 3D learning scenarios simulating interactive clinical cases was developed and adapted to the Smartphones for a continuing medical education program lasting 3 months. A mobile educational platform supporting learning events tracked participant learning progress. A discussion forum accessible via mobile connected participants to a group of HIV specialists available for back-up of the medical information. Learning outcomes were verified through mobile quizzes using multiple choice questions at the end of each module. In December 2009, a mid-term evaluation was conducted, targeting both technical feasibility and user satisfaction. It also highlighted user perception of the program and the technical challenges encountered using mobile devices for lifelong learning. With a response rate of 90% (18/20 questionnaires returned), the overall satisfaction of using mobile tools was generally greater for the iPhone. Access to Skype and Facebook, screen/keyboard size, and image quality were cited as more troublesome for the Nokia N95 compared to the iPhone. Training, supervision and clinical mentoring of health workers are the cornerstone of the scaling up process of HIV/AIDS care in resource-limited settings (RLSs). Educational modules on mobile phones can give flexibility to HCWs for accessing learning content anywhere. However lack of softwares interoperability and the high investment cost for the Smartphones' purchase could represent a limitation to the wide spread use of such kind mLearning programs in RLSs.

  3. Mobile learning for HIV/AIDS healthcare worker training in resource-limited settings

    Directory of Open Access Journals (Sweden)

    Zolfo Maria

    2010-09-01

    Full Text Available Abstract Background We present an innovative approach to healthcare worker (HCW training using mobile phones as a personal learning environment. Twenty physicians used individual Smartphones (Nokia N95 and iPhone, each equipped with a portable solar charger. Doctors worked in urban and peri-urban HIV/AIDS clinics in Peru, where almost 70% of the nation's HIV patients in need are on treatment. A set of 3D learning scenarios simulating interactive clinical cases was developed and adapted to the Smartphones for a continuing medical education program lasting 3 months. A mobile educational platform supporting learning events tracked participant learning progress. A discussion forum accessible via mobile connected participants to a group of HIV specialists available for back-up of the medical information. Learning outcomes were verified through mobile quizzes using multiple choice questions at the end of each module. Methods In December 2009, a mid-term evaluation was conducted, targeting both technical feasibility and user satisfaction. It also highlighted user perception of the program and the technical challenges encountered using mobile devices for lifelong learning. Results With a response rate of 90% (18/20 questionnaires returned, the overall satisfaction of using mobile tools was generally greater for the iPhone. Access to Skype and Facebook, screen/keyboard size, and image quality were cited as more troublesome for the Nokia N95 compared to the iPhone. Conclusions Training, supervision and clinical mentoring of health workers are the cornerstone of the scaling up process of HIV/AIDS care in resource-limited settings (RLSs. Educational modules on mobile phones can give flexibility to HCWs for accessing learning content anywhere. However lack of softwares interoperability and the high investment cost for the Smartphones' purchase could represent a limitation to the wide spread use of such kind mLearning programs in RLSs.

  4. Mnemonic Training Reshapes Brain Networks to Support Superior Memory

    NARCIS (Netherlands)

    Dresler, M.; Shirer, W.R.; Konrad, B.N.; Muller, N.C.J.; Wagner, I.; Fernandez, G.S.E.; Czisch, M.; Greicius, M.D.

    2017-01-01

    Memory skills strongly differ across the general population; however, little is known about the brain characteristics supporting superior memory performance. Here we assess functional brain network organization of 23 of the world's most successful memory athletes and matched controls with fMRI

  5. African Network Operators Group (AfNOG) Training Workshops and ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    What does it mean to "have fiber" or developing a common understanding and terminology. Download PDF. Studies. Supporting learning and research : content opportunities for academic and research libraries / networks in Africa; presentation at AFREN 3, Rabat, 2008. Download PDF. Studies. Broadband infrastructure in ...

  6. Training Convolutional Neural Networks for Translational Invariance on SAR ATR

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten

    2016-01-01

    In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...

  7. Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling

    Science.gov (United States)

    Mohamadian, Masoumeh; Afarideh, Hossein; Ghergherehchi, Mitra

    2017-01-01

    The cyclotron cavity presented in this paper is modeled by a feed-forward neural network trained by the authors’ optimized back-propagation (BP) algorithm. The training samples were obtained from simulation results that are for a number of defined situations and parameters and were achieved parametrically using MWS CST software; furthermore, the conventional BP algorithm with different hidden-neuron numbers, structures, and other optimal parameters such as learning rate that are applied for our purpose was also used here. The present study shows that an optimized FFN can be used to estimate the cyclotron-model parameters with an acceptable error function. A neural network trained by an optimized algorithm therefore shows a proper approximation and an acceptable ability regarding the modeling of the proposed structure. The cyclotron-cavity parameter-modeling results demonstrate that an FNN that is trained by the optimized algorithm could be a suitable method for the estimation of the design parameters in this case.

  8. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Science.gov (United States)

    Tur, Cristina; Castro-Urgal, Rocío; Traveset, Anna

    2013-01-01

    Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled) can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them). Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i) linkage level (number of interactions), (ii) diversity of interactions, and (iii) closeness centrality (a measure of how much a species is connected to other plants via shared pollinators). Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  9. Linking plant specialization to dependence in interactions for seed set in pollination networks.

    Directory of Open Access Journals (Sweden)

    Cristina Tur

    Full Text Available Studies on pollination networks have provided valuable information on the number, frequency, distribution and identity of interactions between plants and pollinators. However, little is still known on the functional effect of these interactions on plant reproductive success. Information on the extent to which plants depend on such interactions will help to make more realistic predictions of the potential impacts of disturbances on plant-pollinator networks. Plant functional dependence on pollinators (all interactions pooled can be estimated by comparing seed set with and without pollinators (i.e. bagging flowers to exclude them. Our main goal in this study was thus to determine whether plant dependence on current insect interactions is related to plant specialization in a pollination network. We studied two networks from different communities, one in a coastal dune and one in a mountain. For ca. 30% of plant species in each community, we obtained the following specialization measures: (i linkage level (number of interactions, (ii diversity of interactions, and (iii closeness centrality (a measure of how much a species is connected to other plants via shared pollinators. Phylogenetically controlled regression analyses revealed that, for the largest and most diverse coastal community, plants highly dependent on pollinators were the most generalists showing the highest number and diversity of interactions as well as occupying central positions in the network. The mountain community, by contrast, did not show such functional relationship, what might be attributable to their lower flower-resource heterogeneity and diversity of interactions. We conclude that plants with a wide array of pollinator interactions tend to be those that are more strongly dependent upon them for seed production and thus might be those more functionally vulnerable to the loss of network interaction, although these outcomes might be context-dependent.

  10. Helping mothers survive bleeding after birth: an educational of simulation-based training in a low resource setting

    NARCIS (Netherlands)

    Nelissen, E.J.T.; Ersdal, H.; Ostergaard, D.; Mduma, E.; Broerse, J.E.W.; Evjen-Olsen, B.; van Roosmalen, J.; Stekelenburg, J.

    2014-01-01

    Objective To evaluate "Helping Mothers Survive Bleeding After Birth" (HMS BAB) simulation-based training in a low-resource setting. Design Educational intervention study. Setting Rural referral hospital in Northern Tanzania. Population Clinicians, nurse-midwives, medical attendants, and ambulance

  11. Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks.

    Science.gov (United States)

    Pratapa, Aditya; Balachandran, Shankar; Raman, Karthik

    2015-10-15

    Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals. We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets. The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0, is available at https://github.com/RamanLab/FastSL CONTACT: kraman@iitm.ac.in Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

    Science.gov (United States)

    Lu, Thomas; Pham, Timothy; Liao, Jason

    2011-01-01

    This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

  13. Multi-link faults localization and restoration based on fuzzy fault set for dynamic optical networks.

    Science.gov (United States)

    Zhao, Yongli; Li, Xin; Li, Huadong; Wang, Xinbo; Zhang, Jie; Huang, Shanguo

    2013-01-28

    Based on a distributed method of bit-error-rate (BER) monitoring, a novel multi-link faults restoration algorithm is proposed for dynamic optical networks. The concept of fuzzy fault set (FFS) is first introduced for multi-link faults localization, which includes all possible optical equipment or fiber links with a membership describing the possibility of faults. Such a set is characterized by a membership function which assigns each object a grade of membership ranging from zero to one. OSPF protocol extension is designed for the BER information flooding in the network. The BER information can be correlated to link faults through FFS. Based on the BER information and FFS, multi-link faults localization mechanism and restoration algorithm are implemented and experimentally demonstrated on a GMPLS enabled optical network testbed with 40 wavelengths in each fiber link. Experimental results show that the novel localization mechanism has better performance compared with the extended limited perimeter vector matching (LVM) protocol and the restoration algorithm can improve the restoration success rate under multi-link faults scenario.

  14. Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

    Science.gov (United States)

    Bennett, C.; Dunne, J. F.; Trimby, S.; Richardson, D.

    2017-02-01

    A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 l, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

  15. Online social networking sites-a novel setting for health promotion?

    Science.gov (United States)

    Loss, Julika; Lindacher, Verena; Curbach, Janina

    2014-03-01

    Among adolescents, online social networking sites (SNS) such as Facebook are popular platforms for social interaction and may therefore be considered as 'novel settings' that could be exploited for health promotion. In this article, we examine the relevant definitions in health promotion and literature in order to analyze whether key characteristics of 'settings for health promotion' and the socio-ecological settings approach can be transferred to SNS. As many of our daily activities have shifted to cyberspace, we argue that online social interaction may gain more importance than geographic closeness for defining a 'setting'. While exposition to positive references to risk behavior by peers may render the SNS environment detrimental to health, SNS may allow people to create their own content and therefore foster participation. However, those health promotion projects delivered on SNS up until today solely relied on health education directed at end users. It remains unclear how health promotion on SNS can meet other requirements of the settings approach (e.g. building partnerships, changing the environment). As yet, one should be cautious in terming SNS a 'setting'. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Communication Training in Corporate Settings: Lessons and Opportunities for the Academe

    OpenAIRE

    Thaddeus McEwen

    1997-01-01

    Today, communication skills are among the most popular content areas in employee training. This survey of training managers examined the status of communication training in businesses and identified the lessons and opportunities for business communication faculty. Results indicated that communication training in businesses tend to focus on managerial and interpersonal communication including teamwork, problem solving, effective meetings, and motivating people. Supervisors and customer service...

  17. Communications Training Courses Across the Leopold Leadership Network

    Science.gov (United States)

    Hayden, T.; Gerber, L. R.; Silver, W. L.

    2012-12-01

    For nearly fifteen years, the Leopold Leadership Program has provided science communication training and support to mid-career academic environmental researchers from across North America. There has been an emphasis throughout on effective communication to non-scientific audiences. Increasingly, Leopold fellows have been developing communications courses for their own students, responding to the need for future scientists to be able to communicate well with the public, the media, policy makers and other audiences. At a June 2012 reunion meeting, a group of past fellows and communications trainers conducted a curriculum exchange, sharing experiences and ideas for successful inclusion of communications training in environmental science curricula. This presentation will present case studies from several institutions, including the use of podcasting, web columns, social media, in-person presentation and other presentation styles for connecting general audiences. We will share best practices, challenges and recommendations for curriculum development and institutional acceptance.

  18. EduCamp Colombia: Social Networked Learning for Teacher Training

    OpenAIRE

    Diego Ernesto Leal Fonseca

    2011-01-01

    This paper describes a learning experience called EduCamp, which was launched by the Ministry of Education of Colombia in 2007, based on emerging concepts such as e-Learning 2.0, connectivism, and personal learning environments. An EduCamp proposes an unstructured collective learning experience, which intends to make palpable the possibilities of social software tools in learning and interaction processes while demonstrating face-to-face organizational forms that reflect social networked lear...

  19. Reward-based training of recurrent neural networks for cognitive and value-based tasks

    Science.gov (United States)

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2017-01-01

    Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

  20. Application of Set Covering Location Problem for Organizing the Public Postal Network

    Directory of Open Access Journals (Sweden)

    Dragana Šarac

    2016-08-01

    Full Text Available Most countries of the European Union ensure certain obligations (criteria which universal service providers must meet to ensure the realization of the universal service. These criteria vary from country to country, giving their own choice of an optimal model for the density of the postal network. Such postal network of the operator providing universal postal service must be organized so that post offices are accessible at the optimal distance from the user. This paper presents two different approaches. The first one is based on the population criteria determined in the previous study. The second one is new, a general method created to determine the minimum number of postal unit applications of Set Covering Location Problem. The authors apply both methods on real data collected from the Serbian municipalities and finally, compare the obtained results.

  1. A link based network route choice model with unrestricted choice set

    DEFF Research Database (Denmark)

    Fosgerau, Mogens; Frejinger, Emma; Karlstrom, Anders

    2013-01-01

    This paper considers the path choice problem, formulating and discussing an econometric random utility model for the choice of path in a network with no restriction on the choice set. Starting from a dynamic specification of link choices we show that it is equivalent to a static model...... of the multinomial logit form but with infinitely many alternatives. The model can be consistently estimated and used for prediction in a computationally efficient way. Similarly to the path size logit model, we propose an attribute called link size that corrects utilities of overlapping paths but that is link...... additive. The model is applied to data recording path choices in a network with more than 3000 nodes and 7000 links....

  2. Interaction of multiple networks modulated by the working memory training based on real-time fMRI

    Science.gov (United States)

    Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie

    2015-03-01

    Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

  3. Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network

    Directory of Open Access Journals (Sweden)

    Kindie Biredagn Nahato

    2015-01-01

    Full Text Available The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets.

  4. A Network Analysis of Online Audience Behaviour: Towards a Better Comprehension of the Agenda Setting Process

    Directory of Open Access Journals (Sweden)

    Sílvia Majó-Vázquez

    2015-06-01

    Full Text Available

    By constructing the network of media audience, this study sheds light on the predominant modes of exposure to online political information in Spain. Novelty data from a panel of thirty thousand individuals is used for the research. The preliminary results bring evidences for reviewing the line of reasoning that advocates for the prevailing fragmentation of the public sphere. More notably, the results contribute to proving  that a substantial level of audience concentration still remains in the web. The highest levels of audience overlapping are found in those media outlets that are driving the media agenda in the offline sphere. Therefore the study proffers evidence that the structure of the online public sphere might guarantee the necessary shared informational experiences for a deliberative democracy.

    The implications of the current networked audience behaviour for the study of the agenda setting process are discussed along with the chances for a shared public agenda in  Spanish society. Observational methods and content analysis have been used in the study of the agenda setting process so far. However, the current communication environment characterized by unlimited, decentralized and abundant sources of political information prompts the application of new analytical approaches. Networks are at the heart of online communication and network science allows for analyzing its structure. It provides the affordances to map and study audience aggregated behaviour when searching for political information. In doing in so, it also unveils the mechanisms that might still guarantee a public agenda in the digital age.

  5. A broadcast-based key agreement scheme using set reconciliation for wireless body area networks.

    Science.gov (United States)

    Ali, Aftab; Khan, Farrukh Aslam

    2014-05-01

    Information and communication technologies have thrived over the last few years. Healthcare systems have also benefited from this progression. A wireless body area network (WBAN) consists of small, low-power sensors used to monitor human physiological values remotely, which enables physicians to remotely monitor the health of patients. Communication security in WBANs is essential because it involves human physiological data. Key agreement and authentication are the primary issues in the security of WBANs. To agree upon a common key, the nodes exchange information with each other using wireless communication. This information exchange process must be secure enough or the information exchange should be minimized to a certain level so that if information leak occurs, it does not affect the overall system. Most of the existing solutions for this problem exchange too much information for the sake of key agreement; getting this information is sufficient for an attacker to reproduce the key. Set reconciliation is a technique used to reconcile two similar sets held by two different hosts with minimal communication complexity. This paper presents a broadcast-based key agreement scheme using set reconciliation for secure communication in WBANs. The proposed scheme allows the neighboring nodes to agree upon a common key with the personal server (PS), generated from the electrocardiogram (EKG) feature set of the host body. Minimal information is exchanged in a broadcast manner, and even if every node is missing a different subset, by reconciling these feature sets, the whole network will still agree upon a single common key. Because of the limited information exchange, if an attacker gets the information in any way, he/she will not be able to reproduce the key. The proposed scheme mitigates replay, selective forwarding, and denial of service attacks using a challenge-response authentication mechanism. The simulation results show that the proposed scheme has a great deal of

  6. A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing

    Directory of Open Access Journals (Sweden)

    Yi-Qing Wang

    2015-09-01

    Full Text Available Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(· is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing.

  7. On the use of harmony search algorithm in the training of wavelet neural networks

    Science.gov (United States)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2015-10-01

    Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.

  8. Structural Damage Identification Based on Rough Sets and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Chengyin Liu

    2014-01-01

    Full Text Available This paper investigates potential applications of the rough sets (RS theory and artificial neural network (ANN method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA. The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.

  9. Rough Set Soft Computing Cancer Classification and Network: One Stone, Two Birds

    Directory of Open Access Journals (Sweden)

    Yue Zhang

    2010-07-01

    Full Text Available Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

  10. The Evaluation on Data Mining Methods of Horizontal Bar Training Based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    Zhang Yanhui

    2015-01-01

    Full Text Available With the rapid development of science and technology, data analysis has become an indispensable part of people’s work and life. Horizontal bar training has multiple categories. It is an emphasis for the re-search of related workers that categories of the training and match should be reduced. The application of data mining methods is discussed based on the problem of reducing categories of horizontal bar training. The BP neural network is applied to the cluster analysis and the principal component analysis, which are used to evaluate horizontal bar training. Two kinds of data mining methods are analyzed from two aspects, namely the operational convenience of data mining and the rationality of results. It turns out that the principal component analysis is more suitable for data processing of horizontal bar training.

  11. Conditions for addressing environmental determinants of health behavior in intersectoral policy networks: A fuzzy set Qualitative Comparative Analysis.

    Science.gov (United States)

    Peters, D T J M; Verweij, S; Grêaux, K; Stronks, K; Harting, J

    2017-12-01

    Improving health requires changes in the social, physical, economic and political determinants of health behavior. For the realization of policies that address these environmental determinants, intersectoral policy networks are considered necessary for the pooling of resources to implement different policy instruments. However, such network diversity may increase network complexity and therefore hamper network performance. Network complexity may be reduced by network management and the provision of financial resources. This study examined whether network diversity - amidst the other conditions - is indeed needed to address environmental determinants of health behavior. We included 25 intersectoral policy networks in Dutch municipalities aimed at reducing overweight, smoking, and alcohol/drugs abuse. For our fuzzy set Qualitative Comparative Analysis we used data from three web-based surveys among (a) project leaders regarding network diversity and size (n = 38); (b) project leaders and project partners regarding management (n = 278); and (c) implementation professionals regarding types of environmental determinants addressed (n = 137). Data on budgets were retrieved from project application forms. Contrary to their intentions, most policy networks typically addressed personal determinants. If the environment was addressed too, it was mostly the social environment. To address environmental determinants of health behavior, network diversity (>50% of the actors are non-public health) was necessary in networks that were either small (network diversity, environmental determinants also were addressed by small networks with large budgets, and by large networks with small budgets, when both provided network management. We conclude that network diversity is important - although not necessary - for resource pooling to address environmental determinants of health behavior, but only effective in the presence of network management. Our findings may support intersectoral

  12. Breast mass segmentation in digital mammography based on pulse coupled neural network and level set method

    Science.gov (United States)

    Xie, Weiying; Ma, Yide; Li, Yunsong

    2015-05-01

    A novel approach to mammographic image segmentation, termed as PCNN-based level set algorithm, is presented in this paper. Just as its name implies, a method based on pulse coupled neural network (PCNN) in conjunction with the variational level set method for medical image segmentation. To date, little work has been done on detecting the initial zero level set contours based on PCNN algorithm for latterly level set evolution. When all the pixels of the input image are fired by PCNN, the small pixel value will be a much more refined segmentation. In mammographic image, the breast tumor presents big pixel value. Additionally, the mammographic image with predominantly dark region, so that we firstly obtain the negative of mammographic image with predominantly dark region except the breast tumor before all the pixels of an input image are fired by PCNN. Therefore, in here, PCNN algorithm is employed to achieve mammary-specific, initial mass contour detection. After that, the initial contours are all extracted. We define the extracted contours as the initial zero level set contours for automatic mass segmentation by variational level set in mammographic image analysis. What's more, a new proposed algorithm improves external energy of variational level set method in terms of mammographic images in low contrast. In accordance with the gray scale of mass region in mammographic image is higher than the region surrounded, so the Laplace operator is used to modify external energy, which could make the bright spot becoming much brighter than the surrounded pixels in the image. A preliminary evaluation of the proposed method performs on a known public database namely MIAS, rather than synthetic images. The experimental results demonstrate that our proposed approach can potentially obtain better masses detection results in terms of sensitivity and specificity. Ultimately, this algorithm could lead to increase both sensitivity and specificity of the physicians' interpretation of

  13. Autocatalytic sets and chemical organizations: modeling self-sustaining reaction networks at the origin of life

    Science.gov (United States)

    Hordijk, Wim; Steel, Mike; Dittrich, Peter

    2018-01-01

    Two related but somewhat different approaches have been proposed to formalize the notion of a self-sustaining chemical reaction network. One is the notion of collectively autocatalytic sets, formalized as RAF theory, and the other is chemical organization theory. Both formalisms have been argued to be relevant to the origin of life. RAF sets and chemical organizations are defined differently, but previously some relationships between the two have been shown. Here, we refine and explore these connections in more detail. In particular, we show that so-called closed RAFs are chemical organizations, but that the converse is not necessarily true. We then introduce and apply a procedure to show how chemical organizations can be used to find all closed RAFs within any chemical reaction system. We end with a discussion of why and how closed RAFs could be important in the context of the origin and early evolution of life.

  14. An analog CMOS chip set for neural networks with arbitrary topologies

    DEFF Research Database (Denmark)

    Lansner, John; Lehmann, Torsten

    1993-01-01

    An analog CMOS chip set for implementations of artificial neural networks (ANNs) has been fabricated and tested. The chip set consists of two cascadable chips: a neuron chip and a synapse chip. Neurons on the neuron chips can be interconnected at random via synapses on the synapse chips thus...... implementing an ANN with arbitrary topology. The neuron test chip contains an array of 4 neurons with well defined hyperbolic tangent activation functions which is implemented by using parasitic lateral bipolar transistors. The synapse test chip is a cascadable 4×4 matrix-vector multiplier with variable, 10-b...... resolution matrix elements. The propagation delay of the test chips was measured to 2.6 μs per layer...

  15. PARTNER: A Marie Curie Initial Training Network for hadron therapy (with french subtitles)

    CERN Multimedia

    CERN BULLETIN; Manuela Cirilli; Nathalie Hospital

    2011-01-01

    PARTNER is a 4-year Marie Curie Training project funded by the European Commission with 5.6 million Euros aimed at the creation of the next generation of experts. Ten academic institutes and research centres and two leading companies are participating in PARTNER, that is coordinated by CERN, forming a unique multidisciplinary and multinational European network.

  16. PARTNER: A Marie Curie Initial Training Network for hadron therapy (with english subtitles)

    CERN Multimedia

    CERN BULLETIN; Manuela Cirilli; Nathalie Hospital

    2011-01-01

    PARTNER is a 4-year Marie Curie Training project funded by the European Commission with 5.6 million Euros aimed at the creation of the next generation of experts. Ten academic institutes and research centres and two leading companies are participating in PARTNER, that is coordinated by CERN, forming a unique multidisciplinary and multinational European network.

  17. Training the "Wizards": A Model for Building Self Efficacy and Peer Networks among Urban Youth Workers

    Science.gov (United States)

    Ross, Laurie; Buglione, Suzanne; Safford-Farquharson, Jennifer

    2011-01-01

    This article presents a community's efforts to address the professional development needs of frontline youth workers. A coalition designed a 13-week Youth Worker Training Institute to increase youth workers' knowledge, skills, self-efficacy, and professional networks. After the Institute, participants reported feeling more skillful, connected to…

  18. Training a multilayer neural network for the Euro-dollar (EUR/ USD) exchange rate

    National Research Council Canada - National Science Library

    Jaime Alberto Villamil Torres; Jesús Alberto Delgado Rivera

    2010-01-01

    ... (as first approximation) a random walk. This paper reports the results of using ANNs for Euro/USD exchange rate trading and the usefulness of the algorithm for chemotaxis leading to training networks thereby maximising an objective function re predicting a trader’s profits. JEL: F310, C450.

  19. A single set of exhaustive exercise before local muscular endurance training improves quadriceps strength and endurance in young men.

    Science.gov (United States)

    Aguiar, Andreo Fernando; Buzzachera, Cosme Franklim; Sanches, Vanda Cristina; Pereira, Rafael Mendes; Da Silva Júnior, Rubens Alexandre; Januário, Renata Selvatici; Rabelo, Lucas Maciel; Dos Santos Silva, André Luís

    2016-01-01

    The purpose of this study was to examine the effects of an additional set of exhaustive exercise before local muscular endurance (LME) training on maximal dynamic strength and endurance of quadriceps muscle in young men. Twenty-seven healthy men (age: 20.9±1.8 years) performed one repetition maximum (1RM), muscular endurance, and magnetic resonance imaging (MRI) tests on two separate occasions (before and after an 8-wk LME training program using a bilateral knee extensor machine). After baseline testing, the subjects were divided into three groups: untrained control (CO, N.=9), traditional training (TR, N.=9), and prior exhaustive training (PE, N.=9). Both the TR and PE groups trained using the same LME training protocol (2 d∙wk-1; 3 sets of 15-20 repetitions at 50% of 1RM) throughout the 8-wk experimental period; the only difference was that the PE group performed an additional set of exhaustive exercise at 80% of 1RM immediately before each training session. After 8 wk, the PE group experienced a greater (Pendurance, and work efficiency than the TR group. Additionally, no changes (P>0.05) in cross-sectional area (CSA), body mass and daily dietary intake were observed from pre- to post-test in either group. These results suggest that the inclusion of a single set of exhaustive exercise at 80% of 1RM immediately before LME training can be a suitable strategy for inducing additional beneficial effects on quadriceps strength and endurance in young men.

  20. The effect of resistance training set configuration on strength, power, and hormonal adaptation in female volleyball players.

    Science.gov (United States)

    Arazi, Hamid; Khanmohammadi, Aida; Asadi, Abbas; Haff, G Gregory

    2017-10-10

    The primary purpose of this investigation was to determine the impact of altering the set structure during an 8-week resistance training program on anthropometric, hormonal, and strength power characteristics. Thirty female volleyball players were recruited for participation and then randomly assigned to 1 of 3 resistance training groups: (i) cluster sets (CRT; n = 10), (ii) traditional sets (TRT; n = 10), or (iii) control (CON; n = 10). All athletes were evaluated for thigh and arm circumference, vertical jump, 20-m sprint, 4 × 9-m shuttle-run, 1-repetition maximum (1RM) back squat, bench press, military press, and deadlift prior to and after an 8-week periodized training intervention. Blood samples were taken before and after the 8-week training period to evaluate resting testosterone, cortisol, and insulin-like growth factor 1 responses to the training period. After 8 weeks of training the CRT group displayed a small but significant improvement in vertical jump (CRT: effect size (ES) = 038, 7.1%) performance when compared with the TRT group (ES = 0.34, 5.6%). Both the CRT and TRT training interventions resulted in very large increases in the 1RM squat (CRT: 8.4% ± 1.2%; TRT: 7.3% ± 0.6%), bench press (CRT: 8.3% ± 2.0%; TRT: 8.7% ± 1.9%), military press (CRT: 5.7% ± 1.2%; TRT: 5.5% ± 1.6%), and deadlift (CRT: 8.2% ± 1.6%; TRT: 8.3% ± 2.2%). There were no significant differences in 20-m sprint or 4 × 9-m shuttle run times between the CRT, TRT, and CON groups. These results suggest that cluster sets allow for greater improvements in vertical jump performance and equal improvements in strength gains to those seen with traditional sets.

  1. Training, Consultation, and Mentoring: Supporting Effective Responses to Challenging Behavior in Early Care and Education Settings

    Science.gov (United States)

    Hirschland, Deborah

    2011-01-01

    Administrators in early care and education programs often turn to training initiatives to bolster staff competence in supporting children with emotional and behavioral challenges. However, training alone rarely results in the wide-ranging changes these administrators seek. This article presents a flexible approach to training, consultation and…

  2. Postural stability and quality of life after guided and self-training among older adults residing in an institutional setting.

    Science.gov (United States)

    Tuunainen, Eeva; Rasku, Jyrki; Jäntti, Pirkko; Moisio-Vilenius, Päivi; Mäkinen, Erja; Toppila, Esko; Pyykkö, Ilmari

    2013-01-01

    To evaluate whether rehabilitation of muscle force or balance improves postural stability and quality of life (QoL), and whether self-administered training is comparable with guided training among older adults residing in an institutional setting. A randomized, prospective intervention study was undertaken among 55 elderly patients. Three intervention groups were evaluated: a muscle force training group; a balance and muscle force training group; and a self-administered training group. Each group underwent 1-hour-long training sessions, twice a week, for 3 months. Postural stability was measured at onset, after 3 months, and after 6 months. Time-domain-dependent body sway variables were calculated. The fall rate was evaluated for 3 years. General health related quality of life (HRQoL) was measured with a 15D instrument. Postural stability was used as a primary outcome, with QoL and falls used as secondary outcomes. Muscle force trainees were able to undertake training, progressing towards more strenuous exercises. In posturography, the number of spiky oscillations was reduced after training, and stationary fields of torque moments of the ankle increased, providing better postural stability in all groups; in particular, the zero crossing rate of weight signal and the number of low variability episodes in the stabilogram were improved after training. While no difference was found between different training groups in posturography outcomes, a reduction of fall rate was significant in only the guided training groups. A significant part of the variability of the QoL could be explained by the posturography outcome (46%). However, the outcome of training was associated with a reduced QoL. Even moderate or severely demented residents could do exercises in five-person groups under the supervision of a physiotherapist. An improvement in postural stability was observed in all training groups, indicating that even self-administered training could be beneficial. Posturography

  3. Exercise order affects the total training volume and the ratings of perceived exertion in response to a super-set resistance training session

    Directory of Open Access Journals (Sweden)

    Balsamo S

    2012-02-01

    Full Text Available Sandor Balsamo1–3, Ramires Alsamir Tibana1,2,4, Dahan da Cunha Nascimento1,2, Gleyverton Landim de Farias1,2, Zeno Petruccelli1,2, Frederico dos Santos de Santana1,2, Otávio Vanni Martins1,2, Fernando de Aguiar1,2, Guilherme Borges Pereira4, Jéssica Cardoso de Souza4, Jonato Prestes41Department of Physical Education, Centro Universitário UNIEURO, Brasília, 2GEPEEFS (Resistance training and Health Research Group, Brasília/DF, 3Graduate Program in Medical Sciences, School of Medicine, Universidade de Brasília (UnB, Brasília, 4Graduation Program in Physical Education, Catholic University of Brasilia (UCB, Brasília/DF, BrazilAbstract: The super-set is a widely used resistance training method consisting of exercises for agonist and antagonist muscles with limited or no rest interval between them – for example, bench press followed by bent-over rows. In this sense, the aim of the present study was to compare the effects of different super-set exercise sequences on the total training volume. A secondary aim was to evaluate the ratings of perceived exertion and fatigue index in response to different exercise order. On separate testing days, twelve resistance-trained men, aged 23.0 ± 4.3 years, height 174.8 ± 6.75 cm, body mass 77.8 ± 13.27 kg, body fat 12.0% ± 4.7%, were submitted to a super-set method by using two different exercise orders: quadriceps (leg extension + hamstrings (leg curl (QH or hamstrings (leg curl + quadriceps (leg extension (HQ. Sessions consisted of three sets with a ten-repetition maximum load with 90 seconds rest between sets. Results revealed that the total training volume was higher for the HQ exercise order (P = 0.02 with lower perceived exertion than the inverse order (P = 0.04. These results suggest that HQ exercise order involving lower limbs may benefit practitioners interested in reaching a higher total training volume with lower ratings of perceived exertion compared with the leg extension plus leg curl

  4. Improving safe consumer transfers in a day treatment setting using training and feedback.

    Science.gov (United States)

    Lebbon, Angela; Austin, John; Rost, Kristen; Stanley, Leslie

    2011-01-01

    An intervention package that included employee training, supervisory feedback, and graphic feedback was developed to increase employees' safe patient-transfers at a day treatment center for adults with disabilities. The intervention was developed based on the center's results from a Performance Diagnostic Checklist (PDC), which focused on antecedents, equipment and processes, knowledge and skills, and consequences related to patient-transfers. A multiple baseline (MBL) across two lifts (pivot and trunk), with one lift (side) remaining in baseline was used to evaluate the effects of the treatment package on three lifts commonly used by three health-care workers. The results indicated a substantial increase in the overall safe performance of the three lifts. The mean increase for group safety performance following intervention was 34% and 29% over baseline measures for the two target transfers, and 28% over baseline measures for the nontargeted transfer. The implications of these findings suggest that in settings where patient transfers are frequent and injuries are likely to occur (e.g., hospitals, day treatment centers), safe lifting and transferring behaviors can improve with an efficient and cost-effective intervention.

  5. Social cognition and interaction training for patients with stable schizophrenia in Chinese community settings.

    Science.gov (United States)

    Wang, Yongguang; Roberts, David L; Xu, Baihua; Cao, Rifang; Yan, Min; Jiang, Qiongping

    2013-12-30

    Accumulated evidence suggests that Social Cognition and Interaction Training (SCIT) is associated with improved performance in social cognition and social skills in patients diagnosed with psychotic disorders. The current study examined the clinical utility of SCIT in patients with schizophrenia in Chinese community settings. Adults with stable schizophrenia were recruited from local community health institutions, and were randomly assigned to SCIT group (n = 22) or a waiting-list control group (n = 17). The SCIT group received the SCIT intervention plus treatment-as-usual, whereas the waiting-list group received only treatment-as-usual during the period of the study. All patients were administered the Chinese versions of the Personal and Social Performance Scale (PSP), Face Emotion Identification Task (FEIT), Eyes task, and Attributional Style Questionnaire (ASQ) at baseline of the SCIT treatment period and at follow-up, 6 months after completion of the 20-week treatment period. Patients in SCIT group showed a significant improvement in the domains of emotion perception, theory of mind, attributional style, and social functioning compared to those in waiting-list group. Findings indicate that SCIT is a feasible and promising method for improving social cognition and social functioning among Chinese outpatients with stable schizophrenia. © 2013 Elsevier Ireland Ltd. All rights reserved.

  6. Mindfulness Meditation Training and Executive Control Network Resting State Functional Connectivity: A Randomized Controlled Trial.

    Science.gov (United States)

    Taren, Adrienne A; Gianaros, Peter J; Greco, Carol M; Lindsay, Emily K; Fairgrieve, April; Brown, Kirk Warren; Rosen, Rhonda K; Ferris, Jennifer L; Julson, Erica; Marsland, Anna L; Creswell, J David

    Mindfulness meditation training has been previously shown to enhance behavioral measures of executive control (e.g., attention, working memory, cognitive control), but the neural mechanisms underlying these improvements are largely unknown. Here, we test whether mindfulness training interventions foster executive control by strengthening functional connections between dorsolateral prefrontal cortex (dlPFC)-a hub of the executive control network-and frontoparietal regions that coordinate executive function. Thirty-five adults with elevated levels of psychological distress participated in a 3-day randomized controlled trial of intensive mindfulness meditation or relaxation training. Participants completed a resting state functional magnetic resonance imaging scan before and after the intervention. We tested whether mindfulness meditation training increased resting state functional connectivity (rsFC) between dlPFC and frontoparietal control network regions. Left dlPFC showed increased connectivity to the right inferior frontal gyrus (T = 3.74), right middle frontal gyrus (MFG) (T = 3.98), right supplementary eye field (T = 4.29), right parietal cortex (T = 4.44), and left middle temporal gyrus (T = 3.97, all p < .05) after mindfulness training relative to the relaxation control. Right dlPFC showed increased connectivity to right MFG (T = 4.97, p < .05). We report that mindfulness training increases rsFC between dlPFC and dorsal network (superior parietal lobule, supplementary eye field, MFG) and ventral network (right IFG, middle temporal/angular gyrus) regions. These findings extend previous work showing increased functional connectivity among brain regions associated with executive function during active meditation by identifying specific neural circuits in which rsFC is enhanced by a mindfulness intervention in individuals with high levels of psychological distress. Clinicaltrials.gov,NCT01628809.

  7. A Trained Network Solution for Multi-State Structural Awareness

    Science.gov (United States)

    2012-03-12

    percentage T12 damaged T23 damaged T34 damaged T41 damaged T13 damaged T24 damaged T12 undamaged T23 undamaged T34 undamaged T41 undamaged T13 undamaged... T24 undamaged 99% 95% 90% 75% 0 50 100 150 200 250 300 pre-set threshold mean-square-error T12 damaged T23 damaged T34 damaged T41 damaged T13...damaged T24 damaged T12 undamaged T23 undamaged T34 undamaged T41 undamaged T13 undamaged T24 undamaged Distribution Statement A: Approved for public

  8. ON OPERATION OF 740 M LONG FREIGHT TRAINS ON CZECH TEN-T RAILWAY NETWORK

    Directory of Open Access Journals (Sweden)

    Michal Drábek

    2016-09-01

    Full Text Available Regulation (EU No 1315/2013 defines actual scope of core and comprehensive TEN-T network, including both networks for railway freight transport. For the core network, possibility to operate 740 m long freight trains is required. The aim of this paper is to analyse availability of appropriate overtaking tracks for 740 m long freight trains. Due to ETCS braking curves and odometry, such trains, after ETCS implementation, will require 780-800 m long overtaking tracks. For practical reasons (e.g. bypass lines, whole Czech railway TEN-T network is analysed. The overtaking track, whose occupation means influence on scheduled traffic or threat to boarding passengers, are excluded. The data was collected from station schemes from Collection of Official Requisites for 2015/16 Timetable, issued by SŽDC, Czech state Infrastructure Manager. Most of appropriate tracks are over 800 m long, but their density in the network and in particular directions varies considerably. For freight traffic, gradient of the line is important, so in the resulting figure, there are marked significant peaks for particular lines as well. Czech TEN-T lines are further segmented on the basis of number of tracks and their traffic character. Then, specific issues on overtaking or crossing of 740 m long freight trains are discussed. As a conclusion, for long-term development of Czech TEN-T lines, targeted investment is recommended not only for passenger railway, but also for freight railway. An attractive capacity offer for railway undertakings, which can stimulate freight traffic on European Rail freight corridors, can be represented by network-bound periodic freight train paths with suitable long overtaking tracks outside bottlenecks. After the overtaking by passenger trains, a freight train should run without stop through large node station or a bottleneck area. Before the sections with high gradients, coupling of additional locomotives should be connected with the overtaking

  9. Evaluation of Life Skills Training and Infused-Life Skills Training in a Rural Setting: Outcomes at Two Years

    Science.gov (United States)

    Smith, Edward A.; Swisher, John D.; Vicary, Judith R.; Bechtel, Lori J.; Minner, Daphne; Henry, Kimberly L.; Palmer, Raymond

    2004-01-01

    This study reports on findings from the first two years of a study to compare a standard Life Skill Training (LST) program with an infused (I-LST) approach. Nine small, rural school districts were randomly assigned to LST, I-LST, or control conditions in grade seven. The LST program significantly reduced alcohol use, binge drinking, marijuana use,…

  10. Reorganization of functional brain networks mediates the improvement of cognitive performance following real-time neurofeedback training of working memory.

    Science.gov (United States)

    Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie

    2015-05-01

    Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.

  11. An Impaled Potential Unexploded Device in the Civilian Training Trauma Setting: A Case Report and Review of the Literature

    Science.gov (United States)

    2017-12-11

    tissue wound . A pressure dressing and tourniquet were applied and he was t ransported 78 to a local, Level IV Trauma Center. At this facility, the...Unexploded Device in the Civilian Training Trauma Setting: A Case Report and Review of the Literature Sb. GRANT NUMBER Sc. PROGRAM ELEMENT NUMBER 6...Unexploded Device in the Civilian Trauma Setting: A Case Report and Review of the literature Article Type: Case Report Corresponding Author: Jamie L

  12. Agenda setting for maternal survival: the power of global health networks and norms.

    Science.gov (United States)

    Smith, Stephanie L; Rodriguez, Mariela A

    2016-04-01

    Nearly 300,000 women--almost all poor women in low-income countries--died from pregnancy-related complications in 2010. This represents a decline since the 1980s, when an estimated half million women died each year, but is still far higher than the aims set in the United Nations Millennium Development Goals (MDGs) at the turn of the century. The 1970s, 1980s and 1990 s witnessed a shift from near complete neglect of the issue to emergence of a network of individuals and organizations with a shared concern for reducing maternal deaths and growth in the number of organizations and governments with maternal health strategies and programmes. Maternal health experienced a marked change in agenda status in the 2000s, attracting significantly higher level attention (e.g. from world leaders) and greater resource commitments (e.g. as one issue addressed by US$40 billion in pledges to the 2010 Global Strategy for Women's and Children's Health) than ever before. Several differences between network and actor features, issue characteristics and the policy environment pre- and post-2000 help to explain the change in agenda status for global maternal mortality reduction. Significantly, a strong poverty reduction norm emerged at the turn of the century; represented by the United Nations MDGs framework, the norm set unusually strong expectations for international development actors to advance included issues. As the norm grew, it drew policy attention to the maternal health goal (MDG 5). Seeking to advance the goals agenda, world leaders launched initiatives addressing maternal and child health. New network governance and framing strategies that closely linked maternal, newborn and child health shaped the initiatives. Diverse network composition--expanding beyond a relatively narrowly focused and technically oriented group to encompass allies and leaders that brought additional resources to bear on the problem--was crucial to maternal health's rise on the agenda in the 2000s

  13. Outcomes from the GLEON fellowship program. Training graduate students in data driven network science.

    Science.gov (United States)

    Dugan, H.; Hanson, P. C.; Weathers, K. C.

    2016-12-01

    In the water sciences there is a massive need for graduate students who possess the analytical and technical skills to deal with large datasets and function in the new paradigm of open, collaborative -science. The Global Lake Ecological Observatory Network (GLEON) graduate fellowship program (GFP) was developed as an interdisciplinary training program to supplement the intensive disciplinary training of traditional graduate education. The primary goal of the GFP was to train a diverse cohort of graduate students in network science, open-web technologies, collaboration, and data analytics, and importantly to provide the opportunity to use these skills to conduct collaborative research resulting in publishable scientific products. The GFP is run as a series of three week-long workshops over two years that brings together a cohort of twelve students. In addition, fellows are expected to attend and contribute to at least one international GLEON all-hands' meeting. Here, we provide examples of training modules in the GFP (model building, data QA/QC, information management, bayesian modeling, open coding/version control, national data programs), as well as scientific outputs (manuscripts, software products, and new global datasets) produced by the fellows, as well as the process by which this team science was catalyzed. Data driven education that lets students apply learned skills to real research projects reinforces concepts, provides motivation, and can benefit their publication record. This program design is extendable to other institutions and networks.

  14. SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method.

    Science.gov (United States)

    Bernal, Javier; Torres-Jimenez, Jose

    2015-01-01

    SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

  15. Setting-up of remote reactor LAB and tapping into CARRN for distance education and training in nuclear field

    Energy Technology Data Exchange (ETDEWEB)

    Park, Eugene [The Nelson Mandeal African Institute of Science and Technology, Arusha (Tanzania, United Republic of)

    2013-07-01

    For a developing country embarking on a research reactor project, building adequate human resource capacity is one of the biggest challenges. Tanzania has been considering a research reactor for some time. The success of future research reactor project impinges on vigorous education and training of necessary personnel to operate and fully utilize the facility. In Africa, underutilization of research reactors is a chronic issue. It is not only misuse of valuable resources but also poses potential safety and security concerns. To mitigate such concerns and to promote education and training, Central African Research Reactor Network (CARRN) was formed in June of 2011. Borrowing from Jordan's success, this paper presents customised curricula to take advantage of CARRN for distance education and training in nuclear field.

  16. CONSIDERATION OF AERODYNAMIC IMPACT IN SETTING THE MAXIMUM PERMISSIBLE SPEEDS OF HIGH-SPEED TRAIN

    Directory of Open Access Journals (Sweden)

    S. T. Djabbarov

    2017-10-01

    Full Text Available Purpose. Studies of the effect of aerodynamic pressure on the maximum permissible speeds of a high-speed train on the existing railway infrastructure. Methodology. The study of the magnitude and direction of the aerodynamic pressure, its effect on the maximum speeds of a high-speed train was carried out on a train model composed of axisymmetric bodies with conical forms of head and tail parts. Findings. Determined the values of the aerodynamic pressure at different distances from the train are, when the high-speed train moves at a speed of 200 km/h or more. The maximum speeds of a high-speed train are determined taking into account the state of the infrastructure of the existing railway, ensuring the safe operation of a high-speed railway. Originality. Theoretical studies of aerodynamic pressure from secondary air currents formed during the movement of high-speed trains are performed on a model of a train composed of identical axisymmetric bodies with conical forms of head and tail moving in a compressible medium. The results of the research allow the regularity of the change in aerodynamic pressure during the movement of a high-speed train. Practical value. The obtained results allow to establish: 1 the maximum permissible speeds of a high-speed train taking into account the technical condition of permanent devices and structures of the existing railway infrastructure; 2 technical parameters of individual objects and structural elements of the infrastructure of high-speed iron subjected to the effect of aerodynamic pressure for a given maximum speed of high-speed trains.

  17. Robust adaptive gradient-descent training algorithm for recurrent neural networks in discrete time domain.

    Science.gov (United States)

    Song, Qing; Wu, Yilei; Soh, Yeng Chai

    2008-11-01

    For a recurrent neural network (RNN), its transient response is a critical issue, especially for real-time signal processing applications. The conventional RNN training algorithms, such as backpropagation through time (BPTT) and real-time recurrent learning (RTRL), have not adequately addressed this problem because they suffer from low convergence speed. While increasing the learning rate may help to improve the performance of the RNN, it can result in unstable training in terms of weight divergence. Therefore, an optimal tradeoff between RNN training speed and weight convergence is desired. In this paper, a robust adaptive gradient-descent (RAGD) training algorithm of RNN is developed based on a novel RNN hybrid training concept. It switches the training patterns between standard real-time online backpropagation (BP) and RTRL according to the derived convergence and stability conditions. The weight convergence and L(2)-stability of the algorithm are derived via the conic sector theorem. The optimized adaptive learning maximizes the training speed of the RNN for each weight update without violating the stability and convergence criteria. Computer simulations are carried out to demonstrate the applicability of the theoretical results.

  18. Toward the training of feed-forward neural networks with the D-optimum input sequence.

    Science.gov (United States)

    Witczak, Marcin

    2006-03-01

    The problem under consideration is to obtain a measurement schedule for training neural networks. This task is perceived as an experimental design in a given design space that is obtained in such a way as to minimize the difference between the neural network and the system being considered. This difference can be expressed in many different ways and one of them, namely, the D-optimality criterion is used in this paper. In particular, the paper presents a unified and comprehensive treatment of this problem by discussing the existing and previously unpublished properties of the optimum experimental design (OED) for neural networks. The consequences of the above properties are discussed as well. A hybrid algorithm that can be used for both the training and data development of neural networks is another important contribution of this paper. A careful analysis of the algorithm is presented and its comprehensive convergence analysis with the help of the Lyapunov method are given. The paper contains a number of numerical examples that justify the application of the OED theory for neural networks. Moreover, an industrial application example is given that deals with the valve actuator.

  19. The Global Network Neonatal Cause of Death algorithm for low-resource settings.

    Science.gov (United States)

    Garces, Ana L; McClure, Elizabeth M; Pérez, Wilton; Hambidge, K Michael; Krebs, Nancy F; Figueroa, Lester; Bose, Carl L; Carlo, Waldemar A; Tenge, Constance; Esamai, Fabian; Goudar, Shivaprasad S; Saleem, Sarah; Patel, Archana B; Chiwila, Melody; Chomba, Elwyn; Tshefu, Antoinette; Derman, Richard J; Hibberd, Patricia L; Bucher, Sherri; Liechty, Edward A; Bauserman, Melissa; Moore, Janet L; Koso-Thomas, Marion; Miodovnik, Menachem; Goldenberg, Robert L

    2017-06-01

    This study estimated the causes of neonatal death using an algorithm for low-resource areas, where 98% of the world's neonatal deaths occur. We enrolled women in India, Pakistan, Guatemala, the Democratic Republic of Congo, Kenya and Zambia from 2014 to 2016 and tracked their delivery and newborn outcomes for up to 28 days. Antenatal care and delivery symptoms were collected using a structured questionnaire, clinical observation and/or a physical examination. The Global Network Cause of Death algorithm was used to assign the cause of neonatal death, analysed by country and day of death. One-third (33.1%) of the 3068 neonatal deaths were due to suspected infection, 30.8% to prematurity, 21.2% to asphyxia, 9.5% to congenital anomalies and 5.4% did not have a cause of death assigned. Prematurity and asphyxia-related deaths were more common on the first day of life (46.7% and 52.9%, respectively), while most deaths due to infection occurred after the first day of life (86.9%). The distribution of causes was similar to global data reported by other major studies. The Global Network algorithm provided a reliable cause of neonatal death in low-resource settings and can be used to inform public health strategies to reduce mortality. ©2017 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

  20. Setting up the On-Site Marriage and Family Therapy Clinical Training Course

    Science.gov (United States)

    Ratanasiripong, Paul; Ghafoori, Bita

    2009-01-01

    The first clinical training experience or practicum for graduate students in a Marriage and Family Therapy (MFT) program is one of the most important aspects of the entire training program. After a year-long journey through textbook and classroom knowledge, students have the opportunity to finally apply their skills to real life environments with…

  1. Effectiveness of Teacher-Child Interaction Training (TCIT) in a Preschool Setting

    Science.gov (United States)

    Lyon, Aaron R.; Gershenson, Rachel A.; Farahmand, Farahnaz K.; Thaxter, Peter J.; Behling, Steven; Budd, Karen S.

    2009-01-01

    This research addressed the need for trained child care staff to support optimal early social-emotional development in urban, low-income, ethnic minority children. We evaluated effectiveness of Teacher-Child Interaction Training (TCIT), an approach adapted from Eyberg's Parent-Child Interaction Therapy (PCIT). TCIT focuses on increasing preschool…

  2. Agenda Trending: Reciprocity and the Predictive Capacity of Social Networking Sites in Intermedia Agenda Setting across Topics over Time

    National Research Council Canada - National Science Library

    Groshek, Jacob; Groshek, Megan Clough

    2013-01-01

    ...-setting effects of social media topics entering traditional media agendas. In addition, this study examines social intermedia agenda setting topically and across time within social networking sites, and in so doing, adds a vital understanding of where traditional media, online uses, and social media content intersect around instances of focusing events, p...

  3. A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

    Directory of Open Access Journals (Sweden)

    Choon Ki Ahn

    2010-01-01

    Full Text Available A new robust training law, which is called an input/output-to-state stable training law (IOSSTL, is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

  4. Modeling of an ionic polymer metal composite actuator based on an extended Kalman filter trained neural network

    Science.gov (United States)

    Quang Truong, Dinh; Ahn, Kyoung Kwan

    2014-07-01

    An ion polymer metal composite (IPMC) is an electroactive polymer that bends in response to a small applied electric field as a result of mobility of cations in the polymer network and vice versa. This paper presents an innovative and accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The model is constructed via a general multilayer perceptron neural network (GMLPNN) integrated with a smart learning mechanism (SLM) that is based on an extended Kalman filter with self-decoupling ability (SDEKF). Here the GMLPNN is built with an ability to autoadjust its structure based on its characteristic vector. Furthermore, by using the SLM based on the SDEKF, the GMLPNN parameters are optimized with small computational effort, and the modeling accuracy is improved. An apparatus employing an IPMC actuator is first set up to investigate the IPMC characteristics and to generate the data for training and validating the model. The advanced NBBM model for the IPMC system is then created with the proper inputs to estimate IPMC tip displacement. Next, the model is optimized using the SLM mechanism with the training data. Finally, the optimized NBBM model is verified with the validating data. A comparison between this model and the previously developed model is also carried out to prove the effectiveness of the proposed modeling technique.

  5. Hip abduction strength training in the clinical setting: with or without external loading?

    DEFF Research Database (Denmark)

    Thorborg, Kristian; Bandholm, T; Petersen, Jesper

    2010-01-01

    The side-lying hip abduction exercise is one of the most commonly used exercises in rehabilitation to increase hip abduction strength, and is often performed without external loading. The aim of this study was to compare the effect of 6 weeks of side-lying hip abduction training, with and without...... external loading, on hip abduction strength in healthy subjects. Thirty-one healthy, physically active men and women were included in a randomised controlled trial and allocated to side-lying hip abduction training, with or without external loading. Training without external loading was performed using...... only the weight of the leg as resistance, whereas training with external loading was performed with a relative load corresponding to 10 repetition maximum. Hip abduction strength was measured pre- and post-intervention. Isometric and eccentric hip abduction strength of the trained leg increased after...

  6. The effects of behavioral skills training on instructor and learner behavior across responses and skill sets.

    Science.gov (United States)

    Fetherston, Anne M; Sturmey, Peter

    2014-02-01

    Behavioral skills training (BST) is effective to train staff to provide intervention to people with developmental disabilities. The purpose of this study was to assess whether: (a) prior studies demonstrating the effectiveness of BST could be systematically replicated while teaching multiple teaching instructors to implement discrete trial teaching, incidental teaching and activity schedules; (b) instructional skills that staff acquired during training on one response generalized to a variety of instructional programs, (c) positive changes in staff performance produced positive behavior change in learners; and (d) positive changes in learner behavior generalized to novel programs. BST resulted in positive behavior change across staff, learners, instructional programs, and various teaching skills. Further, staff generalized teaching skills to novel responses and learners displayed increases in correct responding for all three instructional procedures. Social validity data indicated they these staff training procedures were highly acceptable and effective. Thus, BST is an effective and acceptable staff training procedure. Copyright © 2013. Published by Elsevier Ltd.

  7. Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data.

    Science.gov (United States)

    Assab, Rania; Nekkab, Narimane; Crépey, Pascal; Astagneau, Pascal; Guillemot, Didier; Opatowski, Lulla; Temime, Laura

    2017-08-01

    Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections. Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus). To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.

  8. Fast computation of minimal cut sets in metabolic networks with a Berge algorithm that utilizes binary bit pattern trees.

    Science.gov (United States)

    Jungreuthmayer, Christian; Beurton-Aimar, Marie; Zanghellini, Jürgen

    2013-01-01

    Minimal cut sets are a valuable tool for analyzing metabolic networks and for identifying optimal gene intervention strategies by eliminating unwanted metabolic functions and keeping desired functionality. Minimal cut sets rely on the concept of elementary flux modes, which are sets of indivisible metabolic pathways under steady-state condition. However, the computation of minimal cut sets is nontrivial, as even medium-sized metabolic networks with just 100 reactions easily have several hundred million elementary flux modes. We developed a minimal cut set tool that implements the well-known Berge algorithm and utilizes a novel approach to significantly reduce the program run time by using binary bit pattern trees. By using the introduced tree approach, the size of metabolic models that can be analyzed and optimized by minimal cut sets is pushed to new and considerably higher limits.

  9. The Bilevel Design Problem for Communication Networks on Trains: Model, Algorithm, and Verification

    Directory of Open Access Journals (Sweden)

    Yin Tian

    2014-01-01

    Full Text Available This paper proposes a novel method to solve the problem of train communication network design. Firstly, we put forward a general description of such problem. Then, taking advantage of the bilevel programming theory, we created the cost-reliability-delay model (CRD model that consisted of two parts: the physical topology part aimed at obtaining the networks with the maximum reliability under constrained cost, while the logical topology part focused on the communication paths yielding minimum delay based on the physical topology delivered from upper level. We also suggested a method to solve the CRD model, which combined the genetic algorithm and the Floyd-Warshall algorithm. Finally, we used a practical example to verify the accuracy and the effectiveness of the CRD model and further applied the novel method on a train with six carriages.

  10. Epidemiologic comparison of injured high school basketball athletes reporting to emergency departments and the athletic training setting.

    Science.gov (United States)

    Fletcher, Erica N; McKenzie, Lara B; Comstock, R Dawn

    2014-01-01

    Basketball is a popular US high school sport with more than 1 million participants annually. To compare patterns of athletes with basketball-related injuries presenting to US emergency departments from 2005 through 2010 and the high school athletic training setting from the 2005-2011 seasons. Descriptive epidemiology study. Data from the National Electronic Injury Surveillance System of the US Consumer Product Safety Commission and the High School Reporting Information Online database. Complex sample weights were used to calculate national estimates of basketball-related injuries for comparison. Adolescents from 13 to 19 years of age treated in US emergency departments for basketball-related injuries and athletes from 13 to 19 years of age from schools participating in High School Reporting Information Online who were injured while playing basketball. Nationally, an estimated 1,514,957 (95% confidence interval = 1,337,441, 1,692,474) athletes with basketball-related injuries reported to the emergency department and 1,064,551 (95% confidence interval = 1,055,482, 1,073,620) presented to the athletic training setting. Overall, the most frequent injuries seen in the emergency department were lacerations and fractures (injury proportion ratios [IPRs] = 3.45 and 1.72, respectively), whereas those seen in the athletic training setting were more commonly concussions and strains/sprains (IPRs = 2.23 and 1.19, respectively; all P values basketball players presenting for treatment in the emergency department and the athletic training setting. Understanding differences specific to clinical settings is crucial to grasping the full epidemiologic and clinical picture of sport-related injuries. Certified athletic trainers play an important role in identifying, assessing, and treating athletes with sport-related injuries who might otherwise present to clinical settings with higher costs, such as the emergency department.

  11. Nursing intensive care skills training: a nurse led, short, structured, and practical training program, developed and tested in a resource-limited setting.

    Science.gov (United States)

    De Silva, A Pubudu; Stephens, Tim; Welch, John; Sigera, Chathurani; De Alwis, Sunil; Athapattu, Priyantha; Dharmagunawardene, Dilantha; Olupeliyawa, Asela; de Abrew, Ashwini; Peiris, Lalitha; Siriwardana, Somalatha; Karunathilake, Indika; Dondorp, Arjen; Haniffa, Rashan

    2015-04-01

    To assess the impact of a nurse-led, short, structured training program for intensive care unit (ICU) nurses in a resource-limited setting. A training program using a structured approach to patient assessment and management for ICU nurses was designed and delivered by local nurse tutors in partnership with overseas nurse trainers. The impact of the course was assessed using the following: pre-course and post-course self-assessment, a pre-course and post-course Multiple Choice Questionnaire (MCQ), a post-course Objective Structured Clinical Assessment station, 2 post-course Short Oral Exam (SOE) stations, and post-course feedback questionnaires. In total, 117 ICU nurses were trained. Post-MCQ scores were significantly higher when compared with pre-MCQ (P Nursing Intensive Care Skills Training was highly rated by participants and was effective in improving the knowledge of the participants. This sustainable short course model may be adaptable to other resource-limited settings. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Quantitative systematic review of multi-professional teamwork and leadership training to optimize patient outcomes in acute hospital settings.

    Science.gov (United States)

    Husebø, Sissel Eikeland; Akerjordet, Kristin

    2016-12-01

    To evaluate the impact of multi-professional teamwork (MPTW) and leadership training interventions on patient outcomes in acute hospital settings. Although investigations of teamwork and leadership training in acute hospital settings indicate that such programs can optimize patient outcomes, evidence-based recommendations on the content, duration and frequency of training programs associated with clinical evidence are still absent. Quantitative systematic review. A search was conducted for relevant papers published during the period from 2000-February 2014. Twelve studies met the inclusion criteria and were appraised for quality and a risk-of-bias assessment was conducted. The review used a structured approach for literature search, data evaluation, analysis and presentation. A narrative summary was used to report results. Two MPTW and leadership interventions in stroke units have the greatest impact on patient outcomes in acute hospital settings. The interventions' impact on patient outcomes, explored in the ten remaining studies, is associated with great uncertainty due to several alternative explanations of the findings. Research designs that test such interventions must be improved before recommendations on the ultimate program can be made. This can be achieved by strengthening the design, methodology and descriptions of interventions and the use of more consistent patient outcomes. Building a safety culture adjacent to implementing teamwork and leadership training interventions is essential for improving patient outcomes. © 2016 John Wiley & Sons Ltd.

  13. Enlarge the training set based on inter-class relationship for face recognition from one image per person.

    Directory of Open Access Journals (Sweden)

    Qin Li

    Full Text Available In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA, Fisher linear discriminant analysis (LDA, and locality preserving projections (LPP and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.

  14. Sleep, fatigue, and medical training: setting an agenda for optimal learning and patient care.

    Science.gov (United States)

    Buysse, Daniel J; Barzansky, Barbara; Dinges, David; Hogan, Eileen; Hunt, Carl E; Owens, Judith; Rosekind, Mark; Rosen, Raymond; Simon, Frank; Veasey, Sigrid; Wiest, Francine

    2003-03-15

    The difficult issues surrounding discussions of sleep, fatigue, and medical education stem from an ironic biologic truth: physicians share a common physiology with their patients, a physiology that includes an absolute need for sleep and endogenous circadian rhythms governing alertness and performance. We cannot ignore the fact that patients become ill and require medical care at all times of the day and night, but we also cannot escape the fact that providing such care requires that medical professionals, including medical trainees, be awake and functioning at times that are in conflict with their endogenous sleep and circadian physiology. Finally, we cannot avoid the reality that medical education requires long hours in a constrained number of years. Solutions to the problem of sleep and fatigue in medical education will require the active involvement of numerous parties, ranging from trainees themselves to training program directors, hospital administrators, sleep and circadian scientists, and government funding and regulatory agencies. Each of these parties can be informed by previous laboratory and field studies in a variety of operational settings. including medical environments. Education regarding the known effects of sleep. circadian rhythms, and sleep deprivation can help to elevate the general level of discourse and point to potential solutions. Empiric research addressing the effects of sleep loss on patient safety, education outcomes, and resident health is urgently needed: equally important are the development and assessment of innovative countermeasures to maximize performance and learning. Addressing the economic realities of any changes in resident work hours is an essential component of any discussion of these issues. Finally, work-hour regulations may serve as one component of improved sleep and circadian health for medical trainees. but they should not be seen as substitutes for more original solutions that rely less on enforcement and more on

  15. Comprehensive on-site medical and public health training for local medical practitioners in a refugee setting.

    Science.gov (United States)

    Asgary, Ramin; Jacobson, Karen

    2013-02-01

    In refugee settings, local medical personnel manage a broad range of health problems but commonly lack proper skills and training, which contributes to inefficient use of resources. To fill that gap, we designed, implemented, and evaluated a curriculum for a comprehensive on-site training for medical providers. The comprehensive teaching curriculum provided ongoing on-site training for medical providers (4 physicians, 7 medical officers, 15 nurses and nurse aids, and 30 community health workers) in a sub-Saharan refugee camp. The curriculum included didactic sessions, inpatient and outpatient practice-based teaching, and case-based discussions, which included clinical topics, refugee public health, and organizational skills. The usefulness and efficacy of the training were evaluated through pretraining and posttraining tests, anonymous self-assessment surveys, focus group discussions, and direct clinical observation. Physicians had a 50% (95% CI 17%-82%; range, 25%-75%) improvement in knowledge and skills. They rated the quality and usefulness of lectures 4.75 and practice-based teaching 5.0 on a 5-point scale (1=poor to 5=excellent). Evaluation of medical officers' knowledge revealed improvements in (1) overall test scores (52% [SD 8%] to 80% [SD 5%]; P training prioritization, time constraints, and limited ancillary support. A long-term, ongoing training curriculum for medical providers initiated by aid agencies but integrated into horizontal peer-to-peer education is feasible and effective in refugee settings. Such programs need prioritizing, practice and system-based personnel training, and a comprehensive curriculum to improve clinical decision making.

  16. Fast and efficient second-order method for training radial basis function networks.

    Science.gov (United States)

    Xie, Tiantian; Yu, Hao; Hewlett, Joel; Rózycki, Paweł; Wilamowski, Bogdan

    2012-04-01

    This paper proposes an improved second order (ISO) algorithm for training radial basis function (RBF) networks. Besides the traditional parameters, including centers, widths and output weights, the input weights on the connections between input layer and hidden layer are also adjusted during the training process. More accurate results can be obtained by increasing variable dimensions. Initial centers are chosen from training patterns and other parameters are generated randomly in limited range. Taking the advantages of fast convergence and powerful search ability of second order algorithms, the proposed ISO algorithm can normally reach smaller training/testing error with much less number of RBF units. During the computation process, quasi Hessian matrix and gradient vector are accumulated as the sum of related sub matrices and vectors, respectively. Only one Jacobian row is stored and used for multiplication, instead of the entire Jacobian matrix storage and multiplication. Memory reduction benefits the computation speed and allows the training of problems with basically unlimited number of patterns. Several practical discrete and continuous classification problems are applied to test the properties of the proposed ISO training algorithm.

  17. [Case finding in early prevention networks - a heuristic for ambulatory care settings].

    Science.gov (United States)

    Barth, Michael; Belzer, Florian

    2016-06-01

    One goal of early prevention is the support of families with small children up to three years who are exposed to psychosocial risks. The identification of these cases is often complex and not well-directed, especially in the ambulatory care setting. Development of a model of a feasible and empirical based strategy for case finding in ambulatory care. Based on the risk factors of postpartal depression, lack of maternal responsiveness, parental stress with regulation disorders and poverty a lexicographic and non-compensatory heuristic model with simple decision rules, will be constructed and empirically tested. Therefore the original data set from an evaluation of the pediatric documentary form on psychosocial issues of families with small children in well-child visits will be used and reanalyzed. The first diagnostic step in the non-compensatory and hierarchical classification process is the assessment of postpartal depression followed by maternal responsiveness, parental stress and poverty. The classification model identifies 89.0 % cases from the original study. Compared to the original study the decision process becomes clearer and more concise. The evidence-based and data-driven model exemplifies a strategy for the assessment of psychosocial risk factors in ambulatory care settings. It is based on four evidence-based risk factors and offers a quick and reliable classification. A further advantage of this model is that after a risk factor is identified the diagnostic procedure will be stopped and the counselling process can commence. For further validation of the model studies, in well suited early prevention networks are needed.

  18. Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks

    Directory of Open Access Journals (Sweden)

    Qiushi eZhang

    2015-09-01

    Full Text Available Working memory (WM refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual’s cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex activation using real-time functional magnetic resonance imaging (rtfMRI, which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN, the default mode network (DMN and the salience network (SN, and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.

  19. Impact of real-time fMRI working memory feedback training on the interactions between three core brain networks.

    Science.gov (United States)

    Zhang, Qiushi; Zhang, Gaoyan; Yao, Li; Zhao, Xiaojie

    2015-01-01

    Working memory (WM) refers to the temporary holding and manipulation of information during the performance of a range of cognitive tasks, and WM training is a promising method for improving an individual's cognitive functions. Our previous work demonstrated that WM performance can be improved through self-regulation of dorsal lateral prefrontal cortex (PFC) activation using real-time functional magnetic resonance imaging (rtfMRI), which enables individuals to control local brain activities volitionally according to the neurofeedback. Furthermore, research concerning large-scale brain networks has demonstrated that WM training requires the engagement of several networks, including the central executive network (CEN), the default mode network (DMN) and the salience network (SN), and functional connectivity within the CEN and DMN can be changed by WM training. Although a switching role of the SN between the CEN and DMN has been demonstrated, it remains unclear whether WM training can affect the interactions between the three networks and whether a similar mechanism also exists during the training process. In this study, we investigated the dynamic functional connectivity between the three networks during the rtfMRI feedback training using independent component analysis (ICA) and correlation analysis. The results indicated that functional connectivity within and between the three networks were significantly enhanced by feedback training, and most of the changes were associated with the insula and correlated with behavioral improvements. These findings suggest that the insula plays a critical role in the reorganization of functional connectivity among the three networks induced by rtfMRI training and in WM performance, thus providing new insights into the mechanisms of high-level functions and the clinical treatment of related functional impairments.

  20. PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN

    Directory of Open Access Journals (Sweden)

    Arie Rachmad Syulistyo

    2016-02-01

    Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

  1. High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm.

    Science.gov (United States)

    Gastegger, Michael; Marquetand, Philipp

    2015-05-12

    Artificial neural networks (NNs) represent a relatively recent approach for the prediction of molecular potential energies, suitable for simulations of large molecules and long time scales. By using NNs to fit electronic structure data, it is possible to obtain empirical potentials of high accuracy combined with the computational efficiency of conventional force fields. However, as opposed to the latter, changing bonding patterns and unusual coordination geometries can be described due to the underlying flexible functional form of the NNs. One of the most promising approaches in this field is the high-dimensional neural network (HDNN) method, which is especially adapted to the prediction of molecular properties. While HDNNs have been mostly used to model solid state systems and surface interactions, we present here the first application of the HDNN approach to an organic reaction, the Claisen rearrangement of allyl vinyl ether to 4-pentenal. To construct the corresponding HDNN potential, a new training algorithm is introduced. This algorithm is termed "element-decoupled" global extended Kalman filter (ED-GEKF) and is based on the decoupled Kalman filter. Using a metadynamics trajectory computed with density functional theory as reference data, we show that the ED-GEKF exhibits superior performance - both in terms of accuracy and training speed - compared to other variants of the Kalman filter hitherto employed in HDNN training. In addition, the effect of including forces during ED-GEKF training on the resulting potentials was studied.

  2. A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

    2017-01-01

    In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

    Directory of Open Access Journals (Sweden)

    Dingjun Chen

    2016-01-01

    Full Text Available This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams.

  4. A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.

    Science.gov (United States)

    Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim

    2015-01-01

    The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.

  5. In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression.

    Science.gov (United States)

    Chen, Hai-Feng

    2009-08-01

    Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r(2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.

  6. Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification

    Directory of Open Access Journals (Sweden)

    Ricardo Pérez-Aguila

    2013-01-01

    Full Text Available An approach for nonsupervised segmentation of Computed Tomography (CT brain slices which is based on the use of Vector Quantization Networks (VQNs is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.

  7. The structure of lay consultation networks: managing illness in community settings.

    Science.gov (United States)

    Stoller, Eleanor Palo; Wisniewski, Amy A

    2003-08-01

    We examined the structure of lay consultation networks among elderly people. Data were gathered through interviews with 548 elderly adults living in Florida retirement communities and in Cleveland. Respondents identified people they consulted about symptom or disease information, health worries, what the doctor said, and consulting health providers. Network size, composition, geographic dispersion, gender homogeneity, and division of labor were assessed. Eighty percent identified at least one network member (range = 1 to 7 consultants). Networks largely consisted of family members, particularly spouses and women. Older adults talked most frequently with network members about physician visits. Widowed individuals were more likely to rely on children and friends and have networks outside their neighborhoods than married elders. Women's networks included a broader range of relationships than men's networks. Results reaffirmed the importance of gender in structuring networks in late life. The low prevalence of friends supports Cartensen's Selectivity Theory.

  8. Postural stability and quality of life after guided and self-training among older adults residing in an institutional setting

    Directory of Open Access Journals (Sweden)

    Tuunainen E

    2013-09-01

    Full Text Available Eeva Tuunainen,1 Jyrki Rasku,1 Pirkko Jäntti,2 Päivi Moisio-Vilenius,3 Erja Mäkinen,3 Esko Toppila,4 Ilmari Pyykkö1 1Department of Otolaryngology, Section of Hearing and Balance Research Unit, University of Tampere and University Hospital of Tampere, Finland; 2Department of Geriatric Medicine, Hatanpää City Hospital, Tampere, Finland; 3Koukkuniemi Residential Home, Tampere, Finland; 4Finnish Institute of Occupational Health, Helsinki, Finland Purpose: To evaluate whether rehabilitation of muscle force or balance improves postural stability and quality of life (QoL, and whether self-administered training is comparable with guided training among older adults residing in an institutional setting. Patients and methods: A randomized, prospective intervention study was undertaken among 55 elderly patients. Three intervention groups were evaluated: a muscle force training group; a balance and muscle force training group; and a self-administered training group. Each group underwent 1-hour-long training sessions, twice a week, for 3 months. Postural stability was measured at onset, after 3 months, and after 6 months. Time-domain-dependent body sway variables were calculated. The fall rate was evaluated for 3 years. General health related quality of life (HRQoL was measured with a 15D instrument. Postural stability was used as a primary outcome, with QoL and falls used as secondary outcomes. Results: Muscle force trainees were able to undertake training, progressing towards more strenuous exercises. In posturography, the number of spiky oscillations was reduced after training, and stationary fields of torque moments of the ankle increased, providing better postural stability in all groups; in particular, the zero crossing rate of weight signal and the number of low variability episodes in the stabilogram were improved after training. While no difference was found between different training groups in posturography outcomes, a reduction of fall rate

  9. How can the functioning and effectiveness of networks in the settings approach of health promotion be understood, achieved and researched?

    Science.gov (United States)

    Dietscher, Christina

    2017-02-01

    Networks in health promotion (HP) have, after the launch of WHO's Ottawa Charter [(World Health Organization (WHO) (eds). (1986) Ottawa Charter on Health Promotion. Towards A New Public Health. World Health Organization, Geneva], become a widespread tool to disseminate HP especially in conjunction with the settings approach. Despite their allegedly high importance for HP practice and more than two decades of experiences with networking so far, a sound theoretical basis to support effective planning, formation, coordination and strategy development for networks in the settings approach of HP (HPSN) is still widely missing. Brößkamp-Stone's multi-facetted interorganizational network assessment framework (2004) provides a starting point but falls short of specifying the outcomes that can be reasonably expected from the specific network type of HPSN, and the specific processes/strategies and structures that are needed to achieve them. Based on outcome models in HP, on social, managerial and health science theories of networks, settings and organizations, a sociological systems theory approach and the capacity approach in HP, this article points out why existing approaches to studying networks are insufficient for HPSN, what can be understood by their functioning and effectiveness, what preconditions there are for HPSN effectiveness and how an HPSN functioning and effectiveness framework proposed on these grounds can be used for researching networks in practice, drawing on experiences from the ‘Project on an Internationally Comparative Evaluation Study of the International Network of Health Promoting Hospitals and Health Services’ (PRICES-HPH), which was coordinated by the WHO Collaborating Centre for Health Promotion in Hospitals and Health Services (Vienna WHO-CC) from 2008 to 2012.

  10. Otoscopy simulation training in a classroom setting: a novel approach to teaching otoscopy to medical students.

    Science.gov (United States)

    Davies, Joel; Djelic, Lucas; Campisi, Paolo; Forte, Vito; Chiodo, Albino

    2014-11-01

    To determine the effectiveness of using of an otoscopy stimulator to teach medical students the primary principles of otoscopy in large group training sessions and improve their confidence in making otologic diagnoses. Cross-sectional survey design. In March 2013, the Department of Otolaryngology-Head and Neck Surgery held a large-scale otoscopy simulator teaching session at the MaRS Innovation Center for 92 first and second year University of Toronto medical students. Following the training session, students were provided with an optional electronic, nine-question survey related to their experience with learning otoscopy using the simulators alone, and in comparison to traditional methods of teaching. Thirty-four medical students completed the survey. Ninety-one percent of the respondents indicated that the overall quality of the event was either very good or excellent. A total of 71% of respondents either agreed, or strongly agreed, that the otoscopy simulator training session improved their confidence in diagnosing pathologies of the ear. The majority (70%) of students indicated that the training session had stimulated their interest in otolaryngology-head and neck surgery as a medical specialty. Organizing large-group otoscopy simulator training sessions is one method whereby students can become familiar with a wide variety of pathologies of the ear and improve both their diagnostic accuracy and their confidence in making otologic diagnoses. NA © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

  11. Simulation training for medical emergencies in the dental setting using an inexpensive software application.

    Science.gov (United States)

    Kishimoto, N; Mukai, N; Honda, Y; Hirata, Y; Tanaka, M; Momota, Y

    2017-11-09

    Every dental provider needs to be educated about medical emergencies to provide safe dental care. Simulation training is available with simulators such as advanced life support manikins and robot patients. However, the purchase and development costs of these simulators are high. We have developed a simulation training course on medical emergencies using an inexpensive software application. The purpose of this study was to evaluate the educational effectiveness of this course. Fifty-one dental providers participated in this study from December 2014 to March 2015. Medical simulation software was used to simulate a patient's vital signs. We evaluated participants' ability to diagnose and treat vasovagal syncope or anaphylaxis with an evaluation sheet and conducted a questionnaire before and after the scenario-based simulation training. The median evaluation sheet score for vasovagal syncope increased significantly from 7/9 before to 9/9 after simulation training. The median score for anaphylaxis also increased significantly from 8/12 to 12/12 (P simulation training. This simulation course improved participants' ability to diagnose and treat medical emergencies and improved their confidence. This course can be offered inexpensively using a software application. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  12. District nurses' and registered nurses' training in and use of motivational interviewing in primary care settings.

    Science.gov (United States)

    Östlund, Ann-Sofi; Wadensten, Barbro; Häggström, Elisabeth; Kristofferzon, Marja-Leena

    2014-08-01

    To examine to what extent district nurses and registered nurses have training in motivational interviewing, to what extent they use it and what prerequisites they have for using it; to compare district nurses and registered nurses, as well as to compare users and nonusers of motivational interviewing; and to examine possible relationships between use of motivational interviewing and the variables training, supervision and feedback in motivational interviewing and prerequisites for use. Motivational interviewing is an effective method for motivating patients to change their lifestyle, used increasingly in primary care. A cross-sectional survey study. A study-specific questionnaire was sent to all district nurses and registered nurses (n = 980) in primary care in three counties in Sweden, from September 2011-January 2012; 673 (69%) responded. Differences between groups as well as relationships between study variables were tested. According to self-reports, 59% of the respondents had training in motivational interviewing and 57% used it. Approximately 15% of those who reported using it had no specific training in the method. More district nurses than registered nurses had training in motivational interviewing and used it. The following factors were independently associated with the use of motivational interviewing: training in and knowledge of motivational interviewing, conditions for using it, time and absence of 'other' obstacles. Having knowledge in motivational interviewing and personal as well as workplace prerequisites for using it may promote increased use of motivational interviewing. Having the prerequisites for using motivational interviewing at the workplace is of significance to the use of motivational interviewing. In the context of primary care, district nurses seem to have better prerequisites than registered nurses for using motivational interviewing. © 2013 John Wiley & Sons Ltd.

  13. Evaluation of a train-the-trainer program for stable coronary artery disease management in community settings: A pilot study.

    Science.gov (United States)

    Shen, Zhiyun; Jiang, Changying; Chen, Liqun

    2018-02-01

    To evaluate the feasibility and effectiveness of conducting a train-the-trainer (TTT) program for stable coronary artery disease (SCAD) management in community settings. The study involved two steps: (1) tutors trained community nurses as trainers and (2) the community nurses trained patients. 51 community nurses attended a 2-day TTT program and completed questionnaires assessing knowledge, self-efficacy, and satisfaction. By a feasibility and non-randomized control study, 120 SCAD patients were assigned either to intervention group (which received interventions from trained nurses) or control group (which received routine management). Pre- and post-intervention, patients' self-management behaviors and satisfaction were assessed to determine the program's overall impact. Community nurses' knowledge and self-efficacy improved (Pmanagement behaviors (Pmanagement in community settings in China was generally feasible and effective, but many obstacles remain including patients' noncompliance, nurses' busy work schedules, and lack of policy supports. Finding ways to enhance the motivation of community nurses and patients with SCAD are important in implementing community-based TTT programs for SCAD management; further multicenter and randomized control trials are needed. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Evaluation of tactical training in team handball by means of artificial neural networks.

    Science.gov (United States)

    Hassan, Amr; Schrapf, Norbert; Ramadan, Wael; Tilp, Markus

    2017-04-01

    While tactical performance in competition has been analysed extensively, the assessment of training processes of tactical behaviour has rather been neglected in the literature. Therefore, the purpose of this study is to provide a methodology to assess the acquisition and implementation of offensive tactical behaviour in team handball. The use of game analysis software combined with an artificial neural network (ANN) software enabled identifying tactical target patterns from high level junior players based on their positions during offensive actions. These patterns were then trained by an amateur junior handball team (n = 14, 17 (0.5) years)). Following 6 weeks of tactical training an exhibition game was performed where the players were advised to use the target patterns as often as possible. Subsequently, the position data of the game was analysed with an ANN. The test revealed that 58% of the played patterns could be related to the trained target patterns. The similarity between executed patterns and target patterns was assessed by calculating the mean distance between key positions of the players in the game and the target pattern which was 0.49 (0.20) m. In summary, the presented method appears to be a valid instrument to assess tactical training.

  15. Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions.

    Science.gov (United States)

    Barnes, Jessica J; Nobre, Anna Christina; Woolrich, Mark W; Baker, Kate; Astle, Duncan E

    2016-08-24

    Working memory is a capacity upon which many everyday tasks depend and which constrains a child's educational progress. We show that a child's working memory can be significantly enhanced by intensive computer-based training, relative to a placebo control intervention, in terms of both standardized assessments of working memory and performance on a working memory task performed in a magnetoencephalography scanner. Neurophysiologically, we identified significantly increased cross-frequency phase amplitude coupling in children who completed training. Following training, the coupling between the upper alpha rhythm (at 16 Hz), recorded in superior frontal and parietal cortex, became significantly coupled with high gamma activity (at ∼90 Hz) in inferior temporal cortex. This altered neural network activity associated with cognitive skill enhancement is consistent with a framework in which slower cortical rhythms enable the dynamic regulation of higher-frequency oscillatory activity related to task-related cognitive processes. Whether we can enhance cognitive abilities through intensive training is one of the most controversial topics of cognitive psychology in recent years. This is particularly controversial in childhood, where aspects of cognition, such as working memory, are closely related to school success and are implicated in numerous developmental disorders. We provide the first neurophysiological account of how working memory training may enhance ability in childhood, using a brain recording technique called magnetoencephalography. We borrowed an analysis approach previously used with intracranial recordings in adults, or more typically in other animal models, called "phase amplitude coupling." Copyright © 2016 Barnes et al.

  16. Local or global? How to choose the training set for principal component compression of hyperspectral satellite measurements: a hybrid approach

    Science.gov (United States)

    Hultberg, Tim; August, Thomas; Lenti, Flavia

    2017-09-01

    Principal Component (PC) compression is the method of choice to achieve band-width reduction for dissemination of hyper spectral (HS) satellite measurements and will become increasingly important with the advent of future HS missions (such as IASI-NG and MTG-IRS) with ever higher data-rates. It is a linear transformation defined by a truncated set of the leading eigenvectors of the covariance of the measurements as well as the mean of the measurements. We discuss the strategy for generation of the eigenvectors, based on the operational experience made with IASI. To compute the covariance and mean, a so-called training set of measurements is needed, which ideally should include all relevant spectral features. For the dissemination of IASI PC scores a global static training set consisting of a large sample of measured spectra covering all seasons and all regions is used. This training set was updated once after the start of the dissemination of IASI PC scores in April 2010 by adding spectra from the 2010 Russian wildfires, in which spectral features not captured by the previous training set were identified. An alternative approach, which has sometimes been proposed, is to compute the eigenvectors on the fly from a local training set, for example consisting of all measurements in the current processing granule. It might naively be thought that this local approach would improve the compression rate by reducing the number of PC scores needed to represent the measurements within each granule. This false belief is apparently confirmed, if the reconstruction scores (root mean square of the reconstruction residuals) is used as the sole criteria for choosing the number of PC scores to retain, which would overlook the fact that the decrease in reconstruction score (for the same number of PCs) is achieved only by the retention of an increased amount of random noise. We demonstrate that the local eigenvectors retain a higher amount of noise and a lower amount of atmospheric

  17. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

    Science.gov (United States)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

    2017-03-01

    Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

  18. Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices

    Directory of Open Access Journals (Sweden)

    Andrea Baiocchi

    2015-01-01

    Full Text Available Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.

  19. Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.

    Science.gov (United States)

    Rumschinski, Philipp; Borchers, Steffen; Bosio, Sandro; Weismantel, Robert; Findeisen, Rolf

    2010-05-25

    Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.

  20. A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification

    Directory of Open Access Journals (Sweden)

    Jing Zhao

    2016-01-01

    Full Text Available The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN. To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs and intuitionistic fuzzy cross-entropy (IFCE with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories.

  1. The effects of spaced retrieval training in improving hyperphagia of people living with dementia in residential settings.

    Science.gov (United States)

    Hsu, Chia-Ning; Lin, Li-Chan; Wu, Shiao-Chi

    2017-10-01

    To investigate the effectiveness of spaced retrieval for improving hyperphagia in patients with dementia in residential care settings. Although 10-30% of patients with dementia have hyperphagia, most studies have focused on eating difficulties. Only a few studies have focused on hyperphagia. Various memory problems cause hyperphagia in patients with dementia. Spaced retrieval, a cognitive technique for information learning, can be used as a training method to improve memory loss. Recent studies showed that patients who received the training successfully memorised information learned in the training and correctly applied it to their daily lives. Single-blind experiments were performed. The 97 subjects with dementia were recruited from seven institutions. All research participants were stratified into three groups according to cognitive impairment severity and Hyperphagic Behavior Scale scores and then randomly assigned to the experimental and control groups. The experimental group received a six-week one-by-one spaced retrieval training for hyperphagia behaviour. The control group received routine care. After the intervention, the frequency and severity of hyperphagia in the patients with dementia, and food intake were significantly lower in the experimental group than in the control group. However, body mass index did not significantly differ. Our results suggest that the spaced retrieval training could decrease the frequency and severity of hyperphagia in patients with dementia. The content of this training programme is consistent with the normal manner of eating in daily life and is easy for patients to understand and perform. Therefore, it can be applied in residents' daily lives. This study confirms the efficacy of the spaced retrieval training protocol for hyperphagia in patients with dementia. In future studies, the follow-up duration can be increased to determine the long-term effectiveness of the intervention. © 2016 John Wiley & Sons Ltd.

  2. Social Skills Training in Natural Play Settings: Educating through the Physical Theory to Practice

    Science.gov (United States)

    Aljadeff-Abergel, Elian; Ayvazo, Shiri; Eldar, Eitan

    2012-01-01

    Social skills are prerequisite to academic performance and success in school. Training of these skills is particularly important for students with emotional and behavioral disorders (EBD) who have social deficits and struggle maintaining appropriate and accepted behavior in and outside of the classroom. Educating through the "physical" model is a…

  3. The Long-Term Effects of Functional Communication Training Conducted in Young Children's Home Settings

    Science.gov (United States)

    Wacker, David P.; Schieltz, Kelly M.; Berg, Wendy K.; Harding, Jay W.; Padilla Dalmau, Yaniz C.; Lee, John F.

    2017-01-01

    This article describes the results of a series of studies that involved functional communication training (FCT) conducted in children's homes by their parents. The 103 children who participated were six years old or younger, had developmental delays, and engaged in destructive behaviors such as self-injury. The core procedures used in each study…

  4. Trial-Based Functional Analysis and Functional Communication Training in an Early Childhood Setting

    Science.gov (United States)

    Lambert, Joseph M.; Bloom, Sarah E.; Irvin, Jennifer

    2012-01-01

    Problem behavior is common in early childhood special education classrooms. Functional communication training (FCT; Carr & Durand, 1985) may reduce problem behavior but requires identification of its function. The trial-based functional analysis (FA) is a method that can be used to identify problem behavior function in schools. We conducted…

  5. Developing a Marketing Mind-Set: Training and Mentoring for County Extension Employees

    Science.gov (United States)

    Sneed, Christopher T.; Elizer, Amy Hastings; Hastings, Shirley; Barry, Michael

    2016-01-01

    Marketing the county Extension program is a critical responsibility of the entire county staff. This article describes a unique peer-to-peer training and mentoring program developed to assist county Extension staff in improving marketing skills and successfully developing and implementing a county Extension marketing plan. Data demonstrating…

  6. Sport-specific Outdoor Rehabilitation in a Group Setting : Do the Intentions Match Actual Training Load?

    NARCIS (Netherlands)

    de Bruijn, Jeroen; van der Worp, Henk; Korte, Mark; de Vries, Astrid J; Nijland, Rick; Brink, Michel S

    2017-01-01

    CONTEXT: Previous research has shown a weak relationship between intended and actual training load in various sports. Due to variety in group and content, this relationship is expected to be even weaker during group rehabilitation. OBJECTIVE: The goal of our study was to examine the relationship

  7. A Behavioral Observation Index Designed to Evaluate Training of Correctional Officers in a Prison Setting.

    Science.gov (United States)

    Witherspoon, Arnold Delano

    This study represents an effort to develop an observational instrument to assess a correctional officer's behavior, and to evaluate officer training programs. A list of 73 inmate behaviors to which the officer might respond was assembled. The most relevant, significant, and most often occurring inmate behaviors were selected. Six judges with…

  8. MHC class I epitope binding prediction trained on small data sets

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Nielsen, Morten; Lamberth, K.

    2004-01-01

    The identification of potential T-cell epitopes is important for development of new human or vetenary vaccines, both considering single protein/subunit vaccines, and for epitope/peptide vaccines as such. The highly diverse MHC class I alleles bind very different peptides, and accurate binding pre...... in situations where only very limited data are available for training....

  9. The Language Proficiency Interview (LPI) and Its Applicability in Corporate Language Training Settings.

    Science.gov (United States)

    Stupak, Steven A.

    The Language Proficiency Interview's structure, administration, and rating scale are outlined by an officer of the organization that designed it (Educational Testing Service), and some common mistakes made in its administration are listed. The need for training in the test's administration is emphasized. Its application in the corporate situation…

  10. Mindful Parenting Training in Child Psychiatric Settings : Heightened Parental Mindfulness Reduces Parents' and Children's Psychopathology

    NARCIS (Netherlands)

    Meppelink, Renee; de Bruin, Esther I.; Wanders-Mulder, Femy H.; Vennik, Corinne J.; Bogels, Susan M.

    Mindful parenting training is an application of mindfulness-based interventions that allows parents to perceive their children with unbiased and open attention without prejudgment and become more attentive and less reactive in their parenting. This study examined the effectiveness of mindful

  11. Physician Assistant Student Training for the Inpatient Setting: A Needs Assessment.

    Science.gov (United States)

    Sharma, Poonam; Brooks, Megan; Roomiany, Pahresah; Verma, Lalit; Criscione-Schreiber, Lisa

    2017-12-01

    The number of physician assistants (PAs) practicing hospital medicine is rapidly expanding. Little research has been done to determine which inpatient medicine rotation experiences are most helpful to prepare PA students for a career in inpatient medicine. We aimed to determine those skills that practicing hospitalists believe are most critical for PA students to master and to describe hospitalists' current understanding of PA training. We also sought to evaluate the current performance of our own inpatient medicine rotation for PA students. We surveyed 85 practicing hospitalists, including physicians and advanced-practice providers, from 3 hospitals in the Duke University Health System to identify (1) the clinical topics and skills deemed most essential for PA students on an inpatient medicine rotation, (2) the percentage of hospitalists able to correctly answer basic questions about PA training, and (3) current rotation performance. Descriptive statistics were used to summarize responses. Hospitalists identified the clinical conditions and health care systems with the most educational value for PA students. Hospitalists were found to have variable levels of understanding of the PA training pathway, with more than 20% incorrectly answering questions about the training process. According to mean responses, the rotation performed positively for 15 of 19 medical conditions. The majority of survey respondents suggested that a formal curriculum would help faculty teach and improve the learning experience for PA students. Identifying the most essential content can facilitate curriculum development. Hospitalists have a knowledge gap about the training of PA students. The inpatient medicine rotation was rated positively, but survey responses suggested that a formal curriculum could have a positive effect and would be well received.

  12. Influences on classification accuracy of exam sets: an example from vocational education and training

    NARCIS (Netherlands)

    Hubregtse, M.; Eggen, Theodorus Johannes Hendrikus Maria; Eggen, T.J.H.M.; Veldkamp, B.P.

    2012-01-01

    Classification accuracy of single exams is well studied in the educational measurement literature. However, when making important decisions, such as certification decisions, one usually uses several exams: an exam set. This chapter elaborates on classification accuracy of exam sets. This is

  13. Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

    NARCIS (Netherlands)

    Grinsven, M.J.J.P. van; Ginneken, B. van; Hoyng, C.B.; Theelen, T.; Sanchez, C.I.

    2016-01-01

    Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image

  14. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

    Science.gov (United States)

    Lopes, U K; Valiati, J F

    2017-10-01

    It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists. Significant research can be found on automating diagnosis by applying computational techniques to medical images, thereby eliminating the need for individual image analysis and greatly diminishing overall costs. In addition, recent improvements on deep learning accomplished excellent results classifying images on diverse domains, but its application for tuberculosis diagnosis remains limited. Thus, the focus of this work is to produce an investigation that will advance the research in the area, presenting three proposals to the application of pre-trained convolutional neural networks as feature extractors to detect the disease. The proposals presented in this work are implemented and compared to the current literature. The obtained results are competitive with published works demonstrating the potential of pre-trained convolutional networks as medical image feature extractors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

    Directory of Open Access Journals (Sweden)

    Tayfun Gokmen

    2017-10-01

    Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  16. Manifold absolute pressure estimation using neural network with hybrid training algorithm.

    Science.gov (United States)

    Muslim, Mohd Taufiq; Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

    2017-01-01

    In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

  17. Manifold absolute pressure estimation using neural network with hybrid training algorithm.

    Directory of Open Access Journals (Sweden)

    Mohd Taufiq Muslim

    Full Text Available In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM algorithm, Bayesian Regularization (BR algorithm and Particle Swarm Optimization (PSO algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS. The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value.

  18. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  19. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  20. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  1. The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

    Science.gov (United States)

    Djurfeldt, Mikael

    2012-07-01

    The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

  2. An Analysis Of Personalized Learning Systems For Navy Training And Education Settings

    Science.gov (United States)

    2016-12-01

    of the DT program, the Naval Education and Training Center (NETC) was tasked with completing its own assessment in 2016. NETC’s study used ...much as the students choosing not to adopt tech for use in school. The variety of aptitude, access, and use of ICT in and out of school found in ... the way they created and shared knowledge, and that their preferences for use 8 of

  3. 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.

  4. Estimating Route Choice Models from Stochastically Generated Choice Sets on Large-Scale Networks Correcting for Unequal Sampling Probability

    DEFF Research Database (Denmark)

    Vacca, Alessandro; Prato, Carlo Giacomo; Meloni, Italo

    2015-01-01

    is the dependency of the parameter estimates from the choice set generation technique. Bias introduced in model estimation has been corrected only for the random walk algorithm, which has problematic applicability to large-scale networks. This study proposes a correction term for the sampling probability of routes...

  5. Development of a comprehensive and sustainable gynecologic oncology training program in western Kenya, a low resource setting.

    Science.gov (United States)

    Rosen, Barry; Itsura, Peter; Tonui, Philip; Covens, Alan; van Lonkhuijzen, Luc; Orang'o, Elkanah Omenge

    2017-08-01

    To provide information on the development of a gynecologic oncology training program in a low-resource setting in Kenya. This is a review of a collaboration between Kenyan and North American physicians who worked together to develop a gynecologic oncology training in Kenya. We review the published data on the increase of cancer incidence in sub-Saharan Africa and outline the steps that were taken to develop this program. The incidence of cervical cancer in Kenya is very high and is the leading cause of cancer mortality in Kenya. WHO identifies cancer as a new epidemic affecting countries in sub-Saharan Africa. In Kenya, a country of 45 million, there is limited resources to diagnose and treat cancer. In 2009 in western Kenya, at Moi University there was no strategy to manage oncology in the Reproductive Health department. There was only 1 gynecologic oncologists in Kenya in 2009. A collaboration between Canadian and Kenya physicians resulted in development of a gynecologic oncology clinical program and initiation of fellowship training in Kenya. In the past 4 years, five fellows have graduated from a 2 year fellowship training program. Integration of data collection on all the patients as part of this program provided opportunities to do clinical research and to acquire peer reviewed grants. This is the first recognized fellowship training program in sub-Saharan Africa outside of South Africa. It is an example of a collaborative effort to improve women's health in a low-resource country. This is a Kenyan managed program through Moi University. These subspecialty trained doctors will also provide advice that will shape health care policy and provide sustainable expertise for women diagnosed with a gynecologic cancer.

  6. Training resonant communicators. From raining resonant communicators. From neural networks to network society

    Directory of Open Access Journals (Sweden)

    Gotzon Toral Madariaga

    2013-02-01

    Full Text Available 0 0 1 121 669 USAL 5 1 789 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} Media control is no longer exclusively in the hands of professional broadcasters. New educational projects must empower users so that they can exercise their digital citizenship. As well as acquiring essential technical skills to move in an interconnected world, emotional literacy is also indispensable for people to desire to take part effectively in this augmented community. So, besides teaching audiovisual technology, this project seeks to re-program the relationship that students develop with a situation that is typically stressful. We propose an expanded practice that enables students to recognize and voluntarily activate their communication biology. This way, users can become actors in new communication networks, and global communication flow will be more plural and richer.

  7. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

    Directory of Open Access Journals (Sweden)

    Tayfun Gokmen

    2016-07-01

    Full Text Available In recent years, deep neural networks (DNN have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30,000X compared to state-of-the-art microprocessors while providing power efficiency of 84,000 GigaOps/s/W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things sensors.

  8. Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations.

    Science.gov (United States)

    Gokmen, Tayfun; Vlasov, Yurii

    2016-01-01

    In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

  9. Setting Access Permission through Transitive Relationship in Web-based Social Networks

    Science.gov (United States)

    Hong, Dan; Shen, Vincent Y.

    The rising popularity of various social networking websites has created a huge problem on Internet privacy. Although it is easy to post photos, comments, opinions on some events, etc. on the Web, some of these data (such as a person’s location at a particular time, criticisms of a politician, etc.) are private and should not be accessed by unauthorized users. Although social networks facilitate sharing, the fear of sending sensitive data to a third party without knowledge or permission of the data owners discourages people from taking full advantage of some social networking applications. We exploit the existing relationships on social networks and build a ‘‘trust network’’ with transitive relationship to allow controlled data sharing so that the privacy and preferences of data owners are respected. The trust network linking private data owners, private data requesters, and intermediary users is a directed weighted graph. The permission value for each private data requester can be automatically assigned in this network based on the transitive relationship. Experiments were conducted to confirm the feasibility of constructing the trust network from existing social networks, and to assess the validity of permission value assignments in the query process. Since the data owners only need to define the access rights of their closest contacts once, this privacy scheme can make private data sharing easily manageable by social network participants.

  10. From Cortical Blindness to Conscious Visual Perception: Theories on Neuronal Networks and Visual Training Strategies

    Directory of Open Access Journals (Sweden)

    Vanessa Hadid

    2017-08-01

    Full Text Available Homonymous hemianopia (HH is the most common cortical visual impairment leading to blindness in the contralateral hemifield. It is associated with many inconveniences and daily restrictions such as exploration and visual orientation difficulties. However, patients with HH can preserve the remarkable ability to unconsciously perceive visual stimuli presented in their blindfield, a phenomenon known as blindsight. Unfortunately, the nature of this captivating residual ability is still misunderstood and the rehabilitation strategies in terms of visual training have been insufficiently exploited. This article discusses type I and type II blindsight in a neuronal framework of altered global workspace, resulting from inefficient perception, attention and conscious networks. To enhance synchronization and create global availability for residual abilities to reach visual consciousness, rehabilitation tools need to stimulate subcortical extrastriate pathways through V5/MT. Multisensory bottom-up compensation combined with top-down restitution training could target pre-existing and new neuronal mechanisms to recreate a framework for potential functionality.

  11. From Cortical Blindness to Conscious Visual Perception: Theories on Neuronal Networks and Visual Training Strategies.

    Science.gov (United States)

    Hadid, Vanessa; Lepore, Franco

    2017-01-01

    Homonymous hemianopia (HH) is the most common cortical visual impairment leading to blindness in the contralateral hemifield. It is associated with many inconveniences and daily restrictions such as exploration and visual orientation difficulties. However, patients with HH can preserve the remarkable ability to unconsciously perceive visual stimuli presented in their blindfield, a phenomenon known as blindsight. Unfortunately, the nature of this captivating residual ability is still misunderstood and the rehabilitation strategies in terms of visual training have been insufficiently exploited. This article discusses type I and type II blindsight in a neuronal framework of altered global workspace, resulting from inefficient perception, attention and conscious networks. To enhance synchronization and create global availability for residual abilities to reach visual consciousness, rehabilitation tools need to stimulate subcortical extrastriate pathways through V5/MT. Multisensory bottom-up compensation combined with top-down restitution training could target pre-existing and new neuronal mechanisms to recreate a framework for potential functionality.

  12. Pollution level predictor using artificial neural networks trained with galactic swarm optimization algorithms

    Science.gov (United States)

    Nigam, Nilay; Bessie Amali, D. Geraldine

    2017-11-01

    Pollutant Level Predicator is a system which helps in predicting the amount of pollutants in a specific region. The system uses historic data in order to predict the value for the new input. The prediction system uses Artificial Neural Networks (ANN) trained with different optimization algorithms to classify the pollution level into several classes. This research paper assesses and analyses various techniques which can be used to predict the level of pollutant in Delhi. This study uses daily mean air temperature, relative humidity, wind speed and concentration of PM2.5 in Anand Vihar area of Delhi for a period of 2 years (2015 to 2016). Experimental results show that a ANN trained with Galactic swarm optimization algorithm produces a more accurate predication compared to other optimization algorithms.

  13. Training Graduate Students in Psychological Counseling Module in the Context of Networking

    Directory of Open Access Journals (Sweden)

    Kochetova Y.A.,

    2016-12-01

    Full Text Available The paper provides a general outline of main approbation outcomes of the “Psychological Counseling in Education” module which is part of the master’s programme in School Psychology and is aimed at teaching the methodology of counseling in education and the basic principles of designing the counseling process in the framework of student support at school. A networking algorithm is described for communicating with educational organisations (training sites in the process of internship. In the context of networking, the content and organization of distributed practice is considered the key condition of students’ effective engagement in professional activities, development of professional competencies and readiness for carrying out psychological counseling in education as defined by the professional standard for educational psychologists.

  14. Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks.

    Science.gov (United States)

    Hajimirsadeghi, Hossein; Mori, Greg

    2017-09-01

    We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential functions. Consequently, it can be used to encode different cardinality-based multi-instance assumptions, ranging from the standard MIL assumption to more general assumptions. In addition, this framework can be efficiently used for both binary and multiclass classification. To this end, an efficient inference algorithm and a discriminative latent max-margin learning algorithm are introduced to train and test the proposed multi-instance Markov network models. We evaluate the performance of the proposed framework on binary and multi-class MIL benchmark datasets as well as two challenging computer vision tasks: cyclist helmet recognition and human group activity recognition. Experimental results verify that encoding the degree of ambiguity in data can improve classification performance.

  15. Training a multilayer neural network for the Euro-dollar (EUR/ USD exchange rate

    Directory of Open Access Journals (Sweden)

    Jaime Alberto Villamil Torres

    2010-04-01

    Full Text Available A mathematical tool or model for predicting how an economic variable like the exchange rate (relative price between two currencies will respond is a very important need for investors and policy-makers. Most current techniques are based on statistics, particularly linear time series theory. Artificial neural networks (ANNs are mathematical models which try to emulate biological neural networks’ parallelism and nonlinearity; these models have been successfully applied in Economics and Engineering since the 1980s. ANNs appear to be an alternative for modelling the behaviour of financial variables which resemble (as first approximation a random walk. This paper reports the results of using ANNs for Euro/USD exchange rate trading and the usefulness of the algorithm for chemotaxis leading to training networks thereby maximising an objective function re predicting a trader’s profits. JEL: F310, C450.

  16. Commentary: physician-scientist attrition: stemming the tide through national networks for training and development.

    Science.gov (United States)

    Schwartz, Alan L

    2011-09-01

    Future advances in medicine depend on a reliable pipeline of physician-scientists. However, the changing demographics of physician-scientists, including the advanced age of new MD investigators, and attrition along the physician-scientist developmental pathway are cause for concern. Recently developed National Institutes of Health-funded national networks for physician-scientist training and development-such as the Advanced Research Institute in Geriatric Mental Health and the Pediatric Scientist Development Program-offer valuable approaches to supporting and retaining these trainees.

  17. Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

    Science.gov (United States)

    Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

    2011-01-01

    Background Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. Objective To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Methods Analysis based on the experience of three telemedicine networks (in operation for 7–10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Findings Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Conclusion Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit

  18. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons.

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2017-08-11

    This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

  19. CLUSTERISATION AND INFORMATION TECHNOLOGY IN ADVANCED TRAINING OF THE HEADS OF NETWORK EDUCATIONAL ORGANIZATIONS

    Directory of Open Access Journals (Sweden)

    Victoriia Stoikova

    2017-04-01

    Full Text Available The creation of strong basic schools with the network of branches and other networked educational organizations in the Ukrainian education system requires from leading cadres the possession of the basics of network management. The article deals with the questions of the process of forming professional competencies of heads of networked educational entities in conditions of postgraduate pedagogical education. The features of learning model which based on the active use of information and communication technologies are revealed have been disclosed; components of the open educational environment (cognitive, social and educational and their influence on the process of training leading cadres; advantages of using Internet technologies for educational purposes. The article describes the experience of organizing a continuous educational process by using the funds of information and communication technologies: websites, distance learning courses, social communities, and other Internet services. At the same time, heads of educational institutions are united in cluster formations by type of educational institutions, the level of providing educational services, the direction of professional interests, preferences, and also for the joint development of managerial algorithms in certain typical situations and for solving typical professional problems. In such a model of learning, knowledge is produced by participants independently during active activity by joint search, processing, and analysis of information, solving problem situations, discussions, debates, etc.

  20. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy

    Directory of Open Access Journals (Sweden)

    Nouri S.

    2017-03-01

    Full Text Available Background: The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. Objective: This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO estimating tumor positions in real-time radiotherapy. Method: One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. Results: The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. Conclusion: The internal target volume (ITV should be determined based on the applied neural network algorithm on training steps.

  1. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy.

    Science.gov (United States)

    Nouri, S; Hosseini Pooya, S M; Soltani Nabipour, J

    2017-03-01

    The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO) estimating tumor positions in real-time radiotherapy. One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. The internal target volume (ITV) should be determined based on the applied neural network algorithm on training steps.

  2. Spreading of Excellence in SARNET Network on Severe Accidents: The Education and Training Programme

    Directory of Open Access Journals (Sweden)

    Sandro Paci

    2012-01-01

    Full Text Available The SARNET2 (severe accidents Research NETwork of Excellence project started in April 2009 for 4 years in the 7th Framework Programme (FP7 of the European Commission (EC, following a similar first project in FP6. Forty-seven organisations from 24 countries network their capacities of research in the severe accident (SA field inside SARNET to resolve the most important remaining uncertainties and safety issues on SA in water-cooled nuclear power plants (NPPs. The network includes a large majority of the European actors involved in SA research plus a few non-European relevant ones. The “Education and Training” programme in SARNET is a series of actions foreseen in this network for the “spreading of excellence.” It is focused on raising the competence level of Master and Ph.D. students and young researchers engaged in SA research and on organizing information/training courses for NPP staff or regulatory authorities (but also for researchers interested in SA management procedures.

  3. Why a Train Set Helps Participants Co-Construct Meaning in Business Model Innovation

    DEFF Research Database (Denmark)

    Beuthel, Maria Rosa; Buur, Jacob

    to understand how they construct a concept. We observe that the final result of the workshop is indeed innovative and is co-constructed by all group members. We discuss why the toy train works: It keeps both hands and mind busy, it allows silent participation, and it expands the vocabulary of the discussion....... participant influences this process in a different manner – depending on his/her discipline, role in the group, and character. Based on video data form an industry session, we investigate how participants get to different meanings through analyzing their behaviors, movements, actions and negotiations...

  4. Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

    Science.gov (United States)

    Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

    2017-12-01

    Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive

  5. Video training with peer feedback in real-time consultation: acceptability and feasibility in a general-practice setting.

    Science.gov (United States)

    Eeckhout, Thomas; Gerits, Michiel; Bouquillon, Dries; Schoenmakers, Birgitte

    2016-08-01

    Since many years, teaching and training in communication skills are cornerstones in the medical education curriculum. Although video recording in a real-time consultation is expected to positively contribute to the learning process, research on this topic is scarce. This study will focus on the feasibility and acceptability of video recording during real-time patient encounters performed by general practitioner (GP) trainees. The primary research question addressed the experiences (defined as feasibility and acceptability) of GP trainees in video-recorded vocational training in a general practice. The second research question addressed the appraisal of this training. The procedure of video-recorded training is developed, refined and validated by the Academic Teaching Practice of Leuven since 1974 (Faculty of Medicine of the University of Leuven). The study is set up as a cross-sectional survey without follow-up. Outcome measures were defined as 'feasibility and acceptability' (experiences of trainees) of the video-recorded training and were approached by a structured questionnaire with the opportunity to add free text comments. The studied sample consisted of all first-phase trainees of the GP Master 2011-2012 at the University of Leuven. Almost 70% of the trainees were positive about recording consultations. Nevertheless, over 60% believed that patients felt uncomfortable during the video-recorded encounter. Almost 90% noticed an improvement of own communication skills through the observation and evaluation of. Most students (85%) experienced the logistical issues as major barrier to perform video consultations on a regular base. This study lays the foundation stone for further exploration of the video training in real-time consultations. Both students and teachers on the field acknowledge that the power of imaging is underestimated in the training of communication and vocational skills. The development of supportive material and protocols will lower thresholds

  6. High-Speed Rail Train Timetabling Problem: A Time-Space Network Based Method with an Improved Branch-and-Price Algorithm

    Directory of Open Access Journals (Sweden)

    Bisheng He

    2014-01-01

    Full Text Available A time-space network based optimization method is designed for high-speed rail train timetabling problem to improve the service level of the high-speed rail. The general time-space path cost is presented which considers both the train travel time and the high-speed rail operation requirements: (1 service frequency requirement; (2 stopping plan adjustment; and (3 priority of train types. Train timetabling problem based on time-space path aims to minimize the total general time-space path cost of all trains. An improved branch-and-price algorithm is applied to solve the large scale integer programming problem. When dealing with the algorithm, a rapid branching and node selection for branch-and-price tree and a heuristic train time-space path generation for column generation are adopted to speed up the algorithm computation time. The computational results of a set of experiments on China’s high-speed rail system are presented with the discussions about the model validation, the effectiveness of the general time-space path cost, and the improved branch-and-price algorithm.

  7. Implementing a Modified Version of Parent Management Training (PMT) with an intellectually disabled client in a special education setting.

    Science.gov (United States)

    Schudrich, Wendy

    2012-01-01

    In this article the author discusses how an evidence-based practice was modified to treat an intellectually disabled client with oppositional behavior. Parent Management Training was modified to treat the client. A single-subject A-B design was used. Behavior improved from 1.57 (SD = .78) to 0.63 (SD = .71) episodes of negative behavior per day from baseline to intervention, and findings were significant (t = 2.83, p = .01). Follow-up with the family indicated sustained improvement one year after the intervention was discontinued. Consideration should be given to using principles of Parent Management Training to create formal plans for addressing problem behaviors across settings with intellectually disabled clients.

  8. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-03-27

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

  9. Neural network hydrological modelling: on questions of over-fitting, over-training and over-parameterisation

    Science.gov (United States)

    Abrahart, R. J.; Dawson, C. W.; Heppenstall, A. J.; See, L. M.

    2009-04-01

    The most critical issue in developing a neural network model is generalisation: how well will the preferred solution perform when it is applied to unseen datasets? The reported experiments used far-reaching sequences of model architectures and training periods to investigate the potential damage that could result from the impact of several interrelated items: (i) over-fitting - a machine learning concept related to exceeding some optimal architectural size; (ii) over-training - a machine learning concept related to the amount of adjustment that is applied to a specific model - based on the understanding that too much fine-tuning might result in a model that had accommodated random aspects of its training dataset - items that had no causal relationship to the target function; and (iii) over-parameterisation - a statistical modelling concept that is used to restrict the number of parameters in a model so as to match the information content of its calibration dataset. The last item in this triplet stems from an understanding that excessive computational complexities might permit an absurd and false solution to be fitted to the available material. Numerous feedforward multilayered perceptrons were trialled and tested. Two different methods of model construction were also compared and contrasted: (i) traditional Backpropagation of Error; and (ii) state-of-the-art Symbiotic Adaptive Neuro-Evolution. Modelling solutions were developed using the reported experimental set ups of Gaume & Gosset (2003). The models were applied to a near-linear hydrological modelling scenario in which past upstream and past downstream discharge records were used to forecast current discharge at the downstream gauging station [CS1: River Marne]; and a non-linear hydrological modelling scenario in which past river discharge measurements and past local meteorological records (precipitation and evaporation) were used to forecast current discharge at the river gauging station [CS2: Le Sauzay].

  10. Improving Ambulatory Care Resident Training: Preparing for Opportunities to Treat Mental Illness in the Primary Care Setting.

    Science.gov (United States)

    Farhat, Nada M; Bostwick, Jolene R; Rockafellow, Stuart D

    2017-01-01

    The development of an outpatient psychiatry clinical practice learning experience for PGY2 ambulatory care pharmacy residents in preparation for the treatment of psychiatric disorders in the primary care setting is described. With the increased prevalence of psychiatric disorders, significant mortality, and limited access to care, integration of mental health treatment into the primary care setting is necessary to improve patient outcomes. Given the majority of mental health treatment occurs in the primary care setting, pharmacists in patient-centered medical homes (PCMHs) are in a unique position with direct access to patients to effectively manage these illnesses. However, the increased need for pharmacist education and training in psychiatry has prompted a large, Midwestern academic health system to develop an outpatient psychiatry learning experience for PGY2 (Postgraduate Year 2) ambulatory care pharmacy residents in 2015. The goal of this learning experience is to introduce the PGY2 ambulatory care residents to the role and impact of psychiatric clinical pharmacists and to orient the residents to the basics of psychiatric pharmacotherapy to be applied to their future practice in the primary care setting. The development of an outpatient psychiatry learning experience for PGY2 ambulatory care pharmacy residents will allow for more integrated and comprehensive care for patients with psychiatric conditions, many of whom are treated and managed in the PCMH setting.

  11. Selection, acclimation, training, and preparation of dogs for the research setting.

    Science.gov (United States)

    Meunier, LaVonne D

    2006-01-01

    Dogs have made and will continue to make valuable contributions as animal models in biomedical research. A comprehensive approach from time of breeding through completion of in-life usage is necessary to ensure that high-quality dog models are used in studies. This approach ensures good care and minimizes the impact of interanimal variability on experimental results. Guidance related to choosing and developing high-quality laboratory dogs and managing canine research colonies is provided in this article. Ensuring that dogs are healthy, well adapted, and cooperative involves good communication between vendors, veterinarians, care staff, and researchers to develop appropriate dog husbandry programs. These programs are designed to minimize animal stress and distress from the postweaning period through the transfer and acclimation period within the research facility. Canine socialization and training programs provided by skilled personnel, together with comprehensive veterinary health programs, can further enhance animal welfare and minimize interanimal and group variability in studies.

  12. Using operational data to estimate the running resistance of trains. Estimation of the resistance in a set of Norwegian tunnels

    OpenAIRE

    Halvor Schrøder, Hansen; Nawaz, Muhammad Umer; Olsson, Nils

    2017-01-01

    Two approaches to estimate the running resistance from operational data have been studied: A direct approach based on a measured/estimated acceleration to obtain a resistance time-series, and a velocity-fitting approach based on fitting a predicted to a measured velocity time-series. Two data sets have been considered: The first consists of a velocity time-series, extracted from the train event recorder. The second is logged from the vehicle control unit and includes a time-series of energy c...

  13. The need for leadership training in long-term care settings.

    Science.gov (United States)

    Davis, Jullet A

    2016-10-03

    Purpose Globally, in 1980, approximately 5.8 per cent of the world population was 65 years old and older. By 2050, this number will more than triple to 16 per cent. From a leadership perspective, there is at least one challenge (among many others challenges) to consider. This paper (viewpoint) aims to provide support for the growing need for academically prepared managers. Design/methodology/approach This paper is a viewpoint which presents several characteristics of the long-term care (LTC) field that support the need for academically trained leaders. Findings LTC leaders in all countries must be sufficiently versed in numerous management areas to provide leadership when called on by those assigned to their care. Given local area variations in population needs present across all countries, it may be unwise to advocate for national, countrywide standardization of requirements. Yet, older adults accessing LTC services should expect a minimum level of knowledge from all of their providers - not just those who provide direct, hands-on care. However, similar to those who provide direct care, leaders should receive competency-based education with specific attention to effective communication skills, team-based approaches to care delivery, information technologies and population health. Originality/value Although much of the extant literature focuses on the delivery of care to older persons, there is a dearth of literature addressing the role of LTC leaders in light of global aging. Establishing a minimum level of academic training and increasing transparency focused on the positive experiences of elders residing in LTC facilities should help dispel the notion that placement in an LTC facility reflects filial failure.

  14. Briefing and debriefing during simulation-based training and beyond: Content, structure, attitude and setting.

    Science.gov (United States)

    Kolbe, Michaela; Grande, Bastian; Spahn, Donat R

    2015-03-01

    In this article, we review the debriefing literature and point to the dilemma that although debriefings especially intend to enhance team (rather than individual) learning, it is particularly this team setting that poses risks for debriefing effectiveness (e.g., preference-consistent information sharing, lack of psychological safety inhibiting structured information sharing, ineffective debriefing models). These risks can be managed with a mindful approach with respect to content (e.g., specific learning objectives), structure (e.g., reactions phase, analysis phase, summary phase), attitude (e.g., honesty, curiosity, holding the trainee in positive regard) and setting (e.g., briefings to provide orientation and establish psychological safety). We point to the potential of integrating systemic methods such as circular questions into debriefings, discuss the empirical evidence for debriefing effectiveness and highlight the importance of faculty development. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Enhanced structural connectivity within a brain sub-network supporting working memory and engagement processes after cognitive training.

    Science.gov (United States)

    Román, Francisco J; Iturria-Medina, Yasser; Martínez, Kenia; Karama, Sherif; Burgaleta, Miguel; Evans, Alan C; Jaeggi, Susanne M; Colom, Roberto

    2017-05-01

    The structural connectome provides relevant information about experience and training-related changes in the brain. Here, we used network-based statistics (NBS) and graph theoretical analyses to study structural changes in the brain as a function of cognitive training. Fifty-six young women were divided in two groups (experimental and control). We assessed their cognitive function before and after completing a working memory intervention using a comprehensive battery that included fluid and crystallized abilities, working memory and attention control, and we also obtained structural MRI images. We acquired and analyzed diffusion-weighted images to reconstruct the anatomical connectome and we computed standardized changes in connectivity as well as group differences across time using NBS. We also compared group differences relying on a variety of graph-theory indices (clustering, characteristic path length, global and local efficiency and strength) for the whole network as well as for the sub-network derived from NBS analyses. Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function. Our results revealed enhanced connectivity for the training group within one specific network comprised of nodes/regions supporting cognitive processes required by the training (working memory, interference resolution, inhibition, and task engagement). Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS. Therefore, the connectome approach is a valuable method for tracking the effects of cognitive training interventions across specific sub-networks. Moreover, this approach allowsfor the computation of graph theoretical network metricstoquantifythetopological architecture of the brain networkdetected. The observed structural brain changes support the behavioral results reported earlier (see Colom, Román, et al., 2013

  16. Agenda Trending: Reciprocity and the Predictive Capacity of Social Networking Sites in Intermedia Agenda Setting across Topics over Time

    Directory of Open Access Journals (Sweden)

    Jacob Groshek

    2013-08-01

    Full Text Available In the contemporary converged media environment, agenda setting is being transformed by the dramatic growth of audiences that are simultaneously media users and producers. The study reported here addresses related gaps in the literature by first comparing the topical agendas of two leading traditional media outlets (New York Times and CNN with the most frequently shared stories and trending topics on two widely popular Social Networking Sites (Facebook and Twitter. Time-series analyses of the most prominent topics identify the extent to which traditional media sets the agenda for social media as well as reciprocal agenda-setting effects of social media topics entering traditional media agendas. In addition, this study examines social intermedia agenda setting topically and across time within social networking sites, and in so doing, adds a vital understanding of where traditional media, online uses, and social media content intersect around instances of focusing events, particularly elections. Findings identify core differences between certain traditional and social media agendas, but also within social media agendas that extend from uses examined here. Additional results further suggest important topical and event-oriented limitations upon the predictive capacit of social networking sites to shape traditional media agendas over time.

  17. Echocardiography practice, training and accreditation in the intensive care: document for the World Interactive Network Focused on Critical Ultrasound (WINFOCUS

    Directory of Open Access Journals (Sweden)

    Catena Emanuele

    2008-10-01

    Full Text Available Abstract Echocardiography is increasingly used in the management of the critically ill patient as a non-invasive diagnostic and monitoring tool. Whilst in few countries specialized national training schemes for intensive care unit (ICU echocardiography have been developed, specific guidelines for ICU physicians wishing to incorporate echocardiography into their clinical practice are lacking. Further, existing echocardiography accreditation does not reflect the requirements of the ICU practitioner. The WINFOCUS (World Interactive Network Focused On Critical UltraSound ECHO-ICU Group drew up a document aimed at providing guidance to individual physicians, trainers and the relevant societies of the requirements for the development of skills in echocardiography in the ICU setting. The document is based on recommendations published by the Royal College of Radiologists, British Society of Echocardiography, European Association of Echocardiography and American Society of Echocardiography, together with international input from established practitioners of ICU echocardiography. The recommendations contained in this document are concerned with theoretical basis of ultrasonography, the practical aspects of building an ICU-based echocardiography service as well as the key components of standard adult TTE and TEE studies to be performed on the ICU. Specific issues regarding echocardiography in different ICU clinical scenarios are then described. Obtaining competence in ICU echocardiography may be achieved in different ways – either through completion of an appropriate fellowship/training scheme, or, where not available, via a staged approach designed to train the practitioner to a level at which they can achieve accreditation. Here, peri-resuscitation focused echocardiography represents the entry level – obtainable through established courses followed by mentored practice. Next, a competence-based modular training programme is proposed: theoretical

  18. The Effects of Martial Arts Training on Attentional Networks in Typical Adults.

    Science.gov (United States)

    Johnstone, Ashleigh; Marí-Beffa, Paloma

    2018-01-01

    There is substantial evidence that training in Martial Arts is associated with improvements in cognitive function in children; but little has been studied in healthy adults. Here, we studied the impact of extensive training in Martial Arts on cognitive control in adults. To do so, we used the Attention Network Test (ANT) to test two different groups of participants: with at least 2 years of Martial Arts experience, and with no experience with the sport. Participants were screened from a wider sample of over 500 participants who volunteered to participate. 48 participants were selected: 21 in the Martial Arts group (mean age = 19.68) and 27 in the Non-Martial Arts group (mean age = 19.63). The two groups were matched on a number of demographic variables that included Age and BMI, following the results of a previous pilot study where these factors were found to significantly impact the ANT measures. An effect of Martial Arts experience was found on the Alert network, but not the Orienting or Executive ones. More specifically, Martial Artists showed improved performance when alert had to be sustained endogenously, performing more like the control group when an exogenous cue was provided. This result was further confirmed by a negative correlation between number of years of Martial Arts experience and the costs due to the lack of an exogenous cue suggesting that the longer a person takes part in the sport, the better their endogenous alert is. Results are interpreted in the context of the impact of training a particular attentional state in specific neurocognitive pathways.

  19. Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures.

    Science.gov (United States)

    Cañete, Eduardo; Chen, Jaime; Díaz, Manuel; Llopis, Luis; Reyna, Ana; Rubio, Bartolomé

    2015-06-26

    Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.

  20. Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures

    Directory of Open Access Journals (Sweden)

    Eduardo Cañete

    2015-06-01

    Full Text Available Recently, slab track systems have arisen as a safer and more sustainable option for high speed railway infrastructures, compared to traditional ballasted tracks. Integrating Wireless Sensor Networks within these infrastructures can provide structural health related data that can be used to evaluate their degradation and to not only detect failures but also to predict them. The design of such systems has to deal with a scenario of large areas with inaccessible zones, where neither Internet coverage nor electricity supply is guaranteed. In this paper we propose a monitoring system for slab track systems that measures vibrations and displacements in the track. Collected data is transmitted to passing trains, which are used as data mules to upload the information to a remote control center. On arrival at the station, the data is stored in a database, which is queried by an application in order to detect and predict failures. In this paper, different communication architectures are designed and tested to select the most suitable system meeting such requirements as efficiency, low cost and data accuracy. In addition, to ensure communication between the sensing devices and the train, the communication system must take into account parameters such as train speed, antenna coverage, band and frequency.

  1. Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms.

    Science.gov (United States)

    Vyas, Bhargav Y; Das, Biswarup; Maheshwari, Rudra Prakash

    2016-08-01

    This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg-Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications.

  2. A Partnership Training Program in Breast Cancer Diagnosis: Concept Development of the Next Generation Diagnostic Breast Imaging Using Digital Image Library and Networking Techniques

    National Research Council Canada - National Science Library

    Chouikha, Mohamed F

    2004-01-01

    ...); and Georgetown University (Image Science and Information Systems, ISIS). In this partnership training program, we will train faculty and students in breast cancer imaging, digital image database library techniques and network communication strategy...

  3. Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

    Science.gov (United States)

    Cicero, Mark; Bilbily, Alexander; Colak, Errol; Dowdell, Tim; Gray, Bruce; Perampaladas, Kuhan; Barfett, Joseph

    2017-05-01

    Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set. Our institution's research ethics board approved a single-center retrospective review of 35,038 adult posterior-anterior chest radiographs and final reports performed between 2005 and 2015 (56% men, average age of 56, patient type: 24% inpatient, 39% outpatient, 37% emergency department) with a waiver for informed consent. The GoogLeNet CNN was trained using 3 graphics processing units to automatically classify radiographs as normal (n = 11,702) or into 1 or more of cardiomegaly (n = 9240), consolidation (n = 6788), pleural effusion (n = 7786), pulmonary edema (n = 1286), or pneumothorax (n = 1299). The network's performance was evaluated using receiver operating curve analysis on a test set of 2443 radiographs with the criterion standard being board-certified radiologist interpretation. Using 256 × 256-pixel images as input, the network achieved an overall sensitivity and specificity of 91% with an area under the curve of 0.964 for classifying a study as normal (n = 1203). For the abnormal categories, the sensitivity, specificity, and area under the curve, respectively, were 91%, 91%, and 0.962 for pleural effusion (n = 782), 82%, 82%, and 0.868 for pulmonary edema (n = 356), 74%, 75%, and 0.850 for consolidation (n = 214), 81%, 80%, and 0.875 for cardiomegaly (n = 482), and 78%, 78%, and 0.861 for pneumothorax (n = 167). Current deep CNN architectures can be trained with modest-sized medical data sets to achieve clinically useful performance at detecting and excluding common pathology on chest radiographs.

  4. Bifurcation and chaos in the spontaneously firing spike train of cultured neuronal network

    Science.gov (United States)

    Chen, Wenjuan; Li, Xiangning; Zhu, Geng; Zhou, Wei; Zeng, Shaoqun; Luo, Qingming

    2008-02-01

    Both neuroscience and nonlinear science have focused attention on the dynamics of the neural network. However, litter is known concerning the electrical activity of the cultured neuronal network because of the high complexity and moment change. Instead of traditional methods, we use chaotic time series analysis and temporal coding to analyze the spontaneous firing spike train recorded from hippocampal neuronal network cultured on multi-electrode array. When analyzing interspike interval series of different firing patterns, we found when single spike and burst alternate, the largest Lyapunov exponent of interspike interval (ISI) series is positive. It suggests that chaos should exist. Furthermore, a nonlinear phenomenon of bifurcation is found in the ISI vs. number histogram. It determined that this complex firing pattern of neuron and the irregular ISI series were resulted from deterministic factors and chaos should exist in cultured term.These results suggest that chaotic time series analysis and temporal coding provide us effective methods to investigate the role played by deterministic and stochastic component in neuron information coding, but further research should be carried out because of the high complexity and remarkable noise of the electric activity.

  5. Communicating a terminal prognosis in a palliative care setting: deficiencies in current communication training protocols.

    Science.gov (United States)

    Wittenberg-Lyles, Elaine M; Goldsmith, Joy; Sanchez-Reilly, Sandra; Ragan, Sandra L

    2008-06-01

    The goal of this study was to understand the use and effectiveness of current communication protocols in terminal prognosis disclosures. Data were gathered from an interdisciplinary palliative care consultation service team at a Veterans Hospital in Texas, USA. Medical communication guidelines, a consistent component in United States palliative care education, propose models for delivery of bad news. However, there is little empirical evidence that demonstrates the effectiveness of these guidelines in disclosures of a terminal prognosis. Based on ethnographic observations of terminal prognosis meetings with dying patients, palliative care team meetings, and semi-structured interviews with palliative care team practitioners, this study notes the contradictory conceptualizations of current bad news communication guidelines and highlights that communicating a terminal prognosis also includes (1) adaptive communication based on the patient's acceptability, (2) team based/family communication as opposed to physician-patient dyadic communication, and (3) diffusion of topic through repetition and definition as opposed to singularity of topic. We conclude that environmentally based revision to communication protocol and practice in medical school training is imperative.

  6. Low-Volume High-Intensity Interval Training in a Gym Setting Improves Cardio-Metabolic and Psychological Health.

    Directory of Open Access Journals (Sweden)

    Sam O Shepherd

    Full Text Available Within a controlled laboratory environment, high-intensity interval training (HIT elicits similar cardiovascular and metabolic benefits as traditional moderate-intensity continuous training (MICT. It is currently unclear how HIT can be applied effectively in a real-world environment.To investigate the hypothesis that 10 weeks of HIT, performed in an instructor-led, group-based gym setting, elicits improvements in aerobic capacity (VO2max, cardio-metabolic risk and psychological health which are comparable to MICT.Ninety physically inactive volunteers (42±11 y, 27.7±4.8 kg.m-2 were randomly assigned to HIT or MICT group exercise classes. HIT consisted of repeated sprints (15-60 seconds, >90% HRmax interspersed with periods of recovery cycling (≤25 min.session-1, 3 sessions.week-1. MICT participants performed continuous cycling (~70% HRmax, 30-45 min.session-1, 5 sessions.week-1. VO2max, markers of cardio-metabolic risk, and psychological health were assessed pre and post-intervention.Mean weekly training time was 55±10 (HIT and 128±44 min (MICT (p<0.05, with greater adherence to HIT (83±14% vs. 61±15% prescribed sessions attended, respectively; p<0.05. HIT improved VO2max, insulin sensitivity, reduced abdominal fat mass, and induced favourable changes in blood lipids (p<0.05. HIT also induced beneficial effects on health perceptions, positive and negative affect, and subjective vitality (p<0.05. No difference between HIT and MICT was seen for any of these variables.HIT performed in a real-world gym setting improves cardio-metabolic risk factors and psychological health in physically inactive adults. With a reduced time commitment and greater adherence than MICT, HIT offers a viable and effective exercise strategy to target the growing incidence of metabolic disease and psychological ill-being associated with physical inactivity.

  7. Organizational Infrastructure in the Collegiate Athletic Training Setting, Part II: Benefits of and Barriers in the Athletics Model.

    Science.gov (United States)

    Goodman, Ashley; Mazerolle, Stephanie M; Eason, Christianne M

    2017-01-01

     The athletics model, in which athletic training clinical programs are part of the athletics department, is the predominant model in the collegiate athletic training setting. Little is known about athletic trainers' (ATs') perceptions of this model, particularly as it relates to organizational hierarchy.  To explore the perceived benefits of and barriers in the athletics model.  Qualitative study.  National Collegiate Athletic Association Divisions I and III.  Eight full-time ATs (5 men, 3 women; age = 41 ± 13 years, time employed at the current institution = 14 ± 14 years, experience as a certified AT = 18 ± 13 years) working in the collegiate setting using the athletics model.  We conducted semistructured interviews via telephone or in person and used a general inductive approach to analyze the qualitative data. Multiple-analyst triangulation and peer review established trustworthiness.  Two benefits and 3 barriers emerged from the data. Role identity emerged as a benefit that occurred with role clarity, validation, and acceptance of the collegiate AT personality. Role congruence emerged as a benefit of the athletics model that occurred with 2 lower-order themes: relationship building and physician alignment and support. Role strain, staffing concerns, and work-life conflict emerged as barriers in the athletics model. Role strain occurred with 2 primary lower-order themes: role incongruity and role conflict.  The athletics model is the most common infrastructure for employing ATs in collegiate athletics. Participants expressed positive experiences via character identity, support, trust relationships, and longevity. However, common barriers remain. To reduce role strain, misaligning values, and work-life conflict, ATs working in the athletics model are encouraged to evaluate their relationships with coaches and their supervisor and consider team physician alignment. Moreover, measures to increase quality athletic training staff from a care

  8. Operational Risk Assessment of Distribution Network Equipment Based on Rough Set and D-S Evidence Theory

    Directory of Open Access Journals (Sweden)

    Cunbin Li

    2013-01-01

    Full Text Available With the increasing complication, compaction, and automation of distribution network equipment, a small failure will cause an outbreak chain reaction and lead to operational risk in the power distribution system, even in the whole power system. Therefore, scientific assessment of power distribution equipment operation risk is significant to the security of power distribution system. In order to get the satisfactory assessment conclusions from the complete and incomplete information and improve the assessment level, an operational risk assessment model of distribution network equipment based on rough set and D-S evidence theory was built. In this model, the rough set theory was used to simplify and optimize the operation risk assessment indexes of distribution network equipment and the evidence D-S theory was adopted to combine the optimal indexes. At last, the equipment operational risk level was obtained from the basic probability distribution decision. Taking the transformer as an example, this paper compared the assessment result obtained from the method proposed in this paper with that from the ordinary Rogers ratio method and discussed the application of the proposed method. It proved that the method proposed in this paper is feasible, efficient, and provides a new way to assess the distribution network equipment operational risk.

  9. Proactive Approach for Safe Use of Antimicrobial Coatings in Healthcare Settings: Opinion of the COST Action Network AMiCI

    Directory of Open Access Journals (Sweden)

    Merja Ahonen

    2017-03-01

    Full Text Available Infections and infectious diseases are considered a major challenge to human health in healthcare units worldwide. This opinion paper was initiated by EU COST Action network AMiCI (AntiMicrobial Coating Innovations and focuses on scientific information essential for weighing the risks and benefits of antimicrobial surfaces in healthcare settings. Particular attention is drawn on nanomaterial-based antimicrobial surfaces in frequently-touched areas in healthcare settings and the potential of these nano-enabled coatings to induce (ecotoxicological hazard and antimicrobial resistance. Possibilities to minimize those risks e.g., at the level of safe-by-design are demonstrated.

  10. Generation and quality assessment of route choice sets in public transport networks by means of RP data analysis

    DEFF Research Database (Denmark)

    Larsen, Marie Karen; Nielsen, Otto Anker; Prato, Carlo Giacomo

    2010-01-01

    Literature in route choice modelling shows that a lot of attention has been devoted to route choices of car drivers, but much less attention has been dedicated to route choices of public transport users. As modelling route choice behaviour consists of generating relevant routes and estimating...... discrete choice models, this paper focuses on the issue of choice set generation in public transport networks. Specifically, this paper describes the generation of choice sets for users of the Greater Copenhagen public transport system by applying a doubly stochastic path generation algorithm...

  11. Setting up the speech production network: How oscillations contribute to lateralized information routing

    Directory of Open Access Journals (Sweden)

    Johannes eGehrig

    2012-06-01

    Full Text Available Speech production involves widely distributed brain regions. This study focuses on the spectro-temporal dynamics that contribute to the setup of this network. 26 participants performed a cue-target reading paradigm during MEG. We analyzed local oscillations during preparation for overt and covert reading in the time-frequency domain and localized sources using beamforming. Network dynamics were studied by comparing different dynamic causal models of beta phase coupling. While a broadband low frequency effect was found for any task preparation in bilateral prefrontal cortices, preparation for overt speech production was specifically associated with left-lateralized alpha and beta suppression in temporal cortices and beta suppression in motor-related brain regions. Beta phase coupling in the entire network was modulated by anticipation of overt reading.We propose that the cognitive processes underlying the intention to speak group brain regions belonging to the speech production network by means of beta synchronization and prepare the network for left-lateralized routing of information by suppression of inhibitory alpha and beta oscillations.

  12. Leveraging unsupervised training sets for multi-scale compartmentalization in renal pathology

    Science.gov (United States)

    Lutnick, Brendon; Tomaszewski, John E.; Sarder, Pinaki

    2017-03-01

    Clinical pathology relies on manual compartmentalization and quantification of biological structures, which is time consuming and often error-prone. Application of computer vision segmentation algorithms to histopathological image analysis, in contrast, can offer fast, reproducible, and accurate quantitative analysis to aid pathologists. Algorithms tunable to different biologically relevant structures can allow accurate, precise, and reproducible estimates of disease states. In this direction, we have developed a fast, unsupervised computational method for simultaneously separating all biologically relevant structures from histopathological images in multi-scale. Segmentation is achieved by solving an energy optimization problem. Representing the image as a graph, nodes (pixels) are grouped by minimizing a Potts model Hamiltonian, adopted from theoretical physics, modeling interacting electron spins. Pixel relationships (modeled as edges) are used to update the energy of the partitioned graph. By iteratively improving the clustering, the optimal number of segments is revealed. To reduce computational time, the graph is simplified using a Cantor pairing function to intelligently reduce the number of included nodes. The classified nodes are then used to train a multiclass support vector machine to apply the segmentation over the full image. Accurate segmentations of images with as many as 106 pixels can be completed only in 5 sec, allowing for attainable multi-scale visualization. To establish clinical potential, we employed our method in renal biopsies to quantitatively visualize for the first time scale variant compartments of heterogeneous intra- and extraglomerular structures simultaneously. Implications of the utility of our method extend to fields such as oncology, genomics, and non-biological problems.

  13. Agenda Trending: Reciprocity and the Predictive Capacity of Social Networking Sites in Intermedia Agenda Setting across Topics over Time

    OpenAIRE

    Jacob Groshek; Megan Clough Groshek

    2013-01-01

    In the contemporary converged media environment, agenda setting is being transformed by the dramatic growth of audiences that are simultaneously media users and producers. The study reported here addresses related gaps in the literature by first comparing the topical agendas of two leading traditional media outlets (New York Times and CNN) with the most frequently shared stories and trending topics on two widely popular Social Networking Sites (Facebook and Twitter). Time-series analyses of t...

  14. Development and Operation of International Nuclear Education/Training Program and HRD Cooperation Network

    Energy Technology Data Exchange (ETDEWEB)

    Lee, E. J.; Min, B. J.; Han, K. W. (and others)

    2006-12-15

    The primary result of the project is the establishment of a concept of International Nuclear R and D Academy that integrates the on-going long term activity for international nuclear education/training and a new activity to establish an international cooperation network for nuclear human resources development. For this, the 2007 WNU Summer Institute was hosted with the establishment of an MOU and subsequent preparations. Also, ANENT was promoted through development of a cyber platform for the ANENT web-portal, hosting the third ANENT Coordination Committee meeting, etc. Then a cooperation with universities in Vietnam was launched resulting in preparation of an MOU for the cooperation. Finally, a relevant system framework was established and required procedures were drafted especially for providing students from developing countries with long term education/training programs (e.g. MS and Ph D. courses). The international nuclear education/training programs have offered 13 courses to 182 people from 43 countries. The overall performance of the courses was evaluated to be outstanding. In parallel, the establishment of an MOU for the cooperation of KOICA-IAEA-KAERI courses to ensure their stable and systematic operation. Also, an effort was made to participate in FNCA. Atopia Hall of the International Nuclear Training and Education Center (INTEC) hosted 477 events (corresponding to 18,521 participants) and Nuri Hall (guesthouse) accommodated 4,616 people in 2006. This shows a steady increase of the use rate since the opening of the center, along with a continuous improvement of the equipment.

  15. Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks

    Science.gov (United States)

    Kunimoto, Ryo; Bajorath, Jürgen

    2017-09-01

    Patents from medicinal chemistry represent a rich source of novel compounds and activity data that appear only infrequently in the scientific literature. Moreover, patent information provides a primary focal point for drug discovery. Accordingly, text mining and image extraction approaches have become hot topics in patent analysis and repositories of patent data are being established. In this work, we have generated network representations using alternative similarity measures to systematically compare molecules from patents with other bioactive compounds, visualize similarity relationships, explore the chemical neighbourhood of patent molecules, and identify closely related compounds with different activities. The design of network representations that combine patent molecules and other bioactive compounds and view patent information in the context of current bioactive chemical space aids in the analysis of patents and further extends the use of molecular networks to explore structure-activity relationships.

  16. The Effectiveness of an Interactive Training Program in Developing a Set of Non-Cognitive Skills in Students at University of Petra

    Science.gov (United States)

    Gheith, Eman; Aljaberi, Nahil M.

    2017-01-01

    This study aimed to investigate the effectiveness of interactive training programs in developing a set of non-cognitive skills in students at the University of Petra. Furthermore, it sought to examine the impact of the sex, academic year, and university major variables on developing these skills in students who underwent the training program, as…

  17. The Role of Eif6 in Skeletal Muscle Homeostasis Revealed by Endurance Training Co-expression Networks

    Directory of Open Access Journals (Sweden)

    Kim Clarke

    2017-11-01

    Full Text Available Regular endurance training improves muscle oxidative capacity and reduces the risk of age-related disorders. Understanding the molecular networks underlying this phenomenon is crucial. Here, by exploiting the power of computational modeling, we show that endurance training induces profound changes in gene regulatory networks linking signaling and selective control of translation to energy metabolism and tissue remodeling. We discovered that knockdown of the mTOR-independent factor Eif6, which we predicted to be a key regulator of this process, affects mitochondrial respiration efficiency, ROS production, and exercise performance. Our work demonstrates the validity of a data-driven approach to understanding muscle homeostasis.

  18. Train-Network Interactions and Stability Evaluation in High-Speed Railways--Part I: Phenomena and Modeling

    DEFF Research Database (Denmark)

    Hu, Haitao; Tao, Haidong; Blaabjerg, Frede

    2018-01-01

    This paper presents an impedance-based model to systematically investigate the interaction performance of multiple trains and traction network interaction system, aiming to evaluate the serious phenomena, including low-frequency oscillation (LFO), harmonic resonance and resonance instability....... The train-network interaction mechanism is therefore studied and one presents a detailed coupling model for investigating the three interactive phenomena and their characteristics, influential factors, analysis methods and possible mitigation schemes. In the Part I of the two-part paper, the measured...

  19. Let’s Wiggle with 5-2-1-0: Curriculum Development for Training Childcare Providers to Promote Activity in Childcare Settings

    Directory of Open Access Journals (Sweden)

    Debra M. Vinci

    2016-01-01

    Full Text Available Overweight and obesity are increasing in preschool children in the US. Policy, systems, and environmental change interventions in childcare settings can improve obesity-related behaviors. The aim of this study was to develop and pilot an intervention to train childcare providers to promote physical activity (PA in childcare classrooms. An evidence scan, key informant (n=34 and focus group (n=20 interviews with childcare directors and staff, and environmental self-assessment of childcare facilities (n=22 informed the design of the training curriculum. Feedback from the interviews indicated that childcare providers believed in the importance of teaching children about PA and were supportive of training teachers to incorporate PA into classroom settings. The Promoting Physical Activity in Childcare Setting Curriculum was developed and training was implemented with 16 teachers. Participants reported a positive experience with the hands-on training and reported acquiring new knowledge that they intended to implement in their childcare settings. Our findings highlight the feasibility of working with childcare staff to develop PA training and curriculum. Next steps include evaluating the curriculum in additional childcare settings and childcare staff implementation of the curriculum to understand the effectiveness of the training on PA levels of children.

  20. Pap-smear Classification Using Efficient Second Order Neural Network Training Algorithms

    DEFF Research Database (Denmark)

    Ampazis, Nikolaos; Dounias, George; Jantzen, Jan

    2004-01-01

    . The algorithms are methodologically similar, and are based on iterations of the form employed in the Levenberg-Marquardt (LM) method for non-linear least squares problems with the inclusion of an additional adaptive momentum term arising from the formulation of the training task as a constrained optimization...... problem. The classification results obtained from the application of the algorithms on a standard benchmark pap-smear data set reveal the power of the two methods to obtain excellent solutions in difficult classification problems whereas other standard computational intelligence techniques achieve...

  1. Pilot Integration of HIV Screening and Healthcare Settings with Multi- Component Social Network and Partner Testing for HIV Detection.

    Science.gov (United States)

    Rentz, Michael F; Ruffner, Andrew H; Ancona, Rachel M; Hart, Kimberly W; Kues, John R; Barczak, Christopher M; Lindsell, Christopher J; Fichtenbaum, Carl J; Lyons, Michael S

    2017-11-23

    Healthcare settings screen broadly for HIV. Public health settings use social network and partner testing ("Transmission Network Targeting (TNT)") to select high-risk individuals based on their contacts. HIV screening and TNT systems are not integrated, and healthcare settings have not implemented TNT. The study aimed to evaluate pilot implementation of multi-component, multi-venue TNT in conjunction with HIV screening by a healthcare setting. Our urban, academic health center implemented a TNT program in collaboration with the local health department for five months during 2011. High-risk or HIV positive patients of the infectious diseases clinic and emergency department HIV screening program were recruited to access social and partner networks via compensated peer-referral, testing of companions present with them, and partner notification services. Contacts became the next-generation index cases in a snowball recruitment strategy. The pilot TNT program yielded 485 HIV tests for 482 individuals through eight generations of recruitment with five (1.0%; 95% CI = 0.4%, 2.3%) new diagnoses. Of these, 246 (51.0%; 95% CI = 46.6%, 55.5%) reported that they had not been tested for HIV within the last 12 months and 383 (79.5%; 95% CI = 75.7%, 82.9%) had not been tested by the existing ED screening program within the last five years. TNT complements population screening by more directly targeting high-risk individuals and by expanding the population receiving testing. Information from existing healthcare services could be used to seed TNT programs, or TNT could be implemented within healthcare settings. Research evaluating multi-component, multi-venue HIV detection is necessary to maximize complementary approaches while minimizing redundancy. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. Secure Your Wireless Network: Going Wireless Comes with Its Own Special Set of Security Concerns

    Science.gov (United States)

    Bloomquist, Jane; Musa, Atif

    2004-01-01

    Imagine a completely wireless school, an open network in which all students and staff can roam around using laptops or handheld computers to browse the Internet, access files and applications on the school server, and communicate with each other and the world via e-mail. It's a great picture--and at some schools the future is already here. But…

  3. Summary information and data sets for the HBCU Solar Measurements Network

    Energy Technology Data Exchange (ETDEWEB)

    Marion, W

    1994-08-01

    Since 1985, the National Renewable Energy Laboratory (NREL), formerly the Solar Energy Research Institute (SERI), has operated a solar radiation measurement network of six stations located at Historically Black Colleges and Universities (HBCUs) in the southeastern United States. NREL initiated this network to provide better regional coverage and to comply with President Reagan`s Executive Order 12320, dated September 15, 1981, directing all federal agencies to implement programs to strengthen the nation`s HBCUs. Funding for the HBCU network has been provided by the Department of Energy`s (DOE`s) Resource Assessment Program, Photovoltaic Program, and Solar Thermal Program, and it is currently funded by the Solar Radiation Resource Assessment Project. The objectives of the HBCU network are (1) To significantly improve the assessment of solar radiation resources in the southeastern United States; (2) To enlist the help of the HBCUs in collecting high-quality solar radiation data; (3) To encourage the distribution of solar radiation resource information and the development of solar energy applications in the Southeast; (4) To encourage the development of academic and research programs in solar energy at HBCUs.

  4. Networks and landscapes: a framework for setting goals and evaluating performance at the large landscape scale

    Science.gov (United States)

    R Patrick Bixler; Shawn Johnson; Kirk Emerson; Tina Nabatchi; Melly Reuling; Charles Curtin; Michele Romolini; Morgan Grove

    2016-01-01

    The objective of large landscape conser vation is to mitigate complex ecological problems through interventions at multiple and overlapping scales. Implementation requires coordination among a diverse network of individuals and organizations to integrate local-scale conservation activities with broad-scale goals. This requires an understanding of the governance options...

  5. Recent developments in the setting up of the Malta Seismic Network

    Science.gov (United States)

    Agius, Matthew; Galea, Pauline; D'Amico, Sebastiano

    2015-04-01

    Weak to moderate earthquakes in the Sicily Channel have until now been either poorly located or left undetected. The number of seismic stations operated by various networks: Italy (INGV), Tunisia (TT), and Libya (LNSN) have now improved considerably, however most of the seismicity occurs offshore, in the central part of the Channel, away from the mainland stations. Seismic data availability from island stations across the Channel has been limited or had intermittent transmission hindering proper real-time earthquake monitoring and hypocentre relocation. In order to strengthen the seismic monitoring of the Sicily Channel, in particular the central parts of the Channel, the Seismic Monitoring and Research Unit (SMRU), University of Malta, has, in the last year, been installing a permanent seismic network across the Maltese archipelago: the Malta Seismic Network (ML). Furthermore the SMRU has upgraded its IT facilities to run a virtual regional seismic network composed of the stations on Pantelleria and Lampedusa, together with all the currently publicly available stations in the region. Selected distant seismic stations found elsewhere in the Mediterranean and across the globe have also been incorporated in the system in order to enhance the overall performance of the monitoring and to detect potentially damaging regional earthquakes. Data acquisition and processing of the seismic networks are run by SeisComP. The new installations are part of the project SIMIT (B1-2.19/11) funded by the Italia-Malta Operational Programme 2007-2013. The new system allows the SMRU to rapidly perform more accurate hypocentre locations in the region, and issue automatic SMS alert for potentially felt events in the Sicily Channel detected by the network and for strong earthquakes elsewhere. Within the SIMIT project, the alert system will include civil protection departments in Malta and Sicily. We present the recent developments of the real and virtual seismic network, and discuss the

  6. Effects of drop set resistance training on acute stress indicators and long-term muscle hypertrophy and strength.

    Science.gov (United States)

    Fink, Julius; Schoenfeld, Brad J; Kikuchi, Naoki; Nakazato, Koichi

    2017-04-26

    We investigated the effects of 2 different resistance training (RT) protocols on muscle hypertrophy and strength. The first group (n = 8) performed a single drop set (DS) and the second group (n = 8) performed 3 sets of conventional RT (normal set, NS). Eight young men in each group completed 6 weeks of RT. Muscle hypertrophy was assessed via magnetic resonance imaging (MRI) and strength via 12 RM tests before and after the 6 weeks. Acute stress markers such as muscle thickness (MT), blood lactate (BL), maximal voluntary contraction (MVC), heart rate (HR) and rating of perceived exertion (RPE) before and after one bout of RT. Both groups showed significant increases in triceps muscle cross-sectional area (CSA) (10.0 ± 3.7%, effect size (ES) = 0.47 for DS and 5.1 ± 2.1%, ES = 0.25 for NS). Strength increased in both groups (16.1 ± 12.1%, ES = 0.88 for DS and 25.2 ± 17.5%, ES = 1.34 for NS). Acute pre/post measurements for one bout of RT showed significant changes in MT (18.3 ± 5.8%, p < 0.001) and MVC (-13.3 ± 7.1, p < 0.05) in the DS group only and a significant difference (p < 0.01) in RPE was observed between groups (7.7 ± 1.5 for DS and 5.3 ± 1.4 for NS). Superior muscle gains might be achieved with a single set of DS compared to 3 sets of conventional RT, probably due to higher stress experienced in the DS protocol.

  7. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network

    Directory of Open Access Journals (Sweden)

    Fukuda eMegumi

    2015-03-01

    Full Text Available Motor or perceptual learning is known to influence functional connectivity between brain regions and induce short-term changes in the intrinsic functional networks revealed as correlations in slow blood-oxygen-level dependent (BOLD signal fluctuations. However, no cause-and-effect relationship has been elucidated between a specific change in connectivity and a long-term change in global networks. Here, we examine the hypothesis that functional connectivity (i.e. temporal correlation between two regions is increased and preserved for a long time when two regions are simultaneously activated or deactivated. Using the connectivity-neurofeedback training paradigm, subjects successfully learned to increase the correlation of activity between the lateral parietal and primary motor areas, regions that belong to different intrinsic networks and negatively correlated before training under the resting conditions. Furthermore, whole-brain hypothesis-free analysis as well as functional network analyses demonstrated that the correlation in the resting state between these areas as well as the correlation between the intrinsic networks that include the areas increased for at least two months. These findings indicate that the connectivity-neurofeedback training can cause long-term changes in intrinsic connectivity and that intrinsic networks can be shaped by experience-driven modulation of regional correlation.

  8. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network

    Science.gov (United States)

    Megumi, Fukuda; Yamashita, Ayumu; Kawato, Mitsuo; Imamizu, Hiroshi

    2015-01-01

    Motor or perceptual learning is known to influence functional connectivity between brain regions and induce short-term changes in the intrinsic functional networks revealed as correlations in slow blood-oxygen-level dependent (BOLD) signal fluctuations. However, no cause-and-effect relationship has been elucidated between a specific change in connectivity and a long-term change in global networks. Here, we examine the hypothesis that functional connectivity (i.e., temporal correlation between two regions) is increased and preserved for a long time when two regions are simultaneously activated or deactivated. Using the connectivity-neurofeedback training paradigm, subjects successfully learned to increase the correlation of activity between the lateral parietal and primary motor areas, regions that belong to different intrinsic networks and negatively correlated before training under the resting conditions. Furthermore, whole-brain hypothesis-free analysis as well as functional network analyses demonstrated that the correlation in the resting state between these areas as well as the correlation between the intrinsic networks that include the areas increased for at least 2 months. These findings indicate that the connectivity-neurofeedback training can cause long-term changes in intrinsic connectivity and that intrinsic networks can be shaped by experience-driven modulation of regional correlation. PMID:25870552

  9. Goals of Care Ambulatory Resident Education: Training Residents in Advance Care Planning Conversations in the Outpatient Setting.

    Science.gov (United States)

    Berns, Stephen H; Camargo, Marianne; Meier, Diane E; Yuen, Jacqueline K

    2017-12-01

    Advance care planning (ACP) discussions often occur in the inpatient setting when patients are too ill to participate in decision making. Although the outpatient setting is the preferred time to begin these discussions, few physicians do so in practice. Many internal medicine (IM) residents report inadequate training as a barrier to having outpatient ACP discussions. To assess whether a novel curriculum entitled Goals of Care Ambulatory Resident Education (GOCARE) improved resident physicians' understanding of and preparedness for conducting ACP discussions in the outpatient setting. The curriculum was delivered over four weekly three-hour small group sessions to IM residents. Each session included didactics, a demonstration of skills, and a simulated patient communication laboratory that emphasized deliberate practice. IM residents from an urban, academic ambulatory care practice. Impact of the intervention was evaluated using a retrospective pre-post design. Residents completed surveys immediately after the course and six months later. Forty-two residents participated in the curriculum and 95% completed the postcourse survey. Residents' self-rated level of preparedness increased for ACP discussions overall (4.0 pre vs. 5.2 post on 7-point Likert scale) and for communication steps involved in ACP (p skills (p skills in outpatient ACP discussions.

  10. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

    Science.gov (United States)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-08-01

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online.

  11. Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

    Science.gov (United States)

    Samareh, Jamshid A.; Wong, Jay Ming

    2014-01-01

    Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches.

  12. The Influence of Concentrative Meditation Training on the Development of Attention Networks during Early Adolescence

    Directory of Open Access Journals (Sweden)

    Shruti eBaijal

    2011-07-01

    Full Text Available We investigate if concentrative meditation training (CMT offered during adolescent development benefits subsystems of attention using a quasi-experimental design. Attentional alerting, orienting, and conflict monitoring were examined using the Attention Network Test (ANT in 13, 14, and 15 yo children who received CMT as part of their school curriculum (CMT Group: N=79 vs. those who received no such training (Control Group: N=76. Alerting and conflict monitoring, but not orienting, differed between the CMT and Control Group. Only conflict monitoring demonstrated age-related improvements, with smaller conflict effect scores in older vs. younger participants. The influence of CMT on this system was similar to the influence of developmental maturity, with smaller conflict effects in the CMT vs. Control group. To examine if CMT might also bolster conflict-triggered upregulation of attentional control, conflict effects were evaluated as a function of previous trial conflict demands (high conflict vs. low conflict. Smaller current trial conflict effects were observed when previous conflict was high vs. low, suggesting that similar to adults, when previous conflict was high (vs. low children in this age-range proactively upregulated control so that subsequent trial performance was benefitted. The magnitude of conflict-triggered control upregulation was not bolstered by CMT but CMT did have an effect for current incongruent trials preceded by congruent trials. Thus, CMT’s influence on attention may be tractable and specific; it may bolster attentional alerting, conflict monitoring and reactive control, but does not appear to improve orienting.

  13. Accounting for PMD Temporal Correlation During Lightpath Set Up in Transparent Optical Networks

    DEFF Research Database (Denmark)

    Sambo, Nicola; Secondini, Marco; Andriolli, Nicola

    2010-01-01

    stochastic characteristics. Moreover, PMD depends on time-variant factors, such as the temperature and the fiber stress. When implementing a dynamic GMPLS-controlled transparent optical network, the GMPLS protocol suite must take into account physical impairment information in order to establish lightpaths......In transparent optical networks, the signal transmission is degraded by optical layer physical impairments. Therefore, lightpaths may be blocked due to unacceptable quality of transmission (QoT). Among physical impairments, polarization mode dispersion (PMD) is a detrimental effect which has...... that the instantaneous DGD is not detrimental. Additionally, given PMD temporal correlation properties, once that the instantaneous DGD is not detrimental, it continues to be not detrimental within considerable time ranges. Therefore, more accurate models can be implemented in the GMPLS control plane to account for PMD...

  14. A multi-radio, multi-hop ad-hoc radio communication network for Communications-Based Train Control (CBTC)

    DEFF Research Database (Denmark)

    Farooq, Jahanzeb; Bro, Lars; Karstensen, Rasmus Thystrup

    2017-01-01

    Communications-Based Train Control (CBTC) is a modern signalling system that uses radio communication to transfer train control information between train and wayside. The trackside networks in these systems are mostly based on conventional infrastructure Wi-Fi (IEEE 802.11). It means a train has...... to continuously associate (i.e. perform handshake) with the trackside Wi-Fi Access Points (AP) as it moves, which incurs communication delays. Additionally, these APs are connected to the wayside infrastructure via optical fiber cables that incurs huge costs. This paper presents a novel design in which trackside...... nodes function in ad-hoc Wi-Fi mode, which means no association has to be performed with them prior to transmitting. A node upon receiving packets from a train forwards these packets to the next node, forming a chain of nodes. Following this chain, packets arrive at the destination. To make the design...

  15. Pinning dynamic systems of networks with Markovian switching couplings and controller-node set

    OpenAIRE

    Han, Yujuan; Lu, Wenlian; Li, Zhe; Chen, Tianping

    2014-01-01

    In this paper, we study pinning control problem of coupled dynamical systems with stochastically switching couplings and stochastically selected controller-node set. Here, the coupling matrices and the controller-node sets change with time, induced by a continuous-time Markovian chain. By constructing Lyapunov functions, we establish tractable sufficient conditions for exponentially stability of the coupled system. Two scenarios are considered here. First, we prove that if each subsystem in t...

  16. The impact of brief team communication, leadership and team behavior training on ad hoc team performance in trauma care settings.

    Science.gov (United States)

    Roberts, Nicole K; Williams, Reed G; Schwind, Cathy J; Sutyak, John A; McDowell, Christopher; Griffen, David; Wall, Jarrod; Sanfey, Hilary; Chestnut, Audra; Meier, Andreas H; Wohltmann, Christopher; Clark, Ted R; Wetter, Nathan

    2014-02-01

    Communication breakdowns and care coordination problems often cause preventable adverse patient care events, which can be especially acute in the trauma setting, in which ad hoc teams have little time for advanced planning. Existing teamwork curricula do not address the particular issues associated with ad hoc emergency teams providing trauma care. Ad hoc trauma teams completed a preinstruction simulated trauma encounter and were provided with instruction on appropriate team behaviors and team communication. Teams completed a postinstruction simulated trauma encounter immediately afterward and 3 weeks later, then completed a questionnaire. Blinded raters rated videotapes of the simulations. Participants expressed high levels of satisfaction and intent to change practice after the intervention. Participants changed teamwork and communication behavior on the posttest, and changes were sustained after a 3-week interval, though there was some loss of retention. Brief training exercises can change teamwork and communication behaviors on ad hoc trauma teams. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Social Network Analysis in Transnational Settings: The Case of Mexico City’s AIDS CBOs

    Directory of Open Access Journals (Sweden)

    Nielan Barnes

    2010-02-01

    Full Text Available Using a case study approach, I show how transnational civil society networks both help and hinder community-based HIV/AIDS organizations by providing opportunities for community-state partnerships that favor some local organizations over others, and (reproduce intra-organizational stratification at the local level. In the case of Mexico City, transnational ties encourage community-based AIDS organizations to develop formal organizational forms and strategies (negotiating fronts which often enhance organizational sustainability and draw organizations into a closer relationship with the state institutionalized sphere. However, such ties also create divisions (political fronts between outsider and insider organizations that compromise local inter-organizational collaboration and service delivery. As a result, transnational networks and resources solidify outsider-insider conflicts and balkanize service provision along political lines. The conclusions of this research are helpful to international health practitioners and social scientists seeking to understand how transnational networks and resources shape global civil society, and can both challenge and reproduce existing community-state power regimes and health inequities at local and transnational levels.

  18. The implementation of PEARS training: supporting nurses in non-critical care settings to improve patient outcomes.

    Science.gov (United States)

    Famolare, Nancy; Romano, Jane C

    2013-01-01

    Children's Hospital Boston's Life Support Program began offering the newly developed American Heart Association Pediatric Emergency Assessment, Recognition and Stabilization (PEARS) course for nurses working in non-critical care settings in December of 2007. The goal was to provide an appropriate alternative to pediatric advanced life support (PALS) training for clinical staff caring for the general pediatric population. To date, more than 900 nurses have completed the course with feedback from the participants being extremely positive. Even more impressive is a more appropriate use of the hospital's emergency medical response system promoting early intervention and the significant reduction in cardiac arrests on inpatient units. During a 12-month period, nurses involved in activations of the response system were asked to rate their ability to assess, categorize, decide and act after each event. The overwhelming majority agreed they were able to apply the PEARS systematic approach of assessment and early intervention to the situation. This article describes the planning and implementation of PEARS training for non-critical care nursing staff and provides data that demonstrates improved patient outcomes. Supporting activities and strategies promoting early recognition and interventions contributing to the successful reduction of cardiac arrests on inpatient units are also discussed. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. Efficient Graph-Based Resource Allocation Scheme Using Maximal Independent Set for Randomly- Deployed Small Star Networks.

    Science.gov (United States)

    Zhou, Jian; Wang, Lusheng; Wang, Weidong; Zhou, Qingfeng

    2017-11-06

    In future scenarios of heterogeneous and dense networks, randomly-deployed small star networks (SSNs) become a key paradigm, whose system performance is restricted to inter-SSN interference and requires an efficient resource allocation scheme for interference coordination. Traditional resource allocation schemes do not specifically focus on this paradigm and are usually too time consuming in dense networks. In this article, a very efficient graph-based scheme is proposed, which applies the maximal independent set (MIS) concept in graph theory to help divide SSNs into almost interference-free groups. We first construct an interference graph for the system based on a derived distance threshold indicating for any pair of SSNs whether there is intolerable inter-SSN interference or not. Then, SSNs are divided into MISs, and the same resource can be repetitively used by all the SSNs in each MIS. Empirical parameters and equations are set in the scheme to guarantee high performance. Finally, extensive scenarios both dense and nondense are randomly generated and simulated to demonstrate the performance of our scheme, indicating that it outperforms the classical max K-cut-based scheme in terms of system capacity, utility and especially time cost. Its achieved system capacity, utility and fairness can be close to the near-optimal strategy obtained by a time-consuming simulated annealing search.

  20. SeqEnrich: A tool to predict transcription factor networks from co-expressed Arabidopsis and Brassica napus gene sets.

    Science.gov (United States)

    Becker, Michael G; Walker, Philip L; Pulgar-Vidal, Nadège C; Belmonte, Mark F

    2017-01-01

    Transcription factors and their associated DNA binding sites are key regulatory elements of cellular differentiation, development, and environmental response. New tools that predict transcriptional regulation of biological processes are valuable to researchers studying both model and emerging-model plant systems. SeqEnrich predicts transcription factor networks from co-expressed Arabidopsis or Brassica napus gene sets. The networks produced by SeqEnrich are supported by existing literature and predicted transcription factor-DNA interactions that can be functionally validated at the laboratory bench. The program functions with gene sets of varying sizes and derived from diverse tissues and environmental treatments. SeqEnrich presents as a powerful predictive framework for the analysis of Arabidopsis and Brassica napus co-expression data, and is designed so that researchers at all levels can easily access and interpret predicted transcriptional circuits. The program outperformed its ancestral program ChipEnrich, and produced detailed transcription factor networks from Arabidopsis and Brassica napus gene expression data. The SeqEnrich program is ideal for generating new hypotheses and distilling biological information from large-scale expression data.

  1. Benefits, Barriers, and Motivators to Training Dietetic Interns in Clinical Settings: A Comparison between Preceptors and Nonpreceptors.

    Science.gov (United States)

    AbuSabha, Rayane; Muller, Colette; MacLasco, Jacqueline; George, Mary; Houghton, Erica; Helm, Alison

    2017-10-27

    The shortage of supervised practice sites in dietetics is associated with fewer numbers of preceptors available to supervise interns, especially in the clinical setting. To identify clinical dietitians' perceived benefits and challenges of training dietetic interns and to determine key motivators that would entice nonpreceptors to volunteer for the role. Registered dietitian nutritionists working in clinical settings completed a semi-structured, audiotaped interview followed by a brief questionnaire. Clinical dietitians working in hospitals, long-term care facilities, and outpatient clinics (n=100) participated: 54 preceptors and 46 nonpreceptors. Qualitative analysis was conducted using an iterative process to identify and code common themes. T tests were used to compare mean differences between the opinions of preceptors and nonpreceptors. Preceptors had approximately 5 more years of experience (mean=14.27±12.09 years) than nonpreceptors (mean=8.83±9.72 years) (P< 0.01). Furthermore, preceptors reported twice as many benefits to mentoring interns (mean=6.7 mentions/participant) as nonpreceptors (mean=3.4 mentions/participant), including knowledge gains and staying current. Lack of time was consistently noted as a barrier in interviews and rated as the greatest barrier in the survey. Both groups rated receiving continuing professional education units (CPEUs) for precepting as the greatest potential motivator for taking on interns. Incentive programs should be developed to entice nonpreceptors to take on interns. These programs should include extensive training on the preceptor role and how to alleviate the burden of time spent supervising interns and should provide a significant number of CPEUs to make the added workload worthwhile. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

  2. Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling.

    Science.gov (United States)

    Gramatica, Paola; Pilutti, Pamela; Papa, Ester

    2004-01-01

    The rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous organic compounds is predicted by QSAR modeling. The applied Multiple Linear Regression is based on a variety of theoretical molecular descriptors, selected by the Genetic Algorithms-Variable Subset Selection (GA-VSS) procedure. The models were validated for predictivity by both internal and external validation. For the external validation two splitting approaches, D-optimal Experimental Design and Kohonen Artificial Neural Networks (K-ANN), were applied to the original data set to compare the two methodologies. We emphasize that external validation is the only way to establish a reliable QSAR model for predictive purposes. Predicted data by consensus modeling from different models are also proposed. Copyright 2004 American Chemical Society

  3. The European VLF/LF radio network to search for earthquake precursors: setting up and natural/man-made disturbances

    Directory of Open Access Journals (Sweden)

    P. F. Biagi

    2011-02-01

    Full Text Available In the last years disturbances in VLF/LF radio signals related to seismic activity have been presented. The radio data were collected by receivers located on the ground or on satellites. The ground-based research implies systematic data collection by a network of receivers. Since 2000 the "Pacific VLF network", conducted by Japanese researchers, has been in operation. During 2008 a radio receiver was developed by the Italian factory Elettronika (Palo del Colle, Bari. The receiver is equipment working in VLF and LF bands. It can monitor 10 frequencies distributed in these bands and, for each of them, it saves the power level. At the beginning of 2009, five receivers were made for the realization of the "European VLF/LF Network"; two were planned for Italy and one for Greece, Turkey and Romania, respectively. In 2010 the network was enlarged to include a new receiver installed in Portugal. In this work, first the receiver and its setting up in the different places are described. Then, several disturbances in the radio signals related to the transmitters, receivers, meteorological/geomagnetic conditions are presented and described.

  4. Training Valence, Instrumentality, and Expectancy Scale (T-VIES-it): Factor Structure and Nomological Network in an Italian Sample

    Science.gov (United States)

    Zaniboni, Sara; Fraccaroli, Franco; Truxillo, Donald M.; Bertolino, Marilena; Bauer, Talya N.

    2011-01-01

    Purpose: The purpose of this study is to validate, in an Italian sample, a multidimensional training motivation measure (T-VIES-it) based on expectancy (VIE) theory, and to examine the nomological network surrounding the construct. Design/methodology/approach: Using a cross-sectional design study, 258 public sector employees in Northeast Italy…

  5. Understanding the Construction of Personal Learning Networks to Support Non-Formal Workplace Learning of Training Professionals

    Science.gov (United States)

    Manning, Christin

    2013-01-01

    Workers in the 21st century workplace are faced with rapid and constant developments that place a heavy demand on them to continually learn beyond what the Human Resources and Training groups can meet. As a consequence, professionals must rely on non-formal learning approaches through the development of a personal learning network to keep…

  6. [Current status on management and needs related to education and training programs set for new employees at the provincial Centers for Disease Control and Prevention, in China].

    Science.gov (United States)

    Ma, J; Meng, X D; Luo, H M; Zhou, H C; Qu, S L; Liu, X T; Dai, Z

    2016-06-01

    In order to understand the current management status on education/training and needs for training among new employees working at the provincial CDC in China during 2012-2014, so as to provide basis for setting up related programs at the CDC levels. Based on data gathered through questionnaire surveys run by CDCs from 32 provincial and 5 specifically-designated cities, microsoft excel was used to analyze the current status on management of education and training, for new employees. There were 156 management staff members working on education and training programs in 36 CDCs, with 70% of them having received intermediate or higher levels of education. Large differences were seen on equipment of training hardware in different regions. There were 1 214 teaching staff with 66 percent in the fields or related professional areas on public health, in 2014. 5084 new employees conducted pre/post training programs, from 2012 to 2014 with funding as 750 thousand RMB Yuan. 99.5% of the new employees expressed the needs for further training while. 74% of the new staff members expecting a 2-5 day training program to be implemented. 79% of the new staff members claimed that practice as the most appropriate method for training. Institutional programs set for education and training at the CDCs need to be clarified, with management team organized. It is important to provide more financial support on both hardware, software and human resources related to training programs which are set for new stuff members at all levels of CDCs.

  7. How to set up an effective national primary angioplasty network: lessons learned from five European countries

    DEFF Research Database (Denmark)

    Knot, Jiri; Widimsky, Petr; Wijns, William

    2009-01-01

    Cardiovascular Interventions (EAPCI) recenty launched the Stent For Life Initiative (SFLI). The initial phase of this pan-European project was focused on the positive experience of five countries to provide the best practice examples. The Netherlands, the Czech Republic, Sweden, Denmark and Austria were visited...... and the logistics of ACS treatment was studied. Public campaigns improved patient access to acute PCI. Regional networks involving emergency medical services (EMS), non-PCI hospitals and PCI centres are useful in providing access to acute PCI for most patients. Direct transfer from the first medical contact site...

  8. DEVELOPMENT OF NEURAL NETWORKS FOR FORECASTING OF CHEMICAL SUBSTANCES’ MIGRATION IN SOIL AND ALGORITHMS OF THEIR TRAINING

    Directory of Open Access Journals (Sweden)

    S. P. Kundas

    2010-01-01

    Full Text Available A review of the existing models and methods for forecasting chemical substances' migration in soil is contained in the paper. The paper shows that the most effective decision for solving ecological tasks in this field is an application of artificial neural networks using training «with a tutor» on the basis of an inverse error propagation algorithm. Corresponding structures of  neural networks for solution of the given problem have been developed in the paper.A new method for artificial neural network training based on the modification of an inverse error propagation algorithm while using an additional signal is proposed in the paper. The given method allows to achieve 100% convergence in the forecasting problems pertaining to chemical substances' migration in soil. 

  9. Safety study of 38 503 intravitreal ranibizumab injections performed mainly by physicians in training and nurses in a hospital setting

    DEFF Research Database (Denmark)

    Hasler, Pascal W; Bloch, Sara Brandi; Villumsen, Jørgen

    2015-01-01

    PURPOSE: To evaluate and to compare the safety of intravitreal ranibizumab injections performed by physicians and nurses at a single large hospital clinic in Denmark during 5 years. DESIGN: Retrospective, interventional, non-comparative study. METHODS: SETTING: All eyes that underwent a protocoli......PURPOSE: To evaluate and to compare the safety of intravitreal ranibizumab injections performed by physicians and nurses at a single large hospital clinic in Denmark during 5 years. DESIGN: Retrospective, interventional, non-comparative study. METHODS: SETTING: All eyes that underwent...... a protocolized ranibizumab injection procedure performed in an operating room mainly by nurses and physicians in their first year of ophthalmology training. STUDY POPULATION: A total of 4623 eyes in 3679 patients with subretinal neovascularization secondary to a variety of retinal diseases, mainly neovascular...... detachment from 2007 to 2012. RESULTS: Overall, 38,503 intravitreal ranibizumab injections were performed in 4623 eyes. Injections were performed by nurses (32.5%), ophthalmology residents (61.3%) and vitreoretinal surgeons (6.2%). Severe complications to treatment were observed in 17 eyes: Endophthalmitis...

  10. How is VR used to support training in industry? The INTUITION network of excellence working group on education and training

    NARCIS (Netherlands)

    Cobb, S.C.; Richir, S.; D'Cruz, M.; Klinger, E.; Day, A.; David, P.; Gardeux, F.; van den Broek, Egon; van der Voort, Mascha C.; Meijer, F.; Izkara, J.L.; Mavrikios, D.

    2008-01-01

    INTUITION is the European Network of Excellence on virtual reality and virtual environments applications for future workspaces. The purpose of the network is to gather expertise from partner members and determine the future research agenda for the development and use of virtual reality (VR)

  11. The CRYPTOCHROME photoreceptor gates PDF neuropeptide signaling to set circadian network hierarchy in Drosophila.

    Science.gov (United States)

    Zhang, Luoying; Lear, Bridget C; Seluzicki, Adam; Allada, Ravi

    2009-12-15

    Circadian clocks in the brain are organized as coupled oscillators that integrate seasonal cues such as light and temperature to time daily behaviors. In Drosophila, the PIGMENT DISPERSING FACTOR (PDF) neuropeptide-expressing morning (M) and non-PDF evening (E) cells are coupled cell groups important for morning and evening behavior, respectively. Depending on day length, either M cells (short days) or E cells (long days) dictate both the morning and the evening phase, a phenomenon that we term network hierarchy. To examine the role of PDF in light-dark conditions, we examined flies lacking both the PDF receptor (PDFR) and the circadian photoreceptor CRYPTOCHROME (CRY). We found that subsets of E cells exhibit molecular oscillations antiphase to those of wild-type flies, single cry mutants, or single Pdfr mutants, demonstrating a potent role for PDF in light-mediated entrainment, specifically in the absence of CRY. Moreover, we find that the evening behavioral phase is more strongly reset by PDF(+) M cells in the absence of CRY. On the basis of our findings, we propose that CRY can gate PDF signaling to determine behavioral phase and network hierarchy.

  12. State and Training Effects of Mindfulness Meditation on Brain Networks Reflect Neuronal Mechanisms of Its Antidepressant Effect.

    Science.gov (United States)

    Yang, Chuan-Chih; Barrós-Loscertales, Alfonso; Pinazo, Daniel; Ventura-Campos, Noelia; Borchardt, Viola; Bustamante, Juan-Carlos; Rodríguez-Pujadas, Aina; Fuentes-Claramonte, Paola; Balaguer, Raúl; Ávila, César; Walter, Martin

    2016-01-01

    The topic of investigating how mindfulness meditation training can have antidepressant effects via plastic changes in both resting state and meditation state brain activity is important in the rapidly emerging field of neuroplasticity. In the present study, we used a longitudinal design investigating resting state fMRI both before and after 40 days of meditation training in 13 novices. After training, we compared differences in network connectivity between rest and meditation using common resting state functional connectivity methods. Interregional methods were paired with local measures such as Regional Homogeneity. As expected, significant differences in functional connectivity both between states (rest versus meditation) and between time points (before versus after training) were observed. During meditation, the internal consistency in the precuneus and the temporoparietal junction increased, while the internal consistency of frontal brain regions decreased. A follow-up analysis of regional connectivity of the dorsal anterior cingulate cortex further revealed reduced connectivity with anterior insula during meditation. After meditation training, reduced resting state functional connectivity between the pregenual anterior cingulate and dorsal medical prefrontal cortex was observed. Most importantly, significantly reduced depression/anxiety scores were observed after training. Hence, these findings suggest that mindfulness meditation might be of therapeutic use by inducing plasticity related network changes altering the neuronal basis of affective disorders such as depression.

  13. State and Training Effects of Mindfulness Meditation on Brain Networks Reflect Neuronal Mechanisms of Its Antidepressant Effect

    Science.gov (United States)

    Yang, Chuan-Chih; Barrós-Loscertales, Alfonso; Pinazo, Daniel; Ventura-Campos, Noelia; Borchardt, Viola; Bustamante, Juan-Carlos; Rodríguez-Pujadas, Aina; Fuentes-Claramonte, Paola; Balaguer, Raúl; Ávila, César; Walter, Martin

    2016-01-01

    The topic of investigating how mindfulness meditation training can have antidepressant effects via plastic changes in both resting state and meditation state brain activity is important in the rapidly emerging field of neuroplasticity. In the present study, we used a longitudinal design investigating resting state fMRI both before and after 40 days of meditation training in 13 novices. After training, we compared differences in network connectivity between rest and meditation using common resting state functional connectivity methods. Interregional methods were paired with local measures such as Regional Homogeneity. As expected, significant differences in functional connectivity both between states (rest versus meditation) and between time points (before versus after training) were observed. During meditation, the internal consistency in the precuneus and the temporoparietal junction increased, while the internal consistency of frontal brain regions decreased. A follow-up analysis of regional connectivity of the dorsal anterior cingulate cortex further revealed reduced connectivity with anterior insula during meditation. After meditation training, reduced resting state functional connectivity between the pregenual anterior cingulate and dorsal medical prefrontal cortex was observed. Most importantly, significantly reduced depression/anxiety scores were observed after training. Hence, these findings suggest that mindfulness meditation might be of therapeutic use by inducing plasticity related network changes altering the neuronal basis of affective disorders such as depression. PMID:26998365

  14. State and Training Effects of Mindfulness Meditation on Brain Networks Reflect Neuronal Mechanisms of Its Antidepressant Effect

    Directory of Open Access Journals (Sweden)

    Chuan-Chih Yang

    2016-01-01

    Full Text Available The topic of investigating how mindfulness meditation training can have antidepressant effects via plastic changes in both resting state and meditation state brain activity is important in the rapidly emerging field of neuroplasticity. In the present study, we used a longitudinal design investigating resting state fMRI both before and after 40 days of meditation training in 13 novices. After training, we compared differences in network connectivity between rest and meditation using common resting state functional connectivity methods. Interregional methods were paired with local measures such as Regional Homogeneity. As expected, significant differences in functional connectivity both between states (rest versus meditation and between time points (before versus after training were observed. During meditation, the internal consistency in the precuneus and the temporoparietal junction increased, while the internal consistency of frontal brain regions decreased. A follow-up analysis of regional connectivity of the dorsal anterior cingulate cortex further revealed reduced connectivity with anterior insula during meditation. After meditation training, reduced resting state functional connectivity between the pregenual anterior cingulate and dorsal medical prefrontal cortex was observed. Most importantly, significantly reduced depression/anxiety scores were observed after training. Hence, these findings suggest that mindfulness meditation might be of therapeutic use by inducing plasticity related network changes altering the neuronal basis of affective disorders such as depression.

  15. Conception to set up a new groundwater monitoring network in Serbia

    Directory of Open Access Journals (Sweden)

    Stevanović Zoran

    2015-01-01

    Full Text Available The Water Framework Directive of the European Union (WFD adopted in year 2000. outlines number of water policy and management actions, where monitoring is of primary importance. Following WFD principles Serbia adopted new legislation in water sector aiming to conserve or achieve good ecological, chemical and quantitative status of water resources. Serbia, as most of the countries of former Yugoslavia mostly uses groundwater for drinking water supply (over 75%. However, the current situation in monitoring of groundwater quality and quantity is far from satisfactory. Several hundred piezometers for observation of groundwater level under auspices of the Hydrometeorological Service of Serbia are located mostly in alluviums of major rivers, while some 70 piezometers are used by the Serbian Environmental Protection Agency for controlling groundwater quality. Currently only 20% of delineated groundwater bodies are under observation. This paper evaluates current conditions and proposes to expand national monitoring network to cover most of groundwater bodies or their groups, to raise number of observation points to a density of ca. 1 object /200 km2 and to include as much as possible actual waterworks in this network. Priority in selecting sites for new observation piezometers or springs has to be given to groundwater bodies under threats, either to their water reserves or their water chemical quality. For the former, an assessment of available renewable reserves versus exploitation capacity is needed, while to estimate pressures on water quality, the best way is to compare aquifers’ vulnerability against anthropogenic (diffuse and punctual hazards. [Projekat Ministarstva nauke Republike Srbije, br. 176022

  16. A Method of Forming the Optimal Set of Disjoint Path in Computer Networks

    Directory of Open Access Journals (Sweden)

    As'ad Mahmoud As'ad ALNASER

    2017-04-01

    Full Text Available This work provides a short analysis of algorithms of multipath routing. The modified algorithm of formation of the maximum set of not crossed paths taking into account their metrics is offered. Optimization of paths is carried out due to their reconfiguration with adjacent deadlock path. Reconfigurations are realized within the subgraphs including only peaks of the main and an adjacent deadlock path. It allows to reduce the field of formation of an optimum path and time complexity of its formation.

  17. A prospective randomized study to test the transfer of basic psychomotor skills from virtual reality to physical reality in a comparable training setting.

    Science.gov (United States)

    Lehmann, Kai S; Ritz, Joerg P; Maass, Heiko; Cakmak, Hueseyin K; Kuehnapfel, Uwe G; Germer, Christoph T; Bretthauer, Georg; Buhr, Heinz J

    2005-03-01

    To test whether basic skills acquired on a virtual endoscopic surgery simulator are transferable from virtual reality to physical reality in a comparable training setting. For surgical training in laparoscopic surgery, new training methods have to be developed that allow surgeons to first practice in a simulated setting before operating on real patients. A virtual endoscopic surgery trainer (VEST) has been developed within the framework of a joint project. Because of principal limitations of simulation techniques, it is essential to know whether training with this simulator is comparable to conventional training. Devices used were the VEST system and a conventional video trainer (CVT). Two basic training tasks were constructed identically (a) as virtual tasks and (b) as mechanical models for the CVT. Test persons were divided into 2 groups each consisting of 12 novices and 4 experts. Each group carried out a defined training program over the course of 4 consecutive days on the VEST or the CVT, respectively. To test the transfer of skills, the groups switched devices on the 5th day. The main parameter was task completion time. The novices in both groups showed similar learning curves. The mean task completion times decreased significantly over the 4 training days of the study. The task completion times for the control task on Day 5 were significantly lower than on Days 1 and 2. The experts' task completion times were much lower than those of the novices. This study showed that training with a computer simulator, just as with the CVT, resulted in a reproducible training effect. The control task showed that skills learned in virtual reality are transferable to the physical reality of a CVT. The fact that the experts showed little improvement demonstrates that the simulation trains surgeons in basic laparoscopic skills learned in years of practice.

  18. Global 21 cm Signal Extraction from Foreground and Instrumental Effects. I. Pattern Recognition Framework for Separation Using Training Sets

    Science.gov (United States)

    Tauscher, Keith; Rapetti, David; Burns, Jack O.; Switzer, Eric

    2018-02-01

    The sky-averaged (global) highly redshifted 21 cm spectrum from neutral hydrogen is expected to appear in the VHF range of ∼20–200 MHz and its spectral shape and strength are determined by the heating properties of the first stars and black holes, by the nature and duration of reionization, and by the presence or absence of exotic physics. Measurements of the global signal would therefore provide us with a wealth of astrophysical and cosmological knowledge. However, the signal has not yet been detected because it must be seen through strong foregrounds weighted by a large beam, instrumental calibration errors, and ionospheric, ground, and radio-frequency-interference effects, which we collectively refer to as “systematics.” Here, we present a signal extraction method for global signal experiments which uses Singular Value Decomposition of “training sets” to produce systematics basis functions specifically suited to each observation. Instead of requiring precise absolute knowledge of the systematics, our method effectively requires precise knowledge of how the systematics can vary. After calculating eigenmodes for the signal and systematics, we perform a weighted least square fit of the corresponding coefficients and select the number of modes to include by minimizing an information criterion. We compare the performance of the signal extraction when minimizing various information criteria and find that minimizing the Deviance Information Criterion most consistently yields unbiased fits. The methods used here are built into our widely applicable, publicly available Python package, pylinex, which analytically calculates constraints on signals and systematics from given data, errors, and training sets.

  19. Construction of the minimal dominating set in the design of Wi-Fi-network

    Directory of Open Access Journals (Sweden)

    Y. V. Bugaev

    2016-01-01

    Full Text Available Currently, wireless technology is becoming the preferred, and in some cases the only possible for the information communication devices. If you need coverage of a large space with a complex configuration arises the need to efficiently host multiple Wi-Fi emitters, providing a stable connection with each possible location of the receiver signals. Suppose we have a model that allows to determine the coverage of a stable connection Wi-Fi emitter at a given spatial point. Then the problem of locating Wi-Fi emitters can be formulated as determining a discrete set of locations of transducers satisfying the condition. Otherwise, it is necessary to determine the positions of all the emitters that completely cover a given area, and the number of emitters should be minimal. In this formulation it is the task of the lowest coverage – the problem of finding the smallest set of columns of the matrix, "covering" all of her lines. To develop methods for solving a problem of the smallest covering usually resort to its matrix interpretation, which reduces to the problem of the smallest dominating set of a graph. From the point of view of the formulation of the problem of the smallest covering two cases are possible configuration of the coverage area of the surrounding space Wi-Fi emitters: 1. without variable. Its peculiarity is that the coverage area is completely defined by the location of the emitter. It is possible in the case where the coverage area of the symmetric or the orientation of the directivity diagram of the radiation is fixed. In this case it is possible to build a graph associated with the set of locations of emitters in which each point of placement are associated with adjacent vertices. In this formulation we arrive at the problem of the smallest dominating set of a graph. 2. Variable. It takes place in the case when the diagram of radiation is directed, and the coverage area may vary depending on the orientation of the emitter. That is, the