Wall Shear Stress Distribution in a Patient-Specific Cerebral Aneurysm Model using Reduced Order Modeling

NASA Astrophysics Data System (ADS)

Han, Suyue; Chang, Gary Han; Schirmer, Clemens; Modarres-Sadeghi, Yahya

2016-11-01

We construct a reduced-order model (ROM) to study the Wall Shear Stress (WSS) distributions in image-based patient-specific aneurysms models. The magnitude of WSS has been shown to be a critical factor in growth and rupture of human aneurysms. We start the process by running a training case using Computational Fluid Dynamics (CFD) simulation with time-varying flow parameters, such that these parameters cover the range of parameters of interest. The method of snapshot Proper Orthogonal Decomposition (POD) is utilized to construct the reduced-order bases using the training CFD simulation. The resulting ROM enables us to study the flow patterns and the WSS distributions over a range of system parameters computationally very efficiently with a relatively small number of modes. This enables comprehensive analysis of the model system across a range of physiological conditions without the need to re-compute the simulation for small changes in the system parameters.

[The voice of the singer in the phonetogram].

PubMed

Klingholz, F

1989-01-01

Phonetograms were subdivided into areas approximating voice registers. By means of an analytical description of the areas, parameters could be established for a differentiation of voice categories and efficiency. The evaluation of 21 untrained and 34 trained voices showed a significant difference between the two groups. Male singers demonstrated more efficiency in the head and chest registers than male non-singers; female singers showed a stronger efficiency only in the head voice in comparison with their non-singer counterparts. Proceeding from voice sound alone, voices are often misclassified regarding the voice categories, and voice problems arise. Moreover, enhanced training of only chest or head voice function results in functional disorders in the singing voice. Such cases can be demonstrated by means of phonetograms.

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

NASA Technical Reports Server (NTRS)

Korsmeyer, David (Technical Monitor); Rajkumar, T.; Bardina, Jorge

2003-01-01

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Learning and diagnosing faults using neural networks

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Kiech, Earl L.; Ali, Moonis

1990-01-01

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Impact parameter determination in experimental analysis using a neural network

NASA Astrophysics Data System (ADS)

Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J. B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

1997-03-01

A neural network is used to determine the impact parameter in 40Ca+40Ca reactions. The effect of the detection efficiency as well as the model dependence of the training procedure has been studied carefully. An overall improvement of the impact parameter determination of 25% is obtained using this technique. The analysis of Amphora 40Ca+40Ca data at 35 MeV per nucleon using a neural network shows two well-separated classes of events among the selected ``complete'' events.

Eight weeks of a combination of high intensity interval training and conventional training reduce visceral adiposity and improve physical fitness: a group-based intervention.

PubMed

Giannaki, Christoforos D; Aphamis, George; Sakkis, Panikos; Hadjicharalambous, Marios

2016-04-01

High intensity interval training (HIIT) has been recently promoted as an effective, low volume and time-efficient training method for improving fitness and health related parameters. The aim of the current study was to examine the effect of a combination of a group-based HIIT and conventional gym training on physical fitness and body composition parameters in healthy adults. Thirty nine healthy adults volunteered to participate in this eight-week intervention study. Twenty three participants performed regular gym training 4 days a week (C group), whereas the remaining 16 participants engaged twice a week in HIIT and twice in regular gym training (HIIT-C group) as the other group. Total body fat and visceral adiposity levels were calculated using bioelectrical impedance analysis. Physical fitness parameters such as cardiorespiratory fitness, speed, lower limb explosiveness, flexibility and isometric arm strength were assessed through a battery of field tests. Both exercise programs were effective in reducing total body fat and visceral adiposity (P<0.05) and improving handgrip strength, sprint time, jumping ability and flexibility (P<0.05) whilst only the combination of HIIT and conventional training improved cardiorespiratory fitness levels (P<0.05). A between of group changes analysis revealed that HIIT-C resulted in significantly greater reduction in both abdominal girth and visceral adiposity compared with conventional training (P<0.05). Eight weeks of combined group-based HIIT and conventional training improve various physical fitness parameters and reduce both total and visceral fat levels. This type of training was also found to be superior compared with conventional exercise training alone in terms of reducing more visceral adiposity levels. Group-based HIIT may consider as a good methods for individuals who exercise in gyms and craving to acquire significant fitness benefits in relatively short period of time.

An algorithm for testing the efficient market hypothesis.

PubMed

Boboc, Ioana-Andreea; Dinică, Mihai-Cristian

2013-01-01

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).

An Algorithm for Testing the Efficient Market Hypothesis

PubMed Central

Boboc, Ioana-Andreea; Dinică, Mihai-Cristian

2013-01-01

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH). PMID:24205148

An Emotional ANN (EANN) approach to modeling rainfall-runoff process

NASA Astrophysics Data System (ADS)

Nourani, Vahid

2017-01-01

This paper presents the first hydrological implementation of Emotional Artificial Neural Network (EANN), as a new generation of Artificial Intelligence-based models for daily rainfall-runoff (r-r) modeling of the watersheds. Inspired by neurophysiological form of brain, in addition to conventional weights and bias, an EANN includes simulated emotional parameters aimed at improving the network learning process. EANN trained by a modified version of back-propagation (BP) algorithm was applied to single and multi-step-ahead runoff forecasting of two watersheds with two distinct climatic conditions. Also to evaluate the ability of EANN trained by smaller training data set, three data division strategies with different number of training samples were considered for the training purpose. The overall comparison of the obtained results of the r-r modeling indicates that the EANN could outperform the conventional feed forward neural network (FFNN) model up to 13% and 34% in terms of training and verification efficiency criteria, respectively. The superiority of EANN over classic ANN is due to its ability to recognize and distinguish dry (rainless days) and wet (rainy days) situations using hormonal parameters of the artificial emotional system.

Effects of Ramadan intermittent fasting on middle-distance running performance in well-trained runners.

PubMed

Brisswalter, Jeanick; Bouhlel, Ezzedine; Falola, Jean Marie; Abbiss, Christopher R; Vallier, Jean Marc; Hausswirth, Christophe; Hauswirth, Christophe

2011-09-01

To assess whether Ramadan intermittent fasting (RIF) affects 5000-m running performance and physiological parameters classically associated with middle-distance performance. Two experimental groups (Ramadan fasting, n = 9, vs control, n = 9) participated in 2 experimental sessions, one before RIF and the other at the last week of fasting. For each session, subjects completed 4 tests in the same order: a maximal running test, a maximal voluntary contraction (MVC) of knee extensor, 2 rectangular submaximal exercises on treadmill for 6 minutes at an intensity corresponding to the first ventilatory threshold (VT1), and a running performance test (5000 m). Eighteen, well-trained, middle-distance runners. Maximal oxygen consumption, MVC, running performance, running efficiency, submaximal VO(2) kinetics parameters (VO(2), VO(2)b, time constant τ, and amplitude A1) and anthropometric parameters were recorded or calculated. At the end of Ramadan fasting, a decrease in MVC was observed (-3.2%; P < 0.00001; η, 0.80), associated with an increase in the time constant of oxygen kinetics (+51%; P < 0.00007; η, 0.72) and a decrease in performance (-5%; P < 0.0007; η, 0.51). No effect was observed on running efficiency or maximal aerobic power. These results suggest that Ramadan changes in muscular performance and oxygen kinetics could affect performance during middle-distance events and need to be considered to choose training protocols during RIF.

Efficient design of gain-flattened multi-pump Raman fiber amplifiers using least squares support vector regression

NASA Astrophysics Data System (ADS)

Chen, Jing; Qiu, Xiaojie; Yin, Cunyi; Jiang, Hao

2018-02-01

An efficient method to design the broadband gain-flattened Raman fiber amplifier with multiple pumps is proposed based on least squares support vector regression (LS-SVR). A multi-input multi-output LS-SVR model is introduced to replace the complicated solving process of the nonlinear coupled Raman amplification equation. The proposed approach contains two stages: offline training stage and online optimization stage. During the offline stage, the LS-SVR model is trained. Owing to the good generalization capability of LS-SVR, the net gain spectrum can be directly and accurately obtained when inputting any combination of the pump wavelength and power to the well-trained model. During the online stage, we incorporate the LS-SVR model into the particle swarm optimization algorithm to find the optimal pump configuration. The design results demonstrate that the proposed method greatly shortens the computation time and enhances the efficiency of the pump parameter optimization for Raman fiber amplifier design.

Sprint Interval Training Induces A Sexual Dimorphism but does not Improve Peak Bone Mass in Young and Healthy Mice

PubMed Central

Koenen, Kathrin; Knepper, Isabell; Klodt, Madlen; Osterberg, Anja; Stratos, Ioannis; Mittlmeier, Thomas; Histing, Tina; Menger, Michael D.; Vollmar, Brigitte; Bruhn, Sven; Müller-Hilke, Brigitte

2017-01-01

Elevated peak bone mass in early adulthood reduces the risk for osteoporotic fractures at old age. As sports participation has been correlated with elevated peak bone masses, we aimed to establish a training program that would efficiently stimulate bone accrual in healthy young mice. We combined voluntary treadmill running with sprint interval training modalities that were tailored to the individual performance limits and were of either high or intermediate intensity. Adolescent male and female STR/ort mice underwent 8 weeks of training before the hind legs were analyzed for cortical and trabecular bone parameters and biomechanical strength. Sprint interval training led to increased running speeds, confirming an efficient training. However, males and females responded differently. The males improved their running speeds in response to intermediate intensities only and accrued cortical bone at the expense of mechanical strength. High training intensities induced a significant loss of trabecular bone. The female bones showed neither adverse nor beneficial effects in response to either training intensities. Speculations about the failure to improve geometric alongside mechanical bone properties include the possibility that our training lacked sufficient axial loading, that high cardio-vascular strains adversely affect bone growth and that there are physiological limits to bone accrual. PMID:28303909

Experimental study of a fuel cell power train for road transport application

NASA Astrophysics Data System (ADS)

Corbo, P.; Corcione, F. E.; Migliardini, F.; Veneri, O.

The development of fuel cell electric vehicles requires the on-board integration of fuel cell systems and electric energy storage devices, with an appropriate energy management system. The optimization of performance and efficiency needs an experimental analysis of the power train, which has to be effected in both stationary and transient conditions (including standard driving cycles). In this paper experimental results concerning the performance of a fuel cell power train are reported and discussed. In particular characterization results for a small sized fuel cell system (FCS), based on a 2.5 kW PEM stack, alone and coupled to an electric propulsion chain of 3.7 kW are presented and discussed. The control unit of the FCS allowed the main stack operative parameters (stoichiometric ratio, hydrogen and air pressure, temperature) to be varied and regulated in order to obtain optimized polarization and efficiency curves. Experimental runs effected on the power train during standard driving cycles have allowed the performance and efficiency of the individual components (fuel cell stack and auxiliaries, dc-dc converter, traction batteries, electric engine) to be evaluated, evidencing the role of output current and voltage of the dc-dc converter in directing the energy flows within the propulsion system.

Latent log-linear models for handwritten digit classification.

PubMed

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

Effect of aerobic training on inter-arm coordination in highly trained swimmers.

PubMed

Schnitzler, Christophe; Seifert, Ludovic; Chollet, Didier; Toussaint, Huub

2014-02-01

The effect of three months of aerobic training on spatio-temporal and coordination parameters was examined during a swim trial at maximal aerobic speed. Nine male swimmers swam a 400-m front crawl at maximal speed twice: in trial 1, after summer break, and trial 2, after three months of aerobic training. Video analysis determined the stroke (swimming speed, stroke length, and stroke rate) and coordination (Index of Coordination and propulsive phase duration) parameters for every 50-m segment. All swimmers significantly increased their swimming speed after training. For all swimmers except one, stroke length increased and stroke rate remained constant, whereas the Index of Coordination and the propulsive phase duration decreased (p<.05). This study suggests that aerobic training developed a greater force impulse in the swimmers during the propulsive phases, which allowed them to take advantage of longer non-propulsive phases. In this case, catch-up coordination, if associated with greater stroke length, can be an efficient coordination mode that reflects optimal drag/propulsion adaptation. This finding thus provides new insight into swimmers' adaptations to the middle-distance event. Copyright © 2013 Elsevier B.V. All rights reserved.

An application of deep learning in the analysis of stellar spectra

NASA Astrophysics Data System (ADS)

Fabbro, S.; Venn, K. A.; O'Briain, T.; Bialek, S.; Kielty, C. L.; Jahandar, F.; Monty, S.

2018-04-01

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here, we apply a deep neural network architecture to analyse both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same data sets; however, StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

High-Intensity Interval Training After Stroke: An Opportunity to Promote Functional Recovery, Cardiovascular Health, and Neuroplasticity.

PubMed

Crozier, Jennifer; Roig, Marc; Eng, Janice J; MacKay-Lyons, Marilyn; Fung, Joyce; Ploughman, Michelle; Bailey, Damian M; Sweet, Shane N; Giacomantonio, Nicholas; Thiel, Alexander; Trivino, Michael; Tang, Ada

2018-04-01

Stroke is the leading cause of adult disability. Individuals poststroke possess less than half of the cardiorespiratory fitness (CRF) as their nonstroke counterparts, leading to inactivity, deconditioning, and an increased risk of cardiovascular events. Preserving cardiovascular health is critical to lower stroke risk; however, stroke rehabilitation typically provides limited opportunity for cardiovascular exercise. Optimal cardiovascular training parameters to maximize recovery in stroke survivors also remains unknown. While stroke rehabilitation recommendations suggest the use of moderate-intensity continuous exercise (MICE) to improve CRF, neither is it routinely implemented in clinical practice, nor is the intensity always sufficient to elicit a training effect. High-intensity interval training (HIIT) has emerged as a potentially effective alternative that encompasses brief high-intensity bursts of exercise interspersed with bouts of recovery, aiming to maximize cardiovascular exercise intensity in a time-efficient manner. HIIT may provide an alternative exercise intervention and invoke more pronounced benefits poststroke. To provide an updated review of HIIT poststroke through ( a) synthesizing current evidence; ( b) proposing preliminary considerations of HIIT parameters to optimize benefit; ( c) discussing potential mechanisms underlying changes in function, cardiovascular health, and neuroplasticity following HIIT; and ( d) discussing clinical implications and directions for future research. Preliminary evidence from 10 studies report HIIT-associated improvements in functional, cardiovascular, and neuroplastic outcomes poststroke; however, optimal HIIT parameters remain unknown. Larger randomized controlled trials are necessary to establish ( a) effectiveness, safety, and optimal training parameters within more heterogeneous poststroke populations; (b) potential mechanisms of HIIT-associated improvements; and ( c) adherence and psychosocial outcomes.

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration.

PubMed

Chen, Yunjin; Pock, Thomas

2017-06-01

Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework based on the concept of nonlinear reaction diffusion models for various image restoration problems. By embodying recent improvements in nonlinear diffusion models, we propose a dynamic nonlinear reaction diffusion model with time-dependent parameters (i.e., linear filters and influence functions). In contrast to previous nonlinear diffusion models, all the parameters, including the filters and the influence functions, are simultaneously learned from training data through a loss based approach. We call this approach TNRD-Trainable Nonlinear Reaction Diffusion. The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force. We demonstrate its capabilities with three representative applications, Gaussian image denoising, single image super resolution and JPEG deblocking. Experiments show that our trained nonlinear diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for the tested applications. Our trained models preserve the structural simplicity of diffusion models and take only a small number of diffusion steps, thus are highly efficient. Moreover, they are also well-suited for parallel computation on GPUs, which makes the inference procedure extremely fast.

A Study of Teacher Training in the United States and Europe

ERIC Educational Resources Information Center

Ries, Francis; Yanes Cabrera, Cristina; González Carriedo, Ricardo

2016-01-01

Governments and multilateral organizations frequently employ comparative studies, which are receiving increased attention in the contemporary process of globalization. Within the assessment of educational policies, this comparison is used to define the parameters of quality and the models of efficiency, and it allows us to see the roles that…

Query-based learning for aerospace applications.

PubMed

Saad, E W; Choi, J J; Vian, J L; Wunsch, D C Ii

2003-01-01

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.

The effects of training by virtual reality or gym ball on pelvic floor muscle strength in postmenopausal women: a randomized controlled trial.

PubMed

Martinho, Natalia M; Silva, Valéria R; Marques, Joseane; Carvalho, Leonardo C; Iunes, Denise H; Botelho, Simone

2016-03-22

To evaluate the effectiveness of abdominopelvic training by virtual reality compared to pelvic floor muscle training (PFMT) using a gym ball (a previously tested and efficient protocol) on postmenopausal women's pelvic floor muscle (PFM) strength. A randomized controlled trial was conducted with 60 postmenopausal women, randomly allocated into two groups: Abdominopelvic training by virtual reality - APT_VR (n=30) and PFMT using a gym ball - PFMT_GB (n=30). Both types of training were supervised by the same physical therapist, during 10 sessions each, for 30 minutes. The participants' PFM strength was evaluated by digital palpation and vaginal dynamometry, considering three different parameters: maximum strength, average strength and endurance. An intention-to-treat approach was used to analyze the participants according to original groups. No significant between-group differences were observed in most analyzed parameters. The outcome endurance was higher in the APT_VR group (p=0.003; effect size=0.89; mean difference=1.37; 95% CI=0.46 to 2.28). Both protocols have improved the overall PFM strength, suggesting that both are equally beneficial and can be used in clinical practice. Muscle endurance was higher in patients who trained using virtual reality.

The effects of training by virtual reality or gym ball on pelvic floor muscle strength in postmenopausal women: a randomized controlled trial

PubMed Central

Martinho, Natalia M.; Silva, Valéria R.; Marques, Joseane; Carvalho, Leonardo C.; Iunes, Denise H.; Botelho, Simone

2016-01-01

ABSTRACT Objective To evaluate the effectiveness of abdominopelvic training by virtual reality compared to pelvic floor muscle training (PFMT) using a gym ball (a previously tested and efficient protocol) on postmenopausal women’s pelvic floor muscle (PFM) strength. Method A randomized controlled trial was conducted with 60 postmenopausal women, randomly allocated into two groups: Abdominopelvic training by virtual reality – APT_VR (n=30) and PFMT using a gym ball – PFMT_GB (n=30). Both types of training were supervised by the same physical therapist, during 10 sessions each, for 30 minutes. The participants’ PFM strength was evaluated by digital palpation and vaginal dynamometry, considering three different parameters: maximum strength, average strength and endurance. An intention-to-treat approach was used to analyze the participants according to original groups. Results No significant between-group differences were observed in most analyzed parameters. The outcome endurance was higher in the APT_VR group (p=0.003; effect size=0.89; mean difference=1.37; 95% CI=0.46 to 2.28). Conclusion Both protocols have improved the overall PFM strength, suggesting that both are equally beneficial and can be used in clinical practice. Muscle endurance was higher in patients who trained using virtual reality. PMID:27437716

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

PubMed Central

Song, Fucheng; Zhang, Anling; Liang, Hui; Cui, Lianhua; Li, Wenlian; Si, Hongzong; Duan, Yunbo; Zhai, Honglin

2016-01-01

A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method. PMID:27854309

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Efficient Online Learning Algorithms Based on LSTM Neural Networks.

PubMed

Ergen, Tolga; Kozat, Suleyman Serdar

2017-09-13

We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.

Steering population transfer of the Na2 molecule by an ultrashort pulse train

NASA Astrophysics Data System (ADS)

Niu, Dong-Hua; Wang, Shuo; Zhan, Wei-Shen; Tao, Hong-Cai; Wang, Si-Qi

2018-05-01

We theoretically investigate the complete population transfer among quantum states of the Na2 molecule using ultrashort pulse trains using the time-dependent wave packet method. The population accumulation of the target state can be steered by controlling the laser parameters, such as the variable pulse pairs, the different pulse widths, the time delays and the repetition period between two contiguous pulses; in particular, the pulse pairs and the pulse widths have a great effect on the population transfer. The calculations show that the ultrashort pulse train is a feasible solution, which can steer the population transfer from the initial state to the target state efficiently with lower peak intensities.

Inventory-transportation integrated optimization for maintenance spare parts of high-speed trains

PubMed Central

Wang, Jiaxi; Wang, Huasheng; Wang, Zhongkai; Li, Jian; Lin, Ruixi; Xiao, Jie; Wu, Jianping

2017-01-01

This paper presents a 0–1 programming model aimed at obtaining the optimal inventory policy and transportation mode for maintenance spare parts of high-speed trains. To obtain the model parameters for occasionally-replaced spare parts, a demand estimation method based on the maintenance strategies of China’s high-speed railway system is proposed. In addition, we analyse the shortage time using PERT, and then calculate the unit time shortage cost from the viewpoint of train operation revenue. Finally, a real-world case study from Shanghai Depot is conducted to demonstrate our method. Computational results offer an effective and efficient decision support for inventory managers. PMID:28472097

A system-level mathematical model for evaluation of power train performance of load-leveled electric-vehicles

NASA Technical Reports Server (NTRS)

Purohit, G. P.; Leising, C. J.

1984-01-01

The power train performance of load leveled electric vehicles can be compared with that of nonload leveled systems by use of a simple mathematical model. This method of measurement involves a number of parameters including the degree of load leveling and regeneration, the flywheel mechanical to electrical energy fraction, and efficiencies of the motor, generator, flywheel, and transmission. Basic efficiency terms are defined and representative comparisons of a variety of systems are presented. Results of the study indicate that mechanical transfer of energy into and out of the flywheel is more advantageous than electrical transfer. An optimum degree of load leveling may be achieved in terms of the driving cycle, battery characteristics, mode of mechanization, and the efficiency of the components. For state of the art mechanically coupled flyheel systems, load leveling losses can be held to a reasonable 10%; electrically coupled systems can have losses that are up to six times larger. Propulsion system efficiencies for mechanically coupled flywheel systems are predicted to be approximately the 60% achieved on conventional nonload leveled systems.

A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor

2018-02-01

Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol' method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system.

Using the Nintendo Wii Fit and body weight support to improve aerobic capacity, balance, gait ability, and fear of falling: two case reports.

PubMed

Miller, Carol A; Hayes, Dawn M; Dye, Kelli; Johnson, Courtney; Meyers, Jennifer

2012-01-01

Lower limb amputation in older adults has a significant impact on balance, gait, and cardiovascular fitness, resulting in diminished community participation. The purpose of this case study was to describe the effects of a balance training program utilizing the Nintendo Wii™ Fit (Nintendo of America, Inc, Redmond, Washington) balance board and body-weight supported gait training on aerobic capacity, balance, gait, and fear of falling in two persons with transfemoral amputation. Participant A, a 62 year-old male 32 months post traumatic transfemoral amputation, reported fear of falling and restrictions in community activity. Participant B, a 58 year-old male 9 years post transfemoral amputation, reported limited energy and balance deficits during advanced gait activities. 6-weeks, 2 supervised sessions per week included 20 minutes of Nintendo™ Wii Fit Balance gaming and 20 minutes of gait training using Body Weight Support. Measures included oxygen uptake efficiency slope (OUES), economy of movement, dynamic balance (Biodex platform system), Activities-Specific Balance Confidence (ABC) Scale, and spatial-temporal parameters of gait (GAITRite). Both participants demonstrated improvement in dynamic balance, balance confidence, economy of movement, and spatial-temporal parameters of gait. Participant A reduced the need for an assistive device during community ambulation. Participant B improved his aerobic capacity, indicated by an increase in OUES. This case study illustrated that the use of Nintendo Wii™ Fit training and Body Weight Support were effective interventions to achieve functional goals for improving balance confidence, reducing use of assistive devices, and increasing energy efficiency when ambulating with a transfemoral prosthesis.

Efficient robust conditional random fields.

PubMed

Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A

2015-10-01

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.

Improving training of laparoscopic tissue manipulation skills using various visual force feedback types.

PubMed

Smit, Daan; Spruit, Edward; Dankelman, Jenny; Tuijthof, Gabrielle; Hamming, Jaap; Horeman, Tim

2017-01-01

Visual force feedback allows trainees to learn laparoscopic tissue manipulation skills. The aim of this experimental study was to find the most efficient visual force feedback method to acquire these skills. Retention and transfer validity to an untrained task were assessed. Medical students without prior experience in laparoscopy were randomized in three groups: Constant Force Feedback (CFF) (N = 17), Bandwidth Force Feedback (BFF) (N = 16) and Fade-in Force Feedback (N = 18). All participants performed a pretest, training, post-test and follow-up test. The study involved two dissimilar tissue manipulation tasks, one for training and one to assess transferability. Participants performed six trials of the training task. A force platform was used to record several force parameters. A paired-sample t test showed overall lower force parameter outcomes in the post-test compared to the pretest (p < .001). A week later, the force parameter outcomes were still significantly lower than found in the pretest (p < .005). Participants also performed the transfer task in the post-test (p < .02) and follow-up (p < .05) test with lower force parameter outcomes compared to the pretest. A one-way MANOVA indicated that in the post-test the CFF group applied 50 % less Mean Absolute Nonzero Force (p = .005) than the BFF group. All visual force feedback methods showed to be effective in decreasing tissue manipulation force as no major differences were found between groups in the post and follow-up trials. The BFF method is preferred for it respects individual progress and minimizes distraction.

On the Use of Machine Learning Techniques for the Mechanical Characterization of Soft Biological Tissues.

PubMed

Cilla, M; Pérez-Rey, I; Martínez, M A; Peña, Estefania; Martínez, Javier

2018-06-23

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the SEF and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted, thus the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues. This article is protected by copyright. All rights reserved.

Adaptive optimal training of animal behavior

NASA Astrophysics Data System (ADS)

Bak, Ji Hyun; Choi, Jung Yoon; Akrami, Athena; Witten, Ilana; Pillow, Jonathan

Neuroscience experiments often require training animals to perform tasks designed to elicit various sensory, cognitive, and motor behaviors. Training typically involves a series of gradual adjustments of stimulus conditions and rewards in order to bring about learning. However, training protocols are usually hand-designed, and often require weeks or months to achieve a desired level of task performance. Here we combine ideas from reinforcement learning and adaptive optimal experimental design to formulate methods for efficient training of animal behavior. Our work addresses two intriguing problems at once: first, it seeks to infer the learning rules underlying an animal's behavioral changes during training; second, it seeks to exploit these rules to select stimuli that will maximize the rate of learning toward a desired objective. We develop and test these methods using data collected from rats during training on a two-interval sensory discrimination task. We show that we can accurately infer the parameters of a learning algorithm that describes how the animal's internal model of the task evolves over the course of training. We also demonstrate by simulation that our method can provide a substantial speedup over standard training methods.

A Comparative Evaluation of Public Health Centers with Private Health Training Centers on Primary Healthcare Parameters in India: a Study by Data Envelopment Analysis Technique

PubMed Central

Davey, Sanjeev; Raghav, Santosh Kumar; Singh, Jai Vir; Davey, Anuradha; Singh, Nirankar

2015-01-01

Background: The evaluation of primary healthcare services provided by health training centers of a private medical college has not been studied in comparison with government health facilities in Indian context. Data envelopment analysis (DEA) is one such technique of operations research, which can be used on health facilities for identifying efficient operating practices and strategies for relatively efficient or inefficient health centers by calculating their efficiency scores. Materials and Methods: This study was carried out by DEA technique by using basic radial models (constant ratio to scale (CRS)) in linear programming via DEAOS free online Software among four decision making units (DMUs; by comparing efficiency of two private health centers of a private medical college of India with two public health centers) in district Muzaffarnagar of state Uttar Pradesh. The input and output records of all these health facilities (two from private and two from Government); for 6 months duration from 1st Jan 2014 to 1st July 2014 was taken for deciding their efficiency scores. Results: The efficiency scores of primary healthcare services in presence of doctors (100 vs 30%) and presence of health staff (100 vs 92%) were significantly better from government health facilities as compared to private health facilities (P < 0.0001). Conclusions: The evaluation of primary healthcare services delivery by DEA technique reveals that the government health facilities group were more efficient in delivery of primary healthcare services as compared to private training health facilities group, which can be further clarified in by more in-depth studies in future. PMID:26435598

[The influence of vibration training in combination with general magnetotherapy on dynamics of performance efficiency in athletes].

PubMed

Mikheev, A A; Volchkova, O A; Voronitskiĭ, N E

2010-01-01

The objective of this study was to evaluate effects of a combined treatment including vibrostimulation and magnetotherapy on the working capacity of athletes. Participants of the study were 8 male judo wrestlers. It was shown that implementation of a specialized training program comprising seances of vibration loading and general magnetotherapy 40 and 60 min in duration respectively during 3 consecutive days produced marked beneficial effect on the hormonal status of the athletes. Specifically, the three-day long treatment resulted in a significant increase of blood cortisol and testosterone levels considered to be an objective sign of improved performance parameters in athletes engaged in strength and speed sports. The optimal length of vibration training during 3 days of specialized training is estimated at 20 to 40 minutes supplemented by general magnetotherapy for 60 minutes.

Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm.

PubMed

Sarode, Ketan Dinkar; Kumar, V Ravi; Kulkarni, B D

2016-05-01

An efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. Copyright © 2016 Elsevier Inc. All rights reserved.

Numerical solution of the nonlinear Schrodinger equation by feedforward neural networks

NASA Astrophysics Data System (ADS)

Shirvany, Yazdan; Hayati, Mohsen; Moradian, Rostam

2008-12-01

We present a method to solve boundary value problems using artificial neural networks (ANN). A trial solution of the differential equation is written as a feed-forward neural network containing adjustable parameters (the weights and biases). From the differential equation and its boundary conditions we prepare the energy function which is used in the back-propagation method with momentum term to update the network parameters. We improved energy function of ANN which is derived from Schrodinger equation and the boundary conditions. With this improvement of energy function we can use unsupervised training method in the ANN for solving the equation. Unsupervised training aims to minimize a non-negative energy function. We used the ANN method to solve Schrodinger equation for few quantum systems. Eigenfunctions and energy eigenvalues are calculated. Our numerical results are in agreement with their corresponding analytical solution and show the efficiency of ANN method for solving eigenvalue problems.

Electric train energy consumption modeling

DOE PAGES

Wang, Jinghui; Rakha, Hesham A.

2017-05-01

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

Electric train energy consumption modeling

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

Wang, Jinghui; Rakha, Hesham A.

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

The effects of two different swimming training periodization on physiological parameters at various exercise intensities.

PubMed

Clemente-Suárez, Vicente Javier; Dalamitros, Athanasios; Ribeiro, João; Sousa, Ana; Fernandes, Ricardo J; Vilas-Boas, J Paulo

2017-05-01

This study analysed the effects of two different periodization strategies on physiological parameters at various exercise intensities in competitive swimmers. Seventeen athletes of both sexes were divided to two groups, the traditional periodization (TPG, n = 7) and the reverse periodization group (RPG, n = 10). Each group followed a 10-week training period based on the two different periodization strategies. Before and after training, swimming velocity (SV), energy expenditure (EE), energy cost (EC) and percentage of aerobic (%Aer) and anaerobic (%An) energy contribution to the swimming intensities corresponding to the aerobic threshold (AerT), the anaerobic threshold (AnT) and the velocity at maximal oxygen uptake (vVO 2 max) were measured. Both groups increased the %An at the AerT and AnT intensity (P ≤ .05). In contrast, at the AnT intensity, EE and EC were only increased in TPG. Complementary, %Aer, %An, EE and EC at vVO 2 max did not alter in both groups (P > .05); no changes were observed in SV in TPG and RPG at all three intensities. These results indicate that both periodization schemes confer almost analogous adaptations in specific physiological parameters in competitive swimmers. However, given the large difference in the total training volume between the two groups, it is suggested that the implementation of the reverse periodization model is an effective and time-efficient strategy to improve performance mainly for swimming events where the AnT is an important performance indicator.

Parametric study of closed wet cooling tower thermal performance

NASA Astrophysics Data System (ADS)

Qasim, S. M.; Hayder, M. J.

2017-08-01

The present study involves experimental and theoretical analysis to evaluate the thermal performance of modified Closed Wet Cooling Tower (CWCT). The experimental study includes: design, manufacture and testing prototype of a modified counter flow forced draft CWCT. The modification based on addition packing to the conventional CWCT. A series of experiments was carried out at different operational parameters. In view of energy analysis, the thermal performance parameters of the tower are: cooling range, tower approach, cooling capacity, thermal efficiency, heat and mass transfer coefficients. The theoretical study included develops Artificial Neural Network (ANN) models to predicting various thermal performance parameters of the tower. Utilizing experimental data for training and testing, the models simulated by multi-layer back propagation algorithm for varying all operational parameters stated in experimental test.

Reconstruction of sub-surface archaeological remains from magnetic data using neural computing.

NASA Astrophysics Data System (ADS)

Bescoby, D. J.; Cawley, G. C.; Chroston, P. N.

2003-04-01

The remains of a former Roman colonial settlement, once part of the classical city of Butrint in southern Albania have been the subject of a high resolution magnetic survey using a caesium-vapour magnetometer. The survey revealed the surviving remains of an extensive planned settlement and a number of outlying buildings, today buried beneath over 0.5 m of alluvial deposits. The aim of the current research is to derive a sub-surface model from the magnetic survey measurements, allowing an enhanced archaeological interpretation of the data. Neural computing techniques are used to perform the non-linear mapping between magnetic data and corresponding sub-surface model parameters. The adoption of neural computing paradigms potentially holds several advantages over other modelling techniques, allowing fast solutions for complex data, while having a high tolerance to noise. A multi-layer perceptron network with a feed-forward architecture is trained to estimate the shape and burial depth of wall foundations using a series of representative models as training data. Parameters used to forward model the training data sets are derived from a number of trial trench excavations targeted over features identified by the magnetic survey. The training of the network was optimized by first applying it to synthetic test data of known source parameters. Pre-processing of the network input data, including the use of a rotationally invariant transform, enhanced network performance and the efficiency of the training data. The approach provides good results when applied to real magnetic data, accurately predicting the depths and layout of wall foundations within the former settlement, verified by subsequent excavation. The resulting sub-surface model is derived from the averaged outputs of a ‘committee’ of five networks, trained with individualized training sets. Fuzzy logic inference has also been used to combine individual network outputs through correlation with data from a second geophysical technique, allowing the integration of data from a separate series of measurements.

Acquisition of fundamental laparoscopic skills: is a box really as good as a virtual reality trainer?

PubMed

Vitish-Sharma, P; Knowles, J; Patel, B

2011-01-01

Laparoscopic surgery requires working in a three-dimensional environment with a two-dimensional view. Skills such as depth perception, hand to eye co-ordination and bimanual manipulation are crucial to its efficacy. To compare the efficiency of training in laparoscopic skills on a VR simulator with a traditional box trainer. Twenty medical students were recruited. An initial training session on the relevant anatomy and steps of a laparoscopic cholecystectomy was given. Baseline skills were recorded using a pre-training laparoscopic cholecystectomy on the VR trainer. Parameters measured were: (1) total time taken (mins); (2) number of movements right and left instrument; (3) path length (cms) of right and left instrument was recorded. Ten students trained on a VR simulator, and ten on a box trainer, for three hours each. The box trainer group exercises were based on the Royal College of Surgeons core laparoscopic skills course, and the VR trainer exercises were based on the Simbionix LapMentor basic skills tasks. Following this both groups were reassessed by a laparoscopic cholecystectomy on the VR trainer. Both groups showed improvement in all measured parameters. A student T-test at 95% confidence interval showed no statistically significant difference between the two groups pre and post training. Both the VR and box trainer are effective in the acquisition of laparoscopic skills. Copyright © 2011 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Acute and chronic neuromuscular adaptations to local vibration training.

PubMed

Souron, Robin; Besson, Thibault; Millet, Guillaume Y; Lapole, Thomas

2017-10-01

Vibratory stimuli are thought to have the potential to promote neural and/or muscular (re)conditioning. This has been well described for whole-body vibration (WBV), which is commonly used as a training method to improve strength and/or functional abilities. Yet, this technique may present some limitations, especially in clinical settings where patients are unable to maintain an active position during the vibration exposure. Thus, a local vibration (LV) technique, which consists of applying portable vibrators directly over the tendon or muscle belly without active contribution from the participant, may present an alternative to WBV. The purpose of this narrative review is (1) to provide a comprehensive overview of the literature related to the acute and chronic neuromuscular changes associated with LV, and (2) to show that LV training may be an innovative and efficient alternative method to the 'classic' training programs, including in the context of muscle deconditioning prevention or rehabilitation. An acute LV application (one bout of 20-60 min) may be considered as a significant neuromuscular workload, as demonstrated by an impairment of force generating capacity and LV-induced neural changes. Accordingly, it has been reported that a training period of LV is efficient in improving muscular performance over a wide range of training (duration, number of session) and vibration (frequency, amplitude, site of application) parameters. The functional improvements are principally triggered by adaptations within the central nervous system. A model illustrating the current research on LV-induced adaptations is provided.

Can the efficiency of modified Alvarado scoring system in the diagnosis acute appendicitis be increased with tenesmus?

PubMed

Bulus, Hakan; Tas, Adnan; Morkavuk, Baris; Koklu, Seyfettin; Soy, Derya; Coskun, Ali

2013-01-01

Acute appendicitis is one of the main pathological conditions requiring emergency surgical intervention. The most widely accepted scoring system is modified Alvarado scoring system (MASS). In this study we aimed to improve the efficiency of MASS by adding a new parameter and to evaluate its efficiency in the diagnosis of acute appendicitis. This study included 158 patients who underwent acute appendectomy in Keçiören Training and Research Hospital General Surgery Department. In addition to criteria of MASS, all patients were questioned about the presence of tenesmus. The validity of MASS and MASS with additional parameter was evaluated with respect to sensitivity, specificity and positive and negative predictive values. Accuracy rates of MASS, clinical findings, ultrasonography and MASS with additional parameter in the diagnosis of acute appendicitis were 64, 76, 85 and 80 %. False positivity rates for clinical findings, MASS and MASS with additional parameter in the diagnosis of acute appendicitis were 17, 26 and 10 %, respectively. Sensitivity and specificity of clinical findings in the diagnosis of acute appendicitis were 83 and 66 %, respectively. Sensitivity and specificity of MASS in the diagnosis of acute appendicitis were 74 and 39 %, respectively, and those of MASS with additional parameter were appendicitis increased to 83 and 66 %, respectively. MASS is a simple, cheap and objective scoring system and does not require expertise. When tenesmus is added to standard MASS, rates of accuracy, sensitivity and specificity become better than those in MASS in the diagnosis of acute appendicitis.

Accelerating deep neural network training with inconsistent stochastic gradient descent.

PubMed

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.

Optimal Model-Based Fault Estimation and Correction for Particle Accelerators and Industrial Plants Using Combined Support Vector Machines and First Principles Models

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

Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX

2010-08-25

Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for buildingmore » parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM & FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the parameters of the beam lifetime model) are physically meaningful. (3) Numerical Efficiency of the Training - We investigated the numerical efficiency of the SVM training. More specifically, for the primal formulation of the training, we have developed a problem formulation that avoids the linear increase in the number of the constraints as a function of the number of data points. (4) Flexibility of Software Architecture - The software framework for the training of the support vector machines was designed to enable experimentation with different solvers. We experimented with two commonly used nonlinear solvers for our simulations. The primary application of interest for this project has been the sustained optimal operation of particle accelerators at the Stanford Linear Accelerator Center (SLAC). Particle storage rings are used for a variety of applications ranging from 'colliding beam' systems for high-energy physics research to highly collimated x-ray generators for synchrotron radiation science. Linear accelerators are also used for collider research such as International Linear Collider (ILC), as well as for free electron lasers, such as the Linear Coherent Light Source (LCLS) at SLAC. One common theme in the operation of storage rings and linear accelerators is the need to precisely control the particle beams over long periods of time with minimum beam loss and stable, yet challenging, beam parameters. We strongly believe that beyond applications in particle accelerators, the high fidelity and cost benefits of a combined model-based fault estimation/correction system will attract customers from a wide variety of commercial and scientific industries. Even though the acquisition of Pavilion Technologies, Inc. by Rockwell Automation Inc. in 2007 has altered the small business status of the Pavilion and it no longer qualifies for a Phase II funding, our findings in the course of the Phase I research have convinced us that further research will render a workable model-based fault estimation and correction for particle accelerators and industrial plants feasible.« less

The Changeable Block Distance System Analysis

NASA Astrophysics Data System (ADS)

Lewiński, Andrzej; Toruń, Andrzej

The paper treats about efficiency analysis in Changeable Block Distance (CBD) System connected with wireless positioning and control of train. The analysis is based on modeling of typical ERTMS line and comparison with actual and future traffic. The calculations are related to assumed parameters of railway traffic corresponding to real time - table of distance Psary - Góra Włodowska from CMK line equipped in classic, ETCS Level 1 and ETCS with CBD systems.

Effect of Aerobic Exercise Training on Ventilatory Efficiency and Respiratory Drive in Obese Subjects.

PubMed

Chlif, Mehdi; Chaouachi, Anis; Ahmaidi, Said

2017-07-01

Obese patients show a decline in exercise capacity and diverse degrees of dyspnea in association with mechanical abnormalities, increased ventilatory requirements secondary to the increased metabolic load, and a greater work of breathing. Consequently, obese patients may be particularly predisposed to the development of respiratory muscle fatigue during exercise. The aim of this study was to assess inspiratory muscle performance during incremental exercise in 19 obese male subjects (body mass index 41 ± 6 kg/m 2 ) after aerobic exercise training using the noninvasive, inspiratory muscle tension-time index (T T0.1 ). Measurements performed included anthropometric parameters, lung function assessed by spirometry, rate of perceived breathlessness with the modified Borg dyspnea scale (0-10), breathing pattern, maximal exercise capacity, and inspiratory muscle performance with a breath-by-breath automated exercise metabolic system during an incremental exercise test. T T0.1 was calculated using the equation, T T0.1 = P 0.1 /P Imax × T I /T tot (where P 0.1 represents mouth occlusion pressure, P Imax is maximal inspiratory pressure, and T I /T tot is the duty cycle). At rest, there was no statistically significant difference for spirometric parameters and cardiorespiratory parameters between pre- and post-training. At maximal exercise, the minute ventilation, the rate of exchange ratio, the rate of perceived breathlessness, and the respiratory muscle performance parameters were not significantly different pre- and post-training; in contrast, tidal volume ( P = .037, effect size = 1.51), breathing frequency ( P = .049, effect size = 0.97), power output ( P = .048, effect size = 0.79), peak oxygen uptake ( P = .02, effect size = 0.92) were significantly higher after training. At comparable work load, training induces lower minute ventilation, mouth occlusion pressure, ratio of occlusion pressure to maximal inspiratory pressure, T T0.1 , and rate of perceived breathlessness. Aerobic exercise at ventilatory threshold can induce significant improvement in respiratory muscle strength, maximal exercise capacity, and inspiratory muscle performance and decreased dyspnea perception in obese subjects. Copyright © 2017 by Daedalus Enterprises.

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

NASA Astrophysics Data System (ADS)

Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

2014-03-01

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

Deep neural nets as a method for quantitative structure-activity relationships.

PubMed

Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

2015-02-23

Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

Improving labeling efficiency in automatic quality control of MRSI data.

PubMed

Pedrosa de Barros, Nuno; McKinley, Richard; Wiest, Roland; Slotboom, Johannes

2017-12-01

To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data. 28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling. The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance. Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Modeling the shape and composition of the human body using dual energy X-ray absorptiometry images

PubMed Central

Shepherd, John A.; Fan, Bo; Schwartz, Ann V.; Cawthon, Peggy; Cummings, Steven R.; Kritchevsky, Stephen; Nevitt, Michael; Santanasto, Adam; Cootes, Timothy F.

2017-01-01

There is growing evidence that body shape and regional body composition are strong indicators of metabolic health. The purpose of this study was to develop statistical models that accurately describe holistic body shape, thickness, and leanness. We hypothesized that there are unique body shape features that are predictive of mortality beyond standard clinical measures. We developed algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans into body thickness and leanness images. We performed statistical appearance modeling (SAM) and principal component analysis (PCA) to efficiently encode the variance of body shape, leanness, and thickness across sample of 400 older Americans from the Health ABC study. The sample included 200 cases and 200 controls based on 6-year mortality status, matched on sex, race and BMI. The final model contained 52 points outlining the torso, upper arms, thighs, and bony landmarks. Correlation analyses were performed on the PCA parameters to identify body shape features that vary across groups and with metabolic risk. Stepwise logistic regression was performed to identify sex and race, and predict mortality risk as a function of body shape parameters. These parameters are novel body composition features that uniquely identify body phenotypes of different groups and predict mortality risk. Three parameters from a SAM of body leanness and thickness accurately identified sex (training AUC = 0.99) and six accurately identified race (training AUC = 0.91) in the sample dataset. Three parameters from a SAM of only body thickness predicted mortality (training AUC = 0.66, validation AUC = 0.62). Further study is warranted to identify specific shape/composition features that predict other health outcomes. PMID:28423041

Safety, efficiency and learning curves in robotic surgery: a human factors analysis.

PubMed

Catchpole, Ken; Perkins, Colby; Bresee, Catherine; Solnik, M Jonathon; Sherman, Benjamin; Fritch, John; Gross, Bruno; Jagannathan, Samantha; Hakami-Majd, Niv; Avenido, Raymund; Anger, Jennifer T

2016-09-01

Expense, efficiency of use, learning curves, workflow integration and an increased prevalence of serious incidents can all be barriers to adoption. We explored an observational approach and initial diagnostics to enhance total system performance in robotic surgery. Eighty-nine robotic surgical cases were observed in multiple operating rooms using two different surgical robots (the S and Si), across several specialties (Urology, Gynecology, and Cardiac Surgery). The main measures were operative duration and rate of flow disruptions-described as 'deviations from the natural progression of an operation thereby potentially compromising safety or efficiency.' Contextual parameters collected were surgeon experience level and training, type of surgery, the model of robot and patient factors. Observations were conducted across four operative phases (operating room pre-incision; robot docking; main surgical intervention; post-console). A mean of 9.62 flow disruptions per hour (95 % CI 8.78-10.46) were predominantly caused by coordination, communication, equipment and training problems. Operative duration and flow disruption rate varied with surgeon experience (p = 0.039; p < 0.001, respectively), training cases (p = 0.012; p = 0.007) and surgical type (both p < 0.001). Flow disruption rates in some phases were also sensitive to the robot model and patient characteristics. Flow disruption rate is sensitive to system context and generates improvement diagnostics. Complex surgical robotic equipment increases opportunities for technological failures, increases communication requirements for the whole team, and can reduce the ability to maintain vision in the operative field. These data suggest specific opportunities to reduce the training costs and the learning curve.

Predicting CYP2C19 Catalytic Parameters for Enantioselective Oxidations Using Artificial Neural Networks and a Chirality Code

PubMed Central

Hartman, Jessica H.; Cothren, Steven D.; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A.; Miller, Grover P.

2013-01-01

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224

Efficient photoassociation of ultracold cesium atoms with picosecond pulse laser

NASA Astrophysics Data System (ADS)

Hai, Yang; Hu, Xue-Jin; Li, Jing-Lun; Cong, Shu-Lin

2017-08-01

We investigate theoretically the formation of ultracold Cs2 molecules via photoassociation (PA) with three kinds of pulses (the Gaussian pulse, the asymmetric shaped laser pulse SL1 with a large rising time and a small falling time and the asymmetric shaped laser pulse SL2 with a small rising time and a large falling time). For the three kinds of pulses, the final population on vibrational levels from v‧ = 120 to 175 of the excited state displays a regular oscillation change with pulse width and interaction strength, and a high PA efficiency can be achieved with optimised parameters. The PA efficiency in the excited state steered by the SL1-pulse (SL2-pulse) train with optimised parameters which is composed of four SL1 (SL2) pulses is 1.74 times as much as that by the single SL1 (SL2) pulse due to the population accumulation effect. Moreover, a dump laser is employed to transfer the excited molecules from the excited state to the vibrational level v″ = 12 of the ground state to obtain stable molecules.

Objective evaluation of minimally invasive surgical skills for transplantation. Surgeons using a virtual reality simulator.

PubMed

Dănilă, R; Gerdes, B; Ulrike, H; Domínguez Fernández, E; Hassan, I

2009-01-01

The learning curve in laparoscopic surgery may be associated with higher patient risk, which is unacceptable in the setting of kidney donation. Virtual reality simulators may increase the safety and efficiency of training in laparoscopic surgery. The aim of this study was to investigate if the results of a training session reflect the actual skill level of transplantation surgeons and whether the simulator could differentiate laparoscopic experienced transplantation surgeon from advanced trainees. 16 subjects were assigned to one of two groups: 5 experienced transplantation surgeon and 11 advanced residents, with only assistant role during transplantation. The level of performance was measured by a relative scoring system that combines single parameters assessed by the computer. The higher the level of transplantation experience of a participant, the higher the laparoscopic performance. Experienced transplantation surgeons showed statistically significant better scores than the advanced group for time and precision parameters. Our results show that performance of the various tasks on the simulator corresponds to the respective level of experience in transplantation surgery in our research groups. This study confirms construct validity for the LapSim. It thus measures relevant skills and can be integrated in an endoscopic training and assessment curriculum for transplantations surgeons.

Rootstock and fruit canopy position affect peach [Prunus persica (L.) Batsch] (cv. Rich May) plant productivity and fruit sensorial and nutritional quality.

PubMed

Gullo, Gregorio; Motisi, Antonio; Zappia, Rocco; Dattola, Agostino; Diamanti, Jacopo; Mezzetti, Bruno

2014-06-15

The right combination of rootstock and training system is important for increased yield and fruit sensorial and nutritional homogeneity and quality with peach [Prunus persica (L.) Batsch]. We investigated the effects of rootstock and training system on these parameters, testing the effect of vigorous GF677 and weaker Penta rootstock on 'Rich May' peach cultivar. Fruit position effects regarding photosynthetically active radiation availability, along the canopy profile using the Y training system, were investigated. The positive relationships between total polyphenols content and antioxidant capacity according to canopy vigour and architecture were determined for the two scion/stock combinations. Changes in fruit epicarp colour and content of bioactive compounds were also determined. Lower-vigour trees from Penta rootstock grafting yielded larger fruit with improved skin overcolour, and greater total polyphenols content and antioxidant capacity. GF677 rootstock produced more vigorous trees with fruit with lower sensorial and nutritional parameters. Canopy position strongly affects fruit sensorial and nutritional qualities. These data define potential for improvements to peach production efficiency and fruit quality, particularly for southern Europe peach cultivation conditions. Copyright © 2013 Elsevier Ltd. All rights reserved.

A study of the talent training project management for semiconductor industry in Taiwan: the application of a hybrid data envelopment analysis approach.

PubMed

Kao, Ling-Jing; Chiu, Shu-Yu; Ko, Hsien-Tang

2014-01-01

The purpose of this study is to evaluate the training institution performance and to improve the management of the Manpower Training Project (MTP) administered by the Semiconductor Institute in Taiwan. Much literature assesses the efficiency of an internal training program initiated by a firm, but only little literature studies the efficiency of an external training program led by government. In the study, a hybrid solution of ICA-DEA and ICA-MPI is developed for measuring the efficiency and the productivity growth of each training institution over the period. The technical efficiency change, the technological change, pure technical efficiency change, scale efficiency change, and the total factor productivity change were evaluated according to five inputs and two outputs. According to the results of the study, the training institutions can be classified by their efficiency successfully and the guidelines for the optimal level of input resources can be obtained for each inefficient training institution. The Semiconductor Institute in Taiwan can allocate budget more appropriately and establish withdrawal mechanisms for inefficient training institutions.

A Study of the Talent Training Project Management for Semiconductor Industry in Taiwan: The Application of a Hybrid Data Envelopment Analysis Approach

PubMed Central

Kao, Ling-Jing; Chiu, Shu-Yu; Ko, Hsien-Tang

2014-01-01

The purpose of this study is to evaluate the training institution performance and to improve the management of the Manpower Training Project (MTP) administered by the Semiconductor Institute in Taiwan. Much literature assesses the efficiency of an internal training program initiated by a firm, but only little literature studies the efficiency of an external training program led by government. In the study, a hybrid solution of ICA-DEA and ICA-MPI is developed for measuring the efficiency and the productivity growth of each training institution over the period. The technical efficiency change, the technological change, pure technical efficiency change, scale efficiency change, and the total factor productivity change were evaluated according to five inputs and two outputs. According to the results of the study, the training institutions can be classified by their efficiency successfully and the guidelines for the optimal level of input resources can be obtained for each inefficient training institution. The Semiconductor Institute in Taiwan can allocate budget more appropriately and establish withdrawal mechanisms for inefficient training institutions. PMID:24977192

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

NASA Astrophysics Data System (ADS)

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/.

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

PubMed Central

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. PMID:27493624

Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations.

PubMed

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.

Wheelchair racing efficiency.

PubMed

Cooper, R A; Boninger, M L; Cooper, R; Robertson, R N; Baldini, F D

For individuals with disabilities exercise, such as wheelchair racing, can be an important modality for community reintegration, as well as health promotion. The purpose of this study was to examine selected parameters during racing wheelchair propulsion among a sample of elite wheelchair racers. It was hypothesized that blood lactate accumulation and wheeling economy (i.e. oxygen consumed per minute) would increase with speed and that gross mechanical efficiency would reach an optimum for each athlete. Twelve elite wheelchair racers with paraplegia participated in this study. Nine of the subjects were males and three were females. Each subject used his or her personal wheelchair during the experiments. A computer monitored wheelchair dynamometer was used during all testing. The method used was essentially a discontinuous economy protocol. Mixed model analysis of variance (ANOVA) was used to compare blood lactate concentration, economy (minute oxygen consumption), and gross mechanical efficiency across the stages. The results of this study show that both economy and blood lactate concentration increase linearly with speed if resistance is held constant. The subjects in this study had gross mechanical efficiencies (gme) of about 18%, with the range going from 15.222.7%. The results indicate that at the higher speeds of propulsion, for example near race speeds, analysis of respiratory gases may not give a complete energy profile. While there is a good understanding of training methods to improve cardiovascular fitness for wheelchair racers, little is known about improving efficiency (e.g. technique, equipment), therefore methods need to be developed to determine efficiency while training or in race situations.

Scaling and Systems Considerations in Pulsed Inductive Thrusters

NASA Technical Reports Server (NTRS)

Polzin, Kurt A.

2007-01-01

Performance scaling in pulsed inductive thrusters is discussed in the context of previous experimental studies and modeling results. Two processes, propellant ionization and acceleration, are interconnected where overall thruster performance and operation are concerned, but they are separated here to gain physical insight into each process and arrive at quantitative criteria that should be met to address or mitigate inherent inductive thruster difficulties. The effects of preionization in lowering the discharge energy requirements relative to a case where no preionization is employed, and in influencing the location of the initial current sheet, are described. The relevant performance scaling parameters for the acceleration stage are reviewed, emphasizing their physical importance and the numerical values required for efficient acceleration. The scaling parameters are then related to the design of the pulsed power train providing current to the acceleration stage. The impact of various choices in pulsed power train and circuit topology selection are reviewed, paying special attention to how these choices mitigate or exacerbate switching, lifetime, and power consumption issues.

Sentence alignment using feed forward neural network.

PubMed

Fattah, Mohamed Abdel; Ren, Fuji; Kuroiwa, Shingo

2006-12-01

Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on feed forward neural network classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuate score, and cognate score values. A set of manually prepared training data has been assigned to train the feed forward neural network. Another set of data was used for testing. Using this new approach, we could achieve an error reduction of 60% over length based approach when applied on English-Arabic parallel documents. Moreover this new approach is valid for any language pair and it is quite flexible approach since the feature parameter vector may contain more/less or different features than that we used in our system such as lexical match feature.

High-Intensity Interval Training in Heart Transplant Recipients: A Systematic Review with Meta-Analysis.

PubMed

Perrier-Melo, Raphael José; Figueira, Fernando Augusto Marinho Dos Santos; Guimarães, Guilherme Veiga; Costa, Manoel da Cunha

2018-02-01

Heart transplantation (HTx) is considered an efficient and gold-standard procedure for patients with end-stage heart failure. After surgery, patients have lower aerobic power (VO2max) and compensatory hemodynamic responses. The aim of the present study was to assess through a systematic review with meta-analysis whether high-intensity interval training (HIIT) can provide benefits for those parameters. This is a systematic review with meta-analysis, which searched the databases and data portals PubMed, Web of Science, Scopus, Science Direct and Wiley until December 2016 (pairs). The following terms and descriptors were used: "heart recipient" OR "heart transplant recipient" OR "heart transplant" OR "cardiac transplant" OR "heart graft". Descriptors via DeCS and Mesh were: "heart transplantation'' OR "cardiac transplantation". The words used in combination (AND) were: "exercise training" OR "interval training" OR "high intensity interval training" OR "high intensity training" OR "anaerobic training" OR "intermittent training" OR "sprint training". The initial search identified 1064 studies. Then, only those studies assessing the influence of HIIT on the post-HTx period were added, resulting in three studies analyzed. The significance level adopted was 0.05. Heart transplant recipients showed significant improvement in VO2peak, heart rate and peak blood pressure in 8 to 12 weeks of intervention.

Robustness analysis of bogie suspension components Pareto optimised values

NASA Astrophysics Data System (ADS)

Mousavi Bideleh, Seyed Milad

2017-08-01

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.

Uncertainty Quantification and Regional Sensitivity Analysis of Snow-related Parameters in the Canadian LAnd Surface Scheme (CLASS)

NASA Astrophysics Data System (ADS)

Badawy, B.; Fletcher, C. G.

2017-12-01

The parameterization of snow processes in land surface models is an important source of uncertainty in climate simulations. Quantifying the importance of snow-related parameters, and their uncertainties, may therefore lead to better understanding and quantification of uncertainty within integrated earth system models. However, quantifying the uncertainty arising from parameterized snow processes is challenging due to the high-dimensional parameter space, poor observational constraints, and parameter interaction. In this study, we investigate the sensitivity of the land simulation to uncertainty in snow microphysical parameters in the Canadian LAnd Surface Scheme (CLASS) using an uncertainty quantification (UQ) approach. A set of training cases (n=400) from CLASS is used to sample each parameter across its full range of empirical uncertainty, as determined from available observations and expert elicitation. A statistical learning model using support vector regression (SVR) is then constructed from the training data (CLASS output variables) to efficiently emulate the dynamical CLASS simulations over a much larger (n=220) set of cases. This approach is used to constrain the plausible range for each parameter using a skill score, and to identify the parameters with largest influence on the land simulation in CLASS at global and regional scales, using a random forest (RF) permutation importance algorithm. Preliminary sensitivity tests indicate that snow albedo refreshment threshold and the limiting snow depth, below which bare patches begin to appear, have the highest impact on snow output variables. The results also show a considerable reduction of the plausible ranges of the parameters values and hence reducing their uncertainty ranges, which can lead to a significant reduction of the model uncertainty. The implementation and results of this study will be presented and discussed in details.

The application of neural networks to myoelectric signal analysis: a preliminary study.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1990-03-01

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

Manifold learning of brain MRIs by deep learning.

PubMed

Brosch, Tom; Tam, Roger

2013-01-01

Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with pplications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are (1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 x 128 x 128 practical, and (2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

PubMed Central

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Reference values of left heart echocardiographic dimensions and mass in male peri-pubertal athletes.

PubMed

Cavarretta, Elena; Maffessanti, Francesco; Sperandii, Fabio; Guerra, Emanuele; Quaranta, Federico; Nigro, Antonia; Minati, Monia; Rebecchi, Marco; Fossati, Chiara; Calò, Leonardo; Pigozzi, Fabio

2018-01-01

Background Several articles have proposed reference values in healthy paediatric subjects, but none of them has evaluated a large population of healthy trained adolescents. Design The study purpose was to establish normal echocardiographic measurements of left heart (aortic root, left atrium and left ventricular dimensions and mass) in relation to age, weight, height, body mass index, body surface area and training hours in this specific population. Methods We retrospectively evaluated 2151 consecutive, healthy, peri-pubertal athletes (100% male, mean age 12.4 ± 1.4 years, range 8-18) referred to a single centre for pre-participation screening. All participants were young soccer athletes who trained for a mean of 7.2 ± 1.1 h per week. Results Left ventricular internal diameters, wall thickness, left ventricular mass, aortic root and left atrium diameters were significantly correlated to age, body surface area, height and weight ( p < 0.01). Age, height, weight and body surface area were found associated with chamber size, while body mass index and training hours were not. Inclusion of both age and body size parameters in the statistical models resulted in improved overall explained variance for diameters and left ventricular mass. Conclusion Equations, mean values and percentile charts for the different age groups may be useful as reference data in efficiently assessing left ventricular parameters in young athletes.

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

PubMed

León Blanco, José M; González-R, Pedro L; Arroyo García, Carmen Martina; Cózar-Bernal, María José; Calle Suárez, Marcos; Canca Ortiz, David; Rabasco Álvarez, Antonio María; González Rodríguez, María Luisa

2018-01-01

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

Preliminary experience of a PDCA-cycle and quality management based training curriculum for rat liver transplantation.

PubMed

Jin, Hao; Huang, Hai; Dong, Wei; Sun, Jian; Liu, Anding; Deng, Meihong; Dirsch, Olaf; Dahmen, Uta

2012-08-01

As repeatedly operating rat liver transplantation (LTx) until animals survive is inefficient in respect to time and use of living animals, we developed a new training concept. METHODS AND CONCEPTS: Training was divided into four phases: pretraining-phase, basic-microsurgical-training phase, advanced-microsurgical-training phases, and expert-microsurgical-training phase. Two "productivity-phases" were introduced right after the basic- and advanced-microsurgical-training phases, respectively, to allow the trainee to accumulate experience and to be scientifically productive before proceeding to a more complex procedure. PDCA cycles and quality criteria were employed to control the learning-process and the surgical quality. Predefined quality criteria included survival rate, intraoperative, postoperative, and histologic parameters. Three trainees participated in the LTx training and achieved their first survival record within 4-10 operations. All of them completely mastered the LTx in fewer procedures (31, 60 and 26 procedures) as reported elsewhere, and the more complex arterialized or partial LTx were mastered by trainee A and B in additional 9 and 13 procedures, respectively. Fast progress was possible due to a high number of training in the 2 Productivity-phases. The stepwise and PDCA-based training program increased the efficiency of LTx training, whereas the constant application and development of predefined quality criteria guaranteed the quality of microsurgery. Copyright © 2012 Elsevier Inc. All rights reserved.

High-intensity interval training or continuous training, combined or not with fasting, in obese or overweight women with cardiometabolic risk factors: study protocol for a randomised clinical trial

PubMed Central

Van Bavel, Diogo; de Moraes, Roger; Tibiriça, Eduardo V

2018-01-01

Introduction Physical inactivity and increased caloric intake play important roles in the pathophysiology of obesity. Increasing physical activity and modifying eating behaviours are first-line interventions, frequently hampered by lack of time to exercise and difficulties in coping with different diets. High-intensity interval training (HIIT) may be a time-efficient method compared with moderate-intensity continuous training (CT). Conversely, diets with a fasting component may be more effective than other complex and restrictive diets, as it essentially limits caloric intake to a specified period without major diet composition changes. Therefore, the combination of HIIT and fasting may provide incremental benefits in terms of effectiveness and time efficiency in obese and sedentary populations. The aim of this study is to determine the effect of HIIT versus CT, combined or not with fasting, on microcirculatory function, cardiometabolic parameters, anthropometric indices, cardiorespiratory fitness and quality of life in a population of sedentary overweight or obese women with cardiometabolic risk factors. Methods and analysis Sedentary women aged 30–50 years, with a body mass index ≥25 kg/m2 and cardiometabolic risk factors, will be randomised to HIIT performed in the fasting state, HIIT performed in the fed state, CT in the fasting state or CT in the fed state. Cardiometabolic parameters, anthropometric indices, cardiorespiratory fitness, quality of life and microvascular function (cutaneous capillary density and microvascular reactivity evaluated by laser speckle contrast imaging) will be evaluated before initiation of the interventions and 16 weeks thereafter. Ethics and dissemination The trial complies with the Declaration of Helsinki and has been approved by the local ethics committee (Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil). All patients provide written informed consent before enrolment and randomisation. The study’s results will be disseminated to the healthcare community by publications and presentations at scientific meetings. Trial registration number NCT03236285. PMID:29705753

Learning from Past Classification Errors: Exploring Methods for Improving the Performance of a Deep Learning-based Building Extraction Model through Quantitative Analysis of Commission Errors for Optimal Sample Selection

NASA Astrophysics Data System (ADS)

Swan, B.; Laverdiere, M.; Yang, L.

2017-12-01

In the past five years, deep Convolutional Neural Networks (CNN) have been increasingly favored for computer vision applications due to their high accuracy and ability to generalize well in very complex problems; however, details of how they function and in turn how they may be optimized are still imperfectly understood. In particular, their complex and highly nonlinear network architecture, including many hidden layers and self-learned parameters, as well as their mathematical implications, presents open questions about how to effectively select training data. Without knowledge of the exact ways the model processes and transforms its inputs, intuition alone may fail as a guide to selecting highly relevant training samples. Working in the context of improving a CNN-based building extraction model used for the LandScan USA gridded population dataset, we have approached this problem by developing a semi-supervised, highly-scalable approach to select training samples from a dataset of identified commission errors. Due to the large scope this project, tens of thousands of potential samples could be derived from identified commission errors. To efficiently trim those samples down to a manageable and effective set for creating additional training sample, we statistically summarized the spectral characteristics of areas with rates of commission errors at the image tile level and grouped these tiles using affinity propagation. Highly representative members of each commission error cluster were then used to select sites for training sample creation. The model will be incrementally re-trained with the new training data to allow for an assessment of how the addition of different types of samples affects the model performance, such as precision and recall rates. By using quantitative analysis and data clustering techniques to select highly relevant training samples, we hope to improve model performance in a manner that is resource efficient, both in terms of training process and in sample creation.

Prognostic value of the post-training oxygen uptake efficiency slope in patients with coronary artery disease.

PubMed

Buys, Roselien; Coeckelberghs, Ellen; Cornelissen, Véronique A; Goetschalckx, Kaatje; Vanhees, Luc

2016-09-01

Peak oxygen uptake is an independent predictor of mortality in patients with coronary artery disease (CAD). However, patients with CAD are not always capable of reaching peak effort, and therefore submaximal gas exchange variables such as the oxygen uptake efficiency slope (OUES) have been introduced. Baseline exercise capacity as expressed by OUES provides prognostic information and this parameter responds to training. Therefore, we aimed to assess the prognostic value of post-training OUES in patients with CAD. We included 960 patients with CAD (age 60.6 ± 9.5 years; 853 males) who completed a cardiac rehabilitation program between 2000 and 2011. The OUES was calculated before and after cardiac rehabilitation and information on mortality was obtained. The relationships of post-training OUES with all-cause and cardiovascular (CV) mortality was assessed by Cox proportional hazards regression analyses. Receiver operator characteristic curve analysis was performed in order to obtain the optimal cut-off value. During 7.37 ± 3.20 years of follow-up (range: 0.45-13.75 years), 108 patients died, among whom 47 died due to CV reasons. The post-training OUES was related to all-cause (hazard ratio: 0.50, p < 0.001) and CV (hazard ratio: 0.40, p < 0.001) mortality. When significant covariates, including baseline OUES, were entered into the Cox regression analysis, post-training OUES remained related to all-cause and CV mortality (hazard ratio: 0.40, p < 0.01 and 0.26, p < 0.01, respectively). In addition, the change in OUES due to exercise training was positively related to mortality (hazard ratio: 0.49, p < 0.01). Post-training OUES has stronger prognostic value compared to baseline OUES. The lack of improvement in exercise capacity expressed by OUES after an exercise training program relates to a worse prognosis and can help distinguish patients with favorable and unfavorable prognoses. © The European Society of Cardiology 2016.

Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

NASA Astrophysics Data System (ADS)

Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

2018-06-01

The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.

Exercise training programs to improve hand rim wheelchair propulsion capacity: a systematic review.

PubMed

Zwinkels, Maremka; Verschuren, Olaf; Janssen, Thomas Wj; Ketelaar, Marjolijn; Takken, Tim

2014-09-01

An adequate wheelchair propulsion capacity is required to perform daily life activities. Exercise training may be effective to gain or improve wheelchair propulsion capacity. This review investigates whether different types of exercise training programs are effective in improving wheelchair propulsion capacity. PubMed and EMBASE databases were searched from their respective inceptions in October 2013. Exercise training studies with at least one outcome measure regarding wheelchair propulsion capacity were included. In this study wheelchair propulsion capacity includes four parameters to reflect functional wheelchair propulsion: cardio-respiratory fitness (aerobic capacity), anaerobic capacity, muscular fitness and mechanical efficiency. Articles were not selected on diagnosis, training type or mode. Studies were divided into four training types: interval, endurance, strength, and mixed training. Methodological quality was rated with the PEDro scale, and the level of evidence was determined. The 21 included studies represented 249 individuals with spinal-cord injury (50%), various diagnoses like spina bifida (4%), cerebral palsy (2%), traumatic injury, (3%) and able-bodied participants (38%). All interval training studies found a significant improvement of 18-64% in wheelchair propulsion capacity. Three out of five endurance training studies reported significant effectiveness. Methodological quality was generally poor and there were only two randomised controlled trials. Exercise training programs seem to be effective in improving wheelchair propulsion capacity. However, there is remarkably little research, particularly for individuals who do not have spinal-cord injury. © The Author(s) 2014.

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

PubMed Central

Cheng, Ningtao; Wu, Leihong; Cheng, Yiyu

2013-01-01

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability. PMID:23861920

Effects of a simple prototype respiratory muscle trainer on respiratory muscle strength, quality of life and dyspnea, and oxidative stress in COPD patients: a preliminary study.

PubMed

Leelarungrayub, Jirakrit; Pinkaew, Decha; Puntumetakul, Rungthip; Klaphajone, Jakkrit

2017-01-01

The aim of this study was to evaluate the efficiency of a simple prototype device for training respiratory muscles in lung function, respiratory muscle strength, walking capacity, quality of life (QOL), dyspnea, and oxidative stress in patients with COPD. Thirty COPD patients with moderate severity of the disease were randomized into three groups: control (n=10, 6 males and 4 females), standard training (n=10, 4 males and 6 females), and prototype device (n=10, 5 males and 5 females). Respiratory muscle strength (maximal inspiratory pressure [PImax] and maximal expiratory pressure [PEmax]), lung function (forced vital capacity [FVC], percentage of FVC, forced expiratory volume in 1 second [FEV 1 ], percentage of FEV 1 [FEV 1 %], and FEV 1 /FVC), 6-minute walking distance (6MWD), QOL, and oxidative stress markers (total antioxidant capacity [TAC]), glutathione (GSH), malondialdehyde (MDA), and nitric oxide (NO) were evaluated before and after 6 weeks of training. Moreover, dyspnea scores were assessed before; during week 2, 4, and 6 of training; and at rest after training. All parameters between the groups had no statistical difference before training, and no statistical change in the control group after week 6. FVC, FEV 1 /FVC, PImax, PEmax, QOL, MDA, and NO showed significant changes after 6 weeks of training with either the standard or prototype device, compared to pre-training. FEV 1 , FEV 1 %, 6MWD, TAC, and GSH data did not change statistically. Furthermore, the results of significant changes in all parameters were not statistically different between training groups using the standard and prototype device. The peak dyspnea scores increased significantly in week 4 and 6 when applying the standard or prototype device, and then lowered significantly at rest after 6 weeks of training, compared to pre-training. This study proposes that a simple prototype device can be used clinically in COPD patients as a standard device to train respiratory muscles, improving lung function and QOL, as well as involving MDA and NO levels.

Effectiveness and efficiency of different weight machine-based strength training programmes for patients with hip or knee osteoarthritis: a protocol for a quasi-experimental controlled study in the context of health services research.

PubMed

Krauss, Inga; Müller, Gerhard; Steinhilber, Benjamin; Haupt, Georg; Janssen, Pia; Martus, Peter

2017-01-01

Osteoarthritis is a chronic musculoskeletal disease with a major impact on the individual and the healthcare system. As there is no cure, therapy aims for symptom release and reduction of disease progression. Physical exercises have been defined as a core treatment for osteoarthritis. However, research questions related to dose response, sustainability of effects, economic efficiency and safety are still open and will be evaluated in this trial, investigating a progressive weight machine-based strength training. This is a quasi-experimental controlled trial in the context of health services research. The intervention group (n=300) is recruited from participants of an offer for insurants of a health insurance company suffering from hip or knee osteoarthritis. Potential participants of the control group are selected and written to from the insurance database according to predefined matching criteria. The final statistical twins from the control responders will be determined via propensity score matching (n=300). The training intervention comprises 24 supervised mandatory sessions (2/week) and another 12 facultative sessions (1/week). Exercises include resistance training for the lower extremity and core muscles by use of weight machines and small training devices. The training offer is available at two sites. They differ with respect to the weight machines in use resulting in different dosage parameters. Primary outcomes are self-reported pain and function immediately after the 12-week intervention period. Health-related quality of life, self-efficacy, cost utility and safety will be evaluated as secondary outcomes. Secondary analysis will be undertaken with two strata related to study site. Participants will be followed up 6, 12 and 24 months after baseline. German Clinical Trial Register DRKS00009257. Pre-results.

Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering

NASA Astrophysics Data System (ADS)

Liu, Di; Mishra, Ashok K.; Yu, Zhongbo

2016-07-01

This paper examines the combination of support vector machines (SVM) and the dual ensemble Kalman filter (EnKF) technique to estimate root zone soil moisture at different soil layers up to 100 cm depth. Multiple experiments are conducted in a data rich environment to construct and validate the SVM model and to explore the effectiveness and robustness of the EnKF technique. It was observed that the performance of SVM relies more on the initial length of training set than other factors (e.g., cost function, regularization parameter, and kernel parameters). The dual EnKF technique proved to be efficient to improve SVM with observed data either at each time step or at a flexible time steps. The EnKF technique can reach its maximum efficiency when the updating ensemble size approaches a certain threshold. It was observed that the SVM model performance for the multi-layer soil moisture estimation can be influenced by the rainfall magnitude (e.g., dry and wet spells).

Effects of a short-term personalized Intermittent Work Exercise Program (IWEP) on maximal cardio-respiratory function and endurance parameters among healthy young and older seniors.

PubMed

Vogel, T; Leprêtre, P-M; Brechat, P-H; Lonsdorfer, E; Benetos, A; Kaltenbach, G; Lonsdorfer, J

2011-12-01

The aim of this study was to evaluate the efficiency of a short-term Intermittent Work Exercise Program (IWEP) among healthy elderly subjects. This longitudinal prospective study took place at the Strasbourg University Hospital geriatric department. One hundred and fifty older volunteers, previously determined as being free from cardiac and pulmonary disease, were separated into two age groups: the "young senior" (60.2 ± 3.1 yr) and the "older senior" groups (70.8 ± 5.2 yr). These groups were then subdivided by gender into the "young female senior", "young male senior" "older female senior" and "older male senior" groups. Before and after the IWEP, all subjects were asked to perform an incremental cycle exercise to obtain their first ventilatory threshold (VT1), maximal tolerated power (MTP), peak oxygen uptake (VO2peak) and maximal minute ventilation (MMV). The IWEP consisted of a 30-min cycling exercise which took place twice a week, and was divided into six 5-min stages consisting of 4 min at VT1 intensity and 1 min at 90% MTP. An assessment was made of the effects of the IWEP on maximal cardio-respiratory function (MTP, VO2peak, MMV) and endurance parameters (VT1, heart rate [HR] measured at pretraining VT1 and lactate concentrations at pre-training MTP). This short-term training program resulted in a significant increase of MTP (from 13.2% to 20.6%), VO2peak (from 8.9% to 16.6%) and MMV (from 11.1% to 21.8%) in all groups (p<0.05). VT1 improved from 21% at pretraining to 27%, while HR at pre-training VT1 as well as lactate concentrations at pre-training MTP decreased significantly in all groups (p<0.05). The post-training values for VO2peak and MMV of the "older seniors" were not significantly different (p>0.05) from the "young seniors" pre-training values for the same parameters. The most striking finding in this study is that after only 9 weeks, our short-term "individually-tailored" IWEP significantly improved both maximal cardio-respiratory function and endurance parameters in healthy, previously untrained seniors.

Different modalities of exercise to reduce visceral fat mass and cardiovascular risk in metabolic syndrome: the RESOLVE randomized trial.

PubMed

Dutheil, Frédéric; Lac, Gérard; Lesourd, Bruno; Chapier, Robert; Walther, Guillaume; Vinet, Agnès; Sapin, Vincent; Verney, Julien; Ouchchane, Lemlih; Duclos, Martine; Obert, Philippe; Courteix, Daniel

2013-10-09

Opinions differ over the exercise modalities that best limit cardiovascular risk (CVR) resulting from visceral obesity in individuals with metabolic syndrome (MetS). As little is known about the combined effects of resistance and endurance training at high volumes under sound nutritional conditions, we aimed to analyze the impact of various intensities of physical activity on visceral fat and CVR in individuals with MetS. 100 participants, aged 50-70 years, underwent a diet restriction (protein intake 1.2g/kg/day) with a high exercise volume (15-20 h/week). They were randomized to three training groups: moderate-resistance-moderate-endurance (re), high-resistance-moderate-endurance (Re), or moderate-resistance-high-endurance (rE). A one-year at-home follow-up (M12) commenced with a three-week residential program (Day 0 to Day 21). We measured the change in visceral fat and body composition by DXA, MetS parameters, fitness, the Framingham score and carotid-intima-media-thickness. 78 participants completed the program. At D21, visceral fat loss was highest in Re (-18%, p<.0001) and higher in rE than re (-12% vs. -7%, p<.0001). Similarly, from M3, visceral fat decreased more in high-intensity-groups to reach a visceral fat loss of -21.5% (Re) and -21.1% (rE)>-13.0% (re) at M12 (p<.001). CVR, MetS parameters and fitness improved in all groups. Visceral fat loss correlated with changes in MetS parameters. Increased intensity in high volume training is efficient in improving visceral fat loss and carotid-intima-media-thickness, and is realistic in community dwelling, moderately obese individuals. High-intensity-resistance training induced a faster visceral fat loss, and thus the potential of resistance training should not be undervalued (ClinicalTrials.gov number: NCT00917917). Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Fast and Accurate Poisson Denoising With Trainable Nonlinear Diffusion.

PubMed

Feng, Wensen; Qiao, Peng; Chen, Yunjin; Wensen Feng; Peng Qiao; Yunjin Chen; Feng, Wensen; Chen, Yunjin; Qiao, Peng

2018-06-01

The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision, and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this paper we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly developed trainable nonlinear reaction diffusion (TNRD) model which has proven an extremely fast image restoration approach with performance surpassing recent state-of-the-arts. However, the straightforward direct gradient descent employed in the original TNRD-based denoising task is not applicable in this paper. To solve this problem, we resort to the proximal gradient descent method. We retrain the model parameters, including the linear filters and influence functions by taking into account the Poisson noise statistics, and end up with a well-trained nonlinear diffusion model specialized for Poisson denoising. The trained model provides strongly competitive results against state-of-the-art approaches, meanwhile bearing the properties of simple structure and high efficiency. Furthermore, our proposed model comes along with an additional advantage, that the diffusion process is well-suited for parallel computation on graphics processing units (GPUs). For images of size , our GPU implementation takes less than 0.1 s to produce state-of-the-art Poisson denoising performance.

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

NASA Technical Reports Server (NTRS)

Momoh, James A.; Wang, Yanchun; Dolce, James L.

1997-01-01

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

Effect of aerobic training and resistance training on circulating irisin level and their association with change of body composition in overweight/obese adults: a pilot study.

PubMed

Kim, H-J; Lee, H-J; So, B; Son, J S; Yoon, D; Song, W

2016-06-20

The novel myokine irisin has been reported as a therapeutic target for metabolic disease. The objective of this study is to reveal the effects of aerobic training (AT) and resistance training (RT) on circulating irisin levels and their associations with change of body composition in overweight/obese adults. Twenty eight overweight/obese adults (BMI>23 kg/m(2)) were included in this study and compared before and after 8 weeks of exercise program (60 min/day, 5 times in a week). The subjects, in both aerobic and resistance training, showed significant improvement in anthropometric parameters and exercise capacities including maximal oxygen uptake and muscle strength. Interestingly, the circulating irisin was significantly increased in resistance training group (p=0.002) but not in aerobic training (p=0.426) compared to control group. In addition, we found the positive correlation between change of the circulating irisin and muscle mass (r=0.432, p=0.022) and the negative correlation between change of the circulating irisin and fat mass (r=-0.407, p=0.031). In the present pilot study, we found that circulating irisin level was increased by 8 weeks of resistance training in overweight/obese adults, suggesting that resistance training could be the efficient exercise type in overweight/obese considering positive change of body composition concomitant with increase of irisin levels.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-06-12

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Modeling of transport phenomena in tokamak plasmas with neural networks

DOE PAGES

Meneghini, Orso; Luna, Christopher J.; Smith, Sterling P.; ...

2014-06-23

A new transport model that uses neural networks (NNs) to yield electron and ion heat ux pro les has been developed. Given a set of local dimensionless plasma parameters similar to the ones that the highest delity models use, the NN model is able to efficiently and accurately predict the ion and electron heat transport pro les. As a benchmark, a NN was built, trained, and tested on data from the 2012 and 2013 DIII-D experimental campaigns. It is found that NN can capture the experimental behavior over the majority of the plasma radius and across a broad range ofmore » plasma regimes. Although each radial location is calculated independently from the others, the heat ux pro les are smooth, suggesting that the solution found by the NN is a smooth function of the local input parameters. This result supports the evidence of a well-de ned, non-stochastic relationship between the input parameters and the experimentally measured transport uxes. Finally, the numerical efficiency of this method, requiring only a few CPU-μs per data point, makes it ideal for scenario development simulations and real-time plasma control.« less

Does a single gait training session performed either overground or on a treadmill induce specific short-term effects on gait parameters in patients with hemiparesis? A randomized controlled study.

PubMed

Bonnyaud, Céline; Pradon, Didier; Zory, Raphael; Bensmail, Djamel; Vuillerme, Nicolas; Roche, Nicolas

2013-01-01

Gait training for patients with hemiparesis is carried out independently overground or on a treadmill. Several studies have shown differences in hemiparetic gait parameters during overground versus treadmill walking. However, few studies have compared the effects of these 2 gait training conditions on gait parameters, and no study has compared the short-term effects of these techniques on all biomechanical gait parameters. To determine whether a gait training session performed overground or on a treadmill induces specific short-term effects on biomechanical gait parameters in patients with hemiparesis. Twenty-six subjects with hemiparesis were randomly assigned to a single session of either overground or treadmill gait training. The short-term effects on spatiotemporal, kinematic, and kinetic gait parameters were assessed using gait analysis before and immediately after the training and after a 20-minute rest. Speed, cadence, percentage of single support phase, peak knee extension, peak propulsion, and braking on the paretic side were significantly increased after the gait training session. However, there were no specific changes dependent on the type of gait training performed (overground or on a treadmill). A gait training session performed by subjects with hemiparesis overground or on a treadmill did not induce specific short-term effects on biomechanical gait parameters. The increase in gait velocity that followed a gait training session seemed to reflect specific modifications of the paretic lower limb and adaptation of the nonparetic lower limb.

Innovation Analysis Approach to Design Parameters of High Speed Train Carriage and Their Intrinsic Complexity Relationships

NASA Astrophysics Data System (ADS)

Xiao, Shou-Ne; Wang, Ming-Meng; Hu, Guang-Zhong; Yang, Guang-Wu

2017-09-01

In view of the problem that it's difficult to accurately grasp the influence range and transmission path of the vehicle top design requirements on the underlying design parameters. Applying directed-weighted complex network to product parameter model is an important method that can clarify the relationships between product parameters and establish the top-down design of a product. The relationships of the product parameters of each node are calculated via a simple path searching algorithm, and the main design parameters are extracted by analysis and comparison. A uniform definition of the index formula for out-in degree can be provided based on the analysis of out-in-degree width and depth and control strength of train carriage body parameters. Vehicle gauge, axle load, crosswind and other parameters with higher values of the out-degree index are the most important boundary conditions; the most considerable performance indices are the parameters that have higher values of the out-in-degree index including torsional stiffness, maximum testing speed, service life of the vehicle, and so on; the main design parameters contain train carriage body weight, train weight per extended metre, train height and other parameters with higher values of the in-degree index. The network not only provides theoretical guidance for exploring the relationship of design parameters, but also further enriches the application of forward design method to high-speed trains.

Criterion-based laparoscopic training reduces total training time.

PubMed

Brinkman, Willem M; Buzink, Sonja N; Alevizos, Leonidas; de Hingh, Ignace H J T; Jakimowicz, Jack J

2012-04-01

The benefits of criterion-based laparoscopic training over time-oriented training are unclear. The purpose of this study is to compare these types of training based on training outcome and time efficiency. During four training sessions within 1 week (one session per day) 34 medical interns (no laparoscopic experience) practiced on two basic tasks on the Simbionix LAP Mentor virtual-reality (VR) simulator: 'clipping and grasping' and 'cutting'. Group C (criterion-based) (N = 17) trained to reach predefined criteria and stopped training in each session when these criteria were met, with a maximum training time of 1 h. Group T (time-based) (N = 17) trained for a fixed time of 1 h each session. Retention of skills was assessed 1 week after training. In addition, transferability of skills was established using the Haptica ProMIS augmented-reality simulator. Both groups improved their performance significantly over the course of the training sessions (Wilcoxon signed ranks, P < 0.05). Both groups showed skill transferability and skill retention. When comparing the performance parameters of group C and group T, their performances in the first, the last and the retention training sessions did not differ significantly (Mann-Whitney U test, P > 0.05). The average number of repetitions needed to meet the criteria also did not differ between the groups. Overall, group C spent less time training on the simulator than did group T (74:48 and 120:10 min, respectively; P < 0.001). Group C performed significantly fewer repetitions of each task, overall and in session 2, 3 and 4. Criterion-based training of basic laparoscopic skills can reduce the overall training time with no impact on training outcome, transferability or retention of skills. Criterion-based should be the training of choice in laparoscopic skills curricula.

Optimizing microstimulation using a reinforcement learning framework.

PubMed

Brockmeier, Austin J; Choi, John S; Distasio, Marcello M; Francis, Joseph T; Príncipe, José C

2011-01-01

The ability to provide sensory feedback is desired to enhance the functionality of neuroprosthetics. Somatosensory feedback provides closed-loop control to the motor system, which is lacking in feedforward neuroprosthetics. In the case of existing somatosensory function, a template of the natural response can be used as a template of desired response elicited by electrical microstimulation. In the case of no initial training data, microstimulation parameters that produce responses close to the template must be selected in an online manner. We propose using reinforcement learning as a framework to balance the exploration of the parameter space and the continued selection of promising parameters for further stimulation. This approach avoids an explicit model of the neural response from stimulation. We explore a preliminary architecture--treating the task as a k-armed bandit--using offline data recorded for natural touch and thalamic microstimulation, and we examine the methods efficiency in exploring the parameter space while concentrating on promising parameter forms. The best matching stimulation parameters, from k = 68 different forms, are selected by the reinforcement learning algorithm consistently after 334 realizations.

Locating Groundwater Pollution Source using Breakthrough Curve Characteristics and Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Kumar, J.; Jain, A.; Srivastava, R.

2005-12-01

The identification of pollution sources in aquifers is an important area of research not only for the hydrologists but also for the local and Federal agencies and defense organizations. Once the data in terms of pollutant concentration measurements at observation wells become known, it is important to identify the polluting industry in order to implement punitive or remedial measures. Traditionally, hydrologists have relied on the conceptual methods for the identification of groundwater pollution sources. The problem of identification of groundwater pollution sources using the conceptual methods requires a thorough understanding of the groundwater flow and contaminant transport processes and inverse modeling procedures that are highly complex and difficult to implement. Recently, the soft computing techniques, such as artificial neural networks (ANNs) and genetic algorithms, have provided an attractive and easy to implement alternative to solve complex problems efficiently. Some researchers have used ANNs for the identification of pollution sources in aquifers. A major problem with most previous studies using ANNs has been the large size of the neural networks that are needed to model the inverse problem. The breakthrough curves at an observation well may consist of hundreds of concentration measurements, and presenting all of them to the input layer of an ANN not only results in humongous networks but also requires large amount of training and testing data sets to develop the ANN models. This paper presents the results of a study aimed at using certain characteristics of the breakthrough curves and ANNs for determining the distance of the pollution source from a given observation well. Two different neural network models are developed that differ in the manner of characterizing the breakthrough curves. The first ANN model uses five parameters, similar to the synthetic unit hydrograph parameters, to characterize the breakthrough curves. The five parameters employed are peak concentration, time to peak concentration, the widths of the breakthrough curves at 50% and 75% of the peak concentration, and the time base of the breakthrough curve. The second ANN model employs only the first four parameters leaving out the time base. The measurement of breakthrough curve at an observation well involves very high costs in sample collection at suitable time intervals and analysis for various contaminants. The receding portions of the breakthrough curves are normally very long and excluding the time base from modeling would result in considerable cost savings. The feed-forward multi-layer perceptron (MLP) type neural networks trained using the back-propagation algorithm, are employed in this study. The ANN models for the two approaches were developed using simulated data generated for conservative pollutant transport through a homogeneous aquifer. A new approach for ANN training using back-propagation is employed that considers two different error statistics to prevent over-training and under-training of the ANNs. The preliminary results indicate that the ANNs are able to identify the location of the pollution source very efficiently from both the methods of the breakthrough curves characterization.

Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

NASA Astrophysics Data System (ADS)

Muhammad, Khan; Shah, Akram

2017-12-01

Backbreak, defined as excessive breakage behind the last row of blastholes in blasting operations at a quarry, causes destabilisation of rock slopes, improper fragmentation, minimises drilling efficiency. In this paper an artificial neural network (ANN) is applied to predict backbreak, using 12 input parameters representing various controllable factors, such as the characteristics of explosives and geometrical blast design, at the Dewan Cement limestone quarry in Hattar, Pakistan. This ANN was trained with several model architectures. The 12-2-1 ANN model was selected as the simplest model yielding the best result, with a reported correlation coefficient of 0.98 and 0.97 in the training and validation phases, respectively. Sensitivity analysis of the model suggested that backbreak can be reduced most effectively by reducing powder factor, blasthole inclination, and burden. Field tests were subsequently carried out in which these sensitive parameters were varied accordingly; as a result, backbreak was controlled and reduced from 8 m to less than a metre. The resulting reduction in powder factor (kg of explosives used per m3 of blasted material) also reduced blasting costs.

Influence of Prolonged Spaceflight on Heart Rate and Oxygen Uptake Kinetics

NASA Astrophysics Data System (ADS)

Hoffmann, U.; Moore, A.; Drescher, U.

2013-02-01

During prolonged spaceflight, physical training is used to minimize cardiovascular deconditioning. Measurement of the kinetics of cardiorespiratory parameters, in particular the kinetic analysis of heart rate, respiratory and muscular oxygen uptake, provides useful information with regard to the efficiency and regulation of the cardiorespiratory system. Practically, oxygen uptake kinetics can only be measured at the lung site (V’O2 resp). The dynamics of V’O2 resp, however, is not identical with the dynamics at the site of interest: skeletal muscle. Eight Astronauts were tested pre- and post-flight using pseudo random binary workload changes between 30 and 80 W. Their kinetic responses of heart rate, respiratory as well as muscular V’O2 kinetics were estimated by using time-series analysis. Statistical analysis revealed that the kinetic responses of respiratory as well as muscular V’O2 kinetics are slowed post-flight than pre-flight. Heart rate seems not to be influenced following flight. The influence of other factors (e. g. astronauts’ exercise training) may impact these parameters and is an area for future studies.

Effects of a 12-Week Exercise Training Program on Physical Function in Institutionalized Frail Elderly

PubMed Central

Ferreira, Cristiane Batisti; Teixeira, Pâmela dos Santos; Alves dos Santos, Geiane; Dantas Maya, Athila Teles; Americano do Brasil, Paula; Souza, Vinícius Carolino; Córdova, Cláudio; Lima, Ricardo Moreno; Nóbrega, Otávio de Toledo

2018-01-01

With the increase in life expectancy, the Brazilian elderly population has risen considerably. However, longevity is usually accompanied by problems such as the loss of functional capacity, cognitive decline, frailty syndrome, and deterioration in anthropometric parameters, particularly among those living in long-term care facilities. This randomized controlled trial aimed to verify the effects of exercise training on biochemical, inflammatory, and anthropometric indices and functional performance in institutionalized frail elderly. The sample consisted of 37 elderly people of both genders, aged 76.1 ± 7.7 years, who were randomly allocated into 2 groups: 13 individuals in the exercise group (EG) and 24 in the control group (CG). Anthropometrics, clinical history, functional tests, and biochemical evaluation were measured before and after the completion of a physical exercise program, which lasted for 12 weeks. The 12-week exercise program for frail elderly residents in a long-term care facility was efficient in improving muscle strength, speed, agility, and biochemical variables, with reversal of the frailty condition in a considerable number. However, no effects in anthropometric and inflammatory parameters were noted. PMID:29593907

An efficient approach to the analysis of rail surface irregularities accounting for dynamic train-track interaction and inelastic deformations

NASA Astrophysics Data System (ADS)

Andersson, Robin; Torstensson, Peter T.; Kabo, Elena; Larsson, Fredrik

2015-11-01

A two-dimensional computational model for assessment of rolling contact fatigue induced by discrete rail surface irregularities, especially in the context of so-called squats, is presented. Dynamic excitation in a wide frequency range is considered in computationally efficient time-domain simulations of high-frequency dynamic vehicle-track interaction accounting for transient non-Hertzian wheel-rail contact. Results from dynamic simulations are mapped onto a finite element model to resolve the cyclic, elastoplastic stress response in the rail. Ratcheting under multiple wheel passages is quantified. In addition, low cycle fatigue impact is quantified using the Jiang-Sehitoglu fatigue parameter. The functionality of the model is demonstrated by numerical examples.

System and Method for Outlier Detection via Estimating Clusters

NASA Technical Reports Server (NTRS)

Iverson, David J. (Inventor)

2016-01-01

An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.

Online boosting for vehicle detection.

PubMed

Chang, Wen-Chung; Cho, Chih-Wei

2010-06-01

This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and up-to-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.

Uncertainty Quantification and Sensitivity Analysis in the CICE v5.1 Sea Ice Model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, J. R.; Urban, N. M.

2015-12-01

Changes in the high latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with mid latitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. In this work we characterize parametric uncertainty in Los Alamos Sea Ice model (CICE) and quantify the sensitivity of sea ice area, extent and volume with respect to uncertainty in about 40 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one-at-a-time, this study uses a global variance-based approach in which Sobol sequences are used to efficiently sample the full 40-dimensional parameter space. This approach requires a very large number of model evaluations, which are expensive to run. A more computationally efficient approach is implemented by training and cross-validating a surrogate (emulator) of the sea ice model with model output from 400 model runs. The emulator is used to make predictions of sea ice extent, area, and volume at several model configurations, which are then used to compute the Sobol sensitivity indices of the 40 parameters. A ranking based on the sensitivity indices indicates that model output is most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The main effects and interactions among the most influential parameters are also estimated by a non-parametric regression technique based on generalized additive models. It is recommended research to be prioritized towards more accurately determining these most influential parameters values by observational studies or by improving existing parameterizations in the sea ice model.

High-intensity interval training or continuous training, combined or not with fasting, in obese or overweight women with cardiometabolic risk factors: study protocol for a randomised clinical trial.

PubMed

De Lorenzo, Andrea; Van Bavel, Diogo; de Moraes, Roger; Tibiriça, Eduardo V

2018-04-28

Physical inactivity and increased caloric intake play important roles in the pathophysiology of obesity. Increasing physical activity and modifying eating behaviours are first-line interventions, frequently hampered by lack of time to exercise and difficulties in coping with different diets. High-intensity interval training (HIIT) may be a time-efficient method compared with moderate-intensity continuous training (CT). Conversely, diets with a fasting component may be more effective than other complex and restrictive diets, as it essentially limits caloric intake to a specified period without major diet composition changes. Therefore, the combination of HIIT and fasting may provide incremental benefits in terms of effectiveness and time efficiency in obese and sedentary populations. The aim of this study is to determine the effect of HIIT versus CT, combined or not with fasting, on microcirculatory function, cardiometabolic parameters, anthropometric indices, cardiorespiratory fitness and quality of life in a population of sedentary overweight or obese women with cardiometabolic risk factors. Sedentary women aged 30-50 years, with a body mass index ≥25 kg/m 2 and cardiometabolic risk factors, will be randomised to HIIT performed in the fasting state, HIIT performed in the fed state, CT in the fasting state or CT in the fed state. Cardiometabolic parameters, anthropometric indices, cardiorespiratory fitness, quality of life and microvascular function (cutaneous capillary density and microvascular reactivity evaluated by laser speckle contrast imaging) will be evaluated before initiation of the interventions and 16 weeks thereafter. The trial complies with the Declaration of Helsinki and has been approved by the local ethics committee (Instituto Nacional de Cardiologia, Rio de Janeiro, Brazil). All patients provide written informed consent before enrolment and randomisation. The study's results will be disseminated to the healthcare community by publications and presentations at scientific meetings. NCT03236285. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Voice Range Profiles of Singing Students: The Effects of Training Duration and Institution.

PubMed

Lycke, Hugo; Siupsinskiene, Nora

2016-01-01

The aim of the study was to assess differences in voice parameters measured by the physiological voice range profile (VRP) in groups of vocally healthy subjects differentiated by the duration of vocal training and the training institution. Six basic frequency- and intensity-related VRP parameters and the frequency dip of the register transition zone were determined from VRP recordings of 162 females studying in individual singing lessons (1st-5th level) in Dutch, Belgian, English, and French public or private training facilities. Sixty-seven nonsinging female students served as controls. Singing students in more advanced singing classes demonstrated a significantly greater frequency range, particularly at high frequencies, than did first-year students. Students with private training showed a significantly increased mean intensity range in comparison to those in group classes, while students with musical theater training exhibited significantly increased frequency- and intensity-related VRP parameters in comparison to the students with classical training. When compared to nonsingers, all singing student subgroups showed significant increases in all basic VRP parameters. However, the register transition parameter was not influenced by training duration or institution. Our study suggests that the extension of physiological vocal limits might depend on training duration and institution. © 2016 S. Karger AG, Basel.

Data Mining for Efficient and Accurate Large Scale Retrieval of Geophysical Parameters

NASA Astrophysics Data System (ADS)

Obradovic, Z.; Vucetic, S.; Peng, K.; Han, B.

2004-12-01

Our effort is devoted to developing data mining technology for improving efficiency and accuracy of the geophysical parameter retrievals by learning a mapping from observation attributes to the corresponding parameters within the framework of classification and regression. We will describe a method for efficient learning of neural network-based classification and regression models from high-volume data streams. The proposed procedure automatically learns a series of neural networks of different complexities on smaller data stream chunks and then properly combines them into an ensemble predictor through averaging. Based on the idea of progressive sampling the proposed approach starts with a very simple network trained on a very small chunk and then gradually increases the model complexity and the chunk size until the learning performance no longer improves. Our empirical study on aerosol retrievals from data obtained with the MISR instrument mounted at Terra satellite suggests that the proposed method is successful in learning complex concepts from large data streams with near-optimal computational effort. We will also report on a method that complements deterministic retrievals by constructing accurate predictive algorithms and applying them on appropriately selected subsets of observed data. The method is based on developing more accurate predictors aimed to catch global and local properties synthesized in a region. The procedure starts by learning the global properties of data sampled over the entire space, and continues by constructing specialized models on selected localized regions. The global and local models are integrated through an automated procedure that determines the optimal trade-off between the two components with the objective of minimizing the overall mean square errors over a specific region. Our experimental results on MISR data showed that the combined model can increase the retrieval accuracy significantly. The preliminary results on various large heterogeneous spatial-temporal datasets provide evidence that the benefits of the proposed methodology for efficient and accurate learning exist beyond the area of retrieval of geophysical parameters.

Observation of a high-quality quasi-periodic rapidly propagating wave train using SDO/AIA

NASA Astrophysics Data System (ADS)

Nisticò, G.; Pascoe, D. J.; Nakariakov, V. M.

2014-09-01

Context. We present a new event of quasi-periodic wave trains observed in EUV wavebands that rapidly propagate away from an active region after a flare. Aims: We measured the parameters of a wave train observed on 7 December 2013 after an M1.2 flare, such as the phase speeds, periods and wavelengths, in relationship to the local coronal environment and the energy sources. Methods: We compared our observations with a numerical simulation of fast magnetoacoustic waves that undergo dispersive evolution and leakage in a coronal loop embedded in a potential magnetic field. Results: The wave train is observed to propagate as several arc-shaped intensity disturbances for almost half an hour, with a speed greater than 1000 km s-1 and a period of about 1 min. The wave train followed two different patterns of propagation, in accordance with the magnetic structure of the active region. The oscillatory signal is found to be of high-quality, i.e. there is a large number (10 or more) of subsequent wave fronts observed. The observations are found to be consistent with the numerical simulation of a fast wave train generated by a localised impulsive energy release. Conclusions: Transverse structuring in the corona can efficiently create and guide high-quality quasi-periodic propagating fast wave trains. The movies are available in electronic form at http://www.aanda.org

Examining the Effect of Instructor Experience on Flight Training Time

ERIC Educational Resources Information Center

Polstra, Philip A., Sr.

2012-01-01

Maximizing training efficiency is desirable in many areas of business. The ever increasing costs of flight training combined with a predicted shortage of pilots have resulted in steps being taken to improve flight training efficiency. In the past, the majority of airline pilots received their flight training in the military. Over time a growing…

Nonparametric method for failures detection and localization in the actuating subsystem of aircraft control system

NASA Astrophysics Data System (ADS)

Karpenko, S. S.; Zybin, E. Yu; Kosyanchuk, V. V.

2018-02-01

In this paper we design a nonparametric method for failures detection and localization in the aircraft control system that uses the measurements of the control signals and the aircraft states only. It doesn’t require a priori information of the aircraft model parameters, training or statistical calculations, and is based on algebraic solvability conditions for the aircraft model identification problem. This makes it possible to significantly increase the efficiency of detection and localization problem solution by completely eliminating errors, associated with aircraft model uncertainties.

Factors influencing aircraft ground handling performance

NASA Technical Reports Server (NTRS)

Yager, T. J.

1983-01-01

Problems associated with aircraft ground handling operations on wet runways are discussed and major factors which influence tire/runway braking and cornering traction capability are identified including runway characteristics, tire hydroplaning, brake system anomalies, and pilot inputs. Research results from tests with instrumented ground vehicles and aircraft, and aircraft wet runway accident investigation are summarized to indicate the effects of different aircraft, tire, and runway parameters. Several promising means are described for improving tire/runway water drainage capability, brake system efficiency, and pilot training to help optimize aircraft traction performance on wet runways.

The dependence of neuronal encoding efficiency on Hebbian plasticity and homeostatic regulation of neurotransmitter release

PubMed Central

Faghihi, Faramarz; Moustafa, Ahmed A.

2015-01-01

Synapses act as information filters by different molecular mechanisms including retrograde messenger that affect neuronal spiking activity. One of the well-known effects of retrograde messenger in presynaptic neurons is a change of the probability of neurotransmitter release. Hebbian learning describe a strengthening of a synapse between a presynaptic input onto a postsynaptic neuron when both pre- and postsynaptic neurons are coactive. In this work, a theory of homeostatic regulation of neurotransmitter release by retrograde messenger and Hebbian plasticity in neuronal encoding is presented. Encoding efficiency was measured for different synaptic conditions. In order to gain high encoding efficiency, the spiking pattern of a neuron should be dependent on the intensity of the input and show low levels of noise. In this work, we represent spiking trains as zeros and ones (corresponding to non-spike or spike in a time bin, respectively) as words with length equal to three. Then the frequency of each word (here eight words) is measured using spiking trains. These frequencies are used to measure neuronal efficiency in different conditions and for different parameter values. Results show that neurons that have synapses acting as band-pass filters show the highest efficiency to encode their input when both Hebbian mechanism and homeostatic regulation of neurotransmitter release exist in synapses. Specifically, the integration of homeostatic regulation of feedback inhibition with Hebbian mechanism and homeostatic regulation of neurotransmitter release in the synapses leads to even higher efficiency when high stimulus intensity is presented to the neurons. However, neurons with synapses acting as high-pass filters show no remarkable increase in encoding efficiency for all simulated synaptic plasticity mechanisms. This study demonstrates the importance of cooperation of Hebbian mechanism with regulation of neurotransmitter release induced by rapid diffused retrograde messenger in neurons with synapses as low and band-pass filters to obtain high encoding efficiency in different environmental and physiological conditions. PMID:25972786

[Modern principles of the geriatric analysis in medicine].

PubMed

Volobuev, A N; Zaharova, N O; Romanchuk, N P; Romanov, D V; Romanchuk, P I; Adyshirin-Zade, K A

2016-01-01

The offered methodological principles of the geriatric analysis in medicine enables to plan economic parameters of social protection of the population, necessary amount of medical help financing, to define a structure of the qualified medical personnel training. It is shown that personal health and cognitive longevity of the person depend on the adequate system geriatric analysis and use of biological parameters monitoring in time. That allows estimate efficiency of the combined individual treatment. The geriatric analysis and in particular its genetic-mathematical component aimed at reliability and objectivity of an estimation of the person life expectancy in the country and in region due to the account of influence of mutagen factors as on a gene of the person during his live, and on a population as a whole.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Reduction of N terminal-pro-brain (B-type) natriuretic peptide levels with exercise-based cardiac rehabilitation in patients with left ventricular dysfunction after myocardial infarction.

PubMed

Giallauria, Francesco; De Lorenzo, Anna; Pilerci, Francesco; Manakos, Athanasio; Lucci, Rosa; Psaroudaki, Marianna; D'Agostino, Mariantonietta; Del Forno, Domenico; Vigorito, Carlo

2006-08-01

N-terminal-pro-brain (B-type) natriuretic peptide (NT-pro-BNP) is a peptide hormone released from ventricles in response to myocyte stretch. The aim of the study was to investigate the influence of exercise training on plasma NT-pro-BNP to verify if this parameter could be used as a biological marker of left ventricular remodelling in myocardial infarction patients undergoing an exercise training programme. Forty-four patients after myocardial infarction were enrolled into a cardiac rehabilitation programme, and were randomized in two groups of 22 patients each. Group A patients followed a 3-month exercise training programme, while group B patients received only routine recommendations. All patients underwent NT-pro-BNP assay, and cardiopulmonary exercise test before hospital discharge and after 3 months. In Group A, exercise training reduced NT-pro-BNP levels (from 1498+/-438 to 470+/-375 pg/ml, P=0.0026), increased maximal (VO2peak+4.3+/-2.9 ml/kg per min, P<0.001; Powermax+38+/-7, P<0.001) exercise parameters and work efficiency (Powermax/VO2peak+1.3+/-0.4 Power/ml per kg per min, P<0.001); there was also an inverse correlation between changes in NT-pro-BNP levels and in VO2peak (r=-0.72, P<0.001), E-wave (r=-0.51, P<0.001) and E/A ratio (r=0.59, P<0.001). In group B, at 3 months, no changes were observed in NT-pro-BNP levels, exercise and echocardiographic parameters. Three months exercise training in patients with moderate left ventricular systolic dysfunction after myocardial infarction induced a reduction in NT-pro-BNP levels, an improvement of exercise capacity and early left ventricular diastolic filling, without negative left ventricular remodelling. Whether the reduction of NT-pro-BNP levels could be useful as a surrogate marker of favourable left ventricular remodelling at a later follow-up remains to be further explored.

Neural Network Emulation of Reionization Simulations

NASA Astrophysics Data System (ADS)

Schmit, Claude J.; Pritchard, Jonathan R.

2018-05-01

Next generation radio experiments such as LOFAR, HERA and SKA are expected to probe the Epoch of Reionization and claim a first direct detection of the cosmic 21cm signal within the next decade. One of the major challenges for these experiments will be dealing with enormous incoming data volumes. Machine learning is key to increasing our data analysis efficiency. We consider the use of an artificial neural network to emulate 21cmFAST simulations and use it in a Bayesian parameter inference study. We then compare the network predictions to a direct evaluation of the EoR simulations and analyse the dependence of the results on the training set size. We find that the use of a training set of size 100 samples can recover the error contours of a full scale MCMC analysis which evaluates the model at each step.

Long-term lifestyle intervention with optimized high-intensity interval training improves body composition, cardiometabolic risk, and exercise parameters in patients with abdominal obesity.

PubMed

Gremeaux, Vincent; Drigny, Joffrey; Nigam, Anil; Juneau, Martin; Guilbeault, Valérie; Latour, Elise; Gayda, Mathieu

2012-11-01

The aim of this study was to study the impact of a combined long-term lifestyle and high-intensity interval training intervention on body composition, cardiometabolic risk, and exercise tolerance in overweight and obese subjects. Sixty-two overweight and obese subjects (53.3 ± 9.7 yrs; mean body mass index, 35.8 ± 5 kg/m(2)) were retrospectively identified at their entry into a 9-mo program consisting of individualized nutritional counselling, optimized high-intensity interval exercise, and resistance training two to three times a week. Anthropometric measurements, cardiometabolic risk factors, and exercise tolerance were measured at baseline and program completion. Adherence rate was 97%, and no adverse events occurred with high-intensity interval exercise training. Exercise training was associated with a weekly energy expenditure of 1582 ± 284 kcal. Clinically and statistically significant improvements were observed for body mass (-5.3 ± 5.2 kg), body mass index (-1.9 ± 1.9 kg/m(2)), waist circumference (-5.8 ± 5.4 cm), and maximal exercise capacity (+1.26 ± 0.84 metabolic equivalents) (P < 0.0001 for all parameters). Total fat mass and trunk fat mass, lipid profile, and triglyceride/high-density lipoprotein ratio were also significantly improved (P < 0.0001). At program completion, the prevalence of metabolic syndrome was reduced by 32.5% (P < 0.05). Independent predictors of being a responder to body mass and waist circumference loss were baseline body mass index and resting metabolic rate; those for body mass index decrease were baseline waist circumference and triglyceride/high-density lipoprotein cholesterol ratio. A long-term lifestyle intervention with optimized high-intensity interval exercise improves body composition, cardiometabolic risk, and exercise tolerance in obese subjects. This intervention seems safe, efficient, and well tolerated and could improve adherence to exercise training in this population.

An approach based on Hierarchical Bayesian Graphical Models for measurement interpretation under uncertainty

NASA Astrophysics Data System (ADS)

Skataric, Maja; Bose, Sandip; Zeroug, Smaine; Tilke, Peter

2017-02-01

It is not uncommon in the field of non-destructive evaluation that multiple measurements encompassing a variety of modalities are available for analysis and interpretation for determining the underlying states of nature of the materials or parts being tested. Despite and sometimes due to the richness of data, significant challenges arise in the interpretation manifested as ambiguities and inconsistencies due to various uncertain factors in the physical properties (inputs), environment, measurement device properties, human errors, and the measurement data (outputs). Most of these uncertainties cannot be described by any rigorous mathematical means, and modeling of all possibilities is usually infeasible for many real time applications. In this work, we will discuss an approach based on Hierarchical Bayesian Graphical Models (HBGM) for the improved interpretation of complex (multi-dimensional) problems with parametric uncertainties that lack usable physical models. In this setting, the input space of the physical properties is specified through prior distributions based on domain knowledge and expertise, which are represented as Gaussian mixtures to model the various possible scenarios of interest for non-destructive testing applications. Forward models are then used offline to generate the expected distribution of the proposed measurements which are used to train a hierarchical Bayesian network. In Bayesian analysis, all model parameters are treated as random variables, and inference of the parameters is made on the basis of posterior distribution given the observed data. Learned parameters of the posterior distribution obtained after the training can therefore be used to build an efficient classifier for differentiating new observed data in real time on the basis of pre-trained models. We will illustrate the implementation of the HBGM approach to ultrasonic measurements used for cement evaluation of cased wells in the oil industry.

Heart-Rate Variability During Deep Sleep in World-Class Alpine Skiers: A Time-Efficient Alternative to Morning Supine Measurements.

PubMed

Herzig, David; Testorelli, Moreno; Olstad, Daniela Schäfer; Erlacher, Daniel; Achermann, Peter; Eser, Prisca; Wilhelm, Matthias

2017-05-01

It is increasingly popular to use heart-rate variability (HRV) to tailor training for athletes. A time-efficient method is HRV assessment during deep sleep. To validate the selection of deep-sleep segments identified by RR intervals with simultaneous electroencephalography (EEG) recordings and to compare HRV parameters of these segments with those of standard morning supine measurements. In 11 world-class alpine skiers, RR intervals were monitored during 10 nights, and simultaneous EEGs were recorded during 2-4 nights. Deep sleep was determined from the HRV signal and verified by delta power from the EEG recordings. Four further segments were chosen for HRV determination, namely, a 4-h segment from midnight to 4 AM and three 5-min segments: 1 just before awakening, 1 after waking in supine position, and 1 in standing after orthostatic challenge. Training load was recorded every day. A total of 80 night and 68 morning measurements of 9 athletes were analyzed. Good correspondence between the phases selected by RR intervals vs those selected by EEG was found. Concerning root-mean-squared difference of successive RR intervals (RMSSD), a marker for parasympathetic activity, the best relationship with the morning supine measurement was found in deep sleep. HRV is a simple tool for approximating deep-sleep phases, and HRV measurement during deep sleep could provide a time-efficient alternative to HRV in supine position.

Emulation for probabilistic weather forecasting

NASA Astrophysics Data System (ADS)

Cornford, Dan; Barillec, Remi

2010-05-01

Numerical weather prediction models are typically very expensive to run due to their complexity and resolution. Characterising the sensitivity of the model to its initial condition and/or to its parameters requires numerous runs of the model, which is impractical for all but the simplest models. To produce probabilistic forecasts requires knowledge of the distribution of the model outputs, given the distribution over the inputs, where the inputs include the initial conditions, boundary conditions and model parameters. Such uncertainty analysis for complex weather prediction models seems a long way off, given current computing power, with ensembles providing only a partial answer. One possible way forward that we develop in this work is the use of statistical emulators. Emulators provide an efficient statistical approximation to the model (or simulator) while quantifying the uncertainty introduced. In the emulator framework, a Gaussian process is fitted to the simulator response as a function of the simulator inputs using some training data. The emulator is essentially an interpolator of the simulator output and the response in unobserved areas is dictated by the choice of covariance structure and parameters in the Gaussian process. Suitable parameters are inferred from the data in a maximum likelihood, or Bayesian framework. Once trained, the emulator allows operations such as sensitivity analysis or uncertainty analysis to be performed at a much lower computational cost. The efficiency of emulators can be further improved by exploiting the redundancy in the simulator output through appropriate dimension reduction techniques. We demonstrate this using both Principal Component Analysis on the model output and a new reduced-rank emulator in which an optimal linear projection operator is estimated jointly with other parameters, in the context of simple low order models, such as the Lorenz 40D system. We present the application of emulators to probabilistic weather forecasting, where the construction of the emulator training set replaces the traditional ensemble model runs. Thus the actual forecast distributions are computed using the emulator conditioned on the ‘ensemble runs' which are chosen to explore the plausible input space using relatively crude experimental design methods. One benefit here is that the ensemble does not need to be a sample from the true distribution of the input space, rather it should cover that input space in some sense. The probabilistic forecasts are computed using Monte Carlo methods sampling from the input distribution and using the emulator to produce the output distribution. Finally we discuss the limitations of this approach and briefly mention how we might use similar methods to learn the model error within a framework that incorporates a data assimilation like aspect, using emulators and learning complex model error representations. We suggest future directions for research in the area that will be necessary to apply the method to more realistic numerical weather prediction models.

19F Magnetic resonance imaging of perfluorooctanoic acid encapsulated in liposome for biodistribution measurement.

PubMed

Kimura, Atsuomi; Narazaki, Michiko; Kanazawa, Yoko; Fujiwara, Hideaki

2004-07-01

The tissue distribution of perfluorooctanoic acid (PFOA), which is known to show unique biological responses, has been visualized in female mice by (19)F magnetic resonance imaging (MRI) incorporated with the recent advances in microimaging technique. The chemical shift selected fast spin-echo method was applied to acquire in vivo (19)F MR images of PFOA. The in vivo T(1) and T(2) relaxation times of PFOA were proven to be extremely short, which were 140 (+/- 20) ms and 6.3 (+/- 2.2) ms, respectively. To acquire the in vivo (19)F MR images of PFOA, it was necessary to optimize the parameters of signal selection and echo train length. The chemical shift selection was effectively performed by using the (19)F NMR signal of CF(3) group of PFOA without the signal overlapping because the chemical shift difference between the CF(3) and neighbor signals reaches to 14 kHz. The most optimal echo train length to obtain (19)F images efficiently was determined so that the maximum echo time (TE) value in the fast spin-echo sequence was comparable to the in vivo T(2) value. By optimizing these parameters, the in vivo (19)F MR image of PFOA was enabled to obtain efficiently in 12 minutes. As a result, the time course of the accumulation of PFOA into the mouse liver was clearly pursued in the (19)F MR images. Thus, it was concluded that the (19)F MRI becomes the effective method toward the future pharmacological and toxicological studies of perfluorocarboxilic acids.

Training Efficiency and Transfer Success in an Extended Real-Time Functional MRI Neurofeedback Training of the Somatomotor Cortex of Healthy Subjects

PubMed Central

Auer, Tibor; Schweizer, Renate; Frahm, Jens

2015-01-01

This study investigated the level of self-regulation of the somatomotor cortices (SMCs) attained by an extended functional magnetic resonance imaging (fMRI) neurofeedback training. Sixteen healthy subjects performed 12 real-time functional magnetic resonance imaging neurofeedback training sessions within 4 weeks, involving motor imagery of the dominant right as well as the non-dominant left hand. Target regions of interests in the SMC were individually localized prior to the training by overt finger movements. The feedback signal (FS) was defined as the difference between fMRI activation in the contra- and ipsilateral SMC and visually presented to the subjects. Training efficiency was determined by an off-line general linear model analysis determining the fMRI percent signal changes in the SMC target areas accomplished during the neurofeedback training. Transfer success was assessed by comparing the pre- and post-training transfer task, i.e., the neurofeedback paradigm without the presentation of the FS. Group results show a distinct increase in feedback performance (FP) in the transfer task for the trained group compared to a matched untrained control group, as well as an increase in the time course of the training, indicating an efficient training and a successful transfer. Individual analysis revealed that the training efficiency was not only highly correlated to the transfer success but also predictive. Trainings with at least 12 efficient training runs were associated with a successful transfer outcome. A group analysis of the hemispheric contributions to the FP showed that it is mainly driven by increased fMRI activation in the contralateral SMC, although some individuals relied on ipsilateral deactivation. Training and transfer results showed no difference between left- and right-hand imagery, with a slight indication of more ipsilateral deactivation in the early right-hand trainings. PMID:26500521

Cholinergic parameters and the retrieval of learned and re-learned spatial information: a study using a model of Wernicke-Korsakoff Syndrome.

PubMed

Pires, Rita G W; Pereira, Silvia R C; Oliveira-Silva, Ieda F; Franco, Glaura C; Ribeiro, Angela M

2005-07-01

This is a factorial (2 x 2 x 2) spatial memory and cholinergic parameters study in which the factors are chronic ethanol, thiamine deficiency and naivety in Morris water maze task. Both learning and retention of the spatial version of the water maze were assessed. To assess retrograde retention of spatial information, half of the rats were pre-trained on the maze before the treatment manipulations of pyrithiamine (PT)-induced thiamine deficiency and post-tested after treatment (pre-trained group). The other half of the animals was only trained after treatment to assess anterograde amnesia (post-trained group). Thiamine deficiency, associated to chronic ethanol treatment, had a significant deleterious effect on spatial memory performance of post-trained animals. The biochemical data revealed that chronic ethanol treatment reduced acetylcholinesterase (AChE) activity in the hippocampus while leaving the neocortex unchanged, whereas thiamine deficiency reduced both cortical and hippocampal AChE activity. Regarding basal and stimulated cortical acetylcholine (ACh) release, both chronic ethanol and thiamine deficiency treatments had significant main effects. Significant correlations were found between both cortical and hippocampal AChE activity and behaviour parameters for pre-trained but not for post-trained animals. Also for ACh release, the correlation found was significant only for pre-trained animals. These biochemical parameters were decreased by thiamine deficiency and chronic ethanol treatment, both in pre-trained and post-trained animals. But the correlation with the behavioural parameters was observed only for pre-trained animals, that is, those that were retrained and assessed for retrograde retention.

A Surgical Virtual Reality Simulator Distinguishes Between Expert Gynecologic Laparoscopic Surgeons and Perinatologists

PubMed Central

von Dadelszen, Peter; Allaire, Catherine

2011-01-01

Background: Concern regarding the quality of surgical training in obstetrics and gynecology residency programs is focusing attention on competency based education. Because open surgical skills cannot necessarily be translated into laparoscopic skills and with minimally invasive surgery becoming standard in operative gynecology, the discrepancy in training between obstetrics and gynecology will widen. Training on surgical simulators with virtual reality may improve surgical skills. However, before incorporation into training programs for gynecology residents the validity of such instruments needs to first be established. We sought to prove the construct validity of a virtual reality laparoscopic simulator, the SurgicalSimTM, by showing its ability to distinguish between surgeons with different laparoscopic experience. Methods: Eleven gynecologic surgeons (experts) and 11 perinatologists (controls) completed 3 tasks on the simulator, and 10 performance parameters were compared. Results: The experts performed faster, more efficiently, and with fewer errors, proving the construct validity of the SurgicalSim. Conclusions: Laparoscopic virtual reality simulators can measure relevant surgical skills and so distinguish between subjects having different skill levels. Hence, these simulators could be integrated into gynecology resident endoscopic training and utilized for objective assessment. Second, the skills required for competency in obstetrics cannot necessarily be utilized for better performance in laparoscopic gynecology. PMID:21985726

Realistic and efficient 2D crack simulation

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing; Singh, Abhishek

2010-04-01

Although numerical algorithms for 2D crack simulation have been studied in Modeling and Simulation (M&S) and computer graphics for decades, realism and computational efficiency are still major challenges. In this paper, we introduce a high-fidelity, scalable, adaptive and efficient/runtime 2D crack/fracture simulation system by applying the mathematically elegant Peano-Cesaro triangular meshing/remeshing technique to model the generation of shards/fragments. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level-of-detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanism used for mesh element splitting and merging with minimal memory requirements essential for realistic 2D fragment formation. Upon load impact/contact/penetration, a number of factors including impact angle, impact energy, and material properties are all taken into account to produce the criteria of crack initialization, propagation, and termination leading to realistic fractal-like rubble/fragments formation. The aforementioned parameters are used as variables of probabilistic models of cracks/shards formation, making the proposed solution highly adaptive by allowing machine learning mechanisms learn the optimal values for the variables/parameters based on prior benchmark data generated by off-line physics based simulation solutions that produce accurate fractures/shards though at highly non-real time paste. Crack/fracture simulation has been conducted on various load impacts with different initial locations at various impulse scales. The simulation results demonstrate that the proposed system has the capability to realistically and efficiently simulate 2D crack phenomena (such as window shattering and shards generation) with diverse potentials in military and civil M&S applications such as training and mission planning.

[Efficiency of autogenous training in medical rehabilitation of patients with irritable colon syndrome with constipation dominance].

PubMed

Pakhomova, I V; Aĭvazian, T A; Zaĭtsev, V P; Gusakova, E V; Molina, L P

2008-01-01

It was established that use of autogenous training makes possible to increase efficiency of the therapy, leading to considerable more evident improvement of somatic and psychotic state, decrease of pain syndrome. Predictors of efficiency of autogenous training were marked out. Indications for use the method in medical rehabilitation of patients with irritable colon syndrome with constipation dominance were elaborated.

Successful Training of Filtering Mechanisms in Multiple Object Tracking Does Not Transfer to Filtering Mechanisms in a Visual Working Memory Task: Behavioral and Electrophysiological Evidence

ERIC Educational Resources Information Center

Arend, Anna M.; Zimmer, Hubert D.

2012-01-01

In this training study, we aimed to selectively train participants' filtering mechanisms to enhance visual working memory (WM) efficiency. The highly restricted nature of visual WM capacity renders efficient filtering mechanisms crucial for its successful functioning. Filtering efficiency in visual WM can be measured via the lateralized change…

Laser Fluence Recognition Using Computationally Intelligent Pulsed Photoacoustics Within the Trace Gases Analysis

NASA Astrophysics Data System (ADS)

Lukić, M.; Ćojbašić, Ž.; Rabasović, M. D.; Markushev, D. D.; Todorović, D. M.

2017-11-01

In this paper, the possibilities of computational intelligence applications for trace gas monitoring are discussed. For this, pulsed infrared photoacoustics is used to investigate SF6-Ar mixtures in a multiphoton regime, assisted by artificial neural networks. Feedforward multilayer perceptron networks are applied in order to recognize both the spatial characteristics of the laser beam and the values of laser fluence Φ from the given photoacoustic signal and prevent changes. Neural networks are trained in an offline batch training regime to simultaneously estimate four parameters from theoretical or experimental photoacoustic signals: the laser beam spatial profile R(r), vibrational-to-translational relaxation time τ _{V-T} , distance from the laser beam to the absorption molecules in the photoacoustic cell r* and laser fluence Φ . The results presented in this paper show that neural networks can estimate an unknown laser beam spatial profile and the parameters of photoacoustic signals in real time and with high precision. Real-time operation, high accuracy and the possibility of application for higher intensities of radiation for a wide range of laser fluencies are factors that classify the computational intelligence approach as efficient and powerful for the in situ measurement of atmospheric pollutants.

Motor activity as a way of preventing musculoskeletal problems in string musicians.

PubMed

Wilke, Christiane; Priebus, Julian; Biallas, Bianca; Froböse, Ingo

2011-03-01

The health status of performing artists, especially musicians, was not an issue for medical research until the 1980s. Musicians tend to suffer from health-related problems, as playing an instrument demands long and intensive practice. This paper provides a literature review of health problems of string players in particular. It analyzes whether their problems are playing-related or if various parameters potentially influence their health state, and it subsequently presents a concept of efficient training. Health disorders and diseases are individual. In order to ensure efficient prevention, a profile of qualification, including physical and psychological aspects as well as key skills, allows developing an individual training schedule and thus should be included in the process of prevention. Physical performance plays a decisive role and is more important than commonly thought. Strength, endurance, and flexibility in particular have an immense influence on the musician's performance. Playing an instrument requires both physical and mental skills, and all too often this leads to excessive demands. It is necessary to highlight the possible causes and provide the musician with a therapeutic intervention and educational work. As the demand for preventative work in this field grows steadily, this paper draws a detailed concept of a therapeutic intervention.

Gravity compensation of an upper extremity exoskeleton.

PubMed

Moubarak, S; Pham, M T; Moreau, R; Redarce, T

2010-01-01

This paper presents a new gravity compensation method for an upper extremity exoskeleton mounted on a wheel chair. This new device is dedicated to regular and efficient rehabilitation training for post-stroke and injured people without the continuous presence of a therapist. The exoskeleton is a wearable robotic device attached to the human arm. The user provides information signals to the controller by means of the force sensors around the wrist and the arm, and the robot controller generates the appropriate control signals for different training strategies and paradigms. This upper extremity exoskeleton covers four basic degrees of freedom of the shoulder and the elbow joints with three additional adaptability degrees of freedom in order to match the arm anatomy of different users. For comfortable and efficient rehabilitation, a new heuristic method have been studied and applied on our prototype in order to calculate the gravity compensation model without the need to identify the mass parameters. It is based on the geometric model of the robot and accurate torque measurements of the prototype's actuators in a set of specifically chosen joint positions. The weight effect has been successfully compensated so that the user can move his arm freely while wearing the exoskeleton without feeling its mass.

Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

PubMed

Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert

2010-03-01

We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 10(6) × 10(6) incomplete matrix with 10(5) observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques.

Spectral Regularization Algorithms for Learning Large Incomplete Matrices

PubMed Central

Mazumder, Rahul; Hastie, Trevor; Tibshirani, Robert

2010-01-01

We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we provide a simple and very efficient convex algorithm for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm Soft-Impute iteratively replaces the missing elements with those obtained from a soft-thresholded SVD. With warm starts this allows us to efficiently compute an entire regularization path of solutions on a grid of values of the regularization parameter. The computationally intensive part of our algorithm is in computing a low-rank SVD of a dense matrix. Exploiting the problem structure, we show that the task can be performed with a complexity linear in the matrix dimensions. Our semidefinite-programming algorithm is readily scalable to large matrices: for example it can obtain a rank-80 approximation of a 106 × 106 incomplete matrix with 105 observed entries in 2.5 hours, and can fit a rank 40 approximation to the full Netflix training set in 6.6 hours. Our methods show very good performance both in training and test error when compared to other competitive state-of-the art techniques. PMID:21552465

Higher-order mode-based cavity misalignment measurements at the free-electron laser FLASH

NASA Astrophysics Data System (ADS)

Hellert, Thorsten; Baboi, Nicoleta; Shi, Liangliang

2017-12-01

At the Free-Electron Laser in Hamburg (FLASH) and the European X-Ray Free-Electron Laser, superconducting TeV-energy superconducting linear accelerator (TESLA)-type cavities are used for the acceleration of electron bunches, generating intense free-electron laser (FEL) beams. A long rf pulse structure allows one to accelerate long bunch trains, which considerably increases the efficiency of the machine. However, intrabunch-train variations of rf parameters and misalignments of rf structures induce significant trajectory variations that may decrease the FEL performance. The accelerating cavities are housed inside cryomodules, which restricts the ability for direct alignment measurements. In order to determine the transverse cavity position, we use a method based on beam-excited dipole modes in the cavities. We have developed an efficient measurement and signal processing routine and present its application to multiple accelerating modules at FLASH. The measured rms cavity offset agrees with the specification of the TESLA modules. For the first time, the tilt of a TESLA cavity inside a cryomodule is measured. The preliminary result agrees well with the ratio between the offset and angle dependence of the dipole mode which we calculated with eigenmode simulations.

A systematic review of the effects of different types of therapeutic exercise on physiologic and functional measurements in patients with HIV/AIDS

PubMed Central

Gomes-Neto, Mansueto; Conceição, Cristiano Sena; Carvalho, Vitor Oliveira; Brites, Carlos

2013-01-01

Several studies have reported the benefits of exercise training for adults with HIV, although there is no consensus regarding the most efficient modalities. The aim of this study was to determine the effects of different types of exercise on physiologic and functional measurements in patients with HIV using a systematic strategy for searching randomized controlled trials. The sources used in this review were the Cochrane Library, EMBASE, MEDLINE, and PEDro from 1950 to August 2012. We selected randomized controlled trials examining the effects of exercise on body composition, muscle strength, aerobic capacity, and/or quality of life in adults with HIV. Two independent reviewers screened the abstracts using the Cochrane Collaboration's protocol. The PEDro score was used to evaluate methodological quality. In total, 29 studies fulfilled the inclusion criteria. Individual studies suggested that exercise training contributed to improvement of physiologic and functional parameters, but that the gains were specific to the type of exercise performed. Resistance exercise training improved outcomes related to body composition and muscle strength, with little impact on quality of life. Aerobic exercise training improved body composition and aerobic capacity. Concurrent training produced significant gains in all outcomes evaluated, although moderate intensity and a long duration were necessary. We concluded that exercise training was shown to be a safe and beneficial intervention in the treatment of patients with HIV. PMID:24037014

Core strengthening and synchronized swimming: TRX® suspension training in young female athletes.

PubMed

Tinto, Amalia; Campanella, Marta; Fasano, Milena

2017-06-01

Developing muscle strength and full body stability is essential for the efficient execution of technical moves in synchronized swimming. However, many swimmers find it difficult to control body stability while executing particular figures in water. We evaluated the effects of TRX® suspension training (2 sessions weekly for 6 months on core strength and core stability in young female. Twenty synchronized swimmers (Beginners A category, mean age 10±1 years) are divided in experimental group (EG; N.=10 athletes) and control group (CG; N.=10 athletes). EG received suspension training twice weekly (each session lasting about 15 min) as dryland exercises for 6 months in addition to routine training. CG completed routine training with conventional dryland exercises. Before (T1) and after (T2) completion of the study oblique and transversus abdominis muscle force was measured using a Stabilizer Pressure Biofeedback unit, in prone and supine positions, and isotonic muscle endurance was evaluated with the McGill Test. Non-parametric statistical analysis showed a significant increase (P<0.0001) in the majority of the parameters in the experimental group. The study results provide evidence for the benefit of integrating TRX® suspension training in dryland exercises for muscle strengthening in young athletes practicing synchronized swimming, and in general reiterates the importance of strengthening the core area to ensure stability and specific adaptations, improve the quality of the movement and prevent against injury.

The application of neural networks to the SSME startup transient

NASA Technical Reports Server (NTRS)

Meyer, Claudia M.; Maul, William A.

1991-01-01

Feedforward neural networks were used to model three parameters during the Space Shuttle Main Engine startup transient. The three parameters were the main combustion chamber pressure, a controlled parameter, the high pressure oxidizer turbine discharge temperature, a redlined parameter, and the high pressure fuel pump discharge pressure, a failure-indicating performance parameter. Network inputs consisted of time windows of data from engine measurements that correlated highly to the modeled parameter. A standard backpropagation algorithm was used to train the feedforward networks on two nominal firings. Each trained network was validated with four additional nominal firings. For all three parameters, the neural networks were able to accurately predict the data in the validation sets as well as the training set.

Training-induced annual changes in red blood cell profile in highly-trained endurance and speed-power athletes.

PubMed

Ciekot-Sołtysiak, Monika; Kusy, Krzysztof; Podgórski, Tomasz; Zieliński, Jacek

2017-10-24

An extensive body of literature exists on the effects of training on haematological parameters, but the previous studies have not reported how hematological parameters respond to changes in training loads within consecutive phases of the training cycle in highly-trained athletes in extremely different sport disciplines. The aim of this study was to identify changes in red blood cell (RBC) profile in response to training loads in consecutive phases of the annual training cycle in highly-trained sprinters (8 men, aged 24 ± 3 years) and triathletes (6 men, aged 24 ± 4 years) who competed at the national and international level. Maximal oxygen uptake (VO2max), RBC, haemoglobin (Hb), haematocrit (Ht), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC) and RBC distribution width (RDW) were determined in four characteristic training phases (transition, general subphase of the preparation phase, specific subphase of the preparation phase and competition phase). Our main findings are that (1) Hb, MCH and MCHC in triathletes and MCV in both triathletes and sprinters changed significantly over the annual training cycle, (2) triathletes had significantly higher values than sprinters only in case of MCH and MCHC after the transition and general preparation phases but not after the competition phase when MCH and MCHC were higher in sprinters and (3) in triathletes, Hb, MCH and MCHC substantially decreased after the competition phase, which was not observed in sprinters. The athletes maintained normal ranges of all haematological parameters in four characteristic training phases. Although highly-trained sprinters and triathletes do not significantly differ in their levels of most haematological parameters, these groups are characterized by different patterns of changes during the annual training cycle. Our results suggest that when interpreting the values of haematological parameters in speed-power and endurance athletes, a specific phase of the annual training cycle should be taken into account.

An efficient surrogate-based simulation-optimization method for calibrating a regional MODFLOW model

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.

2017-01-01

Simulation-optimization method entails a large number of model simulations, which is computationally intensive or even prohibitive if the model simulation is extremely time-consuming. Statistical models have been examined as a surrogate of the high-fidelity physical model during simulation-optimization process to tackle this problem. Among them, Multivariate Adaptive Regression Splines (MARS), a non-parametric adaptive regression method, is superior in overcoming problems of high-dimensions and discontinuities of the data. Furthermore, the stability and accuracy of MARS model can be improved by bootstrap aggregating methods, namely, bagging. In this paper, Bagging MARS (BMARS) method is integrated to a surrogate-based simulation-optimization framework to calibrate a three-dimensional MODFLOW model, which is developed to simulate the groundwater flow in an arid hardrock-alluvium region in northwestern Oman. The physical MODFLOW model is surrogated by the statistical model developed using BMARS algorithm. The surrogate model, which is fitted and validated using training dataset generated by the physical model, can approximate solutions rapidly. An efficient Sobol' method is employed to calculate global sensitivities of head outputs to input parameters, which are used to analyze their importance for the model outputs spatiotemporally. Only sensitive parameters are included in the calibration process to further improve the computational efficiency. Normalized root mean square error (NRMSE) between measured and simulated heads at observation wells is used as the objective function to be minimized during optimization. The reasonable history match between the simulated and observed heads demonstrated feasibility of this high-efficient calibration framework.

Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning.

PubMed

Molloy, Katharine; Moore, David R; Sohoglu, Ediz; Amitay, Sygal

2012-01-01

The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions.

Less Is More: Latent Learning Is Maximized by Shorter Training Sessions in Auditory Perceptual Learning

PubMed Central

Molloy, Katharine; Moore, David R.; Sohoglu, Ediz; Amitay, Sygal

2012-01-01

Background The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. Methodology/Principal Findings We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Conclusions/Significance Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions. PMID:22606309

Hematological parameters and anaerobic threshold in Brazilian soccer players throughout a training program.

PubMed

Silva, A S R; Santhiago, V; Papoti, M; Gobatto, C A

2008-04-01

We assessed the responses of hematological parameters and their relationship to the anaerobic threshold of Brazilian soccer players during a training program. Twelve athletes were evaluated at the beginning (week 0, T1), in the middle (week 6, T2), and at the end (week 12, T3) of the soccer training program. On the first day at 7:30 am, before collecting the blood sample at rest for the determination of the hematological parameters, the athletes were conducted to the anthropometric evaluation. On the second day at 8:30 am, the athletes had their anaerobic threshold measured. Analysis of variance with Newman-Keuls'post hoc was used for statistical comparisons between the parameters measured during the soccer training program. Correlations between the parameters analyzed were determined using the Pearson's correlation coefficient. Erythrocytes concentration, hemoglobin, and hematocrit were significantly increased from T1 to T2. The specific soccer training program led to a rise in erythrocytes, hemoglobin, and hematocrit from T1 to T2. We assumed that these results occurred due to the plasma volume reduction and may be explained by the soccer training program characteristics. Furthermore, we did not observe any correlation between the anaerobic threshold and the hematological parameters.

The construction of support vector machine classifier using the firefly algorithm.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

Online measurement for geometrical parameters of wheel set based on structure light and CUDA parallel processing

NASA Astrophysics Data System (ADS)

Wu, Kaihua; Shao, Zhencheng; Chen, Nian; Wang, Wenjie

2018-01-01

The wearing degree of the wheel set tread is one of the main factors that influence the safety and stability of running train. Geometrical parameters mainly include flange thickness and flange height. Line structure laser light was projected on the wheel tread surface. The geometrical parameters can be deduced from the profile image. An online image acquisition system was designed based on asynchronous reset of CCD and CUDA parallel processing unit. The image acquisition was fulfilled by hardware interrupt mode. A high efficiency parallel segmentation algorithm based on CUDA was proposed. The algorithm firstly divides the image into smaller squares, and extracts the squares of the target by fusion of k_means and STING clustering image segmentation algorithm. Segmentation time is less than 0.97ms. A considerable acceleration ratio compared with the CPU serial calculation was obtained, which greatly improved the real-time image processing capacity. When wheel set was running in a limited speed, the system placed alone railway line can measure the geometrical parameters automatically. The maximum measuring speed is 120km/h.

Impact of auditory training for perceptual assessment of voice executed by undergraduate students in Speech-Language Pathology.

PubMed

Silva, Regiane Serafim Abreu; Simões-Zenari, Marcia; Nemr, Nair Kátia

2012-01-01

To analyze the impact of auditory training for auditory-perceptual assessment carried out by Speech-Language Pathology undergraduate students. During two semesters, 17 undergraduate students enrolled in theoretical subjects regarding phonation (Phonation/Phonation Disorders) analyzed samples of altered and unaltered voices (selected for this purpose), using the GRBAS scale. All subjects received auditory training during nine 15-minute meetings. In each meeting, a different parameter was presented using the different voices sample, with predominance of the trained aspect in each session. Sample assessment using the scale was carried out before and after training, and in other four opportunities throughout the meetings. Students' assessments were compared to an assessment carried out by three voice-experts speech-language pathologists who were the judges. To verify training effectiveness, the Friedman's test and the Kappa index were used. The rate of correct answers in the pre-training was considered between regular and good. It was observed maintenance of the number of correct answers throughout assessments, for most of the scale parameters. In the post-training moment, the students showed improvements in the analysis of asthenia, a parameter that was emphasized during training after the students reported difficulties analyzing it. There was a decrease in the number of correct answers for the roughness parameter after it was approached segmented into hoarseness and harshness, and observed in association with different diagnoses and acoustic parameters. Auditory training enhances students' initial abilities to perform the evaluation, aside from guiding adjustments in the dynamics of the university subject.

Multiscale analysis of neural spike trains.

PubMed

Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

2014-01-30

This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code. Copyright © 2013 John Wiley & Sons, Ltd.

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

PubMed Central

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. PMID:28489933

Improving the performance of extreme learning machine for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Li, Jiaojiao; Du, Qian; Li, Wei; Li, Yunsong

2015-05-01

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

The development of multi-model rehabilitation training system for lower limb sitting function

NASA Astrophysics Data System (ADS)

Wu, Jianfeng; Sun, Yue; Wu, Qun

2017-04-01

The multi-model rehabilitation training system was manufactured according to the demands of patients' practical training. Through the use of the patient's exercise physiology information, the ability of muscle force and movement efficiency of the patient were identified. Following with medical rehabilitation therapy, the training model, a combination of active and passive training, was proposed to enhance the training efficiency and rehabilitation effect. Furthermore, taking the sitting movement training as an example, the research theory was applied in knee rehabilitation training. The results of the research provid technical support and practical reference to the relevant training equipment designs and clinical applications.

Prediction of La0.6Sr0.4Co0.2Fe0.8O3 cathode microstructures during sintering: Kinetic Monte Carlo (KMC) simulations calibrated by artificial neural networks

NASA Astrophysics Data System (ADS)

Yan, Zilin; Kim, Yongtae; Hara, Shotaro; Shikazono, Naoki

2017-04-01

The Potts Kinetic Monte Carlo (KMC) model, proven to be a robust tool to study all stages of sintering process, is an ideal tool to analyze the microstructure evolution of electrodes in solid oxide fuel cells (SOFCs). Due to the nature of this model, the input parameters of KMC simulations such as simulation temperatures and attempt frequencies are difficult to identify. We propose a rigorous and efficient approach to facilitate the input parameter calibration process using artificial neural networks (ANNs). The trained ANN reduces drastically the number of trial-and-error of KMC simulations. The KMC simulation using the calibrated input parameters predicts the microstructures of a La0.6Sr0.4Co0.2Fe0.8O3 cathode material during sintering, showing both qualitative and quantitative congruence with real 3D microstructures obtained by focused ion beam scanning electron microscopy (FIB-SEM) reconstruction.

A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

NASA Astrophysics Data System (ADS)

Kong, Xianyu; Che, Xiaowei; Su, Rongguo; Zhang, Chuansong; Yao, Qingzhen; Shi, Xiaoyong

2017-05-01

There is an urgent need to develop efficient evaluation tools that use easily measured variables to make rapid and timely eutrophication assessments, which are important for marine health management, and to implement eutrophication monitoring programs. In this study, an approach for rapidly assessing the eutrophication status of coastal waters with three easily measured parameters (turbidity, chlorophyll a and dissolved oxygen) was developed by the grid search (GS) optimized support vector machine (SVM), with trophic index TRIX classification results as the reference. With the optimized penalty parameter C =64 and the kernel parameter γ =1, the classification accuracy rates reached 89.3% for the training data, 88.3% for the cross-validation, and 88.5% for the validation dataset. Because the developed approach only used three easy-to-measure variables, its application could facilitate the rapid assessment of the eutrophication status of coastal waters, resulting in potential cost savings in marine monitoring programs and assisting in the provision of timely advice for marine management.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

PubMed

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Review of factors affecting aircraft wet runway performance

NASA Technical Reports Server (NTRS)

Yager, T. J.

1983-01-01

Problems associated with aircraft operations on wet runways are discussed and major factors which influence tire/runway braking and cornering traction capability are identified including runway characteristics, tire hydroplaning, brake system anomalies, and pilot inputs. Research results from investigations conducted at the Langley Aircraft Landing Loads and Traction Facility and from tests with instrumented ground vehicles and aircraft are summarized to indicate the effects of different aircraft, tire, and runway parameters. Several promising means are described for improving tire/runway water drainage capability, brake system efficiency, and pilot training to help optimize aircraft traction performance on wet runways.

Impedance Control of the Rehabilitation Robot Based on Sliding Mode Control

NASA Astrophysics Data System (ADS)

Zhou, Jiawang; Zhou, Zude; Ai, Qingsong

As an auxiliary treatment, the 6-DOF parallel robot plays an important role in lower limb rehabilitation. In order to improve the efficiency and flexibility of the lower limb rehabilitation training, this paper studies the impedance controller based on the position control. A nonsingular terminal sliding mode control is developed to ensure the trajectory tracking precision and in contrast to traditional PID control strategy in the inner position loop, the system will be more stable. The stability of the system is proved by Lyapunov function to guarantee the convergence of the control errors. Simulation results validate the effectiveness of the target impedance model and show that the parallel robot can adjust gait trajectory online according to the human-machine interaction force to meet the gait request of patients, and changing the impedance parameters can meet the demands of different stages of rehabilitation training.

The efficiency coefficient of the rat heart and muscular system after physical training and hypokinesia

NASA Technical Reports Server (NTRS)

Alyukhin, Y. S.; Davydov, A. F.

1982-01-01

The efficiency of an isolated heart did not change after prolonged physical training of rats for an extreme load. The increase in oxygen consumption by the entire organism in 'uphill' running as compared to the resting level in the trained rats was 14% lower than in the control animals. Prolonged hypokinesia of the rats did not elicit a change in the efficiency of the isolated heart.

Wearable Performance Devices in Sports Medicine.

PubMed

Li, Ryan T; Kling, Scott R; Salata, Michael J; Cupp, Sean A; Sheehan, Joseph; Voos, James E

2016-01-01

Wearable performance devices and sensors are becoming more readily available to the general population and athletic teams. Advances in technology have allowed individual endurance athletes, sports teams, and physicians to monitor functional movements, workloads, and biometric markers to maximize performance and minimize injury. Movement sensors include pedometers, accelerometers/gyroscopes, and global positioning satellite (GPS) devices. Physiologic sensors include heart rate monitors, sleep monitors, temperature sensors, and integrated sensors. The purpose of this review is to familiarize health care professionals and team physicians with the various available types of wearable sensors, discuss their current utilization, and present future applications in sports medicine. Data were obtained from peer-reviewed literature through a search of the PubMed database. Included studies searched development, outcomes, and validation of wearable performance devices such as GPS, accelerometers, and physiologic monitors in sports. Clinical review. Level 4. Wearable sensors provide a method of monitoring real-time physiologic and movement parameters during training and competitive sports. These parameters can be used to detect position-specific patterns in movement, design more efficient sports-specific training programs for performance optimization, and screen for potential causes of injury. More recent advances in movement sensors have improved accuracy in detecting high-acceleration movements during competitive sports. Wearable devices are valuable instruments for the improvement of sports performance. Evidence for use of these devices in professional sports is still limited. Future developments are needed to establish training protocols using data from wearable devices. © 2015 The Author(s).

Iterative dataset optimization in automated planning: Implementation for breast and rectal cancer radiotherapy.

PubMed

Fan, Jiawei; Wang, Jiazhou; Zhang, Zhen; Hu, Weigang

2017-06-01

To develop a new automated treatment planning solution for breast and rectal cancer radiotherapy. The automated treatment planning solution developed in this study includes selection of the iterative optimized training dataset, dose volume histogram (DVH) prediction for the organs at risk (OARs), and automatic generation of clinically acceptable treatment plans. The iterative optimized training dataset is selected by an iterative optimization from 40 treatment plans for left-breast and rectal cancer patients who received radiation therapy. A two-dimensional kernel density estimation algorithm (noted as two parameters KDE) which incorporated two predictive features was implemented to produce the predicted DVHs. Finally, 10 additional new left-breast treatment plans are re-planned using the Pinnacle 3 Auto-Planning (AP) module (version 9.10, Philips Medical Systems) with the objective functions derived from the predicted DVH curves. Automatically generated re-optimized treatment plans are compared with the original manually optimized plans. By combining the iterative optimized training dataset methodology and two parameters KDE prediction algorithm, our proposed automated planning strategy improves the accuracy of the DVH prediction. The automatically generated treatment plans using the dose derived from the predicted DVHs can achieve better dose sparing for some OARs without compromising other metrics of plan quality. The proposed new automated treatment planning solution can be used to efficiently evaluate and improve the quality and consistency of the treatment plans for intensity-modulated breast and rectal cancer radiation therapy. © 2017 American Association of Physicists in Medicine.

Wearable Performance Devices in Sports Medicine

PubMed Central

Li, Ryan T.; Kling, Scott R.; Salata, Michael J.; Cupp, Sean A.; Sheehan, Joseph; Voos, James E.

2016-01-01

Context: Wearable performance devices and sensors are becoming more readily available to the general population and athletic teams. Advances in technology have allowed individual endurance athletes, sports teams, and physicians to monitor functional movements, workloads, and biometric markers to maximize performance and minimize injury. Movement sensors include pedometers, accelerometers/gyroscopes, and global positioning satellite (GPS) devices. Physiologic sensors include heart rate monitors, sleep monitors, temperature sensors, and integrated sensors. The purpose of this review is to familiarize health care professionals and team physicians with the various available types of wearable sensors, discuss their current utilization, and present future applications in sports medicine. Evidence Acquisition: Data were obtained from peer-reviewed literature through a search of the PubMed database. Included studies searched development, outcomes, and validation of wearable performance devices such as GPS, accelerometers, and physiologic monitors in sports. Study Design: Clinical review. Level of Evidence: Level 4. Results: Wearable sensors provide a method of monitoring real-time physiologic and movement parameters during training and competitive sports. These parameters can be used to detect position-specific patterns in movement, design more efficient sports-specific training programs for performance optimization, and screen for potential causes of injury. More recent advances in movement sensors have improved accuracy in detecting high-acceleration movements during competitive sports. Conclusion: Wearable devices are valuable instruments for the improvement of sports performance. Evidence for use of these devices in professional sports is still limited. Future developments are needed to establish training protocols using data from wearable devices. PMID:26733594

Training-based descreening.

PubMed

Siddiqui, Hasib; Bouman, Charles A

2007-03-01

Conventional halftoning methods employed in electrophotographic printers tend to produce Moiré artifacts when used for printing images scanned from printed material, such as books and magazines. We present a novel approach for descreening color scanned documents aimed at providing an efficient solution to the Moiré problem in practical imaging devices, including copiers and multifunction printers. The algorithm works by combining two nonlinear image-processing techniques, resolution synthesis-based denoising (RSD), and modified smallest univalue segment assimilating nucleus (SUSAN) filtering. The RSD predictor is based on a stochastic image model whose parameters are optimized beforehand in a separate training procedure. Using the optimized parameters, RSD classifies the local window around the current pixel in the scanned image and applies filters optimized for the selected classes. The output of the RSD predictor is treated as a first-order estimate to the descreened image. The modified SUSAN filter uses the output of RSD for performing an edge-preserving smoothing on the raw scanned data and produces the final output of the descreening algorithm. Our method does not require any knowledge of the screening method, such as the screen frequency or dither matrix coefficients, that produced the printed original. The proposed scheme not only suppresses the Moiré artifacts, but, in addition, can be trained with intrinsic sharpening for deblurring scanned documents. Finally, once optimized for a periodic clustered-dot halftoning method, the same algorithm can be used to inverse halftone scanned images containing stochastic error diffusion halftone noise.

The EB factory project. I. A fast, neural-net-based, general purpose light curve classifier optimized for eclipsing binaries

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

Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.

2014-08-01

We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together withmore » a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (∼60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided.« less

Accurate Descriptions of Hot Flow Behaviors Across β Transus of Ti-6Al-4V Alloy by Intelligence Algorithm GA-SVR

NASA Astrophysics Data System (ADS)

Wang, Li-yong; Li, Le; Zhang, Zhi-hua

2016-09-01

Hot compression tests of Ti-6Al-4V alloy in a wide temperature range of 1023-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-3500 machine. In order to accurately and effectively characterize the highly nonlinear flow behaviors, support vector regression (SVR) which is a machine learning method was combined with genetic algorithm (GA) for characterizing the flow behaviors, namely, the GA-SVR. The prominent character of GA-SVR is that it with identical training parameters will keep training accuracy and prediction accuracy at a stable level in different attempts for a certain dataset. The learning abilities, generalization abilities, and modeling efficiencies of the mathematical regression model, ANN, and GA-SVR for Ti-6Al-4V alloy were detailedly compared. Comparison results show that the learning ability of the GA-SVR is stronger than the mathematical regression model. The generalization abilities and modeling efficiencies of these models were shown as follows in ascending order: the mathematical regression model < ANN < GA-SVR. The stress-strain data outside experimental conditions were predicted by the well-trained GA-SVR, which improved simulation accuracy of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles, such as speculating work hardening and dynamic recovery, characterizing dynamic recrystallization evolution, and improving processing maps.

Recurrent neural network based virtual detection line

NASA Astrophysics Data System (ADS)

Kadikis, Roberts

2018-04-01

The paper proposes an efficient method for detection of moving objects in the video. The objects are detected when they cross a virtual detection line. Only the pixels of the detection line are processed, which makes the method computationally efficient. A Recurrent Neural Network processes these pixels. The machine learning approach allows one to train a model that works in different and changing outdoor conditions. Also, the same network can be trained for various detection tasks, which is demonstrated by the tests on vehicle and people counting. In addition, the paper proposes a method for semi-automatic acquisition of labeled training data. The labeling method is used to create training and testing datasets, which in turn are used to train and evaluate the accuracy and efficiency of the detection method. The method shows similar accuracy as the alternative efficient methods but provides greater adaptability and usability for different tasks.

The Combined Intervention with Germinated Vigna radiata and Aerobic Interval Training Protocol Is an Effective Strategy for the Treatment of Non-Alcoholic Fatty Liver Disease (NAFLD) and Other Alterations Related to the Metabolic Syndrome in Zucker Rats.

PubMed

Kapravelou, Garyfallia; Martínez, Rosario; Nebot, Elena; López-Jurado, María; Aranda, Pilar; Arrebola, Francisco; Cantarero, Samuel; Galisteo, Milagros; Porres, Jesus M

2017-07-19

Metabolic syndrome (MetS) is a group of related metabolic alterations that increase the risk of developing non-alcoholic fatty liver disease (NAFLD). Several lifestyle interventions based on dietary treatment with functional ingredients and physical activity are being studied as alternative or reinforcement treatments to the pharmacological ones actually in use. In the present experiment, the combined treatment with mung bean ( Vigna radiata ), a widely used legume with promising nutritional and health benefits that was included in the experimental diet as raw or 4 day-germinated seed flour, and aerobic interval training protocol (65-85% VO₂ max) has been tested in lean and obese Zucker rats following a 2 × 2 × 2 (2 phenotypes, 2 dietary interventions, 2 lifestyles) factorial ANOVA (Analysis of Variance) statistical analysis. Germination of V. radiata over a period of four days originated a significant protein hydrolysis leading to the appearance of low molecular weight peptides. The combination of 4 day-germinated V. radiata and aerobic interval training was more efficient compared to raw V. radiata at improving the aerobic capacity and physical performance, hepatic histology and functionality, and plasma lipid parameters as well as reverting the insulin resistance characteristic of the obese Zucker rat model. In conclusion, the joint intervention with legume sprouts and aerobic interval training protocol is an efficient treatment to improve the alterations of glucose and lipid metabolism as well as hepatic histology and functionality related to the development of NAFLD and the MetS.

Hand rim wheelchair propulsion training using biomechanical real-time visual feedback based on motor learning theory principles.

PubMed

Rice, Ian; Gagnon, Dany; Gallagher, Jere; Boninger, Michael

2010-01-01

As considerable progress has been made in laboratory-based assessment of manual wheelchair propulsion biomechanics, the necessity to translate this knowledge into new clinical tools and treatment programs becomes imperative. The objective of this study was to describe the development of a manual wheelchair propulsion training program aimed to promote the development of an efficient propulsion technique among long-term manual wheelchair users. Motor learning theory principles were applied to the design of biomechanical feedback-based learning software, which allows for random discontinuous real-time visual presentation of key spatiotemporal and kinetic parameters. This software was used to train a long-term wheelchair user on a dynamometer during 3 low-intensity wheelchair propulsion training sessions over a 3-week period. Biomechanical measures were recorded with a SmartWheel during over ground propulsion on a 50-m level tile surface at baseline and 3 months after baseline. Training software was refined and administered to a participant who was able to improve his propulsion technique by increasing contact angle while simultaneously reducing stroke cadence, mean resultant force, peak and mean moment out of plane, and peak rate of rise of force applied to the pushrim after training. The proposed propulsion training protocol may lead to favorable changes in manual wheelchair propulsion technique. These changes could limit or prevent upper limb injuries among manual wheelchair users. In addition, many of the motor learning theory-based techniques examined in this study could be applied to training individuals in various stages of rehabilitation to optimize propulsion early on.

Hand Rim Wheelchair Propulsion Training Using Biomechanical Real-Time Visual Feedback Based on Motor Learning Theory Principles

PubMed Central

Rice, Ian; Gagnon, Dany; Gallagher, Jere; Boninger, Michael

2010-01-01

Background/Objective: As considerable progress has been made in laboratory-based assessment of manual wheelchair propulsion biomechanics, the necessity to translate this knowledge into new clinical tools and treatment programs becomes imperative. The objective of this study was to describe the development of a manual wheelchair propulsion training program aimed to promote the development of an efficient propulsion technique among long-term manual wheelchair users. Methods: Motor learning theory principles were applied to the design of biomechanical feedback-based learning software, which allows for random discontinuous real-time visual presentation of key spatio-temporal and kinetic parameters. This software was used to train a long-term wheelchair user on a dynamometer during 3 low-intensity wheelchair propulsion training sessions over a 3-week period. Biomechanical measures were recorded with a SmartWheel during over ground propulsion on a 50-m level tile surface at baseline and 3 months after baseline. Results: Training software was refined and administered to a participant who was able to improve his propulsion technique by increasing contact angle while simultaneously reducing stroke cadence, mean resultant force, peak and mean moment out of plane, and peak rate of rise of force applied to the pushrim after training. Conclusions: The proposed propulsion training protocol may lead to favorable changes in manual wheelchair propulsion technique. These changes could limit or prevent upper limb injuries among manual wheelchair users. In addition, many of the motor learning theory–based techniques examined in this study could be applied to training individuals in various stages of rehabilitation to optimize propulsion early on. PMID:20397442

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

NetVLAD: CNN Architecture for Weakly Supervised Place Recognition.

PubMed

Arandjelovic, Relja; Gronat, Petr; Torii, Akihiko; Pajdla, Tomas; Sivic, Josef

2018-06-01

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following four principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we create a new weakly supervised ranking loss, which enables end-to-end learning of the architecture's parameters from images depicting the same places over time downloaded from Google Street View Time Machine. Third, we develop an efficient training procedure which can be applied on very large-scale weakly labelled tasks. Finally, we show that the proposed architecture and training procedure significantly outperform non-learnt image representations and off-the-shelf CNN descriptors on challenging place recognition and image retrieval benchmarks.

Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies

PubMed Central

Düking, Peter; Hotho, Andreas; Holmberg, Hans-Christer; Fuss, Franz Konstantin; Sperlich, Billy

2016-01-01

Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health. PMID:27014077

A Study of Relationships among Technical, Tactical, Physical Parameters and Final Outcomes in Elite Soccer Matches as Analyzed by a Semiautomatic Video Tracking System.

PubMed

Filetti, Cristoforo; Ruscello, Bruno; D'Ottavio, Stefano; Fanelli, Vito

2017-06-01

The performance of a soccer team depends on many factors such as decision-making, cognitive and physical skills, and dynamic ever-changing space-time interactions between teammate and opponents in relation to the ball. Seventy ( n = 70) matches of the Italian SERIE A season 2013-2014 were investigated to analyze the mean performance of 360 players in terms of physical (physical efficiency index; PEI) and technical-tactical (technical efficiency index; TEI) standpoints. Using a semiautomatic video analysis system that has incorporated new parameters able to measure technical-tactical and physical efficiency (Patent IB2010/002593, 2011-ISA), the correlation between these new variables and how much it relates to the likelihood of winning were verified. Correlations between TEI and PEI were significant ( n = 140, r = .60, p < .001), and TEI showed a higher likelihood of winning than PEI factors ( p < .0001 vs. .0001, CI 95% [1.64, 3.00] vs. [1.28, 2.07]). Higher TEI and TEI + PEI differences between the teams were associated with a greater likelihood of winning, but PEI differences were not. Key performance indicators and this performance assessment method might be useful to better understand what determines winning and to assist the overall training process and match management.

Improving the Efficiency of Virtual Reality Training by Integrating Partly Observational Learning

ERIC Educational Resources Information Center

Yuviler-Gavish, Nirit; Rodríguez, Jorge; Gutiérrez, Teresa; Sánchez, Emilio; Casado, Sara

2014-01-01

The current study hypothesized that integrating partly observational learning into virtual reality training systems (VRTS) can enhance training efficiency for procedural tasks. A common approach in designing VRTS is the enactive approach, which stresses the importance of physical actions within the environment to enhance perception and improve…

Advanced Flight Simulator: Utilization in A-10 Conversion and Air-to-Surface Attack Training.

DTIC Science & Technology

1981-01-01

ASPT to the A-I0. Finally. the objectivity of the criteria ( parameters of aircraft control. bomb-drop circular error. and percentage of rounds through a...low angle strafe task. Table 4 presents a listing of these tasks and their related release parameters . 12 __ __ _ __ Tab/e .1. A/S Weapons I)eliven...Scoring. Two dependent variables, specific to the phase of student training, were used. In the conversion training phase. specific parameters for

Effective and efficient learning in the operating theater with intraoperative video-enhanced surgical procedure training.

PubMed

van Det, M J; Meijerink, W J H J; Hoff, C; Middel, B; Pierie, J P E N

2013-08-01

INtraoperative Video Enhanced Surgical procedure Training (INVEST) is a new training method designed to improve the transition from basic skills training in a skills lab to procedural training in the operating theater. Traditionally, the master-apprentice model (MAM) is used for procedural training in the operating theater, but this model lacks uniformity and efficiency at the beginning of the learning curve. This study was designed to investigate the effectiveness and efficiency of INVEST compared to MAM. Ten surgical residents with no laparoscopic experience were recruited for a laparoscopic cholecystectomy training curriculum either by the MAM or with INVEST. After a uniform course in basic laparoscopic skills, each trainee performed six cholecystectomies that were digitally recorded. For 14 steps of the procedure, an observer who was blinded for the type of training determined whether the step was performed entirely by the trainee (2 points), partially by the trainee (1 point), or by the supervisor (0 points). Time measurements revealed the total procedure time and the amount of effective procedure time during which the trainee acted as the operating surgeon. Results were compared between both groups. Trainees in the INVEST group were awarded statistically significant more points (115.8 vs. 70.2; p < 0.001) and performed more steps without the interference of the supervisor (46.6 vs. 18.8; p < 0.001). Total procedure time was not lengthened by INVEST, and the part performed by trainees was significantly larger (69.9 vs. 54.1 %; p = 0.004). INVEST enhances effectiveness and training efficiency for procedural training inside the operating theater without compromising operating theater time efficiency.

Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

PubMed Central

Rene, Eldon R.; López, M. Estefanía; Kim, Jung Hoon; Park, Hung Suck

2013-01-01

Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m3 ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m3 ·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R 2) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently. PMID:24307999

Parameterization of MARVELS Spectra Using Deep Learning

NASA Astrophysics Data System (ADS)

Gilda, Sankalp; Ge, Jian; MARVELS

2018-01-01

Like many large-scale surveys, the Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS) was designed to operate at a moderate spectral resolution ($\\sim$12,000) for efficiency in observing large samples, which makes the stellar parameterization difficult due to the high degree of blending of spectral features. Two extant solutions to deal with this issue are to utilize spectral synthesis, and to utilize spectral indices [Ghezzi et al. 2014]. While the former is a powerful and tested technique, it can often yield strongly coupled atmospheric parameters, and often requires high spectral resolution (Valenti & Piskunov 1996). The latter, though a promising technique utilizing measurements of equivalent widths of spectral indices, has only been employed with respect to FKG dwarfs and sub-giants and not red-giant branch stars, which constitute ~30% of MARVELS targets. In this work, we tackle this problem using a convolution neural network (CNN). In particular, we train a one-dimensional CNN on appropriately processed PHOENIX synthetic spectra using supervised training to automatically distinguish the features relevant for the determination of each of the three atmospheric parameters – T_eff, log(g), [Fe/H] – and use the knowledge thus gained by the network to parameterize 849 MARVELS giants. When tested on the synthetic spectra themselves, our estimates of the parameters were consistent to within 11 K, .02 dex, and .02 dex (in terms of mean absolute errors), respectively. For MARVELS dwarfs, the accuracies are 80K, .16 dex and .10 dex, respectively.

Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification

PubMed Central

Zhao, Yuwei; Han, Jiuqi; Chen, Yushu; Sun, Hongji; Chen, Jiayun; Ke, Ang; Han, Yao; Zhang, Peng; Zhang, Yi; Zhou, Jin; Wang, Changyong

2018-01-01

Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB) with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP) methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems. PMID:29867307

Pedagogical Strategies for Training Future Technicians in American Aviation Schools

ERIC Educational Resources Information Center

Pazyura, Natalia

2015-01-01

The article is devoted to the question of improvement of quality and efficiency of professional training of future technicians in aviation industry in the American educational establishments. Main attention is paid to the studies of pedagogical technologies, which are used for the sake of qualitative and efficient training of specialists of…

Effect of Auditory-Perceptual Training With Natural Voice Anchors on Vocal Quality Evaluation.

PubMed

Dos Santos, Priscila Campos Martins; Vieira, Maurílio Nunes; Sansão, João Pedro Hallack; Gama, Ana Cristina Côrtes

2018-01-10

To analyze the effects of auditory-perceptual training with anchor stimuli of natural voices on inter-rater agreement during the assessment of vocal quality. This is a quantitative nature study. An auditory-perceptual training site was developed consisting of Programming Interface A, an auditory training activity, and Programming Interface B, a control activity. Each interface had three stages: pre-training/pre-interval evaluation, training/interval, and post-training/post-interval evaluation. Two experienced evaluators classified 381 voices according to the GRBASI scale (G-grade, R-roughness, B-breathiness, A-asthenia, S-strain, I-instability). Voices were selected that received the same evaluation by both evaluators: 57 voices for evaluation and 56 for training were selected, with varying degrees of deviation across parameters. Fifteen inexperienced evaluators were then selected. In the pre-, post-training, pre-, and postinterval stages, evaluators listened to the voices and classified them via the GRBASI scale. In the stage interval evaluators read a text. In the stage training each parameter was trained separately. Evaluators analyzed the degrees of deviation of the GRBASI parameters based on anchor stimuli, and could only advance after correctly classifying the voices. To quantify inter-rater agreement and provide statistical analyses, the AC1 coefficient, confidence intervals, and percentage variation of agreement were employed. Except for the asthenia parameter, decreased agreement was observed in the control condition. Improved agreement was observed with auditory training, but this improvement did not achieve statistical significance. Training with natural voice anchors suggest an increased inter-rater agreement during perceptual voice analysis, potentially indicating that new internal references were established. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Timing of intermittent torque control with wire-driven gait training robot lifting toe trajectory for trip avoidance.

PubMed

Miyake, Tamon; Kobayashi, Yo; Fujie, Masakatsu G; Sugano, Shigeki

2017-07-01

Gait training robots are useful for changing gait patterns and decreasing risk of trip. Previous research has reported that decreasing duration of the assistance or guidance of the robot is beneficial for efficient gait training. Although robotic intermittent control method for assisting joint motion has been established, the effect of the robot intervention timing on change of toe clearance is unclear. In this paper, we tested different timings of applying torque to the knee, employing the intermittent control of a gait training robot to increase toe clearance throughout the swing phase. We focused on knee flexion motion and designed a gait training robot that can apply flexion torque to the knee with a wire-driven system. We used a method of timing detecting for the robot conducting torque control based on information from the hip, knee, and ankle angles to establish a non-time dependent parameter that can be used to adapt to gait change, such as gait speed. We carried out an experiment in which the conditions were four time points: starting the swing phase, lifting the foot, maintaining knee flexion, and finishing knee flexion. The results show that applying flexion torque to the knee at the time point when people start lifting their toe is effective for increasing toe clearance in the whole swing phase.

Improvement of metabolic and cardiorespiratory responses through treadmill gait training with neuromuscular electrical stimulation in quadriplegic subjects.

PubMed

de Carvalho, Daniela Cristina Leite; Martins, Cristiane Luzia; Cardoso, Simone David; Cliquet, Alberto

2006-01-01

This work assessed the influence of treadmill gait training with neuromuscular electrical stimulation (NMES) on the metabolic and cardiorespiratory responses in quadriplegic subjects. The gait group (GG) (n=11) performed 6 months of treadmill training with 30-50% body weight support and with the help of physiotherapists, twice a week, allotting 20 min for each session. The control group (CG) (n=10), during the 6 months of training, did not perform any activity using NMES, performing instead conventional physiotherapy. Metabolic and cardiorespiratory responses (O(2) uptake [VO(2)], CO(2) production [VCO(2)], pulmonary ventilation (V(E)), heart rate [HR], and blood pressure [BP]) were measured on inclusion and after 6 months. For the GG, differences were found in all parameters after training (P<0.05), except for HR and diastolic BP. During gait, VO(2) (L/min) increased by 36%, VCO(2) (L/min) increased by 42.97%, V(E) (L/min) increased by 30.48%, and systolic BP (mm Hg) increased by 4.8%. For the CG, only VO(2) and VCO(2) (L/min) significantly increased at rest (30.82 and 16.39%, respectively) and during knee-extension exercise (26.29 and 17.37%, respectively). Treadmill gait with NMES was, therefore, more efficient toward increasing the aerobic capacity due to yielding higher metabolic and cardiovascular stresses.

Sprint interval training (SIT) substantially reduces depressive symptoms in major depressive disorder (MDD): A randomized controlled trial.

PubMed

Minghetti, Alice; Faude, Oliver; Hanssen, Henner; Zahner, Lukas; Gerber, Markus; Donath, Lars

2018-07-01

Continuous aerobic exercise training (CAT) is considered a complementary treatment option in patients with major depressive disorder (MDD). Intermittent exercise training protocols, such as sprint interval training (SIT) have gained increasing popularity, but no studies on depressive symptoms following SIT in patients with MDD are available. Fifty-nine in-patients with MDD were randomly assigned to a SIT or CAT group. Medication was counterbalanced in both intervention arms. Both intervention groups received 3 weekly training sessions for 4-weeks (12 sessions in total). SIT comprised 25 bouts of 30 seconds at 80% of maximal power, whereas CAT consisted of 20 minutes of physical activity at 60% of maximal power. The training protocols were isocalorically designed. Maximal bicycle ergometer exercise testing yielded maximal and submaximal physical fitness parameters. The Beck-Depression-Inventory-II (BDI-II) was filled out by the patients before and after the intervention period. BDI-II scores substantially decreased in both groups with an effect size pointing towards a large effect (p < 0.001, η p ² = 0.70) while submaximal (0.07 < d < 0.89) and maximal (0.05 < d < 0.85) fitness variables improved in both groups. Short-term SIT leads to similar results as CAT in patients with MDD and can be regarded as a time-efficient and promising exercise-based treatment strategy. Copyright © 2018 Elsevier B.V. All rights reserved.

Convolutional Dictionary Learning: Acceleration and Convergence

NASA Astrophysics Data System (ADS)

Chun, Il Yong; Fessler, Jeffrey A.

2018-04-01

Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

Natural Language Processing for Joint Fire Observer Training

DTIC Science & Technology

2010-11-01

training system. However, many of the tasks an operator performs are routine and can be automated. The Intelligent Operator Training Assistant ( IOTA ) is...whole JFETS training session might be handled by the IOTA . In other cases, where the soldier departs from pre-defined parameters, the human operator...is able to take over control of the session from the IOTA until the soldier is back within the established parameters. We enable this flexibility

Model's sparse representation based on reduced mixed GMsFE basis methods

NASA Astrophysics Data System (ADS)

Jiang, Lijian; Li, Qiuqi

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.

Model's sparse representation based on reduced mixed GMsFE basis methods

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

Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn; Li, Qiuqi, E-mail: qiuqili@hnu.edu.cn

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a largemore » number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous media is simulated by the proposed sparse representation method.« less

On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

PubMed

Koyama, Shinsuke

2015-07-01

We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

Advanced Proficiency EHR Training: Effect on Physicians’ EHR Efficiency, EHR Satisfaction and Job Satisfaction

PubMed Central

Dastagir, M. Tariq; Chin, Homer L.; McNamara, Michael; Poteraj, Kathy; Battaglini, Sarah; Alstot, Lauren

2012-01-01

The best way to train clinicians to optimize their use of the Electronic Health Record (EHR) remains unclear. Approaches range from web-based training, class-room training, EHR functionality training, case-based training, role-based training, process-based training, mock-clinic training and “on the job” training. Similarly, the optimal timing of training remains unclear--whether to engage in extensive pre go-live training vs. minimal pre go-live training followed by more extensive post go-live training. In addition, the effectiveness of non-clinician trainers, clinician trainers, and peer-trainers, remains unclearly defined. This paper describes a program in which relatively experienced clinician users of an EHR underwent an intensive 3-day Peer-Led EHR advanced proficiency training, and the results of that training based on participant surveys. It highlights the effectiveness of Peer-Led Proficiency Training of existing experienced clinician EHR users in improving self-reported efficiency and satisfaction with an EHR and improvements in perceived work-life balance and job satisfaction. PMID:23304282

Advanced proficiency EHR training: effect on physicians' EHR efficiency, EHR satisfaction and job satisfaction.

PubMed

Dastagir, M Tariq; Chin, Homer L; McNamara, Michael; Poteraj, Kathy; Battaglini, Sarah; Alstot, Lauren

2012-01-01

The best way to train clinicians to optimize their use of the Electronic Health Record (EHR) remains unclear. Approaches range from web-based training, class-room training, EHR functionality training, case-based training, role-based training, process-based training, mock-clinic training and "on the job" training. Similarly, the optimal timing of training remains unclear--whether to engage in extensive pre go-live training vs. minimal pre go-live training followed by more extensive post go-live training. In addition, the effectiveness of non-clinician trainers, clinician trainers, and peer-trainers, remains unclearly defined. This paper describes a program in which relatively experienced clinician users of an EHR underwent an intensive 3-day Peer-Led EHR advanced proficiency training, and the results of that training based on participant surveys. It highlights the effectiveness of Peer-Led Proficiency Training of existing experienced clinician EHR users in improving self-reported efficiency and satisfaction with an EHR and improvements in perceived work-life balance and job satisfaction.

Noncontrast Peripheral MRA with Spiral Echo Train Imaging

PubMed Central

Fielden, Samuel W.; Mugler, John P.; Hagspiel, Klaus D.; Norton, Patrick T.; Kramer, Christopher M.; Meyer, Craig H.

2015-01-01

Purpose To develop a spin echo train sequence with spiral readout gradients with improved artery–vein contrast for noncontrast angiography. Theory Venous T2 becomes shorter as the echo spacing is increased in echo train sequences, improving contrast. Spiral acquisitions, due to their data collection efficiency, facilitate long echo spacings without increasing scan times. Methods Bloch equation simulations were performed to determine optimal sequence parameters, and the sequence was applied in five volunteers. In two volunteers, the sequence was performed with a range of echo times and echo spacings to compare with the theoretical contrast behavior. A Cartesian version of the sequence was used to compare contrast appearance with the spiral sequence. Additionally, spiral parallel imaging was optionally used to improve image resolution. Results In vivo, artery–vein contrast properties followed the general shape predicted by simulations, and good results were obtained in all stations. Compared with a Cartesian implementation, the spiral sequence had superior artery–vein contrast, better spatial resolution (1.2 mm2 versus 1.5 mm2), and was acquired in less time (1.4 min versus 7.5 min). Conclusion The spiral spin echo train sequence can be used for flow-independent angiography to generate threedimensional angiograms of the periphery quickly and without the use of contrast agents. PMID:24753164

Noncontrast peripheral MRA with spiral echo train imaging.

PubMed

Fielden, Samuel W; Mugler, John P; Hagspiel, Klaus D; Norton, Patrick T; Kramer, Christopher M; Meyer, Craig H

2015-03-01

To develop a spin echo train sequence with spiral readout gradients with improved artery-vein contrast for noncontrast angiography. Venous T2 becomes shorter as the echo spacing is increased in echo train sequences, improving contrast. Spiral acquisitions, due to their data collection efficiency, facilitate long echo spacings without increasing scan times. Bloch equation simulations were performed to determine optimal sequence parameters, and the sequence was applied in five volunteers. In two volunteers, the sequence was performed with a range of echo times and echo spacings to compare with the theoretical contrast behavior. A Cartesian version of the sequence was used to compare contrast appearance with the spiral sequence. Additionally, spiral parallel imaging was optionally used to improve image resolution. In vivo, artery-vein contrast properties followed the general shape predicted by simulations, and good results were obtained in all stations. Compared with a Cartesian implementation, the spiral sequence had superior artery-vein contrast, better spatial resolution (1.2 mm(2) versus 1.5 mm(2) ), and was acquired in less time (1.4 min versus 7.5 min). The spiral spin echo train sequence can be used for flow-independent angiography to generate three-dimensional angiograms of the periphery quickly and without the use of contrast agents. © 2014 Wiley Periodicals, Inc.

EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State.

PubMed

Bustos, Alejandro; Rubio, Higinio; Castejón, Cristina; García-Prada, Juan Carlos

2018-03-06

An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation.

Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures.

PubMed

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.

Using Wireless Sensor Networks and Trains as Data Mules to Monitor Slab Track Infrastructures

PubMed Central

Cañete, Eduardo; Chen, Jaime; Díaz, Manuel; Llopis, Luis; Reyna, Ana; Rubio, Bartolomé

2015-01-01

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. PMID:26131668

EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State

PubMed Central

García-Prada, Juan Carlos

2018-01-01

An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation. PMID:29509690

Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

PubMed

Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

2017-06-01

The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

Need to Improve Efficiency of Reserve Training. Report to the Congress.

ERIC Educational Resources Information Center

Comptroller General of the U.S., Washington, DC.

The report discusses the need to vary the training of Reserve and Guard units by skill and readiness requirements and to make more efficient use of training time. It contains recommendations to the Secretaries of Defense, Transportation, Army, Navy, and Air Force. The review was based on questionnaires mailed to 2,209 randomly selected reservists…

Comparison of Critical Power and W' Derived From 2 or 3 Maximal Tests.

PubMed

Simpson, Len Parker; Kordi, Mehdi

2017-07-01

Typically, accessing the asymptote (critical power; CP) and curvature constant (W') parameters of the hyperbolic power-duration relationship requires multiple constant-power exhaustive-exercise trials spread over several visits. However, more recently single-visit protocols and personal power meters have been used. This study investigated the practicality of using a 2-trial, single-visit protocol in providing reliable CP and W' estimates. Eight trained cyclists underwent 3- and 12-min maximal-exercise trials in a single session to derive (2-trial) CP and W' estimates. On a separate occasion a 5-min trial was performed, providing a 3rd trial to calculate (3-trial) CP and W'. There were no differences in CP (283 ± 66 vs 282 ± 65 W) or W' (18.72 ± 6.21 vs 18.27 ± 6.29 kJ) obtained from either the 2-trial or 3-trial method, respectively. After 2 familiarization sessions (completing a 3- and a 12-min trial on both occasions), both CP and W' remained reliable over additional separate measurements. The current study demonstrates that after 2 familiarization sessions, reliable CP and W' parameters can be obtained from trained cyclists using only 2 maximal-exercise trials. These results offer practitioners a practical, time-efficient solution for incorporating power-duration testing into applied athlete support.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

PubMed

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Trend and future of diesel engine: Development of high efficiency and low emission low temperature combustion diesel engine

NASA Astrophysics Data System (ADS)

Ho, R. J.; Yusoff, M. Z.; Palanisamy, K.

2013-06-01

Stringent emission policy has put automotive research & development on developing high efficiency and low pollutant power train. Conventional direct injection diesel engine with diffused flame has reached its limitation and has driven R&D to explore other field of combustion. Low temperature combustion (LTC) and homogeneous charge combustion ignition has been proven to be effective methods in decreasing combustion pollutant emission. Nitrogen Oxide (NOx) and Particulate Matter (PM) formation from combustion can be greatly suppressed. A review on each of method is covered to identify the condition and processes that result in these reductions. The critical parameters that allow such combustion to take place will be highlighted and serves as emphasis to the direction of developing future diesel engine system. This paper is written to explore potential of present numerical and experimental methods in optimizing diesel engine design through adoption of the new combustion technology.

Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

PubMed

Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael

2012-02-01

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

PubMed

Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

2015-11-01

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Successful Diagnosis and Typing of Systemic Amyloidosis Using A Microwave-Assisted Filter-Aided Fast Sample Preparation Method and LC/MS/MS Analysis

PubMed Central

Zou, Lili; Shen, Kaini; Zhong, Dingrong; Zhou, Daobin; Sun, Wei; Li, Jian

2015-01-01

Laser microdissection followed by mass spectrometry has been successfully used for amyloid typing. However, sample contamination can interfere with proteomic analysis, and overnight digestion limits the analytical throughput. Moreover, current quantitative analysis methods are based on the spectrum count, which ignores differences in protein length and may lead to misdiagnoses. Here, we developed a microwave-assisted filter-aided sample preparation (maFASP) method that can efficiently remove contaminants with a 10-kDa cutoff ultrafiltration unit and can accelerate the digestion process with the assistance of a microwave. Additionally, two parameters (P- and D-scores) based on the exponentially modified protein abundance index were developed to define the existence of amyloid deposits and those causative proteins with the greatest abundance. Using our protocol, twenty cases of systemic amyloidosis that were well-typed according to clinical diagnostic standards (training group) and another twenty-four cases without subtype diagnoses (validation group) were analyzed. Using this approach, sample preparation could be completed within four hours. We successfully subtyped 100% of the cases in the training group, and the diagnostic success rate in the validation group was 91.7%. This maFASP-aided proteomic protocol represents an efficient approach for amyloid diagnosis and subtyping, particularly for serum-contaminated samples. PMID:25984759

Efficient patient modeling for visuo-haptic VR simulation using a generic patient atlas.

PubMed

Mastmeyer, Andre; Fortmeier, Dirk; Handels, Heinz

2016-08-01

This work presents a new time-saving virtual patient modeling system by way of example for an existing visuo-haptic training and planning virtual reality (VR) system for percutaneous transhepatic cholangio-drainage (PTCD). Our modeling process is based on a generic patient atlas to start with. It is defined by organ-specific optimized models, method modules and parameters, i.e. mainly individual segmentation masks, transfer functions to fill the gaps between the masks and intensity image data. In this contribution, we show how generic patient atlases can be generalized to new patient data. The methodology consists of patient-specific, locally-adaptive transfer functions and dedicated modeling methods such as multi-atlas segmentation, vessel filtering and spline-modeling. Our full image volume segmentation algorithm yields median DICE coefficients of 0.98, 0.93, 0.82, 0.74, 0.51 and 0.48 regarding soft-tissue, liver, bone, skin, blood and bile vessels for ten test patients and three selected reference patients. Compared to standard slice-wise manual contouring time saving is remarkable. Our segmentation process shows out efficiency and robustness for upper abdominal puncture simulation systems. This marks a significant step toward establishing patient-specific training and hands-on planning systems in a clinical environment. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Preserving information in neural transmission.

PubMed

Sincich, Lawrence C; Horton, Jonathan C; Sharpee, Tatyana O

2009-05-13

Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.

Sampling and data handling methods for inhalable particulate sampling. Final report nov 78-dec 80

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

Smith, W.B.; Cushing, K.M.; Johnson, J.W.

1982-05-01

The report reviews the objectives of a research program on sampling and measuring particles in the inhalable particulate (IP) size range in emissions from stationary sources, and describes methods and equipment required. A computer technique was developed to analyze data on particle-size distributions of samples taken with cascade impactors from industrial process streams. Research in sampling systems for IP matter included concepts for maintaining isokinetic sampling conditions, necessary for representative sampling of the larger particles, while flowrates in the particle-sizing device were constant. Laboratory studies were conducted to develop suitable IP sampling systems with overall cut diameters of 15 micrometersmore » and conforming to a specified collection efficiency curve. Collection efficiencies were similarly measured for a horizontal elutriator. Design parameters were calculated for horizontal elutriators to be used with impactors, the EPA SASS train, and the EPA FAS train. Two cyclone systems were designed and evaluated. Tests on an Andersen Size Selective Inlet, a 15-micrometer precollector for high-volume samplers, showed its performance to be with the proposed limits for IP samplers. A stack sampling system was designed in which the aerosol is diluted in flow patterns and with mixing times simulating those in stack plumes.« less

A preliminary report of music-based training for adult cochlear implant users: Rationales and development.

PubMed

Gfeller, Kate; Guthe, Emily; Driscoll, Virginia; Brown, Carolyn J

2015-09-01

This paper provides a preliminary report of a music-based training program for adult cochlear implant (CI) recipients. Included in this report are descriptions of the rationale for music-based training, factors influencing program development, and the resulting program components. Prior studies describing experience-based plasticity in response to music training, auditory training for persons with hearing impairment, and music training for CI recipients were reviewed. These sources revealed rationales for using music to enhance speech, factors associated with successful auditory training, relevant aspects of electric hearing and music perception, and extant evidence regarding limitations and advantages associated with parameters for music training with CI users. This informed the development of a computer-based music training program designed specifically for adult CI users. Principles and parameters for perceptual training of music, such as stimulus choice, rehabilitation approach, and motivational concerns were developed in relation to the unique auditory characteristics of adults with electric hearing. An outline of the resulting program components and the outcome measures for evaluating program effectiveness are presented. Music training can enhance the perceptual accuracy of music, but is also hypothesized to enhance several features of speech with similar processing requirements as music (e.g., pitch and timbre). However, additional evaluation of specific training parameters and the impact of music-based training on speech perception of CI users is required.

A preliminary report of music-based training for adult cochlear implant users: rationales and development

PubMed Central

Gfeller, Kate; Guthe, Emily; Driscoll, Virginia; Brown, Carolyn J.

2015-01-01

Objective This paper provides a preliminary report of a music-based training program for adult cochlear implant (CI) recipients. Included in this report are descriptions of the rationale for music-based training, factors influencing program development, and the resulting program components. Methods Prior studies describing experience-based plasticity in response to music training, auditory training for persons with hearing impairment, and music training for cochlear implant recipients were reviewed. These sources revealed rationales for using music to enhance speech, factors associated with successful auditory training, relevant aspects of electric hearing and music perception, and extant evidence regarding limitations and advantages associated with parameters for music training with CI users. This information formed the development of a computer-based music training program designed specifically for adult CI users. Results Principles and parameters for perceptual training of music, such as stimulus choice, rehabilitation approach, and motivational concerns were developed in relation to the unique auditory characteristics of adults with electric hearing. An outline of the resulting program components and the outcome measures for evaluating program effectiveness are presented. Conclusions Music training can enhance the perceptual accuracy of music, but is also hypothesized to enhance several features of speech with similar processing requirements as music (e.g., pitch and timbre). However, additional evaluation of specific training parameters and the impact of music-based training on speech perception of CI users are required. PMID:26561884

Effects of consensus training on the reliability of auditory perceptual ratings of voice quality.

PubMed

Iwarsson, Jenny; Reinholt Petersen, Niels

2012-05-01

This study investigates the effect of consensus training of listeners on intrarater and interrater reliability and agreement of perceptual voice analysis. The use of such training, including a reference voice sample, could be assumed to make the internal standards held in memory common and more robust, which is of great importance to reduce the variability of auditory perceptual ratings. A prospective design with testing before and after training. Thirteen students of audiologopedics served as listening subjects. The ratings were made using a multidimensional protocol with four-point equal-appearing interval scales. The stimuli consisted of text reading by authentic dysphonic patients. The consensus training for each perceptual voice parameter included (1) definition, (2) underlying physiology, (3) presentation of carefully selected sound examples representing the parameter in three different grades followed by group discussions of perceived characteristics, and (4) practical exercises including imitation to make use of the listeners' proprioception. Intrarater reliability and agreement showed a marked improvement for intermittent aphonia but not for vocal fry. Interrater reliability was high for most parameters before training with a slight increase after training. Interrater agreement showed marked increases for most voice quality parameters as a result of the training. The results support the recommendation of specific consensus training, including use of a reference voice sample material, to calibrate, equalize, and stabilize the internal standards held in memory by the listeners. Copyright © 2012 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

Integration of leadership training into a problem/case-based learning program for first- and second-year medical students.

PubMed

Ginzburg, Samara B; Deutsch, Susan; Bellissimo, Jaclyn; Elkowitz, David E; Stern, Joel Nh; Lucito, Robert

2018-01-01

The evolution of health care systems in response to societal and financial pressures has changed care delivery models, which presents new challenges for physicians. Leadership training is increasingly being recognized as an essential component of medical education training to prepare physicians to meet these needs. Unfortunately, most medical schools do not include leadership training. It has been suggested that a longitudinal and integrated approach to leadership training should be sought. We hypothesized that integration of leadership training into our hybrid problem-based learning (PBL)/case-based learning (CBL) program, Patient-Centered Explorations in Active Reasoning, Learning and Synthesis (PEARLS), would be an effective way for medical students to develop leadership skills without the addition of curricular time. We designed a unique leadership program in PEARLS in which 98 medical students participated during each of their six courses throughout the first 2 years of school. A program director and trained faculty facilitators educated students and coached them on leadership development throughout this time. Students were assessed by their facilitator at the end of every course on development of leadership skills related to teamwork, meaningful self-assessment, process improvement, and thinking outside the box. Students consistently improved their performance from the first to the final course in all four leadership parameters evaluated. The skills that demonstrated the greatest change were those pertaining to thinking outside the box and process improvement. Incorporation of a longitudinal and integrated approach to leadership training into an existing PBL/CBL program is an effective way for medical students to improve their leadership skills without the addition of curricular time. These results offer a new, time-efficient option for leadership development in schools with existing PBL/CBL programs.

Quiet Clean Short-haul Experimental Engine (QCSEE): Hamilton Standard cam/harmonic drive variable pitch fan actuation system detail design report

NASA Technical Reports Server (NTRS)

1976-01-01

A variable pitch fan actuation system was designed which incorporates a remote nacelle-mounted blade angle regulator. The regulator drives a rotating fan-mounted mechanical actuator through a flexible shaft and differential gear train. The actuator incorporates a high ratio harmonic drive attached to a multitrack spherical cam which changes blade pitch through individual cam follower arms attached to each blade trunnion. Detail design parameters of the actuation system are presented. These include the following: design philosophies, operating limits, mechanical, hydraulic and thermal characteristics, mechanical efficiencies, materials, weights, lubrication, stress analyses, reliability and failure analyses.

Stochastic Investigation of Natural Frequency for Functionally Graded Plates

NASA Astrophysics Data System (ADS)

Karsh, P. K.; Mukhopadhyay, T.; Dey, S.

2018-03-01

This paper presents the stochastic natural frequency analysis of functionally graded plates by applying artificial neural network (ANN) approach. Latin hypercube sampling is utilised to train the ANN model. The proposed algorithm for stochastic natural frequency analysis of FGM plates is validated and verified with original finite element method and Monte Carlo simulation (MCS). The combined stochastic variation of input parameters such as, elastic modulus, shear modulus, Poisson ratio, and mass density are considered. Power law is applied to distribute the material properties across the thickness. The present ANN model reduces the sample size and computationally found efficient as compared to conventional Monte Carlo simulation.

Modeling air concentration over macro roughness conditions by Artificial Intelligence techniques

NASA Astrophysics Data System (ADS)

Roshni, T.; Pagliara, S.

2018-05-01

Aeration is improved in rivers by the turbulence created in the flow over macro and intermediate roughness conditions. Macro and intermediate roughness flow conditions are generated by flows over block ramps or rock chutes. The measurements are taken in uniform flow region. Efficacy of soft computing methods in modeling hydraulic parameters are not common so far. In this study, modeling efficiencies of MPMR model and FFNN model are found for estimating the air concentration over block ramps under macro roughness conditions. The experimental data are used for training and testing phases. Potential capability of MPMR and FFNN model in estimating air concentration are proved through this study.

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

NASA Astrophysics Data System (ADS)

Liu, X. Y.; Alfi, S.; Bruni, S.

2016-06-01

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic 'input-output' model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software 'ADTreS' are utilised as 'virtual measurements' considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

Evaluation of jamming efficiency for the protection of a single ground object

NASA Astrophysics Data System (ADS)

Matuszewski, Jan

2018-04-01

The electronic countermeasures (ECM) include methods to completely prevent or restrict the effective use of the electromagnetic spectrum by the opponent. The most widespread means of disorganizing the operation of electronic devices is to create active and passive radio-electronic jamming. The paper presents the way of jamming efficiency calculations for protecting ground objects against the radars mounted on the airborne platforms. The basic mathematical formulas for calculating the efficiency of active radar jamming are presented. The numerical calculations for ground object protection are made for two different electronic warfare scenarios: the jammer is placed very closely and in a determined distance from the protecting object. The results of these calculations are presented in the appropriate figures showing the minimal distance of effective jamming. The realization of effective radar jamming in electronic warfare systems depends mainly on the precise knowledge of radar and the jammer's technical parameters, the distance between them, the assumed value of the degradation coefficient, the conditions of electromagnetic energy propagation and the applied jamming method. The conclusions from these calculations facilitate making a decision regarding how jamming should be conducted to achieve high efficiency during the electronic warfare training.

Assessing the limitations of the Banister model in monitoring training

PubMed Central

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

2006-01-01

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

Quantitative analysis of professionally trained versus untrained voices.

PubMed

Siupsinskiene, Nora

2003-01-01

The aim of this study was to compare healthy trained and untrained voices as well as healthy and dysphonic trained voices in adults using combined voice range profile and aerodynamic tests, to define the normal range limiting values of quantitative voice parameters and to select the most informative quantitative voice parameters for separation between healthy and dysphonic trained voices. Three groups of persons were evaluated. One hundred eighty six healthy volunteers were divided into two groups according to voice training: non-professional speakers group consisted of 106 untrained voices persons (36 males and 70 females) and professional speakers group--of 80 trained voices persons (21 males and 59 females). Clinical group consisted of 103 dysphonic professional speakers (23 males and 80 females) with various voice disorders. Eighteen quantitative voice parameters from combined voice range profile (VRP) test were analyzed: 8 of voice range profile, 8 of speaking voice, overall vocal dysfunction degree and coefficient of sound, and aerodynamic maximum phonation time. Analysis showed that healthy professional speakers demonstrated expanded vocal abilities in comparison to healthy non-professional speakers. Quantitative voice range profile parameters- pitch range, high frequency limit, area of high frequencies and coefficient of sound differed significantly between healthy professional and non-professional voices, and were more informative than speaking voice or aerodynamic parameters in showing the voice training. Logistic stepwise regression revealed that VRP area in high frequencies was sufficient to discriminate between healthy and dysphonic professional speakers for male subjects (overall discrimination accuracy--81.8%) and combination of three quantitative parameters (VRP high frequency limit, maximum voice intensity and slope of speaking curve) for female subjects (overall model discrimination accuracy--75.4%). We concluded that quantitative voice assessment with selected parameters might be useful for evaluation of voice education for healthy professional speakers as well as for detection of vocal dysfunction and evaluation of rehabilitation effect in dysphonic professionals.

Simulation-Based Training Platforms for Arthroscopy: A Randomized Comparison of Virtual Reality Learning to Benchtop Learning.

PubMed

Middleton, Robert M; Alvand, Abtin; Garfjeld Roberts, Patrick; Hargrove, Caroline; Kirby, Georgina; Rees, Jonathan L

2017-05-01

To determine whether a virtual reality (VR) arthroscopy simulator or benchtop (BT) arthroscopy simulator showed superiority as a training tool. Arthroscopic novices were randomized to a training program on a BT or a VR knee arthroscopy simulator. The VR simulator provided user performance feedback. Individuals performed a diagnostic arthroscopy on both simulators before and after the training program. Performance was assessed using wireless objective motion analysis and a global rating scale. The groups (8 in the VR group, 9 in the BT group) were well matched at baseline across all parameters (P > .05). Training on each simulator resulted in significant performance improvements across all parameters (P < .05). BT training conferred a significant improvement in all parameters when trainees were reassessed on the VR simulator (P < .05). In contrast, VR training did not confer improvement in performance when trainees were reassessed on the BT simulator (P > .05). BT-trained subjects outperformed VR-trained subjects in all parameters during final assessments on the BT simulator (P < .05). There was no difference in objective performance between VR-trained and BT-trained subjects on final VR simulator wireless objective motion analysis assessment (P > .05). Both simulators delivered improvements in arthroscopic skills. BT training led to skills that readily transferred to the VR simulator. Skills acquired after VR training did not transfer as readily to the BT simulator. Despite trainees receiving automated metric feedback from the VR simulator, the results suggest a greater gain in psychomotor skills for BT training. Further work is required to determine if this finding persists in the operating room. This study suggests that there are differences in skills acquired on different simulators and skills learnt on some simulators may be more transferable. Further work in identifying user feedback metrics that enhance learning is also required. Copyright © 2016 Arthroscopy Association of North America. All rights reserved.

Automatic tissue segmentation of breast biopsies imaged by QPI

NASA Astrophysics Data System (ADS)

Majeed, Hassaan; Nguyen, Tan; Kandel, Mikhail; Marcias, Virgilia; Do, Minh; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

2016-03-01

The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

Effects of Short-Interval and Long-Interval Swimming Protocols on Performance, Aerobic Adaptations, and Technical Parameters: A Training Study.

PubMed

Dalamitros, Athanasios A; Zafeiridis, Andreas S; Toubekis, Argyris G; Tsalis, George A; Pelarigo, Jailton G; Manou, Vasiliki; Kellis, Spiridon

2016-10-01

Dalamitros, AA, Zafeiridis, AS, Toubekis, AG, Tsalis, GA, Pelarigo, JG, Manou, V, and Kellis, S. Effects of short-interval and long-interval swimming protocols on performance, aerobic adaptations, and technical parameters: A training study. J Strength Cond Res 30(10): 2871-2879, 2016-This study compared 2-interval swimming training programs of different work interval durations, matched for total distance and exercise intensity, on swimming performance, aerobic adaptations, and technical parameters. Twenty-four former swimmers were equally divided to short-interval training group (INT50, 12-16 × 50 m with 15 seconds rest), long-interval training group (INT100, 6-8 × 100 m with 30 seconds rest), and a control group (CON). The 2 experimental groups followed the specified swimming training program for 8 weeks. Before and after training, swimming performance, technical parameters, and indices of aerobic adaptations were assessed. ΙΝΤ50 and ΙΝΤ100 improved swimming performance in 100 and 400-m tests and the maximal aerobic speed (p ≤ 0.05); the performance in the 50-m swim did not change. Posttraining V[Combining Dot Above]O2max values were higher compared with pretraining values in both training groups (p ≤ 0.05), whereas peak aerobic power output increased only in INT100 (p ≤ 0.05). The 1-minute heart rate and blood lactate recovery values decreased after training in both groups (p < 0.01). Stroke length increased in 100 and 400-m swimming tests after training in both groups (p ≤ 0.05); no changes were observed in stroke rate after training. Comparisons between groups on posttraining mean values, after adjusting for pretraining values, revealed no significant differences between ΙΝΤ50 and ΙΝΤ100 for all variables; however, all measures were improved vs. the respective values in the CON (p < 0.001-0.05). In conclusion, when matched for distance and exercise intensity, the short-interval (50 m) and long-interval (100 m) protocols confer analogous improvements in swimming performance, in stroke cycle parameters, and in indices of aerobic adaptations after 8 weeks of training.

Inter-Firm Cooperation in Training. A National Vocational Education and Training Research and Evaluation Program Report

ERIC Educational Resources Information Center

Cooney, Richard; Long, Michael

2008-01-01

Industry policy emphasises the importance of competition between firms as the basis of efficient markets and as a stimulus for improved firm efficiency. Yet research has shown that much can be gained from cooperation among firms in a range of activities, especially those involving the transfer of knowledge, such as training. These cooperative…

The Effectiveness and Efficiency of Two Types of Simulation as Functions of Level of Elementary Education Training. Final Report.

ERIC Educational Resources Information Center

Girod, Gerald R.

An experiment was performed to determine the efficiency of simulation teaching techniques in training elementary education teachers to identify and correct classroom management problems. The two presentation modes compared were film and audiotape. Twelve hypotheses were tested via analysis of variance to determine the relative efficiency of these…

Effects of a wearable exoskeleton stride management assist system (SMA®) on spatiotemporal gait characteristics in individuals after stroke: a randomized controlled trial.

PubMed

Buesing, Carolyn; Fisch, Gabriela; O'Donnell, Megan; Shahidi, Ida; Thomas, Lauren; Mummidisetty, Chaithanya K; Williams, Kenton J; Takahashi, Hideaki; Rymer, William Zev; Jayaraman, Arun

2015-08-20

Robots offer an alternative, potentially advantageous method of providing repetitive, high-dosage, and high-intensity training to address the gait impairments caused by stroke. In this study, we compared the effects of the Stride Management Assist (SMA®) System, a new wearable robotic device developed by Honda R&D Corporation, Japan, with functional task specific training (FTST) on spatiotemporal gait parameters in stroke survivors. A single blinded randomized control trial was performed to assess the effect of FTST and task-specific walking training with the SMA® device on spatiotemporal gait parameters. Participants (n=50) were randomly assigned to FTST or SMA. Subjects in both groups received training 3 times per week for 6-8 weeks for a maximum of 18 training sessions. The GAITRite® system was used to collect data on subjects' spatiotemporal gait characteristics before training (baseline), at mid-training, post-training, and at a 3-month follow-up. After training, significant improvements in gait parameters were observed in both training groups compared to baseline, including an increase in velocity and cadence, a decrease in swing time on the impaired side, a decrease in double support time, an increase in stride length on impaired and non-impaired sides, and an increase in step length on impaired and non-impaired sides. No significant differences were observed between training groups; except for SMA group, step length on the impaired side increased significantly during self-selected walking speed trials and spatial asymmetry decreased significantly during fast-velocity walking trials. SMA and FTST interventions provided similar, significant improvements in spatiotemporal gait parameters; however, the SMA group showed additional improvements across more parameters at various time points. These results indicate that the SMA® device could be a useful therapeutic tool to improve spatiotemporal parameters and contribute to improved functional mobility in stroke survivors. Further research is needed to determine the feasibility of using this device in a home setting vs a clinic setting, and whether such home use provides continued benefits. This study is registered under the title "Development of walk assist device to improve community ambulation" and can be located in clinicaltrials.gov with the study identifier: NCT01994395 .

NET-ZERO ENERGY BUILDING OPERATOR TRAINING PROGRAM (NZEBOT)

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

Brizendine, Anthony; Byars, Nan; Sleiti, Ahmad

2012-12-31

The primary objective of the Net-Zero Energy Building Operator Training Program (NZEBOT) was to develop certificate level training programs for commercial building owners, managers and operators, principally in the areas of energy / sustainability management. The expected outcome of the project was a multi-faceted mechanism for developing the skill-based competency of building operators, owners, architects/engineers, construction professionals, tenants, brokers and other interested groups in energy efficient building technologies and best practices. The training program draws heavily on DOE supported and developed materials available in the existing literature, as well as existing, modified, and newly developed curricula from the Department ofmore » Engineering Technology & Construction Management (ETCM) at the University of North Carolina at Charlotte (UNC-Charlotte). The project goal is to develop a certificate level training curriculum for commercial energy and sustainability managers and building operators that: 1) Increases the skill-based competency of building professionals in energy efficient building technologies and best practices, and 2) Increases the workforce pool of expertise in energy management and conservation techniques. The curriculum developed in this project can subsequently be used to establish a sustainable energy training program that can contribute to the creation of new “green” job opportunities in North Carolina and throughout the Southeast region, and workforce training that leads to overall reductions in commercial building energy consumption. Three energy training / education programs were developed to achieve the stated goal, namely: 1. Building Energy/Sustainability Management (BESM) Certificate Program for Building Managers and Operators (40 hours); 2. Energy Efficient Building Technologies (EEBT) Certificate Program (16 hours); and 3. Energy Efficent Buildings (EEB) Seminar (4 hours). Training Program 1 incorporates the following topics in the primary five-day Building Energy/Sustainability Management Certificate program in five training modules, namely: 1) Strategic Planning, 2) Sustainability Audits, 3) Information Analysis, 4) Energy Efficiency, and 5) Communication. Training Program 2 addresses the following technical topics in the two-day Building Technologies workshop: 1) Energy Efficient Building Materials, 2) Green Roofing Systems, 3) Energy Efficient Lighting Systems, 4) Alternative Power Systems for Buildings, 5) Innovative Building Systems, and 6) Application of Building Performance Simulation Software. Program 3 is a seminar which provides an overview of elements of programs 1 and 2 in a seminar style presentation designed for the general public to raise overall public awareness of energy and sustainability topics.« less

On the efficiency of FES cycling: a framework and systematic review.

PubMed

Hunt, K J; Fang, J; Saengsuwan, J; Grob, M; Laubacher, M

2012-01-01

Research and development in the art of cycling using functional electrical stimulation (FES) of the paralysed leg muscles has been going on for around thirty years. A range of physiological benefits has been observed in clinical studies but an outstanding problem with FES-cycling is that efficiency and power output are very low. The present work had the following aims: (i) to provide a tutorial introduction to a novel framework and methods of estimation of metabolic efficiency using example data sets, and to propose benchmark measures for evaluating FES-cycling performance; (ii) to systematically review the literature pertaining specifically to the metabolic efficiency of FES-cycling, to analyse the observations and possible explanations for the low efficiency, and to pose hypotheses for future studies which aim to improve performance. We recommend the following as benchmark measures for assessment of the performance of FES-cycling: (i) total work efficiency, delta efficiency and stimulation cost; (ii) we recommend, further, that these benchmark measures be complemented by mechanical measures of maximum power output, sustainable steady-state power output and endurance. Performance assessments should be carried out at a well-defined operating point, i.e. under conditions of well controlled work rate and cadence, because these variables have a strong effect on energy expenditure. Future work should focus on the two main factors which affect FES-cycling performance, namely: (i) unfavourable biomechanics, i.e. crude recruitment of muscle groups, non-optimal timing of muscle activation, and lack of synergistic and antagonistic joint control; (ii) non-physiological recruitment of muscle fibres, i.e. mixed recruitment of fibres of different type and deterministic constant-frequency stimulation. We hypothesise that the following areas may bring better FES-cycling performance: (i) study of alternative stimulation strategies for muscle activation including irregular stimulation patterns (e.g. doublets, triplets, stochastic patterns) and variable frequency stimulation trains, where it appears that increasing frequency over time may be profitable; (ii) study of better timing parameters for the stimulated muscle groups, and addition of more muscle groups: this path may be approached using EMG studies and constrained numerical optimisation employing dynamic models; (iii) development of optimal stimulation protocols for muscle reconditioning and FES-cycle training.

Surrogate-based optimization of hydraulic fracturing in pre-existing fracture networks

NASA Astrophysics Data System (ADS)

Chen, Mingjie; Sun, Yunwei; Fu, Pengcheng; Carrigan, Charles R.; Lu, Zhiming; Tong, Charles H.; Buscheck, Thomas A.

2013-08-01

Hydraulic fracturing has been used widely to stimulate production of oil, natural gas, and geothermal energy in formations with low natural permeability. Numerical optimization of fracture stimulation often requires a large number of evaluations of objective functions and constraints from forward hydraulic fracturing models, which are computationally expensive and even prohibitive in some situations. Moreover, there are a variety of uncertainties associated with the pre-existing fracture distributions and rock mechanical properties, which affect the optimized decisions for hydraulic fracturing. In this study, a surrogate-based approach is developed for efficient optimization of hydraulic fracturing well design in the presence of natural-system uncertainties. The fractal dimension is derived from the simulated fracturing network as the objective for maximizing energy recovery sweep efficiency. The surrogate model, which is constructed using training data from high-fidelity fracturing models for mapping the relationship between uncertain input parameters and the fractal dimension, provides fast approximation of the objective functions and constraints. A suite of surrogate models constructed using different fitting methods is evaluated and validated for fast predictions. Global sensitivity analysis is conducted to gain insights into the impact of the input variables on the output of interest, and further used for parameter screening. The high efficiency of the surrogate-based approach is demonstrated for three optimization scenarios with different and uncertain ambient conditions. Our results suggest the critical importance of considering uncertain pre-existing fracture networks in optimization studies of hydraulic fracturing.

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond.

PubMed

Perdikaris, Paris; Karniadakis, George Em

2016-05-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. © 2016 The Author(s).

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond

PubMed Central

Perdikaris, Paris; Karniadakis, George Em

2016-01-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. PMID:27194481

The Relationship between Parameters of Long-Latency Evoked Potentials in a Multisensory Design.

PubMed

Hernández, Oscar H; García-Martínez, Rolando; Monteón, Victor

2016-10-01

In previous papers, we have shown that parameters of the omitted stimulus potential (OSP), which occurs at the end of a train of sensory stimuli, strongly depend on the modality. A train of stimuli also produces long-latency evoked potentials (LLEP) at the beginning of the train. This study is an extension of the OSP research, and it tested the relationship between parameters (ie, rate of rise, amplitude, and peak latency) of the P2 waves when trains of auditory, visual, or somatosensory stimuli were applied. The dynamics of the first 3 potentials in the train, related to habituation, were also studied. Twenty healthy young college volunteers participated in the study. As in the OSP, the P2 was faster and higher for auditory than for visual or somatosensory stimuli. The first P2 was swifter and higher than the second and the third potentials. The strength of habituation depends on the sensory modality and the parameter used. All these findings support the view that many long-latency brain potentials could share neural mechanisms related to wave generation. © EEG and Clinical Neuroscience Society (ECNS) 2015.

Comparing procedures on the acquisition and generalization of tacts for children with autism spectrum disorder.

PubMed

Schnell, Lauren K; Vladescu, Jason C; Kodak, Tiffany; Nottingham, Casey L

2018-06-17

Generalization is a critical outcome for individuals with autism spectrum disorder (ASD) who display new skills in a limited range of contexts. In the absence of proper planning, generalization may not be observed. The purpose of the current study was to directly compare serial to concurrent multiple exemplar training using total training time per exemplar, mean total training time, and exposures to mastery across three children diagnosed with ASD. Additionally, we assessed the efficiency of presenting secondary targets in the antecedent and consequence portions of learning trials and evaluated generalization to tacts not associated with direct teaching. Results suggested that all training conditions produced acquisition and generalization for trained and untrained exemplars. However, the serial multiple exemplar training condition was more efficient for two participants, whereas the instructive feedback condition was the most efficient for the third. Findings are discussed considering previous studies and areas for future research. © 2018 Society for the Experimental Analysis of Behavior.

Monitoring of performance and training in rowing.

PubMed

Mäestu, Jarek; Jürimäe, Jaak; Jürimäe, Toivo

2005-01-01

Rowing is a strength-endurance type of sport and competition performance depends on factors such as aerobic and anaerobic power, physical power, rowing technique and tactics. Therefore, a rower has to develop several capacities in order to be successful and a valid testing battery of a rower has to include parameters that are highly related to rowing performance. Endurance training is the mainstay in rowing. For the 2000 m race, power training at high velocities should be preferred to resistance training at low velocities in order to train more specifically during the off-season. The specific training of the international rower has to be approximately 70% of the whole training time. Several studies have reported different biochemical parameters for monitoring the training of rowers. There is some evidence that plasma leptin is more sensitive to training volume changes than specific stress hormones (e.g. cortisol, testosterone, growth hormone). In rowing, the stress hormone reactions to training volume and/or intensity changes are controversial. The Recovery-Stress Questionnaire for Athletes measures both stress and recovery, and may therefore be more effective than the previously used Borg ratio scale or the Profile of Mood States, which both focus mainly on the stress component. In the future, probably the most effective way to evaluate the training of rowers is to monitor both stress and recovery components at the same time, using both psychometric data together with the biochemical and performance parameters.

Evaluation of the effectiveness and efficiency of two stimulus prompt strategies with severely handicapped students.

PubMed Central

Steege, M W; Wacker, D P; McMahon, C M

1987-01-01

In this study we compared the effectiveness and efficiency of two treatment packages that used stimulus prompt sequences and task analyses for teaching community living skills to severely handicapped students. Four severely and multiply handicapped students were trained to perform four tasks: (a) making toast, (b) making popcorn, (c) operating a clothes dryer, and (d) operating a washing machine. Following baseline, each student was exposed to two types of training procedures, each involving a task analysis of the target behavior. Training Procedure 1 (Traditional) utilized a least-to-most restrictive prompt sequence. Training Procedure 2 (Prescriptive) utilized ongoing behavioral assessment data to identify discriminative stimuli. The assessment data were used to prescribe instructional prompts across successive training trials. Performance on the tasks was evaluated within a combination multiple baseline (across subjects) and probe (across tasks) design. Training conditions were counterbalanced across subjects and tasks. Results indicated that both training procedures were equally effective in increasing independent task acquisition for subjects on all tasks; however, the prescriptive procedure was the more efficient procedure. PMID:3667479

The Efficiency of the University Teaching and Learning Training Program (UTL) on Developing the Teaching Competencies of the Teaching Staff at Imam University

ERIC Educational Resources Information Center

AlRweithy, Eman; Alsaleem, Basma Issa

2014-01-01

This study aimed at presenting the University Teaching and Learning training program UTL and determining the efficiency of the UTL on developing the teaching competencies of the teaching staff at Imam University in Saudi Arabia. The results revealed that there were statistically significant differences between the performance of the training group…

Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

PubMed Central

Townsend, K A; Wollstein, G; Danks, D; Sung, K R; Ishikawa, H; Kagemann, L; Gabriele, M L; Schuman, J S

2010-01-01

Aims To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes. Methods Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created with backward elimination. Seven types of classifiers, including Support Vector Machines with radial basis (SVM-radial), and Recursive Partitioning and Regression Trees (RPART), were trained on the parameters. The area under the ROC curve (AUC) was calculated for classifiers, individual parameters and HRT3 glaucoma probability scores (GPS). Classifier AUCs and leave-one-out accuracy were compared with the highest individual parameter and GPS AUCs and accuracies. Results The highest AUC and accuracy for an individual parameter were 0.848 and 0.79, for vertical cup/disc ratio (vC/D). For GPS, global GPS performed best with AUC 0.829 and accuracy 0.78. SVM-radial with all parameters showed significant improvement over global GPS and vC/ D with AUC 0.916 and accuracy 0.85. RPART with all parameters provided significant improvement over global GPS with AUC 0.899 and significant improvement over global GPS and vC/D with accuracy 0.875. Conclusions Machine learning classifiers of HRT3 data provide significant enhancement over current methods for detection of glaucoma. PMID:18523087

Traffic sign recognition based on deep convolutional neural network

NASA Astrophysics Data System (ADS)

Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

2017-11-01

Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

Exploring DeepMedic for the purpose of segmenting white matter hyperintensity lesions

NASA Astrophysics Data System (ADS)

Lippert, Fiona; Cheng, Bastian; Golsari, Amir; Weiler, Florian; Gregori, Johannes; Thomalla, Götz; Klein, Jan

2018-02-01

DeepMedic, an open source software library based on a multi-channel multi-resolution 3D convolutional neural network, has recently been made publicly available for brain lesion segmentations. It has already been shown that segmentation tasks on MRI data of patients having traumatic brain injuries, brain tumors, and ischemic stroke lesions can be performed very well. In this paper we describe how it can efficiently be used for the purpose of detecting and segmenting white matter hyperintensity lesions. We examined if it can be applied to single-channel routine 2D FLAIR data. For evaluation, we annotated 197 datasets with different numbers and sizes of white matter hyperintensity lesions. Our experiments have shown that substantial results with respect to the segmentation quality can be achieved. Compared to the original parametrization of the DeepMedic neural network, the timings for training can be drastically reduced if adjusting corresponding training parameters, while at the same time the Dice coefficients remain nearly unchanged. This enables for performing a whole training process within a single day utilizing a NVIDIA GeForce GTX 580 graphics board which makes this library also very interesting for research purposes on low-end GPU hardware.

Effectiveness of functional training on cardiorespiratory parameters: a systematic review and meta-analysis of randomized controlled trials.

PubMed

Rezende Barbosa, Marianne Penachini da Costa de; Oliveira, Vinicius Cunha; Silva, Anne Kastelianne França da; Pérez-Riera, Andrés Ricardo; Vanderlei, Luiz Carlos

2017-07-28

Functional training is a new training vision that was prepared from the gesture imitation of daily activities. Although your use has become popular in clinical practice, the influence of the several cardiorespiratory adjustments performed during the functional training in different populations and conditions is unknown. So, the aim of this systematic review was to gather information in the literature regarding the influence of functional training on cardiorespiratory parameters. We conducted search strategies on MEDLINE, PEDro, EMBASE, SportDiscus and Cochrane to identify randomized controlled trials investigating the effects of functional training on cardiorespiratory parameters. Methodological quality of the included studies was assessed using the PEDro scale. Grading of Recommendations Assessment, Development and Evaluation (GRADE) summarized the evidence. Five original studies were included. Effects favoured functional training on oxygen consumption (VO 2 ) at intermediate-term follow-up: weighted mean difference -1·0 (95% CI: 5·4-3·3), P = 0·642, and a small and not clinically important effect observed on VO 2 favouring control at intermediate-term follow-up (i.e. mean difference of 1·30 (95% CI 1·07-1·53), P<0·001). According to the GRADE system, there is very low quality evidence that functional training is better than other interventions to improve cardiovascular parameters. This result encourages new searches about the theme. © 2017 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Effects of treadmill training with load addition on non-paretic lower limb on gait parameters after stroke: A randomized controlled clinical trial.

PubMed

Ribeiro, Tatiana S; Silva, Emília M G S; Silva, Isaíra A P; Costa, Mayara F P; Cavalcanti, Fabrícia A C; Lindquist, Ana R

2017-05-01

The addition of load on the non-paretic lower limb for the purpose of restraining this limb and stimulating the use of the paretic limb has been suggested to improve hemiparetic gait. However, the results are conflicting and only short-term effects have been observed. This study aims to investigate the effects of adding load on non-paretic lower limb during treadmill gait training as a multisession intervention on kinematic gait parameters after stroke. With this aim, 38 subacute stroke patients (mean time since stroke: 4.5 months) were randomly divided into two groups: treadmill training with load (equivalent to 5% of body weight) on the non-paretic ankle (experimental group) and treadmill training without load (control group). Both groups performed treadmill training during 30min per day, for two consecutive weeks (nine sessions). Spatiotemporal and angular gait parameters were assessed by a motion system analysis at baseline, post-training (at the end of 9days of interventions) and follow-up (40days after the end of interventions). Several post-training effects were demonstrated: patients walked faster and with longer paretic and non-paretic steps compared to baseline, and maintained these gains at follow-up. In addition, patients exhibited greater hip and knee joint excursion in both limbs at post-training, while maintaining most of these benefits at follow-up. All these improvements were observed in both groups. Although the proposal gait training program has provided better gait parameters for these subacute stroke patients, our data indicate that load addition used as a restraint may not provide additional benefits to gait training. Copyright © 2017 Elsevier B.V. All rights reserved.

Improving attention control in dysphoria through cognitive training: transfer effects on working memory capacity and filtering efficiency.

PubMed

Owens, Max; Koster, Ernst H W; Derakshan, Nazanin

2013-03-01

Impaired filtering of irrelevant information from working memory is thought to underlie reduced working memory capacity for relevant information in dysphoria. The current study investigated whether training-related gains in working memory performance on the adaptive dual n-back task could result in improved inhibitory function. Efficacy of training was monitored in a change detection paradigm allowing measurement of a sustained event-related potential asymmetry sensitive to working memory capacity and the efficient filtering of irrelevant information. Dysphoric participants in the training group showed training-related gains in working memory that were accompanied by gains in working memory capacity and filtering efficiency compared to an active control group. Results provide important initial evidence that behavioral performance and neural function in dysphoria can be improved by facilitating greater attentional control. Copyright © 2013 Society for Psychophysiological Research.

Can FES-Augmented Active Cycling Training Improve Locomotion in Post-Acute Elderly Stroke Patients?

PubMed Central

Peri, Elisabetta; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Nava, Claudia; Longoni, Valentina; Monticone, Marco; Ferrante, Simona

2016-01-01

Recent studies advocated the use of active cycling coupled with functional electrical stimulation to induce neuroplasticity and enhance functional improvements in stroke adult patients. The aim of this work was to evaluate whether the benefits induced by such a treatment are superior to standard physiotherapy. A single-blinded randomized controlled trial has been performed on post-acute elderly stroke patients. Patients underwent FES-augmented cycling training combined with voluntary pedaling or standard physiotherapy. The intervention consisted of fifteen 30-minutes sessions carried out within 3 weeks. Patients were evaluated before and after training, through functional scales, gait analysis and a voluntary pedaling test. Results were compared with an age-matched healthy group. Sixteen patients completed the training. After treatment, a general improvement of all clinical scales was obtained for both groups. Only the mechanical efficiency highlighted a group effect in favor of the experimental group. Although a group effect was not found for any other cycling or gait parameters, the experimental group showed a higher percentage of change with respect to the control group (e.g. the gait velocity was improved of 35.4% and 25.4% respectively, and its variation over time was higher than minimal clinical difference for the experimental group only). This trend suggests that differences in terms of motor recovery between the two groups may be achieved increasing the training dose. In conclusion, this study, although preliminary, showed that FES-augmented active cycling training seems to be effective in improving cycling and walking ability in post-acute elderly stroke patients. A higher sample size is required to confirm results. PMID:27990234

Can FES-Augmented Active Cycling Training Improve Locomotion in Post-Acute Elderly Stroke Patients?

PubMed

Peri, Elisabetta; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Nava, Claudia; Longoni, Valentina; Monticone, Marco; Ferrante, Simona

2016-06-13

Recent studies advocated the use of active cycling coupled with functional electrical stimulation to induce neuroplasticity and enhance functional improvements in stroke adult patients. The aim of this work was to evaluate whether the benefits induced by such a treatment are superior to standard physiotherapy. A single-blinded randomized controlled trial has been performed on post-acute elderly stroke patients. Patients underwent FES-augmented cycling training combined with voluntary pedaling or standard physiotherapy. The intervention consisted of fifteen 30-minutes sessions carried out within 3 weeks. Patients were evaluated before and after training, through functional scales, gait analysis and a voluntary pedaling test. Results were compared with an age-matched healthy group. Sixteen patients completed the training. After treatment, a general improvement of all clinical scales was obtained for both groups. Only the mechanical efficiency highlighted a group effect in favor of the experimental group. Although a group effect was not found for any other cycling or gait parameters, the experimental group showed a higher percentage of change with respect to the control group (e.g. the gait velocity was improved of 35.4% and 25.4% respectively, and its variation over time was higher than minimal clinical difference for the experimental group only). This trend suggests that differences in terms of motor recovery between the two groups may be achieved increasing the training dose. In conclusion, this study, although preliminary, showed that FES-augmented active cycling training seems to be effective in improving cycling and walking ability in post-acute elderly stroke patients. A higher sample size is required to confirm results.

Pupillometer-based Neurofeedback Cognitive Training to Improve Processing Speed and Social Functioning in Individuals at Clinical High Risk for Psychosis

PubMed Central

Choi, Jimmy; Stevens, Michael; Deasy, Melissa; Haber, Lawrence C.; Dewberry, Michael J.; Pearlson, Godfrey D.; Corcoran, Cheryl M.; Javitt, Daniel C.; Fiszdon, Joanna M.

2016-01-01

Objective Among individuals at clinical high risk (CHR) for psychosis, processing speed (PS) has been related to social and role functioning regardless of conversion to schizophrenia. This information processing dysfunction is a gateway to broader behavioral deficits such as difficulty executing social behaviors. We examined the feasibility of improving information processing relevant to social situations in CHR, including its sustainability at 2-month follow-up, and its association with concurrent social function. Methods This was a double-blind RCT in which 62 CHR participants were randomized to Processing Speed Training (PST) or an active control matched for training format and the same dose and duration of treatment. PST is a tablet-based program that uses pupillometry-based neurofeedback to continually adjust training parameters for an optimal neurocognitive load and to improve visual scanning efficiency by inhibiting selection of non-essential targets and discriminating figure-ground details. Results The PST group showed faster motoric and non-motoric PS at post training and 2-month follow-up. At 2 month follow-up, the PST group reported better overall social adjustment. Changes in PS from baseline to 2 months were correlated with overall social adjustment and social avoidance in the entire sample. Conclusions and Implications for Practice This is the first study to test focal neurofeedback-based cognitive training for PS deficits in the putatively prodromal phase of schizophrenia to address associated social morbidity. Targeting PS appears to be a promising pathway to decreasing co-morbidity and mitigating a risk factor for psychosis. PMID:27560455

Time to Exhaustion at the VO2max Velocity in Swimming: A Review

PubMed Central

Fernandes, Ricardo J.; Vilas-Boas, J. Paulo

2012-01-01

The aim of this study was to present a review on the time to exhaustion at the minimum swimming velocity corresponding to maximal oxygen consumption (TLim-vVO2max). This parameter is critical both for the aerobic power and the lactate tolerance bioenergetical training intensity zones, being fundamental to characterize it, and to point out its main determinants. The few number of studies conducted in this topic observed that swimmers were able to maintain an exercise intensity corresponding to maximal aerobic power during 215 to 260 s (elite swimmers), 230 to 260 s (high level swimmers) and 310 to 325 s (low level swimmers), and no differences between genders were reported. TLim-vVO2max main bioenergetic and functional determinants were swimming economy and VO2 slow component (direct relationship), and vVO2max, velocity at anaerobic threshold and blood lactate production (inverse relationship); when more homogeneous groups of swimmers were analysed, the inverse correlation value between TLim-vVO2max and vVO2max was not so evident. In general, TLim-vVO2max was not related to VO2max. TLim-vVO2max seems also to be influenced by stroking parameters, with a direct relationship to stroke length and stroke index, and an inverse correlation with stroke rate. Assessing TLim-vVO2max, together with the anaerobic threshold and the biomechanical general parameters, will allow a larger spectrum of testing protocols application, helping to build more objective and efficient training programs. PMID:23486651

Effect of exercise training on ventilatory efficiency in patients with heart disease: a review.

PubMed

Prado, D M L; Rocco, E A; Silva, A G; Rocco, D F; Pacheco, M T; Furlan, V

2016-06-20

The analysis of ventilatory efficiency in cardiopulmonary exercise testing has proven useful for assessing the presence and severity of cardiorespiratory diseases. During exercise, efficient pulmonary gas exchange is characterized by uniform matching of lung ventilation with perfusion. By contrast, mismatching is marked by inefficient pulmonary gas exchange, requiring increased ventilation for a given CO2 production. The etiology of increased and inefficient ventilatory response to exercise in heart disease is multifactorial, involving both peripheral and central mechanisms. Exercise training has been recommended as non-pharmacological treatment for patients with different chronic cardiopulmonary diseases. In this respect, previous studies have reported improvements in ventilatory efficiency after aerobic exercise training in patients with heart disease. Against this background, the primary objective of the present review was to discuss the pathophysiological mechanisms involved in abnormal ventilatory response to exercise, with an emphasis on both patients with heart failure syndrome and coronary artery disease. Secondly, special focus was dedicated to the role of aerobic exercise training in improving indices of ventilatory efficiency among these patients, as well as to the underlying mechanisms involved.

Locomotor-Respiratory Coupling in Wheelchair Racing Athletes: A Pilot Study.

PubMed

Perret, Claudio; Wenger, Martin; Leicht, Christof A; Goosey-Tolfrey, Victoria L

2016-01-01

In wheelchair racing, respiratory muscles of the rib cage are concomitantly involved in non-ventilatory functions during wheelchair propulsion. However, the relationship between locomotor-respiratory coupling (LRC: the ratio between push and breathing frequency), respiratory parameters and work efficiency is unknown. Therefore, the aim of the present study was to investigate the LRC in wheelchair racers over different race distances. Eight trained and experienced wheelchair racers completed three time-trials over the distances of 400, 800, and 5000 m on a training roller in randomized order. During the time trials, ventilatory and gas exchange variables as well as push frequency were continuously registered to determine possible LRC strategies. Four different coupling ratios were identified, namely 1:1; 2:1, 3:1 as well as a 1:1/2:1 alternating type, respectively. The 2:1 coupling was the most dominant type. The 1:1/2:1 alternating coupling type was found predominantly during the 400 m time-trial. Longer race distances tended to result in an increased coupling ratio (e.g., from 1:1 toward 2:1), and an increase in coupling ratio toward a more efficient respiration was found over the 5000 m distance. A significant correlation (r = 0.80, p < 0.05) between respiratory frequency and the respiratory equivalent for oxygen was found for the 400 m and the 800 m time-trials. These findings suggest that a higher coupling ratio indicates enhanced breathing work efficiency with a concomitant deeper and slower respiration during wheelchair racing. Thus, the selection of an appropriate LRC strategy may help to optimize wheelchair racing performance.

Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

NASA Astrophysics Data System (ADS)

Janardhanan, S.; Datta, B.

2011-12-01

Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of saltwater intrusion are considered. The salinity levels resulting at strategic locations due to these pumping are predicted using the ensemble surrogates and are constrained to be within pre-specified levels. Different realizations of the concentration values are obtained from the ensemble predictions corresponding to each candidate solution of pumping. Reliability concept is incorporated as the percent of the total number of surrogate models which satisfy the imposed constraints. The methodology was applied to a realistic coastal aquifer system in Burdekin delta area in Australia. It was found that all optimal solutions corresponding to a reliability level of 0.99 satisfy all the constraints and as reducing reliability level decreases the constraint violation increases. Thus ensemble surrogate model based simulation-optimization was found to be useful in deriving multi-objective optimal pumping strategies for coastal aquifers under parameter uncertainty.

Active relearning for robust supervised classification of pulmonary emphysema

NASA Astrophysics Data System (ADS)

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

Design of a Low-Energy FARAD Thruster

NASA Technical Reports Server (NTRS)

Polzin, K. A.; Rose, M. F.; Miller, R.; Best, S.; Owens, T.; Dankanich, J.

2007-01-01

The design of an electrodeless thruster that relies on a pulsed, rf-assisted discharge and electromagnetic acceleration using an inductive coil is presented. The thruster design is optimized using known performance,scaling parameters, and experimentally-determined design rules, with design targets for discharge energy, plasma exhaust velocity; and thrust efficiency of 100 J/pulse, 25 km/s, and 50%, respectively. Propellant is injected using a high-speed gas valve and preionized by a pulsed-RF signal supplied by a vector inversion generator, allowing for current sheet formation at lower discharge voltages and energies relative to pulsed inductive accelerators that do not employ preionization. The acceleration coil is designed to possess an inductance of at least 700 nH while the target stray (non-coil) inductance in the circuit is 70 nH. A Bernardes and Merryman pulsed power train or a pulse compression power train provide current to the acceleration coil and solid-state components are used to switch both powertrains.

An experimental vestibular neural prosthesis: design and preliminary results with rhesus monkeys stimulated with modulated pulses.

PubMed

Nie, Kaibao; Ling, Leo; Bierer, Steven M; Kaneko, Chris R S; Fuchs, Albert F; Oxford, Trey; Rubinstein, Jay T; Phillips, James O

2013-06-01

A vestibular neural prosthesis was designed on the basis of a cochlear implant for treatment of Meniere's disease and other vestibular disorders. Computer control software was developed to generate patterned pulse stimuli for exploring optimal parameters to activate the vestibular nerve. Two rhesus monkeys were implanted with the prototype vestibular prosthesis and they were behaviorally evaluated post implantation surgery. Horizontal and vertical eye movement responses to patterned electrical pulse stimulations were collected on both monkeys. Pulse amplitude modulated (PAM) and pulse rate modulated (PRM) trains were applied to the lateral canal of each implanted animal. Robust slow-phase nystagmus responses following the PAM or PRM modulation pattern were observed in both implanted monkeys in the direction consistent with the activation of the implanted canal. Both PAM and PRM pulse trains can elicit a significant amount of in-phase modulated eye velocity changes and they could potentially be used for efficiently coding head rotational signals in future vestibular neural prostheses.

Neuromuscular training based on whole body vibration in children with spina bifida: a retrospective analysis of a new physiotherapy treatment program.

PubMed

Stark, C; Hoyer-Kuhn, H-K; Semler, O; Hoebing, L; Duran, I; Cremer, R; Schoenau, E

2015-02-01

Spina bifida is the most common congenital cause of spinal cord lesions resulting in paralysis and secondary conditions like osteoporosis due to immobilization. Physiotherapy is performed for optimizing muscle function and prevention of secondary conditions. Therefore, training of the musculoskeletal system is one of the major aims in the rehabilitation of children with spinal cord lesions. The neuromuscular physiotherapy treatment program Auf die Beine combines 6 months of home-based whole body vibration (WBV) with interval blocks at the rehabilitation center: 13 days of intensive therapy at the beginning and 6 days after 3 months. Measurements are taken at the beginning (M0), after 6 months of training (M6), and after a 6-month follow-up period (M12). Gait parameters are assessed by ground reaction force and motor function by the Gross Motor Function Measurement (GMFM-66). Sixty children (mean age 8.71 ± 4.7 years) who participated in the program until February 2014 were retrospectively analyzed. Walking velocity improved significantly by 0.11 m/s (p = 0.0026) and mobility (GMFM-66) by 2.54 points (p = 0.001) after the training. All changes at follow-up were not significant, but significant changes were observed after the training period. Decreased contractures were observed with increased muscle function. Significant improvements in motor function were observed after the active training period of the new neuromuscular training concept. This first analysis of the new neuromuscular rehabilitation concept Auf die Beine showed encouraging results for a safe and efficient physiotherapy treatment program which increases motor function in children with spina bifida.

Event-based image recognition applied in tennis training assistance

NASA Astrophysics Data System (ADS)

Wawrzyniak, Zbigniew M.; Kowalski, Adam

2016-09-01

This paper presents a concept of a real-time system for individual tennis training assistance. The system is supposed to provide user (player) with information on his strokes accuracy as well as other training quality parameters such as velocity and rotation of the ball during its flight. The method is based on image processing methods equipped with developed explorative analysis of the events and their description by parameters of the movement. There has been presented the concept for further deployment to create a complete system that could assist tennis player during individual training.

Concluding remarks: nutritional strategies to support the adaptive response to prolonged exercise training.

PubMed

van Loon, Luc J C; Tipton, Kevin D

2013-01-01

Nutrition plays a key role in allowing the numerous training hours to be translated into useful adaptive responses of various tissues in the individual athlete. Research over the last decade has shown many examples of the impact of dietary interventions to modulate the skeletal muscle adaptive response to prolonged exercise training. Proper nutritional coaching should be applied throughout both training and competition, each with their specific requirements regarding nutrient provision. Such dietary support will improve exercise training efficiency and, as such, further increase performance capacity. Here, we provide an overview on the properties of various nutritional interventions that may be useful to support the adaptive response to exercise training and competition and, as such, to augment exercise training efficiency. Copyright © 2013 Nestec Ltd., Vevey/S. Karger AG, Basel.

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

PubMed

Law, L A Frey; Shields, R K

2007-01-01

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

Incremental cost and cost-effectiveness of low-dose, high-frequency training in basic emergency obstetric and newborn care as compared to status quo: part of a cluster-randomized training intervention evaluation in Ghana.

PubMed

Willcox, Michelle; Harrison, Heather; Asiedu, Amos; Nelson, Allyson; Gomez, Patricia; LeFevre, Amnesty

2017-12-06

Low-dose, high-frequency (LDHF) training is a new approach best practices to improve clinical knowledge, build and retain competency, and transfer skills into practice after training. LDHF training in Ghana is an opportunity to build health workforce capacity in critical areas of maternal and newborn health and translate improved capacity into better health outcomes. This study examined the costs of an LDHF training approach for basic emergency obstetric and newborn care and calculates the incremental cost-effectiveness of the LDHF training program for health outcomes of newborn survival, compared to the status quo alternative of no training. The costs of LDHF were compared to costs of traditional workshop-based training per provider trained. Retrospective program cost analysis with activity-based costing was used to measure all resources of the LDHF training program over a 3-year analytic time horizon. Economic costs were estimated from financial records, informant interviews, and regional market prices. Health effects from the program's impact evaluation were used to model lives saved and disability-adjusted life years (DALYs) averted. Uncertainty analysis included one-way and probabilistic sensitivity analysis to explore incremental cost-effectiveness results when fluctuating key parameters. For the 40 health facilities included in the evaluation, the total LDHF training cost was $823,134. During the follow-up period after the first LDHF training-1 year at each participating facility-approximately 544 lives were saved. With deterministic calculation, these findings translate to $1497.77 per life saved or $53.07 per DALY averted. Probabilistic sensitivity analysis, with mean incremental cost-effectiveness ratio of $54.79 per DALY averted ($24.42-$107.01), suggests the LDHF training program as compared to no training has 100% probability of being cost-effective above a willingness to pay threshold of $1480, Ghana's gross national income per capita in 2015. This study provides insight into the investment of LDHF training and value for money of this approach to training in-service providers on basic emergency obstetric and newborn care. The LDHF training approach should be considered for expansion in Ghana and integrated into existing in-service training programs and health system organizational structures for lower cost and more efficiency at scale.

Emergence of representations through repeated training on pronouncing novel letter combinations leads to efficient reading.

PubMed

Takashima, Atsuko; Hulzink, Iris; Wagensveld, Barbara; Verhoeven, Ludo

2016-08-01

Printed text can be decoded by utilizing different processing routes depending on the familiarity of the script. A predominant use of word-level decoding strategies can be expected in the case of a familiar script, and an almost exclusive use of letter-level decoding strategies for unfamiliar scripts. Behavioural studies have revealed that frequently occurring words are read more efficiently, suggesting that these words are read in a more holistic way at the word-level, than infrequent and unfamiliar words. To test whether repeated exposure to specific letter combinations leads to holistic reading, we monitored both behavioural and neural responses during novel script decoding and examined changes related to repeated exposure. We trained a group of Dutch university students to decode pseudowords written in an unfamiliar script, i.e., Korean Hangul characters. We compared behavioural and neural responses to pronouncing trained versus untrained two-character pseudowords (equivalent to two-syllable pseudowords). We tested once shortly after the initial training and again after a four days' delay that included another training session. We found that trained pseudowords were pronounced faster and more accurately than novel combinations of radicals (equivalent to letters). Imaging data revealed that pronunciation of trained pseudowords engaged the posterior temporo-parietal region, and engagement of this network was predictive of reading efficiency a month later. The results imply that repeated exposure to specific combinations of graphemes can lead to emergence of holistic representations that result in efficient reading. Furthermore, inter-individual differences revealed that good learners retained efficiency more than bad learners one month later. Copyright © 2016 Elsevier Ltd. All rights reserved.

78 FR 68039 - Privacy Act of 1974; System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-13

... leadership regarding travel, training and supplies. Data is used by leadership to effectively and efficiently... of providing operational metrics, tracking budgets, and presenting work products to senior leadership regarding travel, training, and supply. Data is used by leadership to effectively and efficiently make...

Efficient temporal and interlayer parameter prediction for weighted prediction in scalable high efficiency video coding

NASA Astrophysics Data System (ADS)

Tsang, Sik-Ho; Chan, Yui-Lam; Siu, Wan-Chi

2017-01-01

Weighted prediction (WP) is an efficient video coding tool that was introduced since the establishment of the H.264/AVC video coding standard, for compensating the temporal illumination change in motion estimation and compensation. WP parameters, including a multiplicative weight and an additive offset for each reference frame, are required to be estimated and transmitted to the decoder by slice header. These parameters cause extra bits in the coded video bitstream. High efficiency video coding (HEVC) provides WP parameter prediction to reduce the overhead. Therefore, WP parameter prediction is crucial to research works or applications, which are related to WP. Prior art has been suggested to further improve the WP parameter prediction by implicit prediction of image characteristics and derivation of parameters. By exploiting both temporal and interlayer redundancies, we propose three WP parameter prediction algorithms, enhanced implicit WP parameter, enhanced direct WP parameter derivation, and interlayer WP parameter, to further improve the coding efficiency of HEVC. Results show that our proposed algorithms can achieve up to 5.83% and 5.23% bitrate reduction compared to the conventional scalable HEVC in the base layer for SNR scalability and 2× spatial scalability, respectively.

Effects of 12-week supervised treadmill training on spatio-temporal gait parameters in patients with claudication.

PubMed

Konik, Anita; Kuklewicz, Stanisław; Rosłoniec, Ewelina; Zając, Marcin; Spannbauer, Anna; Nowobilski, Roman; Mika, Piotr

2016-01-01

The purpose of the study was to evaluate selected temporal and spatial gait parameters in patients with intermittent claudication after completion of 12-week supervised treadmill walking training. The study included 36 patients (26 males and 10 females) aged: mean 64 (SD 7.7) with intermittent claudication. All patients were tested on treadmill (Gait Trainer, Biodex). Before the programme and after its completion, the following gait biomechanical parameters were tested: step length (cm), step cycle (cycle/s), leg support time (%), coefficient of step variation (%) as well as pain-free walking time (PFWT) and maximal walking time (MWT) were measured. Training was conducted in accordance with the current TASC II guidelines. After 12 weeks of training, patients showed significant change in gait biomechanics consisting in decreased frequency of step cycle (p < 0.05) and extended step length (p < 0.05). PFWT increased by 96% (p < 0.05). MWT increased by 100% (p < 0.05). After completing the training, patients' gait was more regular, which was expressed via statistically significant decrease of coefficient of variation (p < 0.05) for both legs. No statistically significant relation between the post-training improvement of PFWT and MWT and step length increase and decreased frequency of step cycle was observed (p > 0.05). Twelve-week treadmill walking training programme may lead to significant improvement of temporal and spatial gait parameters in patients with intermittent claudication. Twelve-week treadmill walking training programme may lead to significant improvement of pain-free walking time and maximum walking time in patients with intermittent claudication.

Plyometric Long Jump Training With Progressive Loading Improves Kinetic and Kinematic Swimming Start Parameters.

PubMed

Rebutini, Vanessa Z; Pereira, Gleber; Bohrer, Roberta C D; Ugrinowitsch, Carlos; Rodacki, André L F

2016-09-01

Rebutini, VZ, Pereira, G, Bohrer, RCD, Ugrinowitsch, C, and Rodacki, ALF. Plyometric long jump training with progressive loading improves kinetic and kinematic swimming start parameters. J Strength Cond Res 30(9): 2392-2398, 2016-This study was aimed to determine the effects of a plyometric long jump training program on torque around the lower limb joints and kinetic and kinematics parameters during the swimming jump start. Ten swimmers performed 3 identical assessment sessions, measuring hip and knee muscle extensors during maximal voluntary isometric contraction and kinetic and kinematics parameters during the swimming jump start, at 3 instants: INI (2 weeks before the training program, control period), PRE (2 weeks after INI measurements), and POST (24-48 hours after 9 weeks of training). There were no significant changes from INI to PRE measurements. However, the peak torque and rate of torque development increased significantly from PRE to POST measurements for both hip (47 and 108%) and knee (24 and 41%) joints. There were significant improvements to the horizontal force (7%), impulse (9%), and angle of resultant force (19%). In addition, there were significant improvements to the center of mass displacement (5%), horizontal takeoff velocity (16%), horizontal velocity at water entrance (22%), and peak angle velocity for the knee (15%) and hip joints (16%). Therefore, the plyometric long jump training protocol was effective to enhance torque around the lower limb joints and to control the resultant vector direction, to increase the swimming jump start performance. These findings suggest that coaches should use long jump training instead of vertical jump training to improve swimming start performance.

FDI and Accommodation Using NN Based Techniques

NASA Astrophysics Data System (ADS)

Garcia, Ramon Ferreiro; de Miguel Catoira, Alberto; Sanz, Beatriz Ferreiro

Massive application of dynamic backpropagation neural networks is used on closed loop control FDI (fault detection and isolation) tasks. The process dynamics is mapped by means of a trained backpropagation NN to be applied on residual generation. Process supervision is then applied to discriminate faults on process sensors, and process plant parameters. A rule based expert system is used to implement the decision making task and the corresponding solution in terms of faults accommodation and/or reconfiguration. Results show an efficient and robust FDI system which could be used as the core of an SCADA or alternatively as a complement supervision tool operating in parallel with the SCADA when applied on a heat exchanger.

Geo-Engineering through Internet Informatics (GEMINI)

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

Watney, W. Lynn; Doveton, John H.; Victorine, John R.

GEMINI will resolve reservoir parameters that control well performance; characterize subtle reservoir properties important in understanding and modeling hydrocarbon pore volume and fluid flow; expedite recognition of bypassed, subtle, and complex oil and gas reservoirs at regional and local scale; differentiate commingled reservoirs; build integrated geologic and engineering model based on real-time, iterate solutions to evaluate reservoir management options for improved recovery; provide practical tools to assist the geoscientist, engineer, and petroleum operator in making their tasks more efficient and effective; enable evaluations to be made at different scales, ranging from individual well, through lease, field, to play and regionmore » (scalable information infrastructure); and provide training and technology transfer to evaluate capabilities of the client.« less

Quantum algorithm for support matrix machines

NASA Astrophysics Data System (ADS)

Duan, Bojia; Yuan, Jiabin; Liu, Ying; Li, Dan

2017-09-01

We propose a quantum algorithm for support matrix machines (SMMs) that efficiently addresses an image classification problem by introducing a least-squares reformulation. This algorithm consists of two core subroutines: a quantum matrix inversion (Harrow-Hassidim-Lloyd, HHL) algorithm and a quantum singular value thresholding (QSVT) algorithm. The two algorithms can be implemented on a universal quantum computer with complexity O[log(npq) ] and O[log(pq)], respectively, where n is the number of the training data and p q is the size of the feature space. By iterating the algorithms, we can find the parameters for the SMM classfication model. Our analysis shows that both HHL and QSVT algorithms achieve an exponential increase of speed over their classical counterparts.

Training in the Context of a Reduction in Working Hours.

ERIC Educational Resources Information Center

Trautmann, Jacques

2001-01-01

Discusses the increased importance of training to employers, the need to manage training time efficiently, and the impact of legislation regulated training leave in France. Finds the beginnings of a shift of training from work time to leisure time. (Contains 19 references.) (SK)

Standardized Patient Training Programs: an Efficient Solution to the Call for Quality Improvement in Oncologist Communication Skills.

PubMed

Ju, Melody; Berman, Abigail T; Vapiwala, Neha

2015-09-01

Several key medical and oncologic professional societies have endorsed the importance of physician communication as a quality improvement metric. Despite this clear message, there remain substantial barriers to communication skills training (CST) in oncologic specialties. Herein, we describe the major barriers to communications training and propose standardized patient (SP) programs as efficient and strategic starting points and as expansion opportunities for new and existing CSTs.

Does assist-as-needed upper limb robotic therapy promote participation in repetitive activity-based motor training in sub-acute stroke patients with severe paresis?

PubMed

Grosmaire, Anne-Gaëlle; Duret, Christophe

2017-01-01

Repetitive, active movement-based training promotes brain plasticity and motor recovery after stroke. Robotic therapy provides highly repetitive therapy that reduces motor impairment. However, the effect of assist-as-needed algorithms on patient participation and movement quality is not known. To analyze patient participation and motor performance during highly repetitive assist-as-needed upper limb robotic therapy in a retrospective study. Sixteen patients with sub-acute stroke carried out a 16-session upper limb robotic training program combined with usual care. The Fugl-Meyer Assessment (FMA) score was evaluated pre and post training. Robotic assistance parameters and Performance measures were compared within and across sessions. Robotic assistance did not change within-session and decreased between sessions during the training program. Motor performance did not decrease within-session and improved between sessions. Velocity-related assistance parameters improved more quickly than accuracy-related parameters. An assist-as-needed-based upper limb robotic training provided intense and repetitive rehabilitation and promoted patient participation and motor performance, facilitating motor recovery.

Pelvic floor muscle training protocol for stress urinary incontinence in women: A systematic review.

PubMed

Oliveira, Marlene; Ferreira, Margarida; Azevedo, Maria João; Firmino-Machado, João; Santos, Paula Clara

2017-07-01

Strengthening exercises for pelvic floor muscles (SEPFM) are considered the first approach in the treatment of stress urinary incontinence (SUI). Nevertheless, there is no evidence about training parameters. To identify the protocol and/or most effective training parameters in the treatment of female SUI. A literature research was conducted in the PubMed, Cochrane Library, PEDro, Web of Science and Lilacs databases, with publishing dates ranging from January 1992 to March 2014. The articles included consisted of English-speaking experimental studies in which SEPFM were compared with placebo treatment (usual or untreated). The sample had a diagnosis of SUI and their age ranged between 18 and 65 years. The assessment of methodological quality was performed based on the PEDro scale. Seven high methodological quality articles were included in this review. The sample consisted of 331 women, mean age 44.4±5.51 years, average duration of urinary loss of 64±5.66 months and severity of SUI ranging from mild to severe. SEPFM programs included different training parameters concerning the PFM. Some studies have applied abdominal training and adjuvant techniques. Urine leakage cure rates varied from 28.6 to 80%, while the strength increase of PFM varied from 15.6 to 161.7%. The most effective training protocol consists of SEPFM by digital palpation combined with biofeedback monitoring and vaginal cones, including 12 week training parameters, and ten repetitions per series in different positions compared with SEPFM alone or a lack of treatment.

[Stabilometry, Electromyography and Electroencephalography in Postmenopausal Women after Training of the Leg Support Sensation].

PubMed

Bazanova, O M; Kholodina, N V; Podoinikov, A S; Nikolenko, E D

2015-01-01

Ageing, lack of physical activity and sedentary lifestyle cause disorders of the sensorimotor system of postural control. The role of support afferentation in the changes in cortical activity in balance impairments has not been studied yet. The purpose of this study was to investigate the changes in the stabilographic parameters of the body center of gravity, alpha activity indices of the electroencephalography (EEG) and electromyographic (EMG) measurements of forehead muscle tone in response to visual activation in standing and sitting positions in postmenopausal women after and without training of leg support sensation (LSS) The variables were compared between 3 groups: Group A (n = 12, age: 66 ± 9 years)--women who have trained LSS with the help of Aikido techniques for 8 years; group F (n = 12, age: 65 ± 6 years)--women who have attended Fitness training for 8 years; group N (n = 11, age: 66 ± 7 years)--women who have not taken physical exercises for the last 8 years. It was found that in group N a change in body position from "sitting" to "standing" leads to a much greater increase in the area of stabilogram and in the energy expenditure needed to maintain the bal- ance than in groups A and F. Posture changes from sitting to standing position increases the tension of the forehead muscles and the suppression of alpha-1-amplitude, but decreases the power in high- and low-frequency alpha-band of EEG and the width of alpha-band in group N. In women ofgroup F the posture change does not result in an increase in EMG and signs of activation or tension in EEG; in group A it leads to a decrease of visual activation indices and psychoemotional tension and to an increase in power in alpha-2-band which is a sign of neuronal efficiency. Basing on these data, we can conclude that training focused on support afferentation in postmenopausal women decreases the psychoemotional tension and increases neuronal efficiency ofsensorimotor integration of postural control system and can be used in the prevention of falls in elderly people.

Neuromuscular factors associated with decline in long-distance running performance in master athletes.

PubMed

Brisswalter, Jeanick; Nosaka, Kazunori

2013-01-01

This review focuses on neuromuscular factors that may affect endurance performance in master athletes. During the last decade, due to the rapid increase in the number of master or veteran participants in endurance sporting competitions, many studies attempted to identify metabolic factors associated with the decrease in endurance, especially long-distance running performance with ageing, focusing on decreases in maximal oxygen consumption. However, neuromuscular factors have been less studied despite the well-known phenomena of strength loss with ageing. For master athletes to perform better in long-distance running events, it is important to reduce muscle fatigue and/or muscle damage, to improve locomotion efficiency and to facilitate recovery. To date, no consensus exists that regular endurance training is beneficial for improving locomotion efficiency, reducing muscle fatigue and muscle damage, and enhancing recovery capacity in master athletes. Some recent studies seem to indicate that master athletes have similar muscle damage to young athletes, but they require a longer recovery time after a long-distance running event. Further analyses of these parameters in master athletes require more experimental and practical interest from researchers and coaches. In particular, more attention should be directed towards the capacity to maintain muscle function with training and the role of neuromuscular factors in long-distance performance decline with ageing using a more cellular and molecular approach.

Hydrodynamic profile of young swimmers: changes over a competitive season.

PubMed

Barbosa, T M; Morais, J E; Marques, M C; Silva, A J; Marinho, D A; Kee, Y H

2015-04-01

The aim of this study was to analyze the changes in the hydrodynamic profile of young swimmers over a competitive season and to compare the variations according to a well-designed training periodization. Twenty-five swimmers (13 boys and 12 girls) were evaluated in (a) October (M1); (b) March (M2); and (c) June (M3). Inertial and anthropometrical measures included body mass, swimmer's added water mass, height, and trunk transverse surface area. Swimming efficiency was estimated by the speed fluctuation, stroke index, and approximate entropy. Active drag was estimated with the velocity perturbation method and the passive drag with the gliding decay method. Hydrodynamic dimensionless numbers (Froude and Reynolds numbers) and hull velocity (i.e., speed at Froude number = 0.42) were also calculated. No variable presented a significant gender effect. Anthropometrics and inertial parameters plus dimensionless numbers increased over time. Swimming efficiency improved between M1 and M3. There was a trend for both passive and active drag increase from M1 to M2, but being lower at M3 than at M1. Intra-individual changes between evaluation moments suggest high between- and within-subject variations. Therefore, hydrodynamic changes over a season occur in a non-linear fashion way, where the interplay between growth and training periodization explain the unique path flow selected by each young swimmer. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks.

PubMed

Weng, Jian; Xie, Ye; Wang, Chunjie; Chen, Feiyan

2017-08-18

Previous studies in the field of abacus-based mental calculation (AMC) training have shown that this training has the potential to enhance a wide variety of cognitive abilities. It can also generate specific changes in brain structure and function. However, there is lack of studies investigating the impact of AMC training on the characteristics of brain networks. In this study, utilizing graph-based network analysis, we compared topological properties of brain functional networks between an AMC group and a matched control group. Relative to the control group, the AMC group exhibited higher nodal degrees in bilateral calcarine sulcus and increased local efficiency in bilateral superior occipital gyrus and right cuneus. The AMC group also showed higher nodal local efficiency in right fusiform gyrus, which was associated with better math ability. However, no relationship was significant in the control group. These findings provide evidence that long-term AMC training may improve information processing efficiency in visual-spatial related regions, which extend our understanding of training plasticity at the brain network level.

Effects and dose–response relationships of resistance training on physical performance in youth athletes: a systematic review and meta-analysis

PubMed Central

Lesinski, Melanie; Prieske, Olaf; Granacher, Urs

2016-01-01

Objectives To quantify age, sex, sport and training type-specific effects of resistance training on physical performance, and to characterise dose–response relationships of resistance training parameters that could maximise gains in physical performance in youth athletes. Design Systematic review and meta-analysis of intervention studies. Data sources Studies were identified by systematic literature search in the databases PubMed and Web of Science (1985–2015). Weighted mean standardised mean differences (SMDwm) were calculated using random-effects models. Eligibility criteria for selecting studies Only studies with an active control group were included if these investigated the effects of resistance training in youth athletes (6–18 years) and tested at least one physical performance measure. Results 43 studies met the inclusion criteria. Our analyses revealed moderate effects of resistance training on muscle strength and vertical jump performance (SMDwm 0.8–1.09), and small effects on linear sprint, agility and sport-specific performance (SMDwm 0.58–0.75). Effects were moderated by sex and resistance training type. Independently computed dose–response relationships for resistance training parameters revealed that a training period of >23 weeks, 5 sets/exercise, 6–8 repetitions/set, a training intensity of 80–89% of 1 repetition maximum (RM), and 3–4 min rest between sets were most effective to improve muscle strength (SMDwm 2.09–3.40). Summary/conclusions Resistance training is an effective method to enhance muscle strength and jump performance in youth athletes, moderated by sex and resistance training type. Dose–response relationships for key training parameters indicate that youth coaches should primarily implement resistance training programmes with fewer repetitions and higher intensities to improve physical performance measures of youth athletes. PMID:26851290

Residents' training to retinal photocoagulation: virtual reality compared to conventional apprenticeship

NASA Astrophysics Data System (ADS)

Peugnet, Frederic; Dubois, Patrick; Rouland, Jean-Francois

1998-06-01

Virtual reality is one of these recent technologies which can provide an efficient help in the field of surgical apprenticeship. We achieved an original training simulator for retinal photocoagulation destined to the residents of the ophthalmological department. This paper describes the comparison between this new training tool and the conventional practice. Two groups of residents, randomly selected, were trained exclusively by one of these methods. These two groups were under the responsibility of two distinct experts. A final evaluation was made by a third and different expert, ignoring the training mode practiced by each of the residents. The study lasted six months. The results show that this new training mode is at least as efficient as the current one in terms of elapsed time and efficiency. It may even reduce the training duration. These results confirm that a pedagogical simulator could give a new approach in the medical teaching, particularly in its management. Such a device may solve the problems of practitioner's lack of disponibility and of patients' safety and comfort during a conventional training. Furthermore, it could bring an objective way to value the students; practical ability. On the other hand, this preliminary study emphasizes the difficulties in introducing a new modality in a traditional teaching environment.

Effect of fed- versus fasted state resistance training during Ramadan on body composition and selected metabolic parameters in bodybuilders

PubMed Central

2013-01-01

Background Muslim bodybuilders often continue training during Ramadan. However, the effect of resistance training in a fasted versus a fed state during Ramadan on body composition and metabolic parameters in bodybuilders is not well known. The aim of this study was to evaluate the effects of resistance training in a fasted versus a fed state during Ramadan on body composition and metabolic parameters in bodybuilders. Methods Sixteen men were allocated to two groups: Eight practicing resistance training in the late afternoon in a fasted state (FAST), and eight training in the late evening in an acutely fed state (FED) during Ramadan. All visited the laboratory in the morning two days before the start of Ramadan (Bef-R) and on the 29th day of Ramadan (End-R) for anthropometric measurement, completion of a dietary questionnaire, and provision of fasting blood and urine samples. Results Body mass and body fat percentage remained unchanged in FAST and FED during the whole period of the investigation. Both FAST and FED experienced an increase in the following parameters from Bef-R to End-R: urine specific gravity (1%; p = 0.028, p = 0.004 respectively), serum concentrations of urea (4%, p = 0.006; 7%, p = 0.004 respectively), creatinine (5%, p = 0.015; 6%, p = 0.04 respectively), uric acid (17%; p < 0.001, p = 0.04 respectively), sodium (1%; p = 0.029, p = 0.019 respectively), chloride (2%; p = 0.039, p = 0.004 respectively), and high-density lipoprotein cholesterol (11%, p = 0.04; 10%, p = 0.04 respectively). Conclusion Hypertrophic training in a fasted or in a fed state during Ramadan does not affect body mass and body composition of bodybuilders. Additionally, Ramadan fasting induced changes in urinary and some biochemical parameters, but these changes were not different according to when the training occurred. PMID:23617897

[Psychological effects of preventive voice care training in student teachers].

PubMed

Nusseck, M; Richter, B; Echternach, M; Spahn, C

2017-07-01

Studies on the effectiveness of preventive voice care programs have focused mainly on voice parameters. Psychological parameters, however, have not been investigated in detail so far. The effect of a voice training program for German student teachers on psychological health parameters was investigated in a longitudinal study. The sample of 204 student teachers was divided into the intervention group (n = 123), who participated in the voice training program, and the control group (n = 81), who received no voice training. Voice training contained ten 90-min group courses and an individual visit by the voice trainer in a teaching situation with feedback afterwards. Participants were asked to fill out questionnaires (self-efficacy, Short-Form Health Survey, self-consciousness, voice self-concept, work-related behaviour and experience patterns) at the beginning and the end of their student teacher training period. The training program showed significant positive influences on psychological health, voice self-concept (i.e. more positive perception and increased awareness of one's own voice) and work-related coping behaviour in the intervention group. On average, the mental health status of all participants reduced over time, whereas the status in the trained group diminished significantly less than in the control group. Furthermore, the trained student teachers gained abilities to cope with work-related stress better than those without training. The training program clearly showed a positive impact on mental health. The results maintain the importance of such a training program not only for voice health, but also for wide-ranging aspects of constitutional health.

Support vector machine-an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

PubMed

Liu, Mei; Lu, Jun

2014-09-01

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.

Energy expenditure during rest and treadmill gait training in quadriplegic subjects.

PubMed

de Carvalho, D C L; Cliquet, A

2005-11-01

The analysis of oxygen uptake (VO(2)) and energy consumption in quadriplegics after 6 months of treadmill gait with neuromuscular electrical stimulation (NMES). To compare metabolic responses in quadriplegics after 6 months of treadmill training, with NMES (30-50% body weight relief), with quadriplegics who did not perform gait. Ambulatory of University Hospital, Brazil. Quadriplegics were separated into gait and control groups (CGs). On inclusion, all subjects performed VO(2) test. In the gait group (GG) (n=11), the protocol consisted of 8 min of rest, 10 min of treadmill walking using NMES and 10 min of recovery. In the CG (n=10), testing consisted of 8 min rest, 15 min of quadriceps endurance exercise in sitting position with NMES and 10 min recovery. VO(2), carbon dioxide production (VCO(2)) and energy consumption were measured. The GG performed 6 months of treadmill training, using NMES, for 20 min, twice a week. The CG did not practice any activity with NMES, performing conventional physiotherapy only; the CG was stimulated only during the cardiorespiratory test. All parameters increased significantly for the GG: 36% for VO(2) (l/min), 43% for VCO(2) (l/min) and 32.5% for energy consumption (J/kg/s). For the CG, during knee extension exercise, VO(2) increased without changes in the energy consumption (P<0.05); smaller values were obtained for all parameters when compared to those obtained during gait. Quadriplegic gait was efficient towards increasing VO(2) and energy consumption, which can decrease the risk of cardiovascular diseases. Spinal Cord (2005) 43, 658-663. doi:10.1038/sj.sc.3101776; published online 21 June 2005.

Effect of aquatic interval training with Mediterranean diet counseling in obese patients: results of a preliminary study.

PubMed

Boidin, Maxime; Lapierre, Gabriel; Paquette Tanir, Laurie; Nigam, Anil; Juneau, Martin; Guilbeault, Valérie; Latour, Elise; Gayda, Mathieu

2015-10-01

No previous studies have investigated a high-intensity interval training program (HIIT) with an immersed ergocycle and Mediterranean diet counseling (Med) in obese patients. We aimed to compare the effects of an intensive lifestyle intervention, Med and HIIT with a water-immersed versus dryland ergocycle, on cardiometabolic and exercise parameters in obese patients. We retrospectively identified 95 obese patients at their entry into a 9-month Med and HIIT program: 21 were trained on a water-immersed ergocycle and 74 on a standard dryland ergocycle. Body composition, cardiometabolic and exercise parameters were measured before and after the program. For obese patients performing water- and dryland-exercise (mean age 58±9 years versus 55±7 years), BMI was higher for the water- than dryland-exercise group (39.4±8.3kg/m(2) versus 34.7±5.1kg/m(2), P<0.05), and total fat mass, fasting glycemia and triglycerides level were higher (P<0.05). Both groups showed similarly improved body composition variables (body mass, waist circumference, fat mass, P<0.001), fasting glycemia and triglycerides level (P<0.05). Initial maximal aerobic capacity (metabolic equivalents [METs]) and maximal heart rate (HRmax) were lower for the water- than dryland-exercise group (P<0.05). For both groups, METs, resting HR, resting blood pressure, abdominal and leg muscle endurance were similarly improved (P<0.05). A long-term Mediterranean diet and HIIT program with water-cycling is as effective as a dryland program in improving body composition, fasting glucose, triglycerides level, blood pressure and fitness in obese patients. A Mediterranean diet combined with water-cycling HIIT may be efficient for severely obese patients at high risk of musculoskeletal conditions. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Changes in Gait Symmetry After Training on a Treadmill with Biofeedback in Chronic Stroke Patients: A 6-Month Follow-Up from a Randomized Controlled Trial

PubMed Central

Drużbicki, Mariusz; Guzik, Agnieszka; Przysada, Grzegorz; Kwolek, Andrzej; Brzozowska-Magoń, Agnieszka; Sobolewski, Marek

2016-01-01

Background One of the most significant challenges for patients who survive a stroke is relearning basic motor tasks such as walking. The goal of this study was to evaluate whether training on a treadmill with visual biofeedback improves gait symmetry, as well as spatiotemporal and kinematic gait parameters, in stroke patients. Material/Methods Thirty patients in the chronic phase after a stroke were randomly allocated into groups with a rehabilitation program of treadmill training with or without visual biofeedback. The training program lasted 10 days. Spatiotemporal and kinematic gait parameters were evaluated. For all parameters analyzed, a symmetrical index was calculated. Follow-up studies were performed 6 months after completion of the program. Results The symmetrical index had significantly normalized in terms of the step length (p=0.006), stance phase time, and inter-limb ratio in the intervention group. After 6 months, the improvement in the symmetry of the step length had been maintained. In the control group, no statistically significant change was observed in any of the parameters tested. There was no significant difference between the intervention group and the control group on completion of the program or at 6 months following the completion of the program. Conclusions Training on a treadmill has a significant effect on the improvement of spatiotemporal parameters and symmetry of gait in patients with chronic stroke. In the group with the treadmill training using visual biofeedback, no significantly greater improvement was observed. PMID:27941712

High Intensity Interval Training Reduces the Levels of Serum Inflammatory Cytokine on Women with Metabolic Syndrome.

PubMed

Steckling, F M; Farinha, J B; Santos, D L D; Bresciani, G; Mortari, J A; Stefanello, S T; Courtes, A A; Duarte, T; Duarte, M M M F; Moresco, R N; Cardoso, M S; Soares, F A A

2016-11-01

Objectives: This study investigate the effects of a high intensity interval training (HIIT) and 2 weeks of detraining in functional and body composition parameters, lipoproteins, glucose metabolismand inflammation markers in postmenopausal women with metabolic syndrome (MS). Design: 17 untrained women with MS underwent a HIIT program for 12 weeks. Methods: The training was performed in treadmills, 3 days per week, with intensity ranging from 70-90% of the maximum heart rate (HR max ) and 2 weeks untrained (inactive). Functional and body composition parameters were evaluated before and after the training, while maximal oxygen uptake, lipoprotein and inflammation markers were analyzed before, after training and also in detraining. Results: The HITT program resulted in changesparameters as glucose, HbA1cand NOx after training. In addition, a reduction in pro-inflammatory interleukins and an increase in IL-10 after the HIIT program were found. However, an increase in plasma levels of lipoprotein was found and body composition parameters remain unaltered.Besides, only 2 weeks of detraining are able to revert the effects on inflammatory parameters afforded by the HIIT program. Conclusions: The HIIT program used here positively affected inflammatory profile and other parameters, as glucose, HbA1cand NOx, on postmenopausal women with MS. Moreover, 2 weeks of detraining can reverse the beneficial effects of HIIT program. Our results point out the necessity to aply acontinuous HITT program, in order maintain the benefits detected, to post menopausal women with MS. © Georg Thieme Verlag KG Stuttgart · New York.

Comparison of different vehicle power trains

NASA Astrophysics Data System (ADS)

Mizsey, Peter; Newson, Esmond

Four different alternatives of mobile power train developments (hybrid diesel, fuel cell operating with hydrogen produced on a petrochemical basis, methanol reformer-fuel cell system, gasoline reformer-fuel cell system), are compared with the gasoline internal combustion engine (ICE), for well-to-wheel efficiencies, CO 2 emissions, and investment costs. Although the ICE requires the lowest investment cost, it is not competitive in well-to-wheel efficiencies and less favourable than the above alternatives for CO 2 emissions. The hybrid diesel power train has the highest well-to-wheel efficiency (30%), but its well-to-wheel carbon dioxide emission is similar to that of the fuel cell power train operated with compressed hydrogen produced on a centralised petrochemical basis. This latter case, however, has the advantage over the hybrid diesel power train that the carbon dioxide emission is concentrated and easier to control than the several point-like sources of emissions. Among the five cases studied only the on-board reforming of methanol offers the possibility of using a renewable energy source (biomass).

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

PubMed Central

Dura-Bernal, S.; Neymotin, S. A.; Kerr, C. C.; Sivagnanam, S.; Majumdar, A.; Francis, J. T.; Lytton, W. W.

2017-01-01

Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics. PMID:29200477

Correlations of psycho-physiological parameters influencing the physical fitness of aged women.

PubMed

Bretz, É; Kóbor-Nyakas, D É; Bretz, K J; Hrehuss, N; Radák, Z; Nyakas, Csaba

2014-12-01

Regular assessment of psycho-physiological parameters in aged subjects helps to clarify physical and mental conditions which are important in the prevention of health-endangering events to assure a healthy aging. Thirty older care female residents consented voluntarily to participate in the study. The somatic and psycho-physiological parameters recorded were handgrip force, disjunctive reaction time, balance control and whole body movement coordination, the electrocardiogram and heart rate variability. Significant correlations were found between (a) reaction time and balance control efficiency (r = -0.567, p < 0.009), (b) reaction time and movement coordination accuracy (r = -0.453, p < 0.045), (c) cardiac state and movement coordination accuracy (r = 0.545, p < 0.016), (d) cardiac stress and cardiac state (r = -0.495, p < 0.031), and (e) cardiac stress and force (r = -0.822, p < 0.045). In conclusion, for the aim of establishing basic battery tests for assessing psycho-physiological condition of physical fitness our results emphasize the importance of systematic physical activity, endurance and strength training supporting muscle force, balance control and whole-body movement coordination, in addition to improving the cardiac stress index level. The strong interrelation among these parameters allows the drawing of a more complete view regarding the health condition of aged individuals.

Cognitive Load Theory vs. Constructivist Approaches: Which Best Leads to Efficient, Deep Learning?

ERIC Educational Resources Information Center

Vogel-Walcutt, J. J.; Gebrim, J. B.; Bowers, C.; Carper, T. M.; Nicholson, D.

2011-01-01

Computer-assisted learning, in the form of simulation-based training, is heavily focused upon by the military. Because computer-based learning offers highly portable, reusable, and cost-efficient training options, the military has dedicated significant resources to the investigation of instructional strategies that improve learning efficiency…

Water Resources Division Training Bulletin, July 1973 Through June 1974.

ERIC Educational Resources Information Center

Abrams, R. O.; Brown, D. W.

This bulletin provides information about available training as well as information to assist supervisors and training officers in developing a coordinated, efficient training program in hydrology and other subjects related to water-resources investigations. Most of the training is presented at the Center at Lakewood, Colorado. Information is given…

Effect of process parameters on formability of laser melting deposited 12CrNi2 alloy steel

NASA Astrophysics Data System (ADS)

Peng, Qian; Dong, Shiyun; Kang, Xueliang; Yan, Shixing; Men, Ping

2018-03-01

As a new rapid prototyping technology, the laser melting deposition technology not only has the advantages of fast forming, high efficiency, but also free control in the design and production chain. Therefore, it has drawn extensive attention from community.With the continuous improvement of steel performance requirements, high performance low-carbon alloy steel is gradually integrated into high-tech fields such as aerospace, high-speed train and armored equipment.However, it is necessary to further explore and optimize the difficult process of laser melting deposited alloy steel parts to achieve the performance and shape control.This article took the orthogonal experiment on alloy steel powder by laser melting deposition ,and revealed the influence rule of the laser power, scanning speed, powder gas flow on the quality of the sample than the dilution rate, surface morphology and microstructure analysis were carried out.Finally, under the optimum technological parameters, the Excellent surface quality of the alloy steel forming part with high density, no pore and cracks was obtained.

Tuning support vector machines for minimax and Neyman-Pearson classification.

PubMed

Davenport, Mark A; Baraniuk, Richard G; Scott, Clayton D

2010-10-01

This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning SVM parameters, such as cross-validation, can lead to poor classifier performance. To address this issue, we first prove that the usual cost-sensitive SVM, here called the 2C-SVM, is equivalent to another formulation called the 2nu-SVM. We then exploit a characterization of the 2nu-SVM parameter space to develop a simple yet powerful approach to error estimation based on smoothing. In an extensive experimental study, we demonstrate that smoothing significantly improves the accuracy of cross-validation error estimates, leading to dramatic performance gains. Furthermore, we propose coordinate descent strategies that offer significant gains in computational efficiency, with little to no loss in performance.

Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization

NASA Astrophysics Data System (ADS)

Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang

2018-04-01

Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.

An Automatic User-Adapted Physical Activity Classification Method Using Smartphones.

PubMed

Li, Pengfei; Wang, Yu; Tian, Yu; Zhou, Tian-Shu; Li, Jing-Song

2017-03-01

In recent years, an increasing number of people have become concerned about their health. Most chronic diseases are related to lifestyle, and daily activity records can be used as an important indicator of health. Specifically, using advanced technology to automatically monitor actual activities can effectively prevent and manage chronic diseases. The data used in this paper were obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an efficient Adaboost-Stump running on a smartphone to classify five common activities: cycling, running, sitting, standing, and walking and achieved a satisfactory classification accuracy of 98%. We designed an online learning method, and the classification model requires continuous training with actual data. The parameters in the model then become increasingly fitted to the specific user, which allows the classification accuracy to reach 95% under different use environments. In addition, this paper also utilized the OpenCL framework to design the program in parallel. This process can enhance the computing efficiency approximately ninefold.

Learning toward practical head pose estimation

NASA Astrophysics Data System (ADS)

Sang, Gaoli; He, Feixiang; Zhu, Rong; Xuan, Shibin

2017-08-01

Head pose is useful information for many face-related tasks, such as face recognition, behavior analysis, human-computer interfaces, etc. Existing head pose estimation methods usually assume that the face images have been well aligned or that sufficient and precise training data are available. In practical applications, however, these assumptions are very likely to be invalid. This paper first investigates the impact of the failure of these assumptions, i.e., misalignment of face images, uncertainty and undersampling of training data, on head pose estimation accuracy of state-of-the-art methods. A learning-based approach is then designed to enhance the robustness of head pose estimation to these factors. To cope with misalignment, instead of using hand-crafted features, it seeks suitable features by learning from a set of training data with a deep convolutional neural network (DCNN), such that the training data can be best classified into the correct head pose categories. To handle uncertainty and undersampling, it employs multivariate labeling distributions (MLDs) with dense sampling intervals to represent the head pose attributes of face images. The correlation between the features and the dense MLD representations of face images is approximated by a maximum entropy model, whose parameters are optimized on the given training data. To estimate the head pose of a face image, its MLD representation is first computed according to the model based on the features extracted from the image by the trained DCNN, and its head pose is then assumed to be the one corresponding to the peak in its MLD. Evaluation experiments on the Pointing'04, FacePix, Multi-PIE, and CASIA-PEAL databases prove the effectiveness and efficiency of the proposed method.

Block training periodization in alpine skiing: effects of 11-day HIT on VO2max and performance.

PubMed

Breil, Fabio A; Weber, Simone N; Koller, Stefan; Hoppeler, Hans; Vogt, Michael

2010-08-01

Attempting to achieve the high diversity of training goals in modern competitive alpine skiing simultaneously can be difficult and may lead to compromised overall adaptation. Therefore, we investigated the effect of block training periodization on maximal oxygen consumption (VO2max) and parameters of exercise performance in elite junior alpine skiers. Six female and 15 male athletes were assigned to high-intensity interval (IT, N = 13) or control training groups (CT, N = 8). IT performed 15 high-intensity aerobic interval (HIT) sessions in 11 days. Sessions were 4 x 4 min at 90-95% of maximal heart rate separated by 3-min recovery periods. CT continued their conventionally mixed training, containing endurance and strength sessions. Before and 7 days after training, subjects performed a ramp incremental test followed by a high-intensity time-to-exhaustion (tlim) test both on a cycle ergometer, a 90-s high-box jump test as well as countermovement (CMJ) and squat jumps (SJ) on a force plate. IT significantly improved relative VO2max by 6.0% (P < 0.01; male +7.5%, female +2.1%), relative peak power output by 5.5% (P < 0.01) and power output at ventilatory threshold 2 by 9.6% (P < 0.01). No changes occurred for these measures in CT. tlim remained unchanged in both groups. High-box jump performance was significantly improved in males of IT only (4.9%, P < 0.05). Jump peak power (CMJ -4.8%, SJ -4.1%; P < 0.01), but not height decreased in IT only. For competitive alpine skiers, block periodization of HIT offers a promising way to efficiently improve VO2max and performance. Compromised explosive jump performance might be associated with persisting muscle fatigue.

Adaptations of the aging animal to exercise: role of daily supplementation with melatonin.

PubMed

Mendes, Caroline; Lopes, Ana Maria de Souza; do Amaral, Fernanda Gaspar; Peliciari-Garcia, Rodrigo A; Turati, Ariane de Oliveira; Hirabara, Sandro M; Scialfa Falcão, Julieta H; Cipolla-Neto, José

2013-10-01

The pineal gland, through melatonin, seems to be of fundamental importance in determining the metabolic adaptations of adipose and muscle tissues to physical training. Evidence shows that pinealectomized animals fail to develop adaptive metabolic changes in response to aerobic exercise and therefore do not exhibit the same performance as control-trained animals. The known prominent reduction in melatonin synthesis in aging animals led us to investigate the metabolic adaptations to physical training in aged animals with and without daily melatonin replacement. Male Wistar rats were assigned to four groups: sedentary control (SC), trained control (TC), sedentary treated with melatonin (SM), and trained treated with melatonin (TM). Melatonin supplementation lasted 16 wk, and the animals were subjected to exercise during the last 8 wk of the experiment. After euthanasia, samples of liver, muscle, and adipose tissues were collected for analysis. Trained animals treated with melatonin presented better results in the following parameters: glucose tolerance, physical capacity, citrate synthase activity, hepatic and muscular glycogen content, body weight, protein expression of phosphatidylinositol 3-kinase (PI3K), mitogen-activated protein kinase (MAPK), and protein kinase activated by adenosine monophosphate (AMPK) in the liver, as well as the protein expression of the glucose transporter type 4 (GLUT4) and AMPK in the muscle. In conclusion, these results demonstrate that melatonin supplementation in aging animals is of great importance for the required metabolic adaptations induced by aerobic exercise. Adequate levels of circulating melatonin are, therefore, necessary to improve energetic metabolism efficiency, reducing body weight and increasing insulin sensitivity. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

da Vinci skills simulator for assessing learning curve and criterion-based training of robotic basic skills.

PubMed

Brinkman, Willem M; Luursema, Jan-Maarten; Kengen, Bas; Schout, Barbara M A; Witjes, J Alfred; Bekkers, Ruud L

2013-03-01

To answer 2 research questions: what are the learning curve patterns of novices on the da Vinci skills simulator parameters and what parameters are appropriate for criterion-based robotic training. A total of 17 novices completed 2 simulator sessions within 3 days. Each training session consisted of a warming-up exercise, followed by 5 repetitions of the "ring and rail II" task. Expert participants (n = 3) performed a warming-up exercise and 3 repetitions of the "ring and rail II" task on 1 day. We analyzed all 9 parameters of the simulator. Significant learning occurred on 5 parameters: overall score, time to complete, instrument collision, instruments out of view, and critical errors within 1-10 repetitions (P <.05). Economy of motion and excessive instrument force only showed improvement within the first 5 repetitions. No significant learning on the parameter drops and master workspace range was found. Using the expert overall performance score (n = 3) as a criterion (overall score 90%), 9 of 17 novice participants met the criterion within 10 repetitions. Most parameters showed that basic robotic skills are learned relatively quickly using the da Vinci skills simulator, but that 10 repetitions were not sufficient for most novices to reach an expert level. Some parameters seemed inappropriate for expert-based criterion training because either no learning occurred or the novice performance was equal to expert performance. Copyright © 2013 Elsevier Inc. All rights reserved.

Tongue motor training support system.

PubMed

Sasaki, Makoto; Onishi, Kohei; Nakayama, Atsushi; Kamata, Katsuhiro; Stefanov, Dimitar; Yamaguchi, Masaki

2014-01-01

In this paper, we introduce a new tongue-training system that can be used for improvement of the tongue's range of motion and muscle strength after dysphagia. The training process is organized in game-like manner. Initially, we analyzed surface electromyography (EMG) signals of the suprahyoid muscles of five subjects during tongue-training motions. This test revealed that four types tongue training motions and a swallowing motion could be classified with 93.5% accuracy. Recognized EMG signals during tongue motions were designed to allow control of a mouse cursor via intentional tongue motions. Results demonstrated that simple PC games could be played by tongue motions, achieving in this way efficient, enjoyable and pleasant tongue training. Using the proposed method, dysphagia patients can choose games that suit their preferences and/or state of mind. It is expected that the proposed system will be an efficient tool for long-term tongue motor training and maintaining patients' motivation.

"You've Got to Know Your Apples."

ERIC Educational Resources Information Center

Dettre, Judith

1980-01-01

Presented is a satire on employee training, retraining, efficiency experts, consultants, team training, peer teaching, and behavioral objectives--based on the training of apple sorters at the Fantabalous Fruit Farm. (KC)

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

PubMed

Keshavarz, M; Mojra, A

2015-05-01

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.

Automatic target recognition and detection in infrared imagery under cluttered background

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Koç, Aykut; Alatan, A. Aydın.

2017-10-01

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

Short-term endurance training after coronary artery bypass grafting improves insulin resistance parameters in patients with hypertension.

PubMed

Borowicz-Bieńkowska, Sławomira; Przywarska, Izabela; Dylewicz, Piotr; Pilaczyńska-Szcześniak, Łucja; Rychlewski, Tadeusz; Wilk, Małgorzata; Rózańska, Anna

2004-05-01

It has been shown that short-term exercise training improves insulin resistance parameters in patients with ischaemic heart disease. The effects of such a rehabilitation programme in patients with hypertension have not been well established. To assess whether short-term endurance training after coronary artery bypass grafting (CABG) may improve metabolic parameters and reduce blood pressure in patients with hypertension. The study group consisted of 30 male patients (15 with hypertension and 15 normotensive) aged 55+/-2.1 years who underwent CABG 1 to 6 months before the initiation of a 3-week endurance training. Glucose, insulin and C-peptide blood levels as well as binding and degradation of 125I-insulin by erythrocyte receptors were assessed before and after the training programme. The effects of training on blood pressure values were also evaluated. A significant improvement (p<0.01) in the insulin resistance parameters, i.e. binding and degradation of labelled insulin was noted only in patients with hypertension. This was accompanied by a significant (p<0.05) increase in the HDL-cholesterol level. In the subgroup with hypertension, both the exercise systolic and diastolic pressures decreased significantly (p<0.05 and p<0.01, respectively), and similar changes were noted in the resting systolic and diastolic blood pressures values (p<0.05). Rehabilitation after CABG based on the endurance training was especially effective in patients with hypertension in whom beneficial changes in some metabolic risk factors of ischaemic heart disease as well as the reduction in the blood pressure values were observed.

Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

NASA Astrophysics Data System (ADS)

Maleki, E.

2015-12-01

Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI) systems such as artificial neural networks (ANNs) as an efficient approach to solve the science and engineering problems is considerable. In present study modeling of FSW effective parameters by ANNs is investigated. To train the networks, experimental test results on thirty AA-7075-T6 specimens are considered, and the networks are developed based on back propagation (BP) algorithm. ANNs testing are carried out using different experimental data that they are not used during networks training. In this paper, rotational speed of tool, welding speed, axial force, shoulder diameter, pin diameter and tool hardness are regarded as inputs of the ANNs. Yield strength, tensile strength, notch-tensile strength and hardness of welding zone are gathered as outputs of neural networks. According to the obtained results, predicted values for the hardness of welding zone, yield strength, tensile strength and notch-tensile strength have the least mean relative error (MRE), respectively. Comparison of the predicted and the experimental results confirms that the networks are adjusted carefully, and the ANN can be used for modeling of FSW effective parameters.

A novel fiber laser development for photoacoustic microscopy

NASA Astrophysics Data System (ADS)

Yavas, Seydi; Aytac-Kipergil, Esra; Arabul, Mustafa U.; Erkol, Hakan; Akcaalan, Onder; Eldeniz, Y. Burak; Ilday, F. Omer; Unlu, Mehmet B.

2013-03-01

Photoacoustic microscopy, as an imaging modality, has shown promising results in imaging angiogenesis and cutaneous malignancies like melanoma, revealing systemic diseases including diabetes, hypertension, tracing drug efficiency and assessment of therapy, monitoring healing processes such as wound cicatrization, brain imaging and mapping. Clinically, photoacoustic microscopy is emerging as a capable diagnostic tool. Parameters of lasers used in photoacoustic microscopy, particularly, pulse duration, energy, pulse repetition frequency, and pulse-to-pulse stability affect signal amplitude and quality, data acquisition speed and indirectly, spatial resolution. Lasers used in photoacoustic microscopy are typically Q-switched lasers, low-power laser diodes, and recently, fiber lasers. Significantly, the key parameters cannot be adjusted independently of each other, whereas microvasculature and cellular imaging, e.g., have different requirements. Here, we report an integrated fiber laser system producing nanosecond pulses, covering the spectrum from 600 nm to 1100 nm, developed specifically for photoacoustic excitation. The system comprises of Yb-doped fiber oscillator and amplifier, an acousto-optic modulator and a photonic-crystal fiber to generate supercontinuum. Complete control over the pulse train, including generation of non-uniform pulse trains, is achieved via the AOM through custom-developed field-programmable gate-array electronics. The system is unique in that all the important parameters are adjustable: pulse duration in the range of 1-3 ns, pulse energy up to 10 μJ, repetition rate from 50 kHz to 3 MHz. Different photocoustic imaging probes can be excited with the ultrabroad spectrum. The entire system is fiber-integrated; guided-beam-propagation rendersit misalignment free and largely immune to mechanical perturbations. The laser is robust, low-cost and built using readily available components.

Assessing the impact of a targeted plyometric training on changes in selected kinematic parameters of the swimming start.

PubMed

Rejman, Marek; Bilewski, Marek; Szczepan, Stefan; Klarowicz, Andrzej; Rudnik, Daria; Maćkała, Krzysztof

2017-01-01

The aim of this study was to analyse changes taking place within selected kinematic parameters of the swimming start, after completing a six-week plyometric training, assuming that the take-off power training improves its effectiveness. The experiment included nine male swimmers. In the pre-test the swimmers performed three starts focusing on the best performance. Next, a plyometric training programme, adapted from sprint running, was introduced in order to increase a power of the lower extremities. The programme entailed 75 minute sessions conducted twice a week. Afterwards, a post-test was performed, analogous to the pre-test. Spatio-temporal structure data of the swimming start were gathered from video recordings of the swimmer above and under water. Impulses triggered by the plyometric training contributed to a shorter start time (the main measure of start effectiveness) and glide time as well as increasing average take-off, flight and glide velocities including take-off, entry and glide instantaneous velocities. The glide angle decreased. The changes in selected parameters of the swimming start and its confirmed diagnostic values, showed the areas to be susceptible to plyometric training and suggested that applied plyometric training programme aimed at increasing take-off power enhances the effectiveness of the swimming start.

Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

NASA Astrophysics Data System (ADS)

Qi, D.; Majda, A.

2017-12-01

A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with distinct statistical structures.

Efficient use of historical data for genomic selection: a case study of rust resistance in wheat

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) is a new methodology that can improve wheat breeding efficiency. To implement GS, a training population (TP) with both phenotypic and genotypic data is required to train a statistical model used to predict genotyped selection candidates (SCs). Several factors impact prediction...

Training for Efficiency: Work, Time, and Systems-Based Practice in Medical Residency

ERIC Educational Resources Information Center

Szymczak, Julia E.; Bosk, Charles L.

2012-01-01

Medical residency is a period of intense socialization with a heavy workload. Previous sociological studies have identified efficiency as a practical skill necessary for success. However, many contextual features of the training environment have undergone dramatic change since these studies were conducted. What are the consequences of these…

Impact-Based Training Evaluation Model (IBTEM) for School Supervisors in Indonesia

ERIC Educational Resources Information Center

Sutarto; Usman, Husaini; Jaedun, Amat

2016-01-01

This article represents a study aiming at developing: (1) an IBTEM which is capable to promote partnership between training providers and their client institutions, easy to understand, effective, efficient; and (2) an IBTEM implementation guide which is comprehensive, coherent, easy to understand, effective, and efficient. The method used in the…

Clinical impact of exercise in patients with peripheral arterial disease.

PubMed

Novakovic, Marko; Jug, Borut; Lenasi, Helena

2017-08-01

Increasing prevalence, high morbidity and mortality, and decreased health-related quality of life are hallmarks of peripheral arterial disease. About one-third of peripheral arterial disease patients have intermittent claudication with deleterious effects on everyday activities, such as walking. Exercise training improves peripheral arterial disease symptoms and is recommended as first line therapy for peripheral arterial disease. This review examines the effects of exercise training beyond improvements in walking distance, namely on vascular function, parameters of inflammation, activated hemostasis and oxidative stress, and quality of life. Exercise training not only increases walking distance and physiologic parameters in patients with peripheral arterial disease, but also improves the cardiovascular risk profile by helping patients achieve better control of hypertension, hyperglycemia, obesity and dyslipidemia, thus further reducing cardiovascular risk and the prevalence of coexistent atherosclerotic diseases. American guidelines suggest supervised exercise training, performed for a minimum of 30-45 min, at least three times per week, for at least 12 weeks. Walking is the most studied exercise modality and its efficacy in improving cardiovascular parameters in patients with peripheral arterial disease has been extensively proven. As studies have shown that supervised exercise training improves walking performance, cardiovascular parameters and quality of life in patients with peripheral arterial disease, it should be encouraged and more often prescribed.

Automatic control of negative emotions: evidence that structured practice increases the efficiency of emotion regulation.

PubMed

Christou-Champi, Spyros; Farrow, Tom F D; Webb, Thomas L

2015-01-01

Emotion regulation (ER) is vital to everyday functioning. However, the effortful nature of many forms of ER may lead to regulation being inefficient and potentially ineffective. The present research examined whether structured practice could increase the efficiency of ER. During three training sessions, comprising a total of 150 training trials, participants were presented with negatively valenced images and asked either to "attend" (control condition) or "reappraise" (ER condition). A further group of participants did not participate in training but only completed follow-up measures. Practice increased the efficiency of ER as indexed by decreased time required to regulate emotions and increased heart rate variability (HRV). Furthermore, participants in the ER condition spontaneously regulated their negative emotions two weeks later and reported being more habitual in their use of ER. These findings indicate that structured practice can facilitate the automatic control of negative emotions and that these effects persist beyond training.

Repetition Suppression in the Left Inferior Frontal Gyrus Predicts Tone Learning Performance.

PubMed

Asaridou, Salomi S; Takashima, Atsuko; Dediu, Dan; Hagoort, Peter; McQueen, James M

2016-06-01

Do individuals differ in how efficiently they process non-native sounds? To what extent do these differences relate to individual variability in sound-learning aptitude? We addressed these questions by assessing the sound-learning abilities of Dutch native speakers as they were trained on non-native tone contrasts. We used fMRI repetition suppression to the non-native tones to measure participants' neuronal processing efficiency before and after training. Although all participants improved in tone identification with training, there was large individual variability in learning performance. A repetition suppression effect to tone was found in the bilateral inferior frontal gyri (IFGs) before training. No whole-brain effect was found after training; a region-of-interest analysis, however, showed that, after training, repetition suppression to tone in the left IFG correlated positively with learning. That is, individuals who were better in learning the non-native tones showed larger repetition suppression in this area. Crucially, this was true even before training. These findings add to existing evidence that the left IFG plays an important role in sound learning and indicate that individual differences in learning aptitude stem from differences in the neuronal efficiency with which non-native sounds are processed. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Arm mechanical efficiency and arm exercise capacity are relatively preserved in chronic obstructive pulmonary disease.

PubMed

Franssen, Frits M E; Wouters, Emiel F M; Baarends, Erica M; Akkermans, Marco A; Schols, Annemie M W J

2002-10-01

Previous studies indicate that energy expenditure related to physical activity is enhanced and that mechanical efficiency of leg exercise is reduced in patients with chronic obstructive pulmonary disease (COPD). However, it is yet unclear whether an inefficient energy expenditure is also present during other activities in COPD. This study was carried out to examine arm efficiency and peak arm exercise performance relative to leg exercise in 33 (23 male) patients with COPD ((mean +/- SEM) age: 61 +/- 2 yr; FEV : 40 +/- 2% of predicted) and 20 sex- and age-matched healthy controls. Body composition, pulmonary function, resting energy expenditure (REE), and peak leg and arm exercise performance were determined. To calculate mechanical efficiency, subjects performed submaximal leg and arm ergometry at 50% of achieved peak loads. During exercise testing, metabolic and ventilatory parameters were measured. In contrast to a reduced leg mechanical efficiency in patients compared with controls (15.6 +/- 0.6% and 22.5 +/- 0.6%, respectively; < 0.001), arm mechanical efficiency was comparable in both groups (COPD: 18.3 +/- 0.9%, controls: 21.0 +/- 1.2%; NS). Arm efficiency was not related to leg efficiency, pulmonary function, work of breathing, or REE. Also, arm exercise capacity was relatively preserved in patients with COPD (ratio arm peak work rate/leg peak work rate in patients: 89% vs 53% in controls; < 0.001). Mechanical efficiency and exercise capacity of the upper and lower limbs are not homogeneously affected in COPD, with a relative preservation of the upper limbs. This may have implications for screening of exercise tolerance and prescription of training interventions in patients with COPD. Future studies need to elucidate the mechanism behind this observation.

A dyadic protocol for training complex skills: a replication using female participants.

PubMed

Sanchez-Ku, M L; Arthur, W

2000-01-01

The effectiveness and efficiency of the active interlocked modeling (AIM) dyadic protocol in training complex skills has been extensively demonstrated. However, past evaluation studies have all used male participants exclusively. Consequently, the present study investigated the generalizability of the effectiveness and efficiency gains to women. We randomly assigned 108 female participants to either the AIM-dyad condition or a standard individual control training condition. The results supported the robustness and viability of the AIM protocol. Although their overall performance was lower than that obtained for men in previous studies, women trained in the AIM-dyad condition performed as well as those trained in the individual condition. Thus, the efficiency gains associated with the AIM-dyad protocol, which result from the ability to train two people simultaneously to reach the same performance level as a single person with no increase in training time or machine cost, are generalizable to female participants. The applied and basic research implications of the present study are discussed within the context of well-documented male/female differences in the performance of complex psychomotor tasks. For instance, given the number of women entering the workforce and the significant proportion of women in professions previously deemed to be male-dominated (e.g., air navigation), it is reassuring to know that sex differences in task performance do not necessarily imply sex differences in the effectiveness of training protocols.

Predictors of laparoscopic simulation performance among practicing obstetrician gynecologists.

PubMed

Mathews, Shyama; Brodman, Michael; D'Angelo, Debra; Chudnoff, Scott; McGovern, Peter; Kolev, Tamara; Bensinger, Giti; Mudiraj, Santosh; Nemes, Andreea; Feldman, David; Kischak, Patricia; Ascher-Walsh, Charles

2017-11-01

While simulation training has been established as an effective method for improving laparoscopic surgical performance in surgical residents, few studies have focused on its use for attending surgeons, particularly in obstetrics and gynecology. Surgical simulation may have a role in improving and maintaining proficiency in the operating room for practicing obstetrician gynecologists. We sought to determine if parameters of performance for validated laparoscopic virtual simulation tasks correlate with surgical volume and characteristics of practicing obstetricians and gynecologists. All gynecologists with laparoscopic privileges (n = 347) from 5 academic medical centers in New York City were required to complete a laparoscopic surgery simulation assessment. The physicians took a presimulation survey gathering physician self-reported characteristics and then performed 3 basic skills tasks (enforced peg transfer, lifting/grasping, and cutting) on the LapSim virtual reality laparoscopic simulator (Surgical Science Ltd, Gothenburg, Sweden). The association between simulation outcome scores (time, efficiency, and errors) and self-rated clinical skills measures (self-rated laparoscopic skill score or surgical volume category) were examined with regression models. The average number of laparoscopic procedures per month was a significant predictor of total time on all 3 tasks (P = .001 for peg transfer; P = .041 for lifting and grasping; P < .001 for cutting). Average monthly laparoscopic surgical volume was a significant predictor of 2 efficiency scores in peg transfer, and all 4 efficiency scores in cutting (P = .001 to P = .015). Surgical volume was a significant predictor of errors in lifting/grasping and cutting (P < .001 for both). Self-rated laparoscopic skill level was a significant predictor of total time in all 3 tasks (P < .0001 for peg transfer; P = .009 for lifting and grasping; P < .001 for cutting) and a significant predictor of nearly all efficiency scores and errors scores in all 3 tasks. In addition to total time, there was at least 1 other objective performance measure that significantly correlated with surgical volume for each of the 3 tasks. Higher-volume physicians and those with fellowship training were more confident in their laparoscopic skills. By determining simulation performance as it correlates to active physician practice, further studies may help assess skill and individualize training to maintain skill levels as case volumes fluctuate. Copyright © 2017 Elsevier Inc. All rights reserved.

Designing Empathetic Animated Agents for a B-Learning Training Environment within the Electrical Domain

ERIC Educational Resources Information Center

Hernández, Yasmin; Pérez-Ramírez, Miguel; Zatarain-Cabada, Ramon; Barrón-Estrada, Lucia; Alor-Hernández, Giner

2016-01-01

Electrical tests involve high risk; therefore utility companies require highly qualified electricians and efficient training. Recently, training for electrical tests has been supported by virtual reality systems; nonetheless, these training systems are not yet adaptive. We propose a b-learning model to support adaptive and distance training. The…

General Training System; GENTRAS. Final Report.

ERIC Educational Resources Information Center

International Business Machines Corp., Gaithersburg, MD. Federal Systems Div.

GENTRAS (General Training System) is a computer-based training model for the Marine Corps which makes use of a systems approach. The model defines the skill levels applicable for career growth and classifies and defines the training needed for this growth. It also provides a training cost subsystem which will provide a more efficient means of…

Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

DOE PAGES

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

2017-11-15

In Hezaveh et al. (2017) we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data,more » as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single hyperparameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that neural networks can be a fast alternative to Monte Carlo Markov Chains for parameter uncertainty estimation in many practical applications, allowing more than seven orders of magnitude improvement in speed.« less

Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

NASA Astrophysics Data System (ADS)

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

2017-11-01

In Hezaveh et al. we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational-lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data, as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single variational parameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that the application of approximate Bayesian neural networks to astrophysical modeling problems can be a fast alternative to Monte Carlo Markov Chains, allowing orders of magnitude improvement in speed.

Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

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

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

In Hezaveh et al. (2017) we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data,more » as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single hyperparameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that neural networks can be a fast alternative to Monte Carlo Markov Chains for parameter uncertainty estimation in many practical applications, allowing more than seven orders of magnitude improvement in speed.« less

Are H-reflex and M-wave recruitment curve parameters related to aerobic capacity?

PubMed

Piscione, Julien; Grosset, Jean-François; Gamet, Didier; Pérot, Chantal

2012-10-01

Soleus Hoffmann reflex (H-reflex) amplitude is affected by a training period and type and level of training are also well known to modify aerobic capacities. Previously, paired changes in H-reflex and aerobic capacity have been evidenced after endurance training. The aim of this study was to investigate possible links between H- and M-recruitment curve parameters and aerobic capacity collected on a cohort of subjects (56 young men) that were not involved in regular physical training. Maximal H-reflex normalized with respect to maximal M-wave (H(max)/M(max)) was measured as well as other parameters of the H- or M-recruitment curves that provide information about the reflex or direct excitability of the motoneuron pool, such as thresholds of stimulus intensity to obtain H or M response (H(th) and M(th)), the ascending slope of H-reflex, or M-wave recruitment curves (H(slp) and M(slp)) and their ratio (H(slp)/M(slp)). Aerobic capacity, i.e., maximal oxygen consumption and maximal aerobic power (MAP) were, respectively, estimated from a running field test and from an incremental test on a cycle ergometer. Maximal oxygen consumption was only correlated with M(slp), an indicator of muscle fiber heterogeneity (p < 0.05), whereas MAP was not correlated with any of the tested parameters (p > 0.05). Although higher H-reflex are often described for subjects with a high aerobic capacity because of endurance training, at a basic level (i.e., without training period context) no correlation was observed between maximal H-reflex and aerobic capacity. Thus, none of the H-reflex or M-wave recruitment curve parameters, except M(slp), was related to the aerobic capacity of young, untrained male subjects.

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

PubMed Central

Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

2014-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

PubMed

Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

A cable-driven wrist robotic rehabilitator using a novel torque-field controller for human motion training.

PubMed

Chen, Weihai; Cui, Xiang; Zhang, Jianbin; Wang, Jianhua

2015-06-01

Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a cable-driven wrist robotic rehabilitator (CDWRR) for motion training or assistance to subjects with motor disabilities. The CDWRR utilizes the wrist skeletal joints and arm segments as the supporting structure and takes advantage of cable-driven parallel design to build the system, which brings the properties of flexibility, low-cost, and low-weight. The controller of the CDWRR is designed typically based on a virtual torque-field, which is to plan "assist-as-needed" torques for the spherical motion of wrist responding to the orientation deviation in wrist motion training. The torque-field controller can be customized to different levels of rehabilitation training requirements by tuning the field parameters. Additionally, a rapidly convergent parameter self-identification algorithm is developed to obtain the uncertain parameters automatically for the floating wearable structure of the CDWRR. Finally, experiments on a healthy subject are carried out to demonstrate the performance of the controller and the feasibility of the CDWRR on wrist motion training or assistance.

A cable-driven wrist robotic rehabilitator using a novel torque-field controller for human motion training

NASA Astrophysics Data System (ADS)

Chen, Weihai; Cui, Xiang; Zhang, Jianbin; Wang, Jianhua

2015-06-01

Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a cable-driven wrist robotic rehabilitator (CDWRR) for motion training or assistance to subjects with motor disabilities. The CDWRR utilizes the wrist skeletal joints and arm segments as the supporting structure and takes advantage of cable-driven parallel design to build the system, which brings the properties of flexibility, low-cost, and low-weight. The controller of the CDWRR is designed typically based on a virtual torque-field, which is to plan "assist-as-needed" torques for the spherical motion of wrist responding to the orientation deviation in wrist motion training. The torque-field controller can be customized to different levels of rehabilitation training requirements by tuning the field parameters. Additionally, a rapidly convergent parameter self-identification algorithm is developed to obtain the uncertain parameters automatically for the floating wearable structure of the CDWRR. Finally, experiments on a healthy subject are carried out to demonstrate the performance of the controller and the feasibility of the CDWRR on wrist motion training or assistance.

Pilot study on the effects of a 2-week hiking vacation at moderate versus low altitude on plasma parameters of carbohydrate and lipid metabolism in patients with metabolic syndrome.

PubMed

Gutwenger, Ivana; Hofer, Georg; Gutwenger, Anna K; Sandri, Marco; Wiedermann, Christian J

2015-03-28

Hypoxic and hypobaric conditions may augment the beneficial influence of training on cardiovascular risk factors. This pilot study aimed to explore for effects of a two-week hiking vacation at moderate versus low altitude on adipokines and parameters of carbohydrate and lipid metabolism in patients with metabolic syndrome. Fourteen subjects (mean age: 55.8 years, range: 39 - 69) with metabolic syndrome participated in a 2-week structured training program (3 hours of guided daily hiking 4 times a week, training intensity at 55-65% of individual maximal heart rate; total training time, 24 hours). Participants were divided for residence and training into two groups, one at moderate altitude (1,900 m; n = 8), and the other at low altitude (300 m; n = 6). Anthropometric, cardiovascular and metabolic parameters were measured before and after the training period. In study participants, training overall reduced circulating levels of total cholesterol (p = 0.024), low-density lipoprotein cholesterol (p = 0.025) and adiponectin (p < 0.001). In the group training at moderate altitude (n = 8), lowering effects on circulating levels were significant not only for total cholesterol, low-density-lipoprotein cholesterol and adiponectin (all, p < 0.05) but also for triglycerides (p = 0.025) and leptin (p = 0.015), whereas in the low altitude group (n = 6), none of the lipid parameters was significantly changed (each p > 0.05). Hiking-induced relative changes of triglyceride levels were positively associated with reductions in leptin levels (p = 0.006). As compared to 300 m altitude, training at 1,900 m showed borderline significant differences in the pre-post mean reduction rates of triglyceride (p = 0.050) and leptin levels (p = 0.093). Preliminary data on patients with metabolic syndrome suggest that a 2-week hiking vacation at moderate altitude may be more beneficial for adipokines and parameters of lipid metabolism than training at low altitude. In order to draw firm conclusions regarding better corrections of dyslipidemia and metabolic syndrome by physical exercise under mild hypobaric and hypoxic conditions, a sufficiently powered randomized clinical trial appears warranted. ClinicalTrials.gov ID NCT02013947 (first received November 6, 2013).

Structured Feedback Training for Time-Out: Efficacy and Efficiency in Comparison to a Didactic Method.

PubMed

Jensen, Scott A; Blumberg, Sean; Browning, Megan

2017-09-01

Although time-out has been demonstrated to be effective across multiple settings, little research exists on effective methods for training others to implement time-out. The present set of studies is an exploratory analysis of a structured feedback method for training time-out using repeated role-plays. The three studies examined (a) a between-subjects comparison to more a traditional didactic/video modeling method of time-out training, (b) a within-subjects comparison to traditional didactic/video modeling training for another skill, and (c) the impact of structured feedback training on in-home time-out implementation. Though findings are only preliminary and more research is needed, the structured feedback method appears across studies to be an efficient, effective method that demonstrates good maintenance of skill up to 3 months post training. Findings suggest, though do not confirm, a benefit of the structured feedback method over a more traditional didactic/video training model. Implications and further research on the method are discussed.

Preliminary results of neural networks and zernike polynomials for classification of videokeratography maps.

PubMed

Carvalho, Luis Alberto

2005-02-01

Our main goal in this work was to develop an artificial neural network (NN) that could classify specific types of corneal shapes using Zernike coefficients as input. Other authors have implemented successful NN systems in the past and have demonstrated their efficiency using different parameters. Our claim is that, given the increasing popularity of Zernike polynomials among the eye care community, this may be an interesting choice to add complementing value and precision to existing methods. By using a simple and well-documented corneal surface representation scheme, which relies on corneal elevation information, one can generate simple NN input parameters that are independent of curvature definition and that are also efficient. We have used the Matlab Neural Network Toolbox (MathWorks, Natick, MA) to implement a three-layer feed-forward NN with 15 inputs and 5 outputs. A database from an EyeSys System 2000 (EyeSys Vision, Houston, TX) videokeratograph installed at the Escola Paulista de Medicina-Sao Paulo was used. This database contained an unknown number of corneal types. From this database, two specialists selected 80 corneas that could be clearly classified into five distinct categories: (1) normal, (2) with-the-rule astigmatism, (3) against-the-rule astigmatism, (4) keratoconus, and (5) post-laser-assisted in situ keratomileusis. The corneal height (SAG) information of the 80 data files was fit with the first 15 Vision Science and it Applications (VSIA) standard Zernike coefficients, which were individually used to feed the 15 neurons of the input layer. The five output neurons were associated with the five typical corneal shapes. A group of 40 cases was randomly selected from the larger group of 80 corneas and used as the training set. The NN responses were statistically analyzed in terms of sensitivity [true positive/(true positive + false negative)], specificity [true negative/(true negative + false positive)], and precision [(true positive + true negative)/total number of cases]. The mean values for these parameters were, respectively, 78.75, 97.81, and 94%. Although we have used a relatively small training and testing set, results presented here should be considered promising. They are certainly an indication of the potential of Zernike polynomials as reliable parameters, at least in the cases presented here, as input data for artificial intelligence automation of the diagnosis process of videokeratography examinations. This technique should facilitate the implementation and add value to the classification methods already available. We also discuss briefly certain special properties of Zernike polynomials that are what we think make them suitable as NN inputs for this type of application.

Gait parameters associated with responsiveness to treadmill training with body-weight support after stroke: an exploratory study.

PubMed

Mulroy, Sara J; Klassen, Tara; Gronley, JoAnne K; Eberly, Valerie J; Brown, David A; Sullivan, Katherine J

2010-02-01

Task-specific training programs after stroke improve walking function, but it is not clear which biomechanical parameters of gait are most associated with improved walking speed. The purpose of this study was to identify gait parameters associated with improved walking speed after a locomotor training program that included body-weight-supported treadmill training (BWSTT). A prospective, between-subjects design was used. Fifteen people, ranging from approximately 9 months to 5 years after stroke, completed 1 of 3 different 6-week training regimens. These regimens consisted of 12 sessions of BWSTT alternated with 12 sessions of: lower-extremity resistive cycling; lower-extremity progressive, resistive strengthening; or a sham condition of arm ergometry. Gait analysis was conducted before and after the 6-week intervention program. Kinematics, kinetics, and electromyographic (EMG) activity were recorded from the hemiparetic lower extremity while participants walked at a self-selected pace. Changes in gait parameters were compared in participants who showed an increase in self-selected walking speed of greater than 0.08 m/s (high-response group) and in those with less improvement (low-response group). Compared with participants in the low-response group, those in the high-response group displayed greater increases in terminal stance hip extension angle and hip flexion power (product of net joint moment and angular velocity) after the intervention. The intensity of soleus muscle EMG activity during walking also was significantly higher in participants in the high-response group after the intervention. Only sagittal-plane parameters were assessed, and the sample size was small. Task-specific locomotor training alternated with strength training resulted in kinematic, kinetic, and muscle activation adaptations that were strongly associated with improved walking speed. Changes in both hip and ankle biomechanics during late stance were associated with greater increases in gait speed.

High-Efficiency and High-Power Mid-Wave Infrared Cascade Lasers

DTIC Science & Technology

2012-10-01

internal quantum efficiency () and factor (2) is usually called the optical extraction efficiency (). The optical extraction efficiency ... quantum efficiency involves more fundamental parameters corresponding to the microscopic processes of the device operation, nevertheless, it can be...deriving parameters such as the internal quantum efficiency of a QC laser, the entire injector miniband can be treated as a single virtual state

Robot-assisted gait training in multiple sclerosis patients: a randomized trial.

PubMed

Schwartz, Isabella; Sajin, Anna; Moreh, Elior; Fisher, Iris; Neeb, Martin; Forest, Adina; Vaknin-Dembinsky, Adi; Karusis, Dimitrios; Meiner, Zeev

2012-06-01

Preservation of locomotor activity in multiple sclerosis (MS) patients is of utmost importance. Robotic-assisted body weight-supported treadmill training is a promising method to improve gait functions in neurologically impaired patients, although its effectiveness in MS patients is still unknown. To compare the effectiveness of robot-assisted gait training (RAGT) with that of conventional walking treatment (CWT) on gait and generalized functions in a group of stable MS patients. A prospective randomized controlled trial of 12 sessions of RAGT or CWT in MS patients of EDSS score 5-7. Primary outcome measures were gait parameters and the secondary outcomes were functional and quality of life parameters. All tests were performed at baseline, 3 and 6 months post-treatment by a blinded rater. Fifteen and 17 patients were randomly allocated to RAGT and CWT, respectively. Both groups were comparable at baseline in all parameters. As compared with baseline, although some gait parameters improved significantly following the treatment at each time point there was no difference between the groups. Both FIM and EDSS scores improved significantly post-treatment with no difference between the groups. At 6 months, most gait and functional parameters had returned to baseline. Robot-assisted gait training is feasible and safe and may be an effective additional therapeutic option in MS patients with severe walking disabilities.

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

PubMed

Rácz, A; Bajusz, D; Héberger, K

2015-01-01

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

Training for Efficiency: Work, Time and Systems-based Practice in Medical Residency*

PubMed Central

Szymczak, Julia E.; Bosk, Charles L.

2013-01-01

Medical residency is a period of intense socialization with a heavy workload. Previous sociological studies have identified efficiency as a practical skill necessary for success. However, many contextual features of the training environment have undergone dramatic change since these studies were conducted. What are the consequences of these changes for the socialization of residents to time management and the development of a professional identity? Based on observations of and interviews with internal medicine residents at 3 training programs, we find that efficiency is both a social norm and strategy that residents employ to manage a workload for which the demand for work exceeds the supply of time available to accomplish it. We found that residents struggle to be efficient in the face of seemingly intractable “systems” problems. Residents work around these problems, and in doing so develop a tolerance for organizational vulnerabilities. PMID:22863601

Energy-efficient Public Procurement: Best Practice in Program Delivery

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

Payne, Christopher; Weber, Andrew; Semple, Abby

2013-02-15

This document illustrates the key issues and considerations involved in implementing energy-efficient public procurement. Our primary sources of information have been our partners in the Super Efficient Equipment and Appliance Deployment (SEAD) Initiative Procurement Working Group. Where applicable, we have highlighted specific ways in which working group participants have successfully overcome barriers to delivering effective programs. The following key points emerge from this analysis of programs for energy-efficient public procurement. Lessons for both developed and developing programs are highlighted throughout the guide. 1. Policy: Policy provides the initiative to begin a transition from first cost to life-cycle cost based purchasingmore » methods and culture. Effective policy is well-communicated, establishes accountability from top to bottom of organizations and simplifies the processes necessary to comply. Flexibility and responsiveness are essential in policy development and implementation. Mandatory and voluntary policies may complement one another. 2. Procurement Criteria: Procurement staff must be confident that energy-efficient procurement criteria offer the best long-term value for their organization’s money and represent real environmental gains. Involving multiple stakeholders at the early stages of the criteria creation process can result in greater levels of cooperation from private industry. Criteria should make comparison of products easy for purchasers and require minimal additional calculations. Criteria will need to be regularly updated to reflect market developments. 3. Training: Resources for the creation of training programs are usually very limited, but well-targeted training is necessary in order for a program to be effective. Training must emphasize a process that is efficient for purchasers and simplifies compliance. Purchaser resources and policy must be well designed for training to be effective. Training program development is an excellent opportunity for collaboration amongst public authorities. 4. Procurement Processes: Many tools and guides intended to help buyers comply with energy-efficient procurement policy are designed without detailed knowledge of the procurement process. A deeper understanding of purchasing pathways allows resources to be better directed. Current research by national and international bodies aims to analyze purchasing pathways and can assist in developing future resources.« less

Web-Based Training. ERIC Digest No. 218.

ERIC Educational Resources Information Center

Brown, Bettina Lankard

Reduced training costs, worldwide accessibility, and improved technological capabilities have made Web-based training (WBT) a viable alternative to classroom instruction. WBT enables businesses to cut their training costs. Efficiency of operation is another major advantage of WBT. The flexibility of time, place, and programs offered via WBT…

Effects and dose-response relationships of resistance training on physical performance in youth athletes: a systematic review and meta-analysis.

PubMed

Lesinski, Melanie; Prieske, Olaf; Granacher, Urs

2016-07-01

To quantify age, sex, sport and training type-specific effects of resistance training on physical performance, and to characterise dose-response relationships of resistance training parameters that could maximise gains in physical performance in youth athletes. Systematic review and meta-analysis of intervention studies. Studies were identified by systematic literature search in the databases PubMed and Web of Science (1985-2015). Weighted mean standardised mean differences (SMDwm) were calculated using random-effects models. Only studies with an active control group were included if these investigated the effects of resistance training in youth athletes (6-18 years) and tested at least one physical performance measure. 43 studies met the inclusion criteria. Our analyses revealed moderate effects of resistance training on muscle strength and vertical jump performance (SMDwm 0.8-1.09), and small effects on linear sprint, agility and sport-specific performance (SMDwm 0.58-0.75). Effects were moderated by sex and resistance training type. Independently computed dose-response relationships for resistance training parameters revealed that a training period of >23 weeks, 5 sets/exercise, 6-8 repetitions/set, a training intensity of 80-89% of 1 repetition maximum (RM), and 3-4 min rest between sets were most effective to improve muscle strength (SMDwm 2.09-3.40). Resistance training is an effective method to enhance muscle strength and jump performance in youth athletes, moderated by sex and resistance training type. Dose-response relationships for key training parameters indicate that youth coaches should primarily implement resistance training programmes with fewer repetitions and higher intensities to improve physical performance measures of youth athletes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network

NASA Astrophysics Data System (ADS)

Khan, U. T.

2016-12-01

Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged precipitation and lagged mean daily flow as candidate inputs. Model performance metric show that the CNPSA method had higher performance (with an efficiency of 0.76). Model output was used to assess the risk of extreme peak flows for a given day using an inverse possibility-to-probability transformation.

An interactive tool for visualization of spike train synchronization.

PubMed

Terry, Kevin

2010-08-15

A number of studies have examined the synchronization of central and peripheral spike trains by applying signal analysis techniques in the time and frequency domains. These analyses can reveal the presence of one or more common neural inputs that produce synchronization. However, synchronization measurements can fluctuate significantly due to the inherent variability of neural discharges and a finite data record length. Moreover, the effect of these natural variations is further compounded by the number of parameters available for calculating coherence in the frequency domain and the number of indices used to quantify short-term synchronization (STS) in the time domain. The computational tool presented here provides the user with an interactive environment that dynamically calculates and displays spike train properties along with STS and coherence indices to show how these factors interact. It is intended for a broad range of users, from those who are new to synchronization to experienced researchers who want to develop more meaningful and effective computational and experimental studies. To ensure this freely available tool meets the needs of all users, there are two versions. The first is a stand-alone version for educational use that can run on any computer. The second version can be modified and expanded by researchers who want to explore more in-depth questions about synchronization. Therefore, the distribution and use of this tool should both improve the understanding of fundamental spike train synchronization dynamics and produce more efficient and meaningful synchronization studies. (c) 2010 Elsevier B.V. All rights reserved.

Enhancing Arithmetic and Word-Problem Solving Skills Efficiently by Individualized Computer-Assisted Practice

ERIC Educational Resources Information Center

Schoppek, Wolfgang; Tulis, Maria

2010-01-01

The fluency of basic arithmetical operations is a precondition for mathematical problem solving. However, the training of skills plays a minor role in contemporary mathematics instruction. The authors proposed individualization of practice as a means to improve its efficiency, so that the time spent with the training of skills is minimized. As a…

A Study of the Effects of Shift Operations on Student Achievement in Electronics Training.

ERIC Educational Resources Information Center

Johnson, Frank F., Jr.

This study was designed to determine if the hours during which students participated in electronics training had any influence on their learning efficiency and their ability to function effectively as students, and to identify those factors that contributed to diminished learning efficiency. The three shifts used for the experiment were the night…

Efficiency in Assessment: Can Trained Student Interns Rate Essays as Well as Faculty Members?

ERIC Educational Resources Information Center

Cole, Tracy L.; Cochran, Loretta F.; Troboy, L. Kim; Roach, David W.

2012-01-01

What are the most efficient and effective methods in measuring outcomes for assurance of learning in higher education? This study examines the merits of outsourcing part of the assessment workload by comparing ratings completed by trained student interns to ratings completed by faculty. Faculty evaluation of students' written work samples provides…

Evaluation of an Efficient Method for Training Staff to Implement Stimulus Preference Assessments

ERIC Educational Resources Information Center

Roscoe, Eileen M.; Fisher, Wayne W.

2008-01-01

We used a brief training procedure that incorporated feedback and role-play practice to train staff members to conduct stimulus preference assessments, and we used group-comparison methods to evaluate the effects of training. Staff members were trained to implement the multiple-stimulus-without-replacement assessment in a single session and the…

Cost and Efficiency in Military Specialty Training. Paper No. P-5160.

ERIC Educational Resources Information Center

Gay, Robert M.; Nelson, Gary R.

The paper focuses on one aspect of the training and utilization of military manpower--specialty training for first-term enlisted personnel. Costs of both formal and on-the-job training (OJT) are considered, as well as the returns from training for first-time enlisted personnel. After a description of the conceptual framework the paper describes…

VP-Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography.

PubMed

Huang, Ruobing; Xie, Weidi; Alison Noble, J

2018-04-23

Three-dimensional (3D) fetal neurosonography is used clinically to detect cerebral abnormalities and to assess growth in the developing brain. However, manual identification of key brain structures in 3D ultrasound images requires expertise to perform and even then is tedious. Inspired by how sonographers view and interact with volumes during real-time clinical scanning, we propose an efficient automatic method to simultaneously localize multiple brain structures in 3D fetal neurosonography. The proposed View-based Projection Networks (VP-Nets), uses three view-based Convolutional Neural Networks (CNNs), to simplify 3D localizations by directly predicting 2D projections of the key structures onto three anatomical views. While designed for efficient use of data and GPU memory, the proposed VP-Nets allows for full-resolution 3D prediction. We investigated parameters that influence the performance of VP-Nets, e.g. depth and number of feature channels. Moreover, we demonstrate that the model can pinpoint the structure in 3D space by visualizing the trained VP-Nets, despite only 2D supervision being provided for a single stream during training. For comparison, we implemented two other baseline solutions based on Random Forest and 3D U-Nets. In the reported experiments, VP-Nets consistently outperformed other methods on localization. To test the importance of loss function, two identical models are trained with binary corss-entropy and dice coefficient loss respectively. Our best VP-Net model achieved prediction center deviation: 1.8 ± 1.4 mm, size difference: 1.9 ± 1.5 mm, and 3D Intersection Over Union (IOU): 63.2 ± 14.7% when compared to the ground truth. To make the whole pipeline intervention free, we also implement a skull-stripping tool using 3D CNN, which achieves high segmentation accuracy. As a result, the proposed processing pipeline takes a raw ultrasound brain image as input, and output a skull-stripped image with five detected key brain structures. Copyright © 2018 Elsevier B.V. All rights reserved.

An Efficient Pipeline for Abdomen Segmentation in CT Images.

PubMed

Koyuncu, Hasan; Ceylan, Rahime; Sivri, Mesut; Erdogan, Hasan

2018-04-01

Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient's diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline's optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.

Current and efficiency of Brownian particles under oscillating forces in entropic barriers

NASA Astrophysics Data System (ADS)

Nutku, Ferhat; Aydιner, Ekrem

2015-04-01

In this study, considering the temporarily unbiased force and different forms of oscillating forces, we investigate the current and efficiency of Brownian particles in an entropic tube structure and present the numerically obtained results. We show that different force forms give rise to different current and efficiency profiles in different optimized parameter intervals. We find that an unbiased oscillating force and an unbiased temporal force lead to the current and efficiency, which are dependent on these parameters. We also observe that the current and efficiency caused by temporal and different oscillating forces have maximum and minimum values in different parameter intervals. We conclude that the current or efficiency can be controlled dynamically by adjusting the parameters of entropic barriers and applied force. Project supported by the Funds from Istanbul University (Grant No. 45662).

An Affordable Microsurgical Training System for a Beginning Neurosurgeon: How to Realize the Self-Training Laboratory.

PubMed

Chung, Sang-Bong; Ryu, Jiwook; Chung, Yeongu; Lee, Sung Ho; Choi, Seok Keun

2017-09-01

To provide detailed information about how to realize a self-training laboratory with cost-effective microsurgical instruments, especially pertinent for the novice trainee. Our training model is designed to allow the practice of the microsurgery skills in an efficient and cost-effective manner. A used stereoscopic microscope is prepared for microsurgical training. A sufficient working distance for microsurgical practice is obtained by attaching an auxiliary objective lens. The minimum instrument list includes 2 jeweler's forceps, iris scissors, and alligator clips. The iris scissors and alligator clip provide good alternatives to micro-scissors and microvascular clamp. The short time needed to set up the microscope and suture the gauze with micro-forceps makes the training model suitable for daily practice. It takes about 15 minutes to suture 10 neighboring fibers of the gauze with 10-0 nylon; thus, training can be completed more quickly. We have developed an inexpensive and efficient micro-anastomosis training system using a stereoscopic microscope and minimal micro-instruments. Especially useful for novice trainees, this system provides high accessibility for microsurgical training. Copyright © 2017 Elsevier Inc. All rights reserved.

Microcomputer-Based Organizational Survey Assessment: Applications to Training.

DTIC Science & Technology

1987-08-01

organizations, a growing need for efficient, flexible and cost effective training programs becomes paramount. To cope with these increased training demands, many...and cost effective training programs becomes paramount. To cnpe with these increased training demands, many orga- nizations have turned to Computer...organizational setings the need for better training will .,, For continue to increase (Wexley & Latham, 1981). Recent surveys of the literature document

Detection of nicotine content impact in tobacco manufacturing using computational intelligence.

PubMed

Begic Fazlic, Lejla; Avdagic, Zikrija

2011-01-01

A study is presented for the detection of nicotine impact in different cigarette type, using recorded data and Computational Intelligence techniques. Recorded puffs are processed using Continuous Wavelet Transform and used to extract time-frequency features for normal and abnormal puffs conditions. The wavelet energy distributions are used as inputs to classifiers based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Genetic Algorithms (GAs). The number and the parameters of Membership Functions are used in ANFIS along with the features from wavelet energy distributionare selected using GAs, maximising the diagnosis success. GA with ANFIS (GANFIS) are trained with a subset of data with known nicotine conditions. The trained GANFIS are tested using the other set of data (testing data). A classical method by High-Performance Liquid Chromatography is also introduced to solve this problem, respectively. The results as well as the performances of these two approaches are compared. A combination of these two algorithms is also suggested to improve the efficiency of this solution procedure. Computational results show that this combined algorithm is promising.

Evaluation of the relative roles of a vegetative filter strip and a biofiltration swale in a treatment train for road runoff.

PubMed

Flanagan, Kelsey; Branchu, Philippe; Ramier, David; Gromaire, Marie-Christine

2017-02-01

In order to determine the relative importance of a vegetative filter strip and a biofiltration swale in a treatment train for road runoff, US EPA Storm Water Management Model was used to model infiltration and runoff from the filter strip. The model consisted of a series of subcatchments representing the road, the filter strip and the side-slopes of the swale. Simulations were carried out for different rain scenarios representing a variety of climatic conditions. In addition, a sensitivity analysis was conducted for the model's different parameters (soil characteristics and initial humidity, roughness, geometry, etc.). This exercise showed that for the system studied, the majority of road runoff is treated by the filter strip rather than the biofiltration swale, an effect observed especially during periods of low-intensity rainfall. Additionally, it was observed that the combination of infiltration of road runoff in the filter strip and direct rainfall on the system leads to a significant and variable dilution of the runoff reaching the swale. This result has important implications for evaluating the treatment efficiency of the system.

Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method

PubMed Central

Wang, Hai-peng; Bi, Zheng-yang; Zhou, Yang; Zhou, Yu-xuan; Wang, Zhi-gong; Lv, Xiao-ying

2017-01-01

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training. PMID:28250759

A cluster expansion model for predicting activation barrier of atomic processes

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

Rehman, Tafizur; Jaipal, M.; Chatterjee, Abhijit, E-mail: achatter@iitk.ac.in

2013-06-15

We introduce a procedure based on cluster expansion models for predicting the activation barrier of atomic processes encountered while studying the dynamics of a material system using the kinetic Monte Carlo (KMC) method. Starting with an interatomic potential description, a mathematical derivation is presented to show that the local environment dependence of the activation barrier can be captured using cluster interaction models. Next, we develop a systematic procedure for training the cluster interaction model on-the-fly, which involves: (i) obtaining activation barriers for handful local environments using nudged elastic band (NEB) calculations, (ii) identifying the local environment by analyzing the NEBmore » results, and (iii) estimating the cluster interaction model parameters from the activation barrier data. Once a cluster expansion model has been trained, it is used to predict activation barriers without requiring any additional NEB calculations. Numerical studies are performed to validate the cluster expansion model by studying hop processes in Ag/Ag(100). We show that the use of cluster expansion model with KMC enables efficient generation of an accurate process rate catalog.« less

Hypergraph-based anomaly detection of high-dimensional co-occurrences.

PubMed

Silva, Jorge; Willett, Rebecca

2009-03-01

This paper addresses the problem of detecting anomalous multivariate co-occurrences using a limited number of unlabeled training observations. A novel method based on using a hypergraph representation of the data is proposed to deal with this very high-dimensional problem. Hypergraphs constitute an important extension of graphs which allow edges to connect more than two vertices simultaneously. A variational Expectation-Maximization algorithm for detecting anomalies directly on the hypergraph domain without any feature selection or dimensionality reduction is presented. The resulting estimate can be used to calculate a measure of anomalousness based on the False Discovery Rate. The algorithm has O(np) computational complexity, where n is the number of training observations and p is the number of potential participants in each co-occurrence event. This efficiency makes the method ideally suited for very high-dimensional settings, and requires no tuning, bandwidth or regularization parameters. The proposed approach is validated on both high-dimensional synthetic data and the Enron email database, where p > 75,000, and it is shown that it can outperform other state-of-the-art methods.

Evaluating E-Training for Public Library Staff: A Quasi-Experimental Investigation

ERIC Educational Resources Information Center

Dalston, Teresa

2009-01-01

A comparative evaluation framework of instructional interventions for implementation of online training for public library staff would enable a better understanding of how to improve the effectiveness, efficiency and efficacy of training in certain training environments. This dissertation describes a quasi-experimental study of a two-week,…

Small Community Training & Education

Science.gov Websites

training and adult education professionals. • National Technical Information Service (NTIS) This U.S residents of New Jersey and beyond through education and public service. • Operator Training Committee of Water Wastewater Training Security Conservation & Water Efficiency Water for All Americans Water We

Extensive Functional Evaluations to Monitor Aerobic Training in Becker Muscular Dystrophy: A Case Report.

PubMed

Tramonti, Caterina; Rossi, Bruno; Chisari, Carmelo

2016-06-13

Low-intensity aerobic training seems to have positive effects on muscle strength, endurance and fatigue in Becker Muscular Dystrophy (BMD) patients. We describe the case of a 33-year old BMD man, who performed a four-week aerobic training. Extensive functional evaluations were executed to monitor the efficacy of the rehabilitative treatment. Results evidenced an increased force exertion and an improvement in muscle contraction during sustained exercise. An improvement of walk velocity, together with agility, endurance capacity and oxygen consumption during exercise was observed. Moreover, an enhanced metabolic efficiency was evidenced, as shown by reduced lactate blood levels after training. Interestingly, CK showed higher levels after the training protocol, revealing possible muscle damage. In conclusion, aerobic training may represent an effective method improving exercise performance, functional status and metabolic efficiency. Anyway, a careful functional assessment should be taken into account as a useful approach in the management of the disease's rehabilitative treatment.

Separate and combined effects of exercise training and weight loss on exercise efficiency and substrate oxidation.

PubMed

Amati, Francesca; Dubé, John J; Shay, Chris; Goodpaster, Bret H

2008-09-01

Perturbations in body weight have been shown to affect energy expenditure and efficiency during physical activity. The separate effects of weight loss and exercise training on exercise efficiency or the proportion of energy derived from fat oxidation during physical activity, however, are not known. The purpose of this study was to determine the separate and combined effects of exercise training and weight loss on metabolic efficiency, economy (EC), and fat oxidation during steady-state moderate submaximal exercise. Sixty-four sedentary older (67 +/- 0.5 yr) overweight to obese (30.7 +/- 0.4 kg/m(2)) volunteers completed 4 mo of either diet-induced weight loss (WL; n = 11), exercise training (EX; n = 36), or the combination of both interventions (WLEX; n = 17). Energy expenditure, gross efficiency (GE), EC, and proportion of energy expended from fat (EF) were determined during a 1-h submaximal (50% of peak aerobic capacity) cycle ergometry exercise before the intervention and at the same absolute work rate after the intervention. We found that EX increased GE by 4.7 +/- 2.2%. EC was similarly increased by 4.2 +/- 2.1% by EX. The addition of concomitant WL to EX (WLEX) resulted in greater increases in GE (9.0 +/- 3.3%) compared with WL alone but not compared with EX alone. These effects remained after adjusting for changes in lean body mass. The proportion of energy derived from fat during the bout of moderate exercise increased with EX and WLEX but not with WL. From these findings, we conclude that exercise training, either alone or in combination with weight loss, increases both exercise efficiency and the utilization of fat during moderate physical activity in previously sedentary, obese older adults. Weight loss alone, however, significantly improves neither efficiency nor utilization of fat during exercise.

Acoustic Analysis and Electroglottography in Elite Vocal Performers.

PubMed

Villafuerte-Gonzalez, Rocio; Valadez-Jimenez, Victor M; Sierra-Ramirez, Jose A; Ysunza, Pablo Antonio; Chavarria-Villafuerte, Karen; Hernandez-Lopez, Xochiquetzal

2017-05-01

Acoustic analysis of voice (AAV) and electroglottography (EGG) have been used for assessing vocal quality in patients with voice disorders. The effectiveness of these procedures for detecting mild disturbances in vocal quality in elite vocal performers has been controversial. To compare acoustic parameters obtained by AAV and EGG before and after vocal training to determine the effectiveness of these procedures for detecting vocal improvements in elite vocal performers. Thirty-three elite vocal performers were studied. The study group included 14 males and 19 females, ages 18-40 years, without a history of voice disorders. Acoustic parameters were obtained through AAV and EGG before and after vocal training using the Linklater method. Nonsignificant differences (P > 0.05) were found between values of fundamental frequency (F 0 ), shimmer, and jitter obtained by both procedures before vocal training. Mean F 0 was similar after vocal training. Jitter percentage as measured by AAV showed nonsignificant differences (P > 0.05) before and after vocal training. Shimmer percentage as measured by AAV demonstrated a significant reduction (P < 0.05) after vocal training. As measured by EGG after vocal training, shimmer and jitter were significantly reduced (P < 0.05); open quotient was significantly increased (P < 0.05); and irregularity was significantly reduced (P < 0.05). AAV and EGG were effective for detecting improvements in vocal function after vocal training in male and female elite vocal performers undergoing vocal training. EGG demonstrated better efficacy for detecting improvements and provided additional parameters as compared to AAV. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Context-Aware Based Efficient Training System Using Augmented Reality and Gravity Sensor for Healthcare Services

NASA Astrophysics Data System (ADS)

Kim, Seoksoo; Jung, Sungmo; Song, Jae-Gu; Kang, Byong-Ho

As augmented reality and a gravity sensor is of growing interest, siginificant developement is being made on related technology, which allows application of the technology in a variety of areas with greater expectations. In applying Context-aware to augmented reality, it can make useful programs. A traning system suggested in this study helps a user to understand an effcienct training method using augmented reality and make sure if his exercise is being done propery based on the data collected by a gravity sensor. Therefore, this research aims to suggest an efficient training environment that can enhance previous training methods by applying augmented reality and a gravity sensor.

A Semiautomated Multilayer Picking Algorithm for Ice-sheet Radar Echograms Applied to Ground-Based Near-Surface Data

NASA Technical Reports Server (NTRS)

Onana, Vincent De Paul; Koenig, Lora Suzanne; Ruth, Julia; Studinger, Michael; Harbeck, Jeremy P.

2014-01-01

Snow accumulation over an ice sheet is the sole mass input, making it a primary measurement for understanding the past, present, and future mass balance. Near-surface frequency-modulated continuous-wave (FMCW) radars image isochronous firn layers recording accumulation histories. The Semiautomated Multilayer Picking Algorithm (SAMPA) was designed and developed to trace annual accumulation layers in polar firn from both airborne and ground-based radars. The SAMPA algorithm is based on the Radon transform (RT) computed by blocks and angular orientations over a radar echogram. For each echogram's block, the RT maps firn segmented-layer features into peaks, which are picked using amplitude and width threshold parameters of peaks. A backward RT is then computed for each corresponding block, mapping the peaks back into picked segmented-layers. The segmented layers are then connected and smoothed to achieve a final layer pick across the echogram. Once input parameters are trained, SAMPA operates autonomously and can process hundreds of kilometers of radar data picking more than 40 layers. SAMPA final pick results and layer numbering still require a cursory manual adjustment to correct noncontinuous picks, which are likely not annual, and to correct for inconsistency in layer numbering. Despite the manual effort to train and check SAMPA results, it is an efficient tool for picking multiple accumulation layers in polar firn, reducing time over manual digitizing efforts. The trackability of good detected layers is greater than 90%.

MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations

NASA Astrophysics Data System (ADS)

Vergara-Perez, Sandra; Marucho, Marcelo

2016-01-01

One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson-Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post-analysis of structural and electrical properties of biomolecules.

MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations

PubMed Central

Vergara-Perez, Sandra; Marucho, Marcelo

2015-01-01

One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post- analysis of structural and electrical properties of biomolecules. PMID:26924848

MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations.

PubMed

Vergara-Perez, Sandra; Marucho, Marcelo

2016-01-01

One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post- analysis of structural and electrical properties of biomolecules.

Designing train-speed trajectory with energy efficiency and service quality

NASA Astrophysics Data System (ADS)

Jia, Jiannan; Yang, Kai; Yang, Lixing; Gao, Yuan; Li, Shukai

2018-05-01

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency and service quality, this article formulates a bi-objective train-speed trajectory optimization model to minimize simultaneously the energy consumption and travel time in an inter-station section. This article is distinct from previous studies in that more sophisticated train driving strategies characterized by the acceleration/deceleration gear, the cruising speed, and the speed-shift site are specifically considered. For obtaining an optimal train-speed trajectory which has equal satisfactory degree on both objectives, a fuzzy linear programming approach is applied to reformulate the objectives. In addition, a genetic algorithm is developed to solve the proposed train-speed trajectory optimization problem. Finally, a series of numerical experiments based on a real-world instance of Beijing-Tianjin Intercity Railway are implemented to illustrate the practicability of the proposed model as well as the effectiveness of the solution methodology.

Visual Working Memory Capacity Can Be Increased by Training on Distractor Filtering Efficiency.

PubMed

Li, Cui-Hong; He, Xu; Wang, Yu-Juan; Hu, Zhe; Guo, Chun-Yan

2017-01-01

It is generally considered that working memory (WM) capacity is limited and that WM capacity affects cognitive processes. Distractor filtering efficiency has been suggested to be an important factor in determining the visual working memory (VWM) capacity of individuals. In the present study, we investigated whether training in visual filtering efficiency (FE) could improve VWM capacity, as measured by performance on the change detection task (CDT) and changes of contralateral delay activity (CDA) (contralateral delay activity) of different conditions, and evaluated the transfer effect of visual FE training on verbal WM and fluid intelligence, as indexed by performance on the verbal WM span task and Raven's Standard Progressive Matrices (RSPM) test, respectively. Participants were divided into high- and low-capacity groups based on their performance in a CDT designed to test VWM capacity, and then the low-capacity individuals received 20 days of FE training. The training significantly improved the group's performance in the CDT, and their CDA models of different conditions became more similar with high capacity group, and the effect generalized to improve verbal WM span. These gains were maintained at a 3-month follow-up test. Participants' RSPM scores were not changed by the training. These findings support the notion that WM capacity is determined, at least in part, by distractor FE and can be enhanced through training.

Interactive internet-based clinical education: an efficient and cost-savings approach to point-of-care test training.

PubMed

Knapp, Herschel; Chan, Kee; Anaya, Henry D; Goetz, Matthew B

2011-06-01

We successfully created and implemented an effective HIV rapid testing training and certification curriculum using traditional in-person training at multiple sites within the U.S. Department of Veterans Affairs (VA) Healthcare System. Considering the multitude of geographically remote facilities in the nationwide VA system, coupled with the expansion of HIV diagnostics, we developed an alternate training method that is affordable, efficient, and effective. Using materials initially developed for in-person HIV rapid test in-services, we used a distance learning model to offer this training via live audiovisual online technology to educate clinicians at a remote outpatient primary care VA facility. Participants' evaluation metrics showed that this form of remote education is equivalent to in-person training; additionally, HIV testing rates increased considerably in the months following this intervention. Although there is a one-time setup cost associated with this remote training protocol, there is potential cost savings associated with the point-of-care nurse manager's time productivity by using the Internet in-service learning module for teaching HIV rapid testing. If additional in-service training modules are developed into Internet-based format, there is the potential for additional cost savings. Our cost analysis demonstrates that the remote in-service method provides a more affordable and efficient alternative compared with in-person training. The online in-service provided training that was equivalent to in-person sessions based on first-hand supervisor observation, participant satisfaction surveys, and follow-up results. This method saves time and money, requires fewer personnel, and affords access to expert trainers regardless of geographic location. Further, it is generalizable to training beyond HIV rapid testing. Based on these consistent implementation successes, we plan to expand use of online training to include remote VA satellite facilities spanning several states for a variety of diagnostic devices. Ultimately, Internet-based training has the potential to provide "big city" quality of care to patients at remote (rural) clinics.

The efficiency of multimedia learning into old age.

PubMed

Van Gerven, Pascal W M; Paas, Fred; Van Merriënboer, Jeroen J G; Hendriks, Maaike; Schmidt, Henk G

2003-12-01

On the basis of a multimodal model of working memory, cognitive load theory predicts that a multimedia-based instructional format leads to a better acquisition of complex subject matter than a purely visual instructional format. This study investigated the extent to which age and instructional format had an impact on training efficiency among both young and old adults. It was hypothesised that studying worked examples that are presented as a narrated animation (multimedia condition) is a more efficient means of complex skill training than studying visually presented worked examples (unimodal condition) and solving conventional problems. Furthermore, it was hypothesised that multimedia-based worked examples are especially helpful for elderly learners, who have to deal with a general decline of working-memory resources, because they address both mode-specific working-memory stores. The sample consisted of 60 young (mean age = 15.98 years) and 60 old adults (mean age = 64.48 years). Participants of both age groups were trained in either a conventional, a unimodal, or a multimedia condition. Subsequently, they had to solve a series of test problems. Dependent variables were perceived cognitive load during the training, performance on the test, and efficiency in terms of the ratio between these two variables. Results showed that for both age groups multimedia-based worked examples were more efficient than the other training formats in that less cognitive load led to at least an equal performance level. Although no difference in the beneficial effect of multimedia learning was found between the age groups, multimedia-based instructions seem promising for the elderly.

Virtual reality: Avatars in human spaceflight training

NASA Astrophysics Data System (ADS)

Osterlund, Jeffrey; Lawrence, Brad

2012-02-01

With the advancements in high spatial and temporal resolution graphics, along with advancements in 3D display capabilities to model, simulate, and analyze human-to-machine interfaces and interactions, the world of virtual environments is being used to develop everything from gaming, movie special affects and animations to the design of automobiles. The use of multiple object motion capture technology and digital human tools in aerospace has demonstrated to be a more cost effective alternative to the cost of physical prototypes, provides a more efficient, flexible and responsive environment to changes in the design and training, and provides early human factors considerations concerning the operation of a complex launch vehicle or spacecraft. United Space Alliance (USA) has deployed this technique and tool under Research and Development (R&D) activities on both spacecraft assembly and ground processing operations design and training on the Orion Crew Module. USA utilizes specialized products that were chosen based on functionality, including software and fixed based hardware (e.g., infrared and visible red cameras), along with cyber gloves to ensure fine motor dexterity of the hands. The key findings of the R&D were: mock-ups should be built to not obstruct cameras from markers being tracked; a mock-up toolkit be assembled to facilitate dynamic design changes; markers should be placed in accurate positions on humans and flight hardware to help with tracking; 3D models used in the virtual environment be striped of non-essential data; high computational capable workstations are required to handle the large model data sets; and Technology Interchange Meetings with vendors and other industries also utilizing virtual reality applications need to occur on a continual basis enabling USA to maintain its leading edge within this technology. Parameters of interest and benefit in human spaceflight simulation training that utilizes virtual reality technologies are to familiarize and assess operational processes, allow the ability to train virtually, experiment with "what if" scenarios, and expedite immediate changes to validate the design implementation are all parameters of interest in human spaceflight. Training benefits encompass providing 3D animation for post-training assessment, placement of avatars within 3D replicated work environments in assembling or processing hardware, offering various viewpoints of processes viewed and assessed giving the evaluators the ability to assess task feasibility and identify potential support equipment needs; and provide human factors determinations, such as reach, visibility, and accessibility. Multiple object motion capture technology provides an effective tool to train and assess ergonomic risks, simulations for determination of negative interactions between technicians and their proposed workspaces, and evaluation of spaceflight systems prior to, and as part of, the design process to contain costs and reduce schedule delays.

49 CFR 239.301 - Operational (efficiency) tests.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Operational (efficiency) tests. 239.301 Section... ADMINISTRATION, DEPARTMENT OF TRANSPORTATION PASSENGER TRAIN EMERGENCY PREPAREDNESS Operational (Efficiency) Tests; Inspection of Records and Recordkeeping § 239.301 Operational (efficiency) tests. (a) Each...

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

PubMed Central

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-01-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. PMID:29194393

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.

PubMed

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-12-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

Investigating the Structural Relationships among Organisational Support, Learning Flow, Learners' Satisfaction and Learning Transfer in Corporate E-Learning

ERIC Educational Resources Information Center

Joo, Young Ju; Lim, Kyu Yon; Park, Su Yeong

2011-01-01

E-learning in corporate training has been growing rapidly because of the pursuit of time and budget efficiency in course development and delivery. However, according to previous studies, efficiency does not always guarantee training effectiveness, which is the major concern of human resource development. It is therefore necessary to identify the…

Estimating synaptic parameters from mean, variance, and covariance in trains of synaptic responses.

PubMed

Scheuss, V; Neher, E

2001-10-01

Fluctuation analysis of synaptic transmission using the variance-mean approach has been restricted in the past to steady-state responses. Here we extend this method to short repetitive trains of synaptic responses, during which the response amplitudes are not stationary. We consider intervals between trains, long enough so that the system is in the same average state at the beginning of each train. This allows analysis of ensemble means and variances for each response in a train separately. Thus, modifications in synaptic efficacy during short-term plasticity can be attributed to changes in synaptic parameters. In addition, we provide practical guidelines for the analysis of the covariance between successive responses in trains. Explicit algorithms to estimate synaptic parameters are derived and tested by Monte Carlo simulations on the basis of a binomial model of synaptic transmission, allowing for quantal variability, heterogeneity in the release probability, and postsynaptic receptor saturation and desensitization. We find that the combined analysis of variance and covariance is advantageous in yielding an estimate for the number of release sites, which is independent of heterogeneity in the release probability under certain conditions. Furthermore, it allows one to calculate the apparent quantal size for each response in a sequence of stimuli.

Effects of Endurance and Endurance Strength Training on Body Composition and Physical Capacity in Women with Abdominal Obesity.

PubMed

Skrypnik, Damian; Bogdański, Paweł; Mądry, Edyta; Karolkiewicz, Joanna; Ratajczak, Marzena; Kryściak, Jakub; Pupek-Musialik, Danuta; Walkowiak, Jarosław

2015-01-01

To compare the effects of endurance training with endurance strength training on the anthropometric, body composition, physical capacity, and circulatory parameters in obese women. 44 women with abdominal obesity were randomized into groups A and B, and asked to perform endurance (A) and endurance strength training (B) for 3 months, 3 times/week, for 60 min. Dual-energy X-ray absorptiometry and Graded Exercise Test were performed before and after training. Significant decreases in body mass, BMI, total body fat, total body fat mass, and waist and hip circumference were observed after both types of intervention. Marked increases in total body lean and total body fat-free mass were documented in group B. In both groups, significant increases in peak oxygen uptake, time to exhaustion, maximal work rate, and work rate at ventilatory threshold were accompanied by noticeably decreased resting heart rate, resting systolic blood pressure, and resting and exercise diastolic blood pressure. No significant differences were noticed between groups for the investigated parameters. Our findings demonstrate evidence for a favorable and comparable effect of 3-month endurance and endurance strength training on anthropometric parameters, body composition, physical capacity, and circulatory system function in women with abdominal obesity. © 2015 S. Karger GmbH, Freiburg.

Effects of Endurance and Endurance Strength Training on Body Composition and Physical Capacity in Women with Abdominal Obesity

PubMed Central

Skrypnik, Damian; Bogdański, Paweł; Mądry, Edyta; Karolkiewicz, Joanna; Ratajczak, Marzena; Kryściak, Jakub; Pupek-Musialik, Danuta; Walkowiak, Jarosław

2015-01-01

Aims To compare the effects of endurance training with endurance strength training on the anthropometric, body composition, physical capacity, and circulatory parameters in obese women. Methods 44 women with abdominal obesity were randomized into groups A and B, and asked to perform endurance (A) and endurance strength training (B) for 3 months, 3 times/week, for 60 min. Dual-energy X-ray absorptiometry and Graded Exercise Test were performed before and after training. Results Significant decreases in body mass, BMI, total body fat, total body fat mass, and waist and hip circumference were observed after both types of intervention. Marked increases in total body lean and total body fat-free mass were documented in group B. In both groups, significant increases in peak oxygen uptake, time to exhaustion, maximal work rate, and work rate at ventilatory threshold were accompanied by noticeably decreased resting heart rate, resting systolic blood pressure, and resting and exercise diastolic blood pressure. No significant differences were noticed between groups for the investigated parameters. Conclusion Our findings demonstrate evidence for a favorable and comparable effect of 3-month endurance and endurance strength training on anthropometric parameters, body composition, physical capacity, and circulatory system function in women with abdominal obesity. PMID:25968470

Effects of two aerobic exercise training protocols on parameters of oxidative stress in the blood and liver of obese rats.

PubMed

Delwing-de Lima, Daniela; Ulbricht, Ariene Sampaio Souza Farias; Werlang-Coelho, Carla; Delwing-Dal Magro, Débora; Joaquim, Victor Hugo Antonio; Salamaia, Eloise Mariani; de Quevedo, Silvana Rodrigues; Desordi, Larissa

2017-12-08

We evaluated the effects of moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT) protocols on the alterations in oxidative stress parameters caused by a high-fat diet (HFD), in the blood and liver of rats. The HFD enhanced thiobarbituric acid reactive substances (TBA-RS) and protein carbonyl content, while reducing total sulfhydryl content and catalase (CAT) and glutathione peroxidase (GSH-Px) activities in the blood. Both training protocols prevented an increase in TBA-RS and protein carbonyl content, and prevented a reduction in CAT. HIIT protocol enhanced SOD activity. In the liver, HFD didn't alter TBA-RS, total sulfhydryl content or SOD, but increased protein carbonyl content and CAT and decreased GSH-Px. The exercise protocols prevented the increase in protein carbonyl content and the MICT protocol prevented an alteration in CAT. In conclusion, HFD elicits oxidative stress in the blood and liver and both protocols prevented most of the alterations in the oxidative stress parameters.

The Pilates method and cardiorespiratory adaptation to training.

PubMed

Tinoco-Fernández, Maria; Jiménez-Martín, Miguel; Sánchez-Caravaca, M Angeles; Fernández-Pérez, Antonio M; Ramírez-Rodrigo, Jesús; Villaverde-Gutiérrez, Carmen

2016-01-01

Although all authors report beneficial health changes following training based on the Pilates method, no explicit analysis has been performed of its cardiorespiratory effects. The objective of this study was to evaluate possible changes in cardiorespiratory parameters with the Pilates method. A total of 45 university students aged 18-35 years (77.8% female and 22.2% male), who did not routinely practice physical exercise or sports, volunteered for the study and signed informed consent. The Pilates training was conducted over 10 weeks, with three 1-hour sessions per week. Physiological cardiorespiratory responses were assessed using a MasterScreen CPX apparatus. After the 10-week training, statistically significant improvements were observed in mean heart rate (135.4-124.2 beats/min), respiratory exchange ratio (1.1-0.9) and oxygen equivalent (30.7-27.6) values, among other spirometric parameters, in submaximal aerobic testing. These findings indicate that practice of the Pilates method has a positive influence on cardiorespiratory parameters in healthy adults who do not routinely practice physical exercise activities.

The effects of working memory training on functional brain network efficiency.

PubMed

Langer, Nicolas; von Bastian, Claudia C; Wirz, Helen; Oberauer, Klaus; Jäncke, Lutz

2013-10-01

The human brain is a highly interconnected network. Recent studies have shown that the functional and anatomical features of this network are organized in an efficient small-world manner that confers high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of functional brain networks is related to performance in working memory (WM) tasks and if these networks can be modified by WM training. Therefore, we conducted a double-blind training study enrolling 66 young adults. Half of the subjects practiced three WM tasks and were compared to an active control group practicing three tasks with low WM demand. High-density resting-state electroencephalography (EEG) was recorded before and after training to analyze graph-theoretical functional network characteristics at an intracortical level. WM performance was uniquely correlated with power in the theta frequency, and theta power was increased by WM training. Moreover, the better a person's WM performance, the more their network exhibited small-world topology. WM training shifted network characteristics in the direction of high performers, showing increased small-worldness within a distributed fronto-parietal network. Taken together, this is the first longitudinal study that provides evidence for the plasticity of the functional brain network underlying WM. Copyright © 2013 Elsevier Ltd. All rights reserved.

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Iron, Hematological Parameters and Blood Plasma Lipid Profile in Vitamin D Supplemented and Non-Supplemented Young Soccer Players Subjected to High-Intensity Interval Training.

PubMed

Jastrzebska, Maria; Kaczmarczyk, Mariusz; Suárez, Arturo Diaz; Sánchez, Guillermo Felipe López; Jastrzebska, Joanna; Radziminski, Lukasz; Jastrzebski, Zbigniew

2017-01-01

Vitamin D deficiency has been associated with increased risk for cardiovascular disease and anemia. Vitamin D-related changes in lipid profile have been studied extensively but the relationship between vitamin D and lipid metabolism is not completely understood. As both vitamin D and intermittent training may potentially affect iron and lipid metabolism, the aim of the study was to evaluate whether a daily supplementation of vitamin D can modulate the response of hematological and lipid parameters to high-intensity interval training (HIIT) in soccer players. Thirty-six young elite junior soccer players were included in the placebo-controlled, double-blind study. Participants were non-randomly allocated into either a supplemented group (SG, n=20, HIIT and 5,000 IU of vitamin D daily) or placebo group (PG, n=16, HIIT and sunflower oil). Hematological parameters were ascertained before and after the 8-wk training. The change score (post- and pre-training difference) was calculated for each individual and the mean change score (MCS) was compared between SG and PG using the t test and analysis of covariance. There were no differences between SG and PG at baseline. The red and white cell count, hemoglobin, hematocrit, MCHC, ferritin, and HDL-cholesterol changed significantly over the 8-wk HIIT. However, no significant differences in MCS were observed between SG and PG for any variable. A daily vitamin D supplement did not have any impact on alteration in hematological or lipid parameters in young soccer players in the course of high-intensity interval training.

Voice characteristics of children aged between 6 and 13 years: impact of age, gender, and vocal training.

PubMed

Pribuisiene, Ruta; Uloza, Virgilijus; Kardisiene, Vilija

2011-12-01

To determine impact of age, gender, and vocal training on voice characteristics of children aged 6-13 years. Voice acoustic and phonetogram parameters were determined for the group of 44 singing and 31 non-singing children. No impact of gender and/or age on phonetogram, acoustic voice parameters, and maximum phonation time was detected. Voice ranges of all children represented a pre-pubertal soprano type with a voice range of 22 semitones for non-singing and of 26 semitones for singing individuals. The mean maximum voice intensity was 81 dB. Vocal training had a positive impact on voice intensity parameters in girls. The presented data on average voice characteristics may be applicable in the clinical practice and provide relevant support for voice assessment.

Therapeutic effects of an anti-gravity treadmill (AlterG) training on gait and lower limbs kinematics and kinetics in children with cerebral palsy.

PubMed

Lotfian, M; Kharazi, M R; Mirbagheri, A; Dadashi, F; Nourian, R; Mirbagheri, M M

2017-07-01

We aimed to investigate the effects of the lower body weight support treadmill (AlterG) training on kinetics and kinematics of the lower extremities in children with cerebral palsy (CP). We provided a 45-minute training program, 3 times a week for 8 weeks. AlterG can support the subject's weight up to 70% so that the subject will be able to walk more comfortably to reach a more correct walking pattern. The kinematics and kinetics were evaluated using an isokinetic dynamometer. The locomotion parameters were assessed in the gait laboratory. Subjects performance was evaluated at four time points: baseline (prior to training), 1 and 2 months after the beginning of training, and one month after the end of the training (as a follow-up evaluation). The results showed that the major gait, kinematic, and kinetic parameters improved after the AlterG training and were persistent. These findings suggest that the AlterG training can be considered as a therapeutic tool for improving the lower limb performance and locomotion in children with CP.

Large Scale Gaussian Processes for Atmospheric Parameter Retrieval and Cloud Screening

NASA Astrophysics Data System (ADS)

Camps-Valls, G.; Gomez-Chova, L.; Mateo, G.; Laparra, V.; Perez-Suay, A.; Munoz-Mari, J.

2017-12-01

Current Earth-observation (EO) applications for image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. Spatio-temporally explicit classification methods are a requirement in a variety of Earth system data processing applications. Upcoming missions such as the super-spectral Copernicus Sentinels EnMAP and FLEX will soon provide unprecedented data streams. Very high resolution (VHR) sensors like Worldview-3 also pose big challenges to data processing. The challenge is not only attached to optical sensors but also to infrared sounders and radar images which increased in spectral, spatial and temporal resolution. Besides, we should not forget the availability of the extremely large remote sensing data archives already collected by several past missions, such ENVISAT, Cosmo-SkyMED, Landsat, SPOT, or Seviri/MSG. These large-scale data problems require enhanced processing techniques that should be accurate, robust and fast. Standard parameter retrieval and classification algorithms cannot cope with this new scenario efficiently. In this work, we review the field of large scale kernel methods for both atmospheric parameter retrieval and cloud detection using infrared sounding IASI data and optical Seviri/MSG imagery. We propose novel Gaussian Processes (GPs) to train problems with millions of instances and high number of input features. Algorithms can cope with non-linearities efficiently, accommodate multi-output problems, and provide confidence intervals for the predictions. Several strategies to speed up algorithms are devised: random Fourier features and variational approaches for cloud classification using IASI data and Seviri/MSG, and engineered randomized kernel functions and emulation in temperature, moisture and ozone atmospheric profile retrieval from IASI as a proxy to the upcoming MTG-IRS sensor. Excellent compromise between accuracy and scalability are obtained in all applications.

SOD2 gene polymorphism and response of oxidative stress parameters in young wrestlers to a three-month training.

PubMed

Jówko, Ewa; Gierczuk, Dariusz; Cieśliński, Igor; Kotowska, Jadwiga

2017-05-01

The aim of the study was to analyse the effect of Val 16Ala polymorphism in SOD2 gene on oxidative stress parameters and lipid profile of the blood during a three-month wrestling training. The study included 53 Polish young wrestlers. Blood samples were collected at the beginning of the programme and following three months of the training. The list of analysed parameters included erythrocyte and serum activities of superoxide dismutase (SOD), whole blood glutathione peroxidase (GPx) activity, total glutathione (tGSH) level, concentration of lipid hydroperoxides (LHs), total antioxidant capacity (TAC) and creatine kinase (CK) activity in the serum, as well as lipid profile parameters: triglycerides (TG), total cholesterol (TC), high-density (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Three-month training resulted in a decrease in CK activity, an increase in serum SOD activity, as well as in unfavourable changes in serum lipid profile: an increase in TC, LDL-C, and TG, and a decrease in HDL-C. Aside from CK activity, all these changes seemed to be associated with presence of Val allele. Prior to the training programme, subjects with Ala/Ala genotype presented with lower levels of LHs, lower whole blood GPx activity, and lower serum concentrations of TC than the individuals with Ala/Val genotype. Both prior to and after three-month training, higher levels of tGSH were observed in Val/Val genotype as compared to Ala/Val genotype carriers. Moreover, multiple regression analysis demonstrated that SOD2 genotype was a significant predictor of pre-training whole blood GPx activity and erythrocyte SOD activity (Val/Val > Ala/Val > Ala/Ala). Altogether, these findings suggest that Val 16Ala polymorphism in SOD2 gene contributes to individual variability in oxidative stress status and lipid profile of the blood in young wrestlers, and may modulate biochemical response to training.

Sensitivity of a Bayesian atmospheric-transport inversion model to spatio-temporal sensor resolution applied to the 2006 North Korean nuclear test

NASA Astrophysics Data System (ADS)

Lundquist, K. A.; Jensen, D. D.; Lucas, D. D.

2017-12-01

Atmospheric source reconstruction allows for the probabilistic estimate of source characteristics of an atmospheric release using observations of the release. Performance of the inversion depends partially on the temporal frequency and spatial scale of the observations. The objective of this study is to quantify the sensitivity of the source reconstruction method to sparse spatial and temporal observations. To this end, simulations of atmospheric transport of noble gasses are created for the 2006 nuclear test at the Punggye-ri nuclear test site. Synthetic observations are collected from the simulation, and are taken as "ground truth". Data denial techniques are used to progressively coarsen the temporal and spatial resolution of the synthetic observations, while the source reconstruction model seeks to recover the true input parameters from the synthetic observations. Reconstructed parameters considered here are source location, source timing and source quantity. Reconstruction is achieved by running an ensemble of thousands of dispersion model runs that sample from a uniform distribution of the input parameters. Machine learning is used to train a computationally-efficient surrogate model from the ensemble simulations. Monte Carlo sampling and Bayesian inversion are then used in conjunction with the surrogate model to quantify the posterior probability density functions of source input parameters. This research seeks to inform decision makers of the tradeoffs between more expensive, high frequency observations and less expensive, low frequency observations.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

An assessment of emergency medicine residents' ability to perform in a multitasking environment.

PubMed

Ledrick, David; Fisher, Susan; Thompson, Justin; Sniadanko, Mark

2009-09-01

Multitasking (MT) is a term often applied to emergency medicine (EM), but it is still poorly understood. In an effort to facilitate MT research in EM, the authors conducted this pilot study to describe EM residents' scores on a Multi-Tasking Assessment Tool (MTAT) and compare these scores with the residents' work efficiency in the emergency department. The authors administered a previously developed test of MT ability to EM residents. They performed a multiple regression analysis to determine the effect of MT ability on resident work efficiency, defining efficiency as the number of relative value units billed per hour. They controlled the analysis for year of training and medical knowledge using as a standard the in-service exam administered by the American Board of Emergency Medicine. Complete data for 35 residents were available for analysis. Work efficiency was multivariately correlated with MTAT scores and year of training (P < .05). Whereas year of training explained the majority of the variance, a resident's MT ability accounted for a smaller but still significant portion. This pilot study further validates the MTAT and lays the groundwork for further research in MT in EM. Resident year of training and MTAT scores explain the variability in resident work efficiency significantly more than medical knowledge. Understanding MT ability may ultimately help in resident selection, education, and remediation as well as career counseling and improvement of practice systems in EM.

Development of an operationally efficient PTC braking enforcement algorithm for freight trains.

DOT National Transportation Integrated Search

2013-08-01

Software algorithms used in positive train control (PTC) systems designed to predict freight train stopping distance and enforce a penalty brake application have been shown to be overly conservative, which can lead to operational inefficiencies by in...

Effect of Different Training Methods on Stride Parameters in Speed Maintenance Phase of 100-m Sprint Running.

PubMed

Cetin, Emel; Hindistan, I Ethem; Ozkaya, Y Gul

2018-05-01

Cetin, E, Hindistan, IE, Ozkaya, YG. Effect of different training methods on stride parameters in speed maintenance phase of 100-m sprint running. J Strength Cond Res 32(5): 1263-1272, 2018-This study examined the effects of 2 different training methods relevant to sloping surface on stride parameters in speed maintenance phase of 100-m sprint running. Twenty recreationally active students were assigned into one of 3 groups: combined training (Com), horizontal training (H), and control (C) group. Com group performed uphill and downhill training on a sloping surface with an angle of 4°, whereas H group trained on a horizontal surface, 3 days a week for 8 weeks. Speed maintenance and deceleration phases were divided into distances with 10-m intervals, and running time (t), running velocity (RV), step frequency (SF), and step length (SL) were measured at preexercise, and postexercise period. After 8 weeks of training program, t was shortened by 3.97% in Com group, and 2.37% in H group. Running velocity also increased for totally 100 m of running distance by 4.13 and 2.35% in Com, and H groups, respectively. At the speed maintenance phase, although t and maximal RV (RVmax) found to be statistically unaltered during overall phase, t was found to be decreased, and RVmax was preceded by 10 m in distance in both training groups. Step length was increased at 60-70 m, and SF was decreased at 70-80 m in H group. Step length was increased with concomitant decrease in SF at 80-90 m in Com group. Both training groups maintained the RVmax with a great percentage at the speed maintenance phase. In conclusion, although both training methods resulted in an increase in running time and RV, Com training method was more prominently effective method in improving RV, and this improvement was originated from the positive changes in SL during the speed maintaining phase.

A Fully Automated Drosophila Olfactory Classical Conditioning and Testing System for Behavioral Learning and Memory Assessment

PubMed Central

Jiang, Hui; Hanna, Eriny; Gatto, Cheryl L.; Page, Terry L.; Bhuva, Bharat; Broadie, Kendal

2016-01-01

Background Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies. New Method The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays. Results The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24 hours) are comparable to traditional manual experiments, while minimizing experimenter involvement. Comparison with Existing Methods The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ~$500US, making it affordable to a wide range of investigators. Conclusions This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays. PMID:26703418

A fully automated Drosophila olfactory classical conditioning and testing system for behavioral learning and memory assessment.

PubMed

Jiang, Hui; Hanna, Eriny; Gatto, Cheryl L; Page, Terry L; Bhuva, Bharat; Broadie, Kendal

2016-03-01

Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies. The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays. The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24h) are comparable to traditional manual experiments, while minimizing experimenter involvement. The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ∼$500US, making it affordable to a wide range of investigators. This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays. Copyright © 2015 Elsevier B.V. All rights reserved.

Changes in balance, functional performance and fall risk following whole body vibration training and vitamin D supplementation in institutionalized elderly women. A 6 month randomized controlled trial.

PubMed

Bogaerts, An; Delecluse, Christophe; Boonen, Steven; Claessens, Albrecht L; Milisen, Koen; Verschueren, Sabine M P

2011-03-01

Falls in the elderly constitute a growing public health problem. This randomized controlled trial investigated the potential benefit of 6 months of whole body vibration (WBV) training and/or vitamin D supplementation on balance, functionality and estimated fall risk in institutionalized elderly women. A total of 113 women (mean age: 79.6) were randomly assigned to either a WBV or a no-training group, receiving either a conventional dose (880 IU/d) or a high dose (1600 IU/d) of vitamin D3. The WBV group performed exercises on a vibration platform 3×/week. Balance was evaluated by computerized posturography. Functionality was assessed by 10 m walk test, Timed up and Go (TUG) performance and endurance capacity (Shuttle Walk). Fall risk was determined with the Physiological Profile Assessment. Performance on the 10 m walk test and on TUG improved over time in all groups. For none of the parameters, high-dose vitamin D resulted in a better performance than conventional dosing. The improvements in the WBV group in endurance capacity, walking at preferred speed, and TUG were significantly larger than the changes with supplementation alone. No additional benefit of WBV training could be detected on fall risk and postural control, although sway velocity and maximal isometric knee extension strength improved only in the WBV group. This trial showed that a high-dose vitamin D supplementation is not more efficient than conventional dosing in improving functionality in institutionalized elderly. WBV training on top of vitamin D supplementation provided an added benefit with regard to walking, TUG performance, and endurance capacity. Copyright © 2010 Elsevier B.V. All rights reserved.

Automatic Classification of volcano-seismic events based on Deep Neural Networks.

NASA Astrophysics Data System (ADS)

Titos Luzón, M.; Bueno Rodriguez, A.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Seismic monitoring of active volcanoes is a popular remote sensing technique to detect seismic activity, often associated to energy exchanges between the volcano and the environment. As a result, seismographs register a wide range of volcano-seismic signals that reflect the nature and underlying physics of volcanic processes. Machine learning and signal processing techniques provide an appropriate framework to analyze such data. In this research, we propose a new classification framework for seismic events based on deep neural networks. Deep neural networks are composed by multiple processing layers, and can discover intrinsic patterns from the data itself. Internal parameters can be initialized using a greedy unsupervised pre-training stage, leading to an efficient training of fully connected architectures. We aim to determine the robustness of these architectures as classifiers of seven different types of seismic events recorded at "Volcán de Fuego" (Colima, Mexico). Two deep neural networks with different pre-training strategies are studied: stacked denoising autoencoder and deep belief networks. Results are compared to existing machine learning algorithms (SVM, Random Forest, Multilayer Perceptron). We used 5 LPC coefficients over three non-overlapping segments as training features in order to characterize temporal evolution, avoid redundancy and encode the signal, regardless of its duration. Experimental results show that deep architectures can classify seismic events with higher accuracy than classical algorithms, attaining up to 92% recognition accuracy. Pre-training initialization helps these models to detect events that occur simultaneously in time (such explosions and rockfalls), increase robustness against noisy inputs, and provide better generalization. These results demonstrate deep neural networks are robust classifiers, and can be deployed in real-environments to monitor the seismicity of restless volcanoes.

Payload Crew Training Complex (PCTC) utilization and training plan

NASA Technical Reports Server (NTRS)

Self, M. R.

1980-01-01

The physical facilities that comprise the payload crew training complex (PCTC) are described including the host simulator; experiment simulators; Spacelab aft flight deck, experiment pallet, and experiment rack mockups; the simulation director's console; payload operations control center; classrooms; and supporting soft- and hardware. The parameters of a training philosophy for payload crew training at the PCTC are established. Finally the development of the training plan is addressed including discussions of preassessment, and evaluation options.

Analysing the 21 cm signal from the epoch of reionization with artificial neural networks

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; Semelin, Benoit

2017-07-01

The 21 cm signal from the epoch of reionization should be observed within the next decade. While a simple statistical detection is expected with Square Kilometre Array (SKA) pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here, we test another possible inversion method: artificial neural networks (ANNs). We produce a training set that consists of 70 individual samples. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the seminumerical simulations that describe astrophysical processes. Using this set, we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

The influence of different training schedules on the learning of psychomotor skills for endoscopic surgery.

PubMed

Verdaasdonk, E G G; Stassen, L P S; van Wijk, R P J; Dankelman, J

2007-02-01

Psychomotor skills for endoscopic surgery can be trained with virtual reality simulators. Distributed training is more effective than massed training, but it is unclear whether distributed training over several days is more effective than distributed training within 1 day. This study aimed to determine which of these two options is the most effective for training endoscopic psychomotor skills. Students with no endoscopic experience were randomly assigned either to distributed training on 3 consecutive days (group A, n = 10) or distributed training within 1 day (group B, n = 10). For this study the SIMENDO virtual reality simulator for endoscopic skills was used. The training involved 12 repetitions of three different exercises (drop balls, needle manipulation, 30 degree endoscope) in differently distributed training schedules. All the participants performed a posttraining test (posttest) for the trained tasks 7 days after the training. The parameters measured were time, nontarget environment collisions, and instrument path length. There were no significant differences between the groups in the first training session for all the parameters. In the posttest, group A (training over several days) performed 18.7% faster than group B (training on 1 day) (p = 0.013). The collision and path length scores for group A did not differ significantly from the scores for group B. The distributed group trained over several days was faster, with the same number of errors and the same instrument path length used. Psychomotor skill training for endoscopic surgery distributed over several days is superior to training on 1 day.

Instructor Considerations in the Design of Optimal Training Devices

DTIC Science & Technology

1988-08-18

the training device development project, both in terms of cost and impact on training effectiveness. Simulation-based training devices have had a long... impact on training efficiency, the 1OS should be well designed. Taxonomy of Training Terms The architecture for this expert system includes the following...Here the impact of cost and benefit factors are evaluated and displayed in such a manner as to assist the analyst in selecting one configuration. An

Parameters of hormetic stress and resilience to trauma in rats.

PubMed

Plumb, Traci N; Cullen, Patrick K; Minor, Thomas R

2015-01-01

Hormesis is the process by which small stresses build resilience to large stresses. We pre-exposed rats to various parameters of mild-to-moderate stress prior to traumatic stress in the present experiments to assess the potential benefits of hormetic training on resilience to traumatic, uncontrollable stress. Rats underwent varying stress pre-training parameters prior to exposure to uncontrollable traumatic stress in the learned helplessness procedure. The ability to prevent the exaggerated fear responding and escape deficits that normally follow experience with traumatic stress were used as a measure of the benefits of hormetic training. Four experiments examined the effects of number of training sessions, stressor severity and pattern of rest between pre-training stress sessions. Repeated exposure to mild restraint stress or moderate shock stress eliminated both the enhanced fear conditioning and shuttle-escape deficits that result from exposure to traumatic, inescapable shock. The pattern of rest did not contribute to resilience when the pre-exposure stressor was mild, but was vital when the pre-exposure stressor was moderate, with an alternation of stress and rest being the most effective procedure. The data also suggest that the level of resilience may increase with the number of pre-exposure sessions.

[Quant efficiency of the detection as a quality parameter of the visualization equipment].

PubMed

Morgun, O N; Nemchenko, K E; Rogov, Iu V

2003-01-01

The critical parameter of notion "quant efficiency of detection" is defined in the paper. Different methods of specifying the detection quant efficiency (DQE) are under discussion. Thus, techniques of DQE determination for a whole unit and means of DQE finding at terminal space frequency are addressed. The notion of DQE at zero frequency is in the focus of attention. Finally, difficulties occurring in determining the above parameter as well as its disadvantages (as a parameter characterizing the quality of X-ray irradiation visualizing systems) are also discussed.

Variation in Behavioral Reactivity Is Associated with Cooperative Restraint Training Efficiency

PubMed Central

Bliss-Moreau, Eliza; Moadab, Gilda

2016-01-01

Training techniques that prepare laboratory animals to participate in testing via cooperation are useful tools that have the potential to benefit animal wellbeing. Understanding how animals systematically vary in their cooperative training trajectories will help trainers to design effective and efficient training programs. In the present report we document an updated method for training rhesus monkeys to cooperatively participate in restraint in a ‘primate chair.’ We trained 14 adult male macaques to raise their head above a yoke and accept yoke closure in an average of 6.36 training days in sessions that lasted an average of 10.52 min. Behavioral observations at 2 time points prior to training (approximately 3 y and 1.3 y prior) were used to quantify behavioral reactivity directed toward humans and toward other macaques. Individual differences in submissive–affiliative reactivity to humans but not reactivity toward other monkeys were related to learning outcomes. Macaques that were more reactive to humans were less willing to participate in training, were less attentive to the trainer, were more reactive during training sessions, and required longer training sessions, longer time to yoke, and more instances of negative reinforcement. These results suggest that rhesus macaques can be trained to cooperate with restraint rapidly and that individual difference data can be used to structure training programs to accommodate variation in animal temperament. PMID:26817979

Facial Emotions Recognition using Gabor Transform and Facial Animation Parameters with Neural Networks

NASA Astrophysics Data System (ADS)

Harit, Aditya; Joshi, J. C., Col; Gupta, K. K.

2018-03-01

The paper proposed an automatic facial emotion recognition algorithm which comprises of two main components: feature extraction and expression recognition. The algorithm uses a Gabor filter bank on fiducial points to find the facial expression features. The resulting magnitudes of Gabor transforms, along with 14 chosen FAPs (Facial Animation Parameters), compose the feature space. There are two stages: the training phase and the recognition phase. Firstly, for the present 6 different emotions, the system classifies all training expressions in 6 different classes (one for each emotion) in the training stage. In the recognition phase, it recognizes the emotion by applying the Gabor bank to a face image, then finds the fiducial points, and then feeds it to the trained neural architecture.

Sleep, anxiety and electronic device use by athletes in the training and competition environments.

PubMed

Romyn, Georgia; Robey, Elisa; Dimmock, James A; Halson, Shona L; Peeling, Peter

2016-01-01

This study subjectively assessed sleep quality and quantity, state anxiety and electronic device use during a 7-day training week (TRAIN) and a 7-day competitive tournament (COMP). Eight state-level netball players used wrist-watch actigraphy to provide indirect sleep measures of bedtime, wake time, sleep duration, sleep onset latency, sleep efficiency, wake after sleep onset and fragmentation index. State anxiety was reported using the anxiety sub-scale in the Profile of Mood States-Adolescents. Before bed duration of electronic device use and the estimated time to sleep after finishing electronic device use was also recorded. Significant main effects showed that sleep efficiency (p = 0.03) was greater in COMP as compared to TRAIN. Furthermore, the bedtime and wake time were earlier (p = 0.01) during COMP. No further differences existed between conditions (p > 0.05). However, strong negative associations were seen between state anxiety and the sleep quality rating. Here, sleep efficiency was likely greater in COMP due to the homeostatic need for recovery sleep, resulting from the change in environment from training to competition. Furthermore, an increased anxiety before bed seems to influence sleep quality and should be considered in athletes portraying poor sleep habits.

Tact training versus bidirectional intraverbal training in teaching a foreign language.

PubMed

Dounavi, Katerina

2014-01-01

The current study involved an evaluation of the emergence of untrained verbal relations as a function of 3 different foreign-language teaching strategies. Two Spanish-speaking adults received foreign-language (English) tact training and native-to-foreign and foreign-to-native intraverbal training. Tact training and native-to-foreign intraverbal training resulted in the emergence of a greater number of untrained responses, and may thus be more efficient than foreign-to-native intraverbal training. © Society for the Experimental Analysis of Behavior.

Oxidative stress biomarker monitoring in elite women volleyball athletes during a 6-week training period.

PubMed

Martinović, Jelena; Dopsaj, Violeta; Kotur-Stevuljević, Jelena; Dopsaj, Milivoj; Vujović, Ana; Stefanović, Aleksandra; Nešić, Goran

2011-05-01

The objectives of this study were to determine (a) if reactive oxygen metabolites (ROMs) are a reliable parameter for monitoring oxidative stress in athletes alone or in association with other parameters of oxidative stress and depending on whether antioxidant supplements are taken or not; (b) the level of oxidative stress in athletes before the competition season; and (c) if oxidative status could be improved in volleyball athletes. Sixteen women athletes (supplemented group) received an antioxidant cocktail containing vitamin E, vitamin C, zinc gluconate, and selenium as a dietary supplement during a 6-week training period, whereas 12 of them (control group) received no dietary supplement. Blood samples were taken before and after the training period. The following parameters were measured: ROMs, superoxide anion (O2⁻₂), malondialdehyde (MDA), advanced oxidation protein products (AOPP), lipid hydroperoxide (LOOH), biological antioxidative potential (BAP), paraoxonase activity toward paraoxon (POase) and diazoxon (DZOase), superoxide dismutase(SOD), total sulfydryl group concentration (SH groups), and lipid status. Reactive oxygen metabolites were taken as the dependent variable and MDA, O2⁻₂, AOPP, and LOOH as independent variables. In the group of athletes who have received supplementation, linear regression analysis revealed that the implemented model had a lower influence on dROMs (70.4 vs. 27.9%) after the training period. The general linear model showed significant differences between parameters before and after training/supplementation (Wilks' lambda = 0.074, F = 11.76, p < 0.01). At the partial level, significant increases in ROM levels (p <0.05, 95% confidence interval [CI]: 286-337), SOD activity (CI: 113-144), and BAP (CI: 2,388-2,580) (p < 0.01) were observed. The association between ROMs and other parameters of oxidative stress was reduced in athletes who received supplements. During the precompetition training period, treatment with dietary supplements prevented the depletion of antioxidative defense in volleyball athletes.

Custodial Training Makes Sense and Saves Dollars.

ERIC Educational Resources Information Center

Petersen, David

2002-01-01

Explains that due to the complexity of today's custodial work, extensive education and training is required. This includes basic commercial/industrial cleaning techniques; hygiene procedures; asbestos awareness; management, scheduling, and budgeting; chemical usage; and calculating operations efficiency. Details the in-depth custodial training of…

Effects of an individualized home-based unsupervised aerobic training on body composition and physiological parameters in obese adults are independent of gender.

PubMed

Emerenziani, G P; Gallotta, M C; Migliaccio, S; Ferrari, D; Greco, E A; Saavedra, F J; Iazzoni, S; Aversa, A; Donini, L M; Lenzi, A; Baldari, C; Guidetti, L

2018-04-01

Evaluation of the effects of an individualized home-based unsupervised aerobic training on body composition, physical and physiological parameters in female and male obese adults. Two hundred and twenty obese adults (age 47.9 ± 12.4 years; BMI 38.0 ± 7.2 kg/m 2 ) entered the 4-month training program. Body composition, physiological and functional capacities were assessed pre- and post-intervention. All subjects were requested to perform unsupervised aerobic training with the intensity based on heart rate, walking speed and OMNI-RPE score corresponding to the individual ventilatory threshold for at least 5 days/week. After 4-month study period, 40% of patients completed the protocol, 24% had high compliance (HC) (exercise ≥ 3 days/week), while 16% had low compliance (LC) to exercise prescription (exercise < than 3 days/week). In HC group, a significant improvement of body composition variables after training was performed. Moreover, oxygen uptake and metabolic equivalent at peak significantly increased after training. Six-minute walking test (6MWT) distance significantly increased while heart rate during 6MWT was significantly lower after training. No significant differences were found in LC group between pre- and post-intervention in all variables. Interestingly, gender does not influence the effects of training. Our results indicate that subjects, independent of gender, with high compliance to the aerobic training based on a new individualized method can achieve a significant reduction in weight loss and also an improvement in physical and physiological parameters. This innovative personalized prescription could be a valuable tool for exercise physiologist, endocrinologists, and nutritionists to approach and correct life style of obese subjects.

High-performance vision training improves batting statistics for University of Cincinnati baseball players.

PubMed

Clark, Joseph F; Ellis, James K; Bench, Johnny; Khoury, Jane; Graman, Pat

2012-01-01

Baseball requires an incredible amount of visual acuity and eye-hand coordination, especially for the batters. The learning objective of this work is to observe that traditional vision training as part of injury prevention or conditioning can be added to a team's training schedule to improve some performance parameters such as batting and hitting. All players for the 2010 to 2011 season underwent normal preseason physicals and baseline testing that is standard for the University of Cincinnati Athletics Department. Standard vision training exercises were implemented 6 weeks before the start of the season. Results are reported as compared to the 2009 to 2010 season. Pre season conditioning was followed by a maintenance program during the season of vision training. The University of Cincinnati team batting average increased from 0.251 in 2010 to 0.285 in 2011 and the slugging percentage increased by 0.033. The rest of the Big East's slugging percentage fell over that same time frame 0.082. This produces a difference of 0.115 with 95% confidence interval (0.024, 0.206). As with the batting average, the change for University of Cincinnati is significantly different from the rest of the Big East (p = 0.02). Essentially all batting parameters improved by 10% or more. Similar differences were seen when restricting the analysis to games within the Big East conference. Vision training can combine traditional and technological methodologies to train the athletes' eyes and improve batting. Vision training as part of conditioning or injury prevention can be applied and may improve batting performance in college baseball players. High performance vision training can be instituted in the pre-season and maintained throughout the season to improve batting parameters.

Clinical usefulness of the virtual reality-based postural control training on the gait ability in patients with stroke.

PubMed

Park, Yu-Hyung; Lee, Chi-Ho; Lee, Byoung-Hee

2013-01-01

This study is a single blind randomized controlled trial to determine the effect of virtual reality-based postural control training on the gait ability in patients with chronic stroke. Sixteen subjects were randomly assigned to either experimental group (VR, n= 8) or control group (CPT, n= 8). Subjects in both groups received conventional physical therapy for 60 min per day, five days per week during a period of four weeks. Subjects in the VR group received additional augmented reality-based training for 30 min per day, three days per week during a period of four weeks. The subjects were evaluated one week before and after participating in a four week training and follow-up at one month post-training. Data derived from the gait analyses included spatiotemporal gait parameters, 10 meters walking test (10 mWT). In the gait parameters, subjects in the VR group showed significant improvement, except for cadence at post-training and follow-up within the experimental group. However, no obvious significant improvement was observed within the control group. In between group comparisons, the experimental group (VR group) showed significantly greater improvement only in stride length compared with the control group (P< 0.05), however, no significant difference was observed in other gait parameters. In conclusion, we demonstrate significant improvement in gait ability in chronic stroke patients who received virtual reality based postural control training. These findings suggest that virtual reality (VR) postural control training using real-time information may be a useful approach for enhancement of gait ability in patients with chronic stroke.

[The use of controlled physical training in patients with acute coronary syndrome treated with intervention - assessment of effects on biochemical parameters and functional myocardial].

PubMed

Kapusta, Joanna; Kapusta, Anna; Pawlicki, Lucjan; Irzmański, Robert

2016-06-01

Diseases of the cardiovascular system is one of the most common causes of death among people over 65 years. Due to its course and incidence are a major cause of disability and impaired quality of life for seniors, as well as a serious economic problem in health care. Important role in the prevention of cardiovascular disease plays making systematic physical activity, which is a component of any rehabilitation program. Regular physical training by doing cardio-and vasoprotective has a beneficial effect on cardiovascular status and physical performance in patients with diagnosed coronary heart disease, regardless of age. The aim of this study was to evaluate the effect of controlled exercise on selected biochemical parameters and functional myocardial infarction. A group of 89 patients were divided into 3 subgroups. In group I (n = 30) was performed 2 weeks cardiac rehabilitation program, in group II (n = 30) 4 weekly. Streamline the program consisted of a series of interval training performed using a bicycle ergometer and general exercise. The remaining group (gr. III, n = 29) participated in individually selected training program. In all subjects before and after the training cycle underwent thoracic impedance plethysmography, also determined the level of plasma natriuretic peptide NT-proBNP and echocardiography and exercise test. After training, in groups, which carried out a controlled physical training, improvement was observed: exercise capacity of patients respectively in group I (p = 0.0003), group II (p = 0.0001) and group III (p = 0.032), stroke volume SV, cardiac output CO and global myocardial contractility, there was also reduction in the concentration of natriuretic peptide NT-proBNP. Furthermore, the correlation between the results shown pletyzmography parameters and NT-proBNP, SV, CO and EF. Regular physical training as part of the cardiac rehabilitation has a beneficial effect on biochemical parameters and functional myocardial infarction in patients with ACS. Size of the observed changes conditioned by the nature and duration of the training. © 2016 MEDPRESS.

Comparison of the Efficacy and Efficiency of the Use of Virtual Reality Simulation With High-Fidelity Mannequins for Simulation-Based Training of Fiberoptic Bronchoscope Manipulation.

PubMed

Jiang, Bailin; Ju, Hui; Zhao, Ying; Yao, Lan; Feng, Yi

2018-04-01

This study compared the efficacy and efficiency of virtual reality simulation (VRS) with high-fidelity mannequin in the simulation-based training of fiberoptic bronchoscope manipulation in novices. Forty-six anesthesia residents with no experience in fiberoptic intubation were divided into two groups: VRS (group VRS) and mannequin (group M). After a standard didactic teaching session, group VRS trained 25 times on VRS, whereas group M performed the same process on a mannequin. After training, participants' performance was assessed on a mannequin five consecutive times. Procedure times during training were recorded as pooled data to construct learning curves. Procedure time and global rating scale scores of manipulation ability were compared between groups, as well as changes in participants' confidence after training. Plateaus in the learning curves were achieved after 19 (95% confidence interval = 15-26) practice sessions in group VRS and 24 (95% confidence interval = 20-32) in group M. There was no significant difference in procedure time [13.7 (6.6) vs. 11.9 (4.1) seconds, t' = 1.101, P = 0.278] or global rating scale [3.9 (0.4) vs. 3.8 (0.4), t = 0.791, P = 0.433] between groups. Participants' confidence increased after training [group VRS: 1.8 (0.7) vs. 3.9 (0.8), t = 8.321, P < 0.001; group M = 2.0 (0.7) vs. 4.0 (0.6), t = 13.948, P < 0.001] but did not differ significantly between groups. Virtual reality simulation is more efficient than mannequin in simulation-based training of flexible fiberoptic manipulation in novices, but similar effects can be achieved in both modalities after adequate training.

Personnel training experience in the radioactive waste management: 10 years of Moscow SIA 'RADON' international education training centre

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

Batyukhnova, Olga; Dmitriev, Sergey; Arustamov, Artur

Available in abstract form only. Full text of publication follows: The education service for specialists dealing with radioactive waste was established in Russia (former USSR) in 1983 and was based on the capabilities of two organisations: the Moscow Scientific and Industrial Association 'Radon' (SIA 'Radon') and the Chemical Department of Lomonosov's Moscow State University. These two organizations are able to offer training programs in the science fundamentals, applied research and in practical operational areas of the all pre-disposal activities of the radioactive waste management. Since 1997 this system was upgraded to the international level and now acts as International Educationmore » Training Centre (IETC) at SIA 'Radon' under the guidance of the IAEA. During 10 years more than 300 specialists from 26 European and Asian countries enhanced their knowledge and skills in radioactive waste management. The IAEA supported specialized regional training courses and workshops, fellowships, on-the-job training, and scientific visits are additional means to assure development of personnel capabilities. Efficiency of training was carefully analysed using the structural adaptation of educational process as well as factors, which have influence on education quality. Social-psychological aspects were also taken into account in assessing the overall efficiency. The analysis of the effect of individual factors and the efficiency of education activity were carried out based on attestation results and questioning attendees. A number of analytical methods were utilised such as Ishikawa's diagram method and Pareto's principle for improving of training programs and activities. (authors)« less

A New Parameter for Cardiac Efficiency Analysis

NASA Astrophysics Data System (ADS)

Borazjani, Iman; Rajan, Navaneetha Krishnan; Song, Zeying; Hoffmann, Kenneth; MacMahon, Eileen; Belohlavek, Marek

2014-11-01

Detecting and evaluating a heart with suboptimal pumping efficiency is a significant clinical goal. However, the routine parameters such as ejection fraction, quantified with current non-invasive techniques are not predictive of heart disease prognosis. Furthermore, they only represent left-ventricular (LV) ejection function and not the efficiency, which might be affected before apparent changes in the function. We propose a new parameter, called the hemodynamic efficiency (H-efficiency) and defined as the ratio of the useful to total power, for cardiac efficiency analysis. Our results indicate that the change in the shape/motion of the LV will change the pumping efficiency of the LV even if the ejection fraction is kept constant at 55% (normal value), i.e., H-efficiency can be used for suboptimal cardiac performance diagnosis. To apply H-efficiency on a patient-specific basis, we are developing a system that combines echocardiography (echo) and computational fluid dynamics (CFD) to provide the 3D pressure and velocity field to directly calculate the H-efficiency parameter. Because the method is based on clinically used 2D echo, which has faster acquisition time and lower cost relative to other imaging techniques, it can have a significant impact on a large number of patients. This work is partly supported by the American Heart Association.

Tethered Swimming for the Evaluation and Prescription of Resistance Training in Young Swimmers.

PubMed

Papoti, Marcelo; da Silva, Adelino S R; Kalva-Filho, Carlos Augusto; Araujo, Gustavo Gomes; Santiago, Vanessa; Martins, LuizEduardo Barreto; Cunha, Sérgio Augusto; Gobatto, Claudio Alexandre

2017-02-01

The aims of the present study were 1) to evaluate the effects of 11 weeks of a typical free-swimming training program on aerobic and stroke parameters determined in tethered swimming (Study 1; n=13) and 2) to investigate the responses of tethered swimming efforts, in addition to free-swimming sessions, through 7 weeks of training (Study 2; n=21). In both studies, subjects performed a graded exercise test in tethered swimming (GET) to determine anaerobic threshold (AnT), stroke rate at AnT (SR AnT ), peak force at GET (PF GET ) and peak blood lactate ([La-] GET ). Participants also swam 100-, 200- and 400-m lengths to evaluate performance. In Study 2, swimmers were divided into control (i. e., only free-swimming; GC [n=11]) and tethered swimming group (i. e., 50% of the main session; G TS [n=10]). The results of Study 1 demonstrate that AnT, PF GET , [La - ] GET and 200-m performance were improved with free-swimming training. The SR AnT decreased with training. In Study 2, free-swimming performance and most of the graded exercise test parameters were not altered in either group. However, [La-] GET improved only for G TS . These results demonstrate that aerobic parameters obtained in tethered swimming can be used to evaluate free-swimming training responses, and the addition of tethered efforts during training routine improves the lactate production capacity of swimmers. © Georg Thieme Verlag KG Stuttgart · New York.

Endurance exercise training blunts the deleterious effect of high-fat feeding on whole body efficiency

PubMed Central

Holloway, Cameron J.; Murray, Andrew J.; Knight, Nicholas S.; Carter, Emma E.; Kemp, Graham J.; Thompson, Campbell H.; Tyler, Damian J.; Neubauer, Stefan; Robbins, Peter A.; Clarke, Kieran

2011-01-01

We recently showed that a week-long, high-fat diet reduced whole body exercise efficiency in sedentary men by >10% (Edwards LM, Murray AJ, Holloway CJ, Carter EE, Kemp GJ, Codreanu I, Brooker H, Tyler DJ, Robbins PA, Clarke K. FASEB J 25: 1088–1096, 2011). To test if a similar dietary regime would blunt whole body efficiency in endurance-trained men and, as a consequence, hinder aerobic exercise performance, 16 endurance-trained men were given a short-term, high-fat (70% kcal from fat) and a moderate carbohydrate (50% kcal from carbohydrate) diet, in random order. Efficiency was assessed during a standardized exercise task on a cycle ergometer, with aerobic performance assessed during a 1-h time trial and mitochondrial function later measured using 31P-magnetic resonance spectroscopy. The subjects then underwent a 2-wk wash-out period, before the study was repeated with the diets crossed over. Muscle biopsies, for mitochondrial protein analysis, were taken at the start of the study and on the 5th day of each diet. Plasma fatty acids were 60% higher on the high-fat diet compared with moderate carbohydrate diet (P < 0.05). However, there was no change in whole body efficiency and no change in mitochondrial function. Endurance exercise performance was significantly reduced (P < 0.01), most probably due to glycogen depletion. Neither diet led to changes in citrate synthase, ATP synthase, or mitochondrial uncoupling protein 3. We conclude that prior exercise training blunts the deleterious effect of short-term, high-fat feeding on whole body efficiency. PMID:21632846

Living and Training at 825 m for 8 Weeks Supplemented With Intermittent Hypoxic Training at 3,000 m Improves Blood Parameters and Running Performance.

PubMed

Wonnabussapawich, Preetiwat; Hamlin, Michael J; Lizamore, Catherine A; Manimmanakorn, Nuttaset; Leelayuwat, Naruemon; Tunkamnerdthai, Orathai; Thuwakum, Worrawut; Manimmanakorn, Apiwan

2017-12-01

Wonnabussapawich, P, Hamlin, MJ, Lizamore, CA, Manimmanakorn, N, Leelayuwat, N, Tunkamnerdthai, O, Thuwakum, W, and Manimmanakorn, A. Living and training at 825 m for 8 weeks supplemented with intermittent hypoxic training at 3,000 m improves blood parameters and running performance. J Strength Cond Res 31(12): 3287-3294, 2017-We aimed to investigate the effect of an 8-week low-altitude training block supplemented with intermittent hypoxic training, on blood and performance parameters in soccer players. Forty university-level male soccer players were separated into altitude (n = 20, 825 m) or sea-level (n = 20, 125 m) groups. Before (1-2 days ago) and after (1 and 14 days later) training, players were asked to give a resting venous blood sample and complete a series of performance tests. Compared with sea level, the altitude group increased erythropoietin, red blood cell (RBC) count, and hematocrit 1 day after training (42.6 ± 24.0%, 1.8 ± 1.3%, 1.4 ± 1.1%, mean ± 95% confidence limits (CL), respectively). By 14 days after training, only RBC count and hemoglobin were substantially higher in the altitude compared with the sea-level group (3.2 ± 1.8%, 2.9 ± 2.1% respectively). Compared with sea level, the altitude group 1-2 days after training improved their 50-m (-2.9 ± 1.4%) and 2,800-m (-2.9 ± 4.4%) run times and demonstrated a higher maximal aerobic speed (4.7 ± 7.4%). These performance changes remained at 14 days after training with the addition of a likely higher estimated V[Combining Dot Above]O2max in the altitude compared with the sea-level group (3.2 ± 3.0%). Eight weeks of low-altitude training, supplemented with regular bouts of intermittent hypoxic training at higher altitude, produced beneficial performance improvements in team-sport athletes, which may increase the viability of such training to coaches and players that cannot access more traditional high altitude venues.

Alteration in cardiac uncoupling proteins and eNOS gene expression following high-intensity interval training in favor of increasing mechanical efficiency.

PubMed

Fallahi, Ali Asghar; Shekarfroush, Shahnaz; Rahimi, Mostafa; Jalali, Amirhossain; Khoshbaten, Ali

2016-03-01

High-intensity interval training (HIIT) increases energy expenditure and mechanical energy efficiency. Although both uncoupling proteins (UCPs) and endothelial nitric oxide synthase (eNOS) affect the mechanical efficiency and antioxidant capacity, their effects are inverse. The aim of this study was to determine whether the alterations of cardiac UCP2, UCP3, and eNOS mRNA expression following HIIT are in favor of increased mechanical efficiency or decreased oxidative stress. Wistar rats were divided into five groups: control group (n=12), HIIT for an acute bout (AT1), short term HIIT for 3 and 5 sessions (ST3 and ST5), long-term training for 8 weeks (LT) (6 in each group). The rats of the training groups were made to run on a treadmill for 60 min in three stages: 6 min running for warm-up, 7 intervals of 7 min running on treadmill with a slope of 5° to 20° (4 min with an intensity of 80-110% VO2max and 3 min at 50-60% VO2max), and 5-min running for cool-down. The control group did not participate in any exercise program. Rats were sacrificed and the hearts were extracted to analyze the levels of UCP2, UCP3 and eNOS mRNA by RT-PCR. UCP3 expression was increased significantly following an acute training bout. Repeated HIIT for 8 weeks resulted in a significant decrease in UCPs mRNA and a significant increase in eNOS expression in cardiac muscle. This study indicates that Long term HIIT through decreasing UCPs mRNA and increasing eNOS mRNA expression may enhance energy efficiency and physical performance.

Alteration in cardiac uncoupling proteins and eNOS gene expression following high-intensity interval training in favor of increasing mechanical efficiency

PubMed Central

Fallahi, Ali Asghar; Shekarfroush, Shahnaz; Rahimi, Mostafa; Jalali, Amirhossain; Khoshbaten, Ali

2016-01-01

Objective(s): High-intensity interval training (HIIT) increases energy expenditure and mechanical energy efficiency. Although both uncoupling proteins (UCPs) and endothelial nitric oxide synthase (eNOS) affect the mechanical efficiency and antioxidant capacity, their effects are inverse. The aim of this study was to determine whether the alterations of cardiac UCP2, UCP3, and eNOS mRNA expression following HIIT are in favor of increased mechanical efficiency or decreased oxidative stress. Materials and Methods: Wistar rats were divided into five groups: control group (n=12), HIIT for an acute bout (AT1), short term HIIT for 3 and 5 sessions (ST3 and ST5), long-term training for 8 weeks (LT) (6 in each group). The rats of the training groups were made to run on a treadmill for 60 min in three stages: 6 min running for warm-up, 7 intervals of 7 min running on treadmill with a slope of 5° to 20° (4 min with an intensity of 80-110% VO2max and 3 min at 50-60% VO2max), and 5-min running for cool-down. The control group did not participate in any exercise program. Rats were sacrificed and the hearts were extracted to analyze the levels of UCP2, UCP3 and eNOS mRNA by RT-PCR. Results: UCP3 expression was increased significantly following an acute training bout. Repeated HIIT for 8 weeks resulted in a significant decrease in UCPs mRNA and a significant increase in eNOS expression in cardiac muscle. Conclusion: This study indicates that Long term HIIT through decreasing UCPs mRNA and increasing eNOS mRNA expression may enhance energy efficiency and physical performance. PMID:27114795

Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies.

PubMed

Ette, E I; Howie, C A; Kelman, A W; Whiting, B

1995-05-01

Monte Carlo simulation technique used to evaluate the effect of the arrangement of concentrations on the efficiency of estimation of population pharmacokinetic parameters in the preclinical setting is described. Although the simulations were restricted to the one compartment model with intravenous bolus input, they provide the basis of discussing some structural aspects involved in designing a destructive ("quantic") preclinical population pharmacokinetic study with a fixed sample size as is usually the case in such studies. The efficiency of parameter estimation obtained with sampling strategies based on the three and four time point designs were evaluated in terms of the percent prediction error, design number, individual and joint confidence intervals coverage for parameter estimates approaches, and correlation analysis. The data sets contained random terms for both inter- and residual intra-animal variability. The results showed that the typical population parameter estimates for clearance and volume were efficiently (accurately and precisely) estimated for both designs, while interanimal variability (the only random effect parameter that could be estimated) was inefficiently (inaccurately and imprecisely) estimated with most sampling schedules of the two designs. The exact location of the third and fourth time point for the three and four time point designs, respectively, was not critical to the efficiency of overall estimation of all population parameters of the model. However, some individual population pharmacokinetic parameters were sensitive to the location of these times.

20 CFR 664.420 - What are leadership development opportunities?

Code of Federal Regulations, 2013 CFR

2013-04-01

... 20 Employees' Benefits 4 2013-04-01 2013-04-01 false What are leadership development opportunities..., Elements, and Parameters § 664.420 What are leadership development opportunities? Leadership development... and team work training, including team leadership training; (e) Training in decision-making, including...

20 CFR 664.420 - What are leadership development opportunities?

Code of Federal Regulations, 2012 CFR

2012-04-01

... 20 Employees' Benefits 4 2012-04-01 2012-04-01 false What are leadership development opportunities..., Elements, and Parameters § 664.420 What are leadership development opportunities? Leadership development... and team work training, including team leadership training; (e) Training in decision-making, including...

20 CFR 664.420 - What are leadership development opportunities?

Code of Federal Regulations, 2014 CFR

2014-04-01

... 20 Employees' Benefits 4 2014-04-01 2014-04-01 false What are leadership development opportunities..., Elements, and Parameters § 664.420 What are leadership development opportunities? Leadership development... and team work training, including team leadership training; (e) Training in decision-making, including...

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

NASA Astrophysics Data System (ADS)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Estimating a Logistic Discrimination Functions When One of the Training Samples Is Subject to Misclassification: A Maximum Likelihood Approach.

PubMed

Nagelkerke, Nico; Fidler, Vaclav

2015-01-01

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

Estimation of parameters in Shot-Noise-Driven Doubly Stochastic Poisson processes using the EM algorithm--modeling of pre- and postsynaptic spike trains.

PubMed

Mino, H

2007-01-01

To estimate the parameters, the impulse response (IR) functions of some linear time-invariant systems generating intensity processes, in Shot-Noise-Driven Doubly Stochastic Poisson Process (SND-DSPP) in which multivariate presynaptic spike trains and postsynaptic spike trains can be assumed to be modeled by the SND-DSPPs. An explicit formula for estimating the IR functions from observations of multivariate input processes of the linear systems and the corresponding counting process (output process) is derived utilizing the expectation maximization (EM) algorithm. The validity of the estimation formula was verified through Monte Carlo simulations in which two presynaptic spike trains and one postsynaptic spike train were assumed to be observable. The IR functions estimated on the basis of the proposed identification method were close to the true IR functions. The proposed method will play an important role in identifying the input-output relationship of pre- and postsynaptic neural spike trains in practical situations.

Can an anti-gravity treadmill improve stability of children with cerebral palsy?

PubMed

Birgani, P M; Ashtiyani, M; Rasooli, A; Shahrokhnia, M; Shahrokhi, A; Mirbagheri, M M

2016-08-01

We aimed to study the effects of an anti-gravity treadmill (AlterG) training on balance and postural stability in children with cerebral palsy (CP). AlterG training was performed 3 days/week for 8 weeks, with up to 45 minutes of training per session. The subject was evaluated before and after the 8-week training. The effects of training on the balance and postural stability was evaluated based on the Romberg test that was performed by using a posturography device. The parameters quantifying Center-of-Pressure (CoP) were calculated using different analytical approaches including power spectral density and principal components analyses. All of the key parameters including the Stabilogram, the Fast Fourier Transform (FFT) Energy, the Eigenvectors, and the Eigenvalues of CoP were modified between 14%-84%. The results indicated that the balance features were improved substantially after training. The clinical implication is that the AlterG has the potential to effectively improve postural stability in children with cerebral palsy.

Retrospective analysis of the utility of multiparametric MRI for differentiating between benign and malignant breast lesions in women in China

PubMed Central

Fan, Wei Xiong; Chen, Xiao Feng; Cheng, Feng Yan; Cheng, Ya Bao; Xu, Tai; Zhu, Wen Biao; Zhu, Xiao Lei; Li, Gui Jin; Li, Shuai

2018-01-01

Abstract We explored the utility of time-resolved angiography with interleaved stochastic trajectories dynamic contrast-enhanced magnetic resonance imaging (TWIST DCE-MRI), readout segmentation of long variable echo-trains diffusion-weighted magnetic resonance imaging- diffusion-weighted magnetic resonance imaging (RESOLVE-DWI), and echo-planar imaging- diffusion-weighted magnetic resonance imaging (EPI-DWI) for distinguishing between malignant and benign breast lesions. This retrospective analysis included female patients with breast lesions seen at a single center in China between January 2016 and April 2016. Patients were allocated to a benign or malignant group based on pathologic diagnosis. All patients received routine MRI, RESOLVE-DWI, EPI-DWI, and TWIST DCE-T1WI. Variables measured included quantitative parameters (Ktrans, Kep, and Ve), semiquantitative parameters (rate of contrast enhancement for contrast agent inflow [W-in], rate of contrast decay for contrast agent outflow [W-out], and time-to-peak enhancement after contrast agent injection [TTP]) and apparent diffusion coefficient (ADC) values for RESOLVE-DWI (ADCr) and EPI-DWI (ADCe). Receiver-operating characteristic (ROC) curve analysis was used to evaluate the diagnostic utility of each parameter for differentiating malignant from benign breast lesions. A total of 87 patients were included (benign, n = 20; malignant, n = 67). Compared with the benign group, the malignant group had significantly higher Ktrans, Kep and W-in and significantly lower W-out, TTP, ADCe, and ADCr (all P < .05); Ve was not significantly different between groups. RESOLVE-DWI was superior to conventional EPI-DWI at illustrating lesion boundary and morphology, while ADCr was significantly lower than ADCe in all patients. Kep, W-out, ADCr, and ADCe showed the highest diagnostic efficiency (based on AUC value) for differentiating between benign and malignant lesions. Combining 3 parameters (Kep, W-out, and ADCr) had a higher diagnostic efficiency (AUC, 0.965) than any individual parameter and distinguished between benign and malignant lesions with high sensitivity (91.0%), specificity (95.0%), and accuracy (91.9%). An index combining Kep, W-out, and ADCr could potentially be used for the differential diagnosis of breast lesions. PMID:29369183

Retrospective analysis of the utility of multiparametric MRI for differentiating between benign and malignant breast lesions in women in China.

PubMed

Fan, Wei Xiong; Chen, Xiao Feng; Cheng, Feng Yan; Cheng, Ya Bao; Xu, Tai; Zhu, Wen Biao; Zhu, Xiao Lei; Li, Gui Jin; Li, Shuai

2018-01-01

We explored the utility of time-resolved angiography with interleaved stochastic trajectories dynamic contrast-enhanced magnetic resonance imaging (TWIST DCE-MRI), readout segmentation of long variable echo-trains diffusion-weighted magnetic resonance imaging- diffusion-weighted magnetic resonance imaging (RESOLVE-DWI), and echo-planar imaging- diffusion-weighted magnetic resonance imaging (EPI-DWI) for distinguishing between malignant and benign breast lesions.This retrospective analysis included female patients with breast lesions seen at a single center in China between January 2016 and April 2016. Patients were allocated to a benign or malignant group based on pathologic diagnosis. All patients received routine MRI, RESOLVE-DWI, EPI-DWI, and TWIST DCE-T1WI. Variables measured included quantitative parameters (K, Kep, and Ve), semiquantitative parameters (rate of contrast enhancement for contrast agent inflow [W-in], rate of contrast decay for contrast agent outflow [W-out], and time-to-peak enhancement after contrast agent injection [TTP]) and apparent diffusion coefficient (ADC) values for RESOLVE-DWI (ADCr) and EPI-DWI (ADCe). Receiver-operating characteristic (ROC) curve analysis was used to evaluate the diagnostic utility of each parameter for differentiating malignant from benign breast lesions.A total of 87 patients were included (benign, n = 20; malignant, n = 67). Compared with the benign group, the malignant group had significantly higher K, Kep and W-in and significantly lower W-out, TTP, ADCe, and ADCr (all P < .05); Ve was not significantly different between groups. RESOLVE-DWI was superior to conventional EPI-DWI at illustrating lesion boundary and morphology, while ADCr was significantly lower than ADCe in all patients. Kep, W-out, ADCr, and ADCe showed the highest diagnostic efficiency (based on AUC value) for differentiating between benign and malignant lesions. Combining 3 parameters (Kep, W-out, and ADCr) had a higher diagnostic efficiency (AUC, 0.965) than any individual parameter and distinguished between benign and malignant lesions with high sensitivity (91.0%), specificity (95.0%), and accuracy (91.9%).An index combining Kep, W-out, and ADCr could potentially be used for the differential diagnosis of breast lesions.

Reduced posterior parietal cortex activation after training on a visual search task.

PubMed

Bueichekú, Elisenda; Miró-Padilla, Anna; Palomar-García, María-Ángeles; Ventura-Campos, Noelia; Parcet, María-Antonia; Barrós-Loscertales, Alfonso; Ávila, César

2016-07-15

Gaining experience on a cognitive task improves behavioral performance and is thought to enhance brain efficiency. Despite the body of literature already published on the effects of training on brain activation, less research has been carried out on visual search attention processes under well controlled conditions. Thirty-six healthy adults divided into trained and control groups completed a pre-post letter-based visual search task fMRI study in one day. Twelve letters were used as targets and ten as distractors. The trained group completed a training session (840 trials) with half the targets between scans. The effects of training were studied at the behavioral and brain levels by controlling for repetition effects using both between-subjects (trained vs. control groups) and within-subject (trained vs. untrained targets) controls. The trained participants reduced their response speed by 31% as a result of training, maintaining their accuracy scores, whereas the control group hardly changed. Neural results revealed that brain changes associated with visual search training were circumscribed to reduced activation in the posterior parietal cortex (PPC) when controlling for group, and they included inferior occipital areas when controlling for targets. The observed behavioral and brain changes are discussed in relation to automatic behavior development. The observed training-related decreases could be associated with increased neural efficiency in specific key regions for task performance. Copyright © 2016 Elsevier Inc. All rights reserved.

Transfer of skills on LapSim virtual reality laparoscopic simulator into the operating room in urology.

PubMed

Alwaal, Amjad; Al-Qaoud, Talal M; Haddad, Richard L; Alzahrani, Tarek M; Delisle, Josee; Anidjar, Maurice

2015-01-01

Assessing the predictive validity of the LapSim simulator within a urology residency program. Twelve urology residents at McGill University were enrolled in the study between June 2008 and December 2011. The residents had weekly training on the LapSim that consisted of 3 tasks (cutting, clip-applying, and lifting and grasping). They underwent monthly assessment of their LapSim performance using total time, tissue damage and path length among other parameters as surrogates for their economy of movement and respect for tissue. The last residents' LapSim performance was compared with their first performance of radical nephrectomy on anesthetized porcine models in their 4(th) year of training. Two independent urologic surgeons rated the resident performance on the porcine models, and kappa test with standardized weight function was used to assess for inter-observer bias. Nonparametric spearman correlation test was used to compare each rater's cumulative score with the cumulative score obtained on the porcine models in order to test the predictive validity of the LapSim simulator. The kappa results demonstrated acceptable agreement between the two observers among all domains of the rating scale of performance except for confidence of movement and efficiency. In addition, poor predictive validity of the LapSim simulator was demonstrated. Predictive validity was not demonstrated for the LapSim simulator in the context of a urology residency training program.

An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances.

PubMed

Fairbank, Michael; Li, Shuhui; Fu, Xingang; Alonso, Eduardo; Wunsch, Donald

2014-01-01

We present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets. The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with the random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscillation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than has been achieved with adaptive critic designs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Understanding visual search patterns of dermatologists assessing pigmented skin lesions before and after online training.

PubMed

Krupinski, Elizabeth A; Chao, Joseph; Hofmann-Wellenhof, Rainer; Morrison, Lynne; Curiel-Lewandrowski, Clara

2014-12-01

The goal of this investigation was to explore the feasibility of characterizing the visual search characteristics of dermatologists evaluating images corresponding to single pigmented skin lesions (PSLs) (close-ups and dermoscopy) as a venue to improve training programs for dermoscopy. Two Board-certified dermatologists and two dermatology residents participated in a phased study. In phase I, they viewed a series of 20 PSL cases ranging from benign nevi to melanoma. The close-up and dermoscopy images of the PSL were evaluated sequentially and rated individually as benign or malignant, while eye position was recorded. Subsequently, the participating subjects completed an online dermoscopy training module that included a pre- and post-test assessing their dermoscopy skills (phase 2). Three months later, the subjects repeated their assessment on the 20 PSLs presented during phase I of the study. Significant differences in viewing time and eye-position parameters were observed as a function of level of expertise. Dermatologists overall have more efficient search than residents generating fewer fixations with shorter dwells. Fixations and dwells associated with decisions changing from benign to malignant or vice versa from photo to dermatoscopic viewing were longer than any other decision, indicating increased visual processing for those decisions. These differences in visual search may have implications for developing tools to teach dermatologists and residents about how to better utilize dermoscopy in clinical practice.

Sputum smear microscopy: evaluation of impact of training, microscope distribution, and use of external quality assessment guidelines for resource-poor settings.

PubMed

Van Rie, A; Fitzgerald, D; Kabuya, G; Van Deun, A; Tabala, M; Jarret, N; Behets, F; Bahati, E

2008-03-01

Sputum smear microscopy is the main and often only laboratory technique used for the diagnosis of tuberculosis in resource-poor countries, making quality assurance (QA) of smear microscopy an important activity. We evaluated the effects of a 5-day refresher training course for laboratory technicians and the distribution of new microscopes on the quality of smear microscopy in 13 primary health care laboratories in Kinshasa, Democratic Republic of Congo. The 2002 external QA guidelines for acid-fast bacillus smear microscopy were implemented, and blinded rechecking of the slides was performed before and 9 months after the training course and microscope distribution. We observed that the on-site checklist was highly time-consuming but could be tailored to capture frequent problems. Random blinded rechecking by the lot QA system method decreased the number of slides to be reviewed. Most laboratories needed further investigation for possible unacceptable performance, even according to the least-stringent interpretation. We conclude that the 2002 external QA guidelines are feasible for implementation in resource-poor settings, that the efficiency of external QA can be increased by selecting sample size parameters and interpretation criteria that take into account the local working conditions, and that greater attention should be paid to the provision of timely feedback and correction of the causes of substandard performance at poorly performing laboratories.

The effects of differential and variable training on the quality parameters of a handball throw.

PubMed

Wagner, Herbert; Müller, Erich

2008-01-01

The aim of this study was to undertake a comprehensive temporal, effective, and practical training study (variable and differential learning) that would offer athletes the opportunity to increase their performance, and to analyse the effects by measuring kinematics and quality parameters. Two participants of differing standards--a player of the first Austrian League and an Olympic and World Champion--but of similar anthropometric characteristics were recruited. One of the participants (Austrian League) was tested on five different occasions (pre-test and four retests) to measure the effects of four different training phases using kinematic analysis. The results of the study indicate an increase in ball velocity within the differential training phases (first, second, and fourth phases), different proximal-to-distal sequences of the participants, and a change of movement pattern during training measured by the segment velocities and the angle-time courses.

Driving style recognition method using braking characteristics based on hidden Markov model

PubMed Central

Wu, Chaozhong; Lyu, Nengchao; Huang, Zhen

2017-01-01

Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style. PMID:28837580

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Stacked Multilayer Self-Organizing Map for Background Modeling.

PubMed

Zhao, Zhenjie; Zhang, Xuebo; Fang, Yongchun

2015-09-01

In this paper, a new background modeling method called stacked multilayer self-organizing map background model (SMSOM-BM) is proposed, which presents several merits such as strong representative ability for complex scenarios, easy to use, and so on. In order to enhance the representative ability of the background model and make the parameters learned automatically, the recently developed idea of representative learning (or deep learning) is elegantly employed to extend the existing single-layer self-organizing map background model to a multilayer one (namely, the proposed SMSOM-BM). As a consequence, the SMSOM-BM gains several merits including strong representative ability to learn background model of challenging scenarios, and automatic determination for most network parameters. More specifically, every pixel is modeled by a SMSOM, and spatial consistency is considered at each layer. By introducing a novel over-layer filtering process, we can train the background model layer by layer in an efficient manner. Furthermore, for real-time performance consideration, we have implemented the proposed method using NVIDIA CUDA platform. Comparative experimental results show superior performance of the proposed approach.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

An Integrated Approach to Change the Outcome Part II: Targeted Neuromuscular Training Techniques to Reduce Identified ACL Injury Risk Factors

PubMed Central

Myer, Gregory D.; Ford, Kevin R.; Brent, Jensen L.; Hewett, Timothy E.

2014-01-01

Prior reports indicate that female athletes who demonstrate high knee abduction moments (KAMs) during landing are more responsive to neuromuscular training designed to reduce KAM. Identification of female athletes who demonstrate high KAM, which accurately identifies those at risk for noncontact anterior cruciate ligament (ACL) injury, may be ideal for targeted neuromuscular training. Specific neuromuscular training targeted to the underlying biomechanical components that increase KAM may provide the most efficient and effective training strategy to reduce noncontact ACL injury risk. The purpose of the current commentary is to provide an integrative approach to identify and target mechanistic underpinnings to increased ACL injury in female athletes. Specific neuromuscular training techniques will be presented that address individual algorithm components related to high knee load landing patterns. If these integrated techniques are employed on a widespread basis, prevention strategies for noncontact ACL injury among young female athletes may prove both more effective and efficient. PMID:22580980

Shape-driven 3D segmentation using spherical wavelets.

PubMed

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2006-01-01

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.

Learning to represent spatial transformations with factored higher-order Boltzmann machines.

PubMed

Memisevic, Roland; Hinton, Geoffrey E

2010-06-01

To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a three-dimensional interaction tensor. We describe a low-rank approximation to this interaction tensor that uses a sum of factors, each of which is a three-way outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.

Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

NASA Astrophysics Data System (ADS)

Ghosh, Arpita; Das, Papita; Sinha, Keka

2015-06-01

In the present work, spent tea leaves were modified with Ca(OH)2 and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a two-level three-factor (23) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12 %.

Validity of a smartphone protractor to measure sagittal parameters in adult spinal deformity.

PubMed

Kunkle, William Aaron; Madden, Michael; Potts, Shannon; Fogelson, Jeremy; Hershman, Stuart

2017-10-01

Smartphones have become an integral tool in the daily life of health-care professionals (Franko 2011). Their ease of use and wide availability often make smartphones the first tool surgeons use to perform measurements. This technique has been validated for certain orthopedic pathologies (Shaw 2012; Quek 2014; Milanese 2014; Milani 2014), but never to assess sagittal parameters in adult spinal deformity (ASD). This study was designed to assess the validity, reproducibility, precision, and efficiency of using a smartphone protractor application to measure sagittal parameters commonly measured in ASD assessment and surgical planning. This study aimed to (1) determine the validity of smartphone protractor applications, (2) determine the intra- and interobserver reliability of smartphone protractor applications when used to measure sagittal parameters in ASD, (3) determine the efficiency of using a smartphone protractor application to measure sagittal parameters, and (4) elucidate whether a physician's level of experience impacts the reliability or validity of using a smartphone protractor application to measure sagittal parameters in ASD. An experimental validation study was carried out. Thirty standard 36″ standing lateral radiographs were examined. Three separate measurements were performed using a marker and protractor; then at a separate time point, three separate measurements were performed using a smartphone protractor application for all 30 radiographs. The first 10 radiographs were then re-measured two more times, for a total of three measurements from both the smartphone protractor and marker and protractor. The parameters included lumbar lordosis, pelvic incidence, and pelvic tilt. Three raters performed all measurements-a junior level orthopedic resident, a senior level orthopedic resident, and a fellowship-trained spinal deformity surgeon. All data, including the time to perform the measurements, were recorded, and statistical analysis was performed to determine intra- and interobserver reliability, as well as accuracy, efficiency, and precision. Statistical analysis using the intra- and interclass correlation coefficient was calculated using R (version 3.3.2, 2016) to determine the degree of intra- and interobserver reliability. High rates of intra- and interobserver reliability were observed between the junior resident, senior resident, and attending surgeon when using the smartphone protractor application as demonstrated by high inter- and intra-class correlation coefficients greater than 0.909 and 0.874 respectively. High rates of inter- and intraobserver reliability were also seen between the junior resident, senior resident, and attending surgeon when a marker and protractor were used as demonstrated by high inter- and intra-class correlation coefficients greater than 0.909 and 0.807 respectively. The lumbar lordosis, pelvic incidence, and pelvic tilt values were accurately measured by all three raters, with excellent inter- and intra-class correlation coefficient values. When the first 10 radiographs were re-measured at different time points, a high degree of precision was noted. Measurements performed using the smartphone application were consistently faster than using a marker and protractor-this difference reached statistical significance of p<.05. Adult spinal deformity radiographic parameters can be measured accurately, precisely, reliably, and more efficiently using a smartphone protractor application than with a standard protractor and wax pencil. A high degree of intra- and interobserver reliability was seen between the residents and attending surgeon, indicating measurements made with a smartphone protractor are unaffected by an observer's level of experience. As a result, smartphone protractors may be used when planning ASD surgery. Copyright © 2017 Elsevier Inc. All rights reserved.

Electronegativity Equalization Method: Parameterization and Validation for Large Sets of Organic, Organohalogene and Organometal Molecule

PubMed Central

Vařeková, Radka Svobodová; Jiroušková, Zuzana; Vaněk, Jakub; Suchomel, Šimon; Koča, Jaroslav

2007-01-01

The Electronegativity Equalization Method (EEM) is a fast approach for charge calculation. A challenging part of the EEM is the parameterization, which is performed using ab initio charges obtained for a set of molecules. The goal of our work was to perform the EEM parameterization for selected sets of organic, organohalogen and organometal molecules. We have performed the most robust parameterization published so far. The EEM parameterization was based on 12 training sets selected from a database of predicted 3D structures (NCI DIS) and from a database of crystallographic structures (CSD). Each set contained from 2000 to 6000 molecules. We have shown that the number of molecules in the training set is very important for quality of the parameters. We have improved EEM parameters (STO-3G MPA charges) for elements that were already parameterized, specifically: C, O, N, H, S, F and Cl. The new parameters provide more accurate charges than those published previously. We have also developed new parameters for elements that were not parameterized yet, specifically for Br, I, Fe and Zn. We have also performed crossover validation of all obtained parameters using all training sets that included relevant elements and confirmed that calculated parameters provide accurate charges.

Application of Fragment Ion Information as Further Evidence in Probabilistic Compound Screening Using Bayesian Statistics and Machine Learning: A Leap Toward Automation.

PubMed

Woldegebriel, Michael; Zomer, Paul; Mol, Hans G J; Vivó-Truyols, Gabriel

2016-08-02

In this work, we introduce an automated, efficient, and elegant model to combine all pieces of evidence (e.g., expected retention times, peak shapes, isotope distributions, fragment-to-parent ratio) obtained from liquid chromatography-tandem mass spectrometry (LC-MS/MS/MS) data for screening purposes. Combining all these pieces of evidence requires a careful assessment of the uncertainties in the analytical system as well as all possible outcomes. To-date, the majority of the existing algorithms are highly dependent on user input parameters. Additionally, the screening process is tackled as a deterministic problem. In this work we present a Bayesian framework to deal with the combination of all these pieces of evidence. Contrary to conventional algorithms, the information is treated in a probabilistic way, and a final probability assessment of the presence/absence of a compound feature is computed. Additionally, all the necessary parameters except the chromatographic band broadening for the method are learned from the data in training and learning phase of the algorithm, avoiding the introduction of a large number of user-defined parameters. The proposed method was validated with a large data set and has shown improved sensitivity and specificity in comparison to a threshold-based commercial software package.

Using Neural Networks to Classify Digitized Images of Galaxies

NASA Astrophysics Data System (ADS)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

Research of carbon composite material for nonlinear finite element method

NASA Astrophysics Data System (ADS)

Kim, Jung Ho; Garg, Mohit; Kim, Ji Hoon

2012-04-01

Works on the absorption of collision energy in the structural members are carried out widely with various material and cross-sections. And, with ever increasing safety concerns, they are presently applied in various fields including railroad trains, air crafts and automobiles. In addition to this, problem of lighting structural members became important subject by control of exhaust gas emission, fuel economy and energy efficiency. CFRP(Carbon Fiber Reinforced Plastics) usually is applying the two primary structural members because of different result each design parameter as like stacking thickness, stacking angle, moisture absorption ect. We have to secure the data for applying primary structural members. But it always happens to test design parameters each for securing the data. So, it has much more money and time. We can reduce the money and the time, if can ensure the CFRP material properties each design parameters. In this study, we experiment the coupon test each tension, compression and shear using CFRP prepreg sheet and simulate non-linear analyze at the sources - test result, Caron longitudinal modulus and matrix poisson's ratio using GENOAMQC is specialized at Composite analysis. And then we predict the result that specimen manufacture changing stacking angle and experiment in such a way of test method using GENOA-MCQ.

Baryonic effects in cosmic shear tomography: PCA parametrization and importance of extreme baryonic models

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

Mohammed, Irshad; Gnedin, Nickolay Y.

Baryonic effects are amongst the most severe systematics to the tomographic analysis of weak lensing data which is the principal probe in many future generations of cosmological surveys like LSST, Euclid etc.. Modeling or parameterizing these effects is essential in order to extract valuable constraints on cosmological parameters. In a recent paper, Eifler et al. (2015) suggested a reduction technique for baryonic effects by conducting a principal component analysis (PCA) and removing the largest baryonic eigenmodes from the data. In this article, we conducted the investigation further and addressed two critical aspects. Firstly, we performed the analysis by separating the simulations into training and test sets, computing a minimal set of principle components from the training set and examining the fits on the test set. We found that using only four parameters, corresponding to the four largest eigenmodes of the training set, the test sets can be fitted thoroughly with an RMSmore » $$\\sim 0.0011$$. Secondly, we explored the significance of outliers, the most exotic/extreme baryonic scenarios, in this method. We found that excluding the outliers from the training set results in a relatively bad fit and degraded the RMS by nearly a factor of 3. Therefore, for a direct employment of this method to the tomographic analysis of the weak lensing data, the principle components should be derived from a training set that comprises adequately exotic but reasonable models such that the reality is included inside the parameter domain sampled by the training set. The baryonic effects can be parameterized as the coefficients of these principle components and should be marginalized over the cosmological parameter space.« less

Full scale evaluation of diffuser ageing with clean water oxygen transfer tests.

PubMed

Krampe, J

2011-01-01

Aeration is a crucial part of the biological wastewater treatment in activated sludge systems and the main energy user of WWTPs. Approximately 50 to 60% of the total energy consumption of a WWTP can be attributed to the aeration system. The performance of the aeration system, and in the case of fine bubble diffused aeration the diffuser performance, has a significant impact on the overall plant efficiency. This paper seeks to isolate the changes of the diffuser performance over time by eliminating all other influencing parameters like sludge retention time, surfactants and reactor layout. To achieve this, different diffusers have been installed and tested in parallel treatment trains in two WWTPs. The diffusers have been performance tested in clean water tests under new conditions and after one year of operation. A set of material property tests describing the diffuser membrane quality was also performed. The results showed a significant drop in the performance of the EPDM diffuser in the first year which resulted in similar oxygen transfer efficiency around 16 g/m3/m for all tested systems. Even though the tested silicone diffusers did not show a drop in performance they had a low efficiency in the initial tests. The material properties indicate that the EPDM performance loss is partly due to the washout of additives.

Effect of Table Tennis Trainings on Certain Physical and Physiological Parameters in Children Aged 10-12

ERIC Educational Resources Information Center

Tas, Murat; Sinanoglu, Ahmet

2017-01-01

In the research it was aimed to examine the effects of basic table tennis trainings, which were implemented on girls aged 10-12 for 16 weeks, on certain physical and physiological parameters. A total of 40 students, as randomly selected 20 test groups and 20 control groups at an age range of 10-12 participated in the research. These students were…

The association between the maximum step length test and the walking efficiency in children with cerebral palsy.

PubMed

Kimoto, Minoru; Okada, Kyoji; Sakamoto, Hitoshi; Kondou, Takanori

2017-05-01

[Purpose] To improve walking efficiency could be useful for reducing fatigue and extending possible period of walking in children with cerebral palsy (CP). For this purpose, current study compared conventional parameters of gross motor performance, step length, and cadence in the evaluation of walking efficiency in children with CP. [Subjects and Methods] Thirty-one children with CP (21 boys, 10 girls; mean age, 12.3 ± 2.7 years) participated. Parameters of gross motor performance, including the maximum step length (MSL), maximum side step length, step number, lateral step up number, and single leg standing time, were measured in both dominant and non-dominant sides. Spatio-temporal parameters of walking, including speed, step length, and cadence, were calculated. Total heart beat index (THBI), a parameter of walking efficiency, was also calculated from heartbeats and walking distance in 10 minutes of walking. To analyze the relationships between these parameters and the THBI, the coefficients of determination were calculated using stepwise analysis. [Results] The MSL of the dominant side best accounted for the THBI (R 2 =0.759). [Conclusion] The MSL of the dominant side was the best explanatory parameter for walking efficiency in children with CP.

Development of a Neural Network Simulator for Studying the Constitutive Behavior of Structural Composite Materials

DOE PAGES

Na, Hyuntae; Lee, Seung-Yub; Üstündag, Ersan; ...

2013-01-01

This paper introduces a recent development and application of a noncommercial artificial neural network (ANN) simulator with graphical user interface (GUI) to assist in rapid data modeling and analysis in the engineering diffraction field. The real-time network training/simulation monitoring tool has been customized for the study of constitutive behavior of engineering materials, and it has improved data mining and forecasting capabilities of neural networks. This software has been used to train and simulate the finite element modeling (FEM) data for a fiber composite system, both forward and inverse. The forward neural network simulation precisely reduplicates FEM results several orders ofmore » magnitude faster than the slow original FEM. The inverse simulation is more challenging; yet, material parameters can be meaningfully determined with the aid of parameter sensitivity information. The simulator GUI also reveals that output node size for materials parameter and input normalization method for strain data are critical train conditions in inverse network. The successful use of ANN modeling and simulator GUI has been validated through engineering neutron diffraction experimental data by determining constitutive laws of the real fiber composite materials via a mathematically rigorous and physically meaningful parameter search process, once the networks are successfully trained from the FEM database.« less

Multi-Objective Aerodynamic Optimization of the Streamlined Shape of High-Speed Trains Based on the Kriging Model.

PubMed

Xu, Gang; Liang, Xifeng; Yao, Shuanbao; Chen, Dawei; Li, Zhiwei

2017-01-01

Minimizing the aerodynamic drag and the lift of the train coach remains a key issue for high-speed trains. With the development of computing technology and computational fluid dynamics (CFD) in the engineering field, CFD has been successfully applied to the design process of high-speed trains. However, developing a new streamlined shape for high-speed trains with excellent aerodynamic performance requires huge computational costs. Furthermore, relationships between multiple design variables and the aerodynamic loads are seldom obtained. In the present study, the Kriging surrogate model is used to perform a multi-objective optimization of the streamlined shape of high-speed trains, where the drag and the lift of the train coach are the optimization objectives. To improve the prediction accuracy of the Kriging model, the cross-validation method is used to construct the optimal Kriging model. The optimization results show that the two objectives are efficiently optimized, indicating that the optimization strategy used in the present study can greatly improve the optimization efficiency and meet the engineering requirements.

Efficient high density train operations

DOEpatents

Gordon, Susanna P.; Evans, John A.

2001-01-01

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

Optimization of output power and transmission efficiency of magnetically coupled resonance wireless power transfer system

NASA Astrophysics Data System (ADS)

Yan, Rongge; Guo, Xiaoting; Cao, Shaoqing; Zhang, Changgeng

2018-05-01

Magnetically coupled resonance (MCR) wireless power transfer (WPT) system is a promising technology in electric energy transmission. But, if its system parameters are designed unreasonably, output power and transmission efficiency will be low. Therefore, optimized parameters design of MCR WPT has important research value. In the MCR WPT system with designated coil structure, the main parameters affecting output power and transmission efficiency are the distance between the coils, the resonance frequency and the resistance of the load. Based on the established mathematical model and the differential evolution algorithm, the change of output power and transmission efficiency with parameters can be simulated. From the simulation results, it can be seen that output power and transmission efficiency of the two-coil MCR WPT system and four-coil one with designated coil structure are improved. The simulation results confirm the validity of the optimization method for MCR WPT system with designated coil structure.

An Analysis of Marine Corps Training

DTIC Science & Technology

1978-06-01

total systems approach to datarmine how it can be made more effective and efficient with the ultimate goal of improved combat readiness. Six major...their attention in recent years on the costs and the effectiveness of military train- ing and ieducation.- This concern has -caused each of the Services...more-effi- cient-without a- loss- of effectiveness . To achieve--optimum effectiveness and-efficiency,_ decisions €o6ncerning -manage- ment of the-tr

Is high-intensity interval training a time-efficient exercise strategy to improve health and fitness?

PubMed

Gillen, Jenna B; Gibala, Martin J

2014-03-01

Growing research suggests that high-intensity interval training (HIIT) is a time-efficient exercise strategy to improve cardiorespiratory and metabolic health. "All out" HIIT models such as Wingate-type exercise are particularly effective, but this type of training may not be safe, tolerable or practical for many individuals. Recent studies, however, have revealed the potential for other models of HIIT, which may be more feasible but are still time-efficient, to stimulate adaptations similar to more demanding low-volume HIIT models and high-volume endurance-type training. As little as 3 HIIT sessions per week, involving ≤10 min of intense exercise within a time commitment of ≤30 min per session, including warm-up, recovery between intervals and cool down, has been shown to improve aerobic capacity, skeletal muscle oxidative capacity, exercise tolerance and markers of disease risk after only a few weeks in both healthy individuals and people with cardiometabolic disorders. Additional research is warranted, as studies conducted have been relatively short-term, with a limited number of measurements performed on small groups of subjects. However, given that "lack of time" remains one of the most commonly cited barriers to regular exercise participation, low-volume HIIT is a time-efficient exercise strategy that warrants consideration by health practitioners and fitness professionals.

High-intensity interval training beneficial effects on body mass, blood pressure, and oxidative stress in diet-induced obesity in ovariectomized mice.

PubMed

Pimenta, Marcel; Bringhenti, Isabele; Souza-Mello, Vanessa; Dos Santos Mendes, Iara Karise; Aguila, Marcia B; Mandarim-de-Lacerda, Carlos A

2015-10-15

To investigate the possible beneficial effect of high-intensity interval training (HIIT) on skeletal muscle oxidative stress, body mass (BM) and systolic blood pressure (SBP) in ovariectomized mice fed or not fed a high-fat diet. Three-month-old female C57BL/6 mice were bilaterally ovariectomized (OVX group) or submitted to surgical stress without ovariectomy (SHAM group) and separated into standard chow (SHAM-SC; OVX-SC) and high-fat diet (SHAM-HF; OVX-HF) groups. After 13 weeks, an HIIT program (swimming) was carried out for 8 weeks in non-trained (NT) and trained (T) groups. The significant reduction of uterine mass and the cytological examination of vaginal smears in the OVX group confirmed that ovariectomy was successful. Before the HIIT protocol, the ovariectomized groups showed a greater BM than the SHAM group, irrespective of the diet they received. The HIIT minimized BM gain in animals fed an HF diet and/or ovariectomized. SBP and total cholesterol were increased in the OVX and HF animals compared to their counterparts, and the HIIT efficiently reduced these factors. In the HF and OVX mice, the muscular superoxide dismutase and catalase levels were low while their glutathione peroxidase and glutathione reductase levels were high and the HIIT normalized these parameters. Diet-induced obesity maximizes the deleterious effects of an ovariectomy. The HIIT protocol significantly reduced BM, SBP and oxidative stress in the skeletal muscle indicating that HIIT diminishes the cardiovascular and metabolic risk that is inherent to obesity and menopause. Copyright © 2015 Elsevier Inc. All rights reserved.

Electrical stimulation superimposed onto voluntary muscular contraction.

PubMed

Paillard, Thierry; Noé, Frédéric; Passelergue, Philippe; Dupui, Philippe

2005-01-01

Electrical stimulation (ES) reverses the order of recruitment of motor units (MU) observed with voluntary muscular contraction (VOL) since under ES, large MU are recruited before small MU. The superimposition of ES onto VOL (superimposed technique: application of an electrical stimulus during a voluntary muscle action) can theoretically activate more motor units than VOL performed alone, which can engender an increase of the contraction force. Two superimposed techniques can be used: (i) the twitch interpolation technique (ITT), which consists of interjecting an electrical stimulus onto the muscle nerve; and (ii) the percutaneous superimposed electrical stimulation technique (PST), where the stimulation is applied to the muscle belly. These two superimposed techniques can be used to evaluate the ability to fully activate a muscle. They can thus be employed to distinguish the central or peripheral nature of fatigue after exhausting exercise. In general, whatever the technique employed, the superimposition of ES onto volitional exercise does not recruit more MU than VOL, except with eccentric actions. Nevertheless, the neuromuscular response associated with the use of the superimposed technique (ITT and PST) depends on the parameter of the superimposed current. The sex and the training level of the subjects can also modify the physiological impact of the superimposed technique. Although the motor control differs drastically between training with ES and VOL, the integration of the superimposed technique in training programmes with healthy subjects does not reveal significant benefits compared with programmes performed only with voluntary exercises. Nevertheless, in a therapeutic context, training programmes using ES superimposition compensate volume and muscle strength deficit with more efficiency than programmes using VOL or ES separately.

Protein Supplementation to Augment the Effects of High Intensity Resistance Training in Untrained Middle-Aged Males: The Randomized Controlled PUSH Trial.

PubMed

Wittke, Andreas; von Stengel, Simon; Hettchen, Michael; Fröhlich, Michael; Giessing, Jürgen; Lell, Michael; Scharf, Michael; Bebenek, Michael; Kohl, Matthias; Kemmler, Wolfgang

2017-01-01

High intensity (resistance exercise) training (HIT) defined as a "single set resistance exercise to muscular failure" is an efficient exercise method that allows people with low time budgets to realize an adequate training stimulus. Although there is an ongoing discussion, recent meta-analysis suggests the significant superiority of multiple set (MST) methods for body composition and strength parameters. The aim of this study is to determine whether additional protein supplementation may increase the effect of a HIT-protocol on body composition and strength to an equal MST-level. One hundred and twenty untrained males 30-50 years old were randomly allocated to three groups: (a) HIT, (b) HIT and protein supplementation (HIT&P), and (c) waiting-control (CG) and (after cross-over) high volume/high-intensity-training (HVHIT). HIT was defined as "single set to failure protocol" while HVHIT consistently applied two equal sets. Protein supplementation provided an overall intake of 1.5-1.7 g/kg/d/body mass. Primary study endpoint was lean body mass (LBM). LBM significantly improved in all exercise groups ( p ≤ 0.043); however only HIT&P and HVHIT differ significantly from control ( p ≤ 0.002). HIT diverges significantly from HIT&P ( p = 0.017) and nonsignificantly from HVHIT ( p = 0.059), while no differences were observed for HIT&P versus HVHIT ( p = 0.691). In conclusion, moderate to high protein supplementation significantly increases the effects of a HIT-protocol on LBM in middle-aged untrained males.

Running for your life: A review of physical activity and cardiovascular disease risk reduction in individuals with schizophrenia.

PubMed

Chalfoun, Claire; Karelis, Antony D; Stip, Emmanuel; Abdel-Baki, Amal

2016-08-01

Individuals with schizophrenia have a greater risk for cardiometabolic risk factors (e.g. central obesity, insulin resistance, hypertension and dyslipidaemia), cardiovascular diseases and mortality. This risky profile may be explained by the adverse effects of antipsychotic medications and an unhealthy lifestyle (e.g. smoking, poor nutrition and low physical activity). In the general population, physical activity has been shown to be the optimal strategy to improve both cardiometabolic parameters and cardiorespiratory fitness levels. Accordingly, an emerging literature of non-pharmacological interventions (e.g. cognitive behavioural therapy, diet and physical activity) has been studied in individuals with schizophrenia. Therefore, the purpose of this review was 1) to conduct a critical literature review of non-pharmacological interventions that included some kind of physical activity (including supervised and unsupervised exercise training) and target cardiometabolic risk factors in individuals with schizophrenia. 2) To describe the contribution of physical activity alone by reviewing trials of supervised exercise training programmes only. A literature review via systematic keyword search for publications in Medline, PubMed, Embase and PsycINFO was performed. Many non-pharmacological interventions are efficient in reducing cardiovascular disease risk factors when combined with physical activity. Supervised physical activity has been successful in decreasing cardiovascular disease risk, and aerobic interval training appears to provide more benefits by specifically targeting cardiorespiratory fitness levels. In conclusion, physical activity is an effective strategy for addressing cardiovascular disease risk in individuals with schizophrenia. Long-term studies are needed to evaluate the feasibility and impact of exercise training programmes in individuals with schizophrenia.

Effects of Preseason Training on the Sleep Characteristics of Professional Rugby League Players.

PubMed

Thornton, Heidi R; Delaney, Jace A; Duthie, Grant M; Dascombe, Ben J

2018-02-01

To investigate the influence of daily and exponentially weighted moving training loads on subsequent nighttime sleep. Sleep of 14 professional rugby league athletes competing in the National Rugby League was recorded using wristwatch actigraphy. Physical demands were quantified using GPS technology, including total distance, high-speed distance, acceleration/deceleration load (SumAccDec; AU), and session rating of perceived exertion (AU). Linear mixed models determined effects of acute (daily) and subacute (3- and 7-d) exponentially weighted moving averages (EWMA) on sleep. Higher daily SumAccDec was associated with increased sleep efficiency (effect-size correlation; ES = 0.15; ±0.09) and sleep duration (ES = 0.12; ±0.09). Greater 3-d EWMA SumAccDec was associated with increased sleep efficiency (ES = 0.14; ±0.09) and an earlier bedtime (ES = 0.14; ±0.09). An increase in 7-d EWMA SumAccDec was associated with heightened sleep efficiency (ES = 0.15; ±0.09) and earlier bedtimes (ES = 0.15; ±0.09). The direction of the associations between training loads and sleep varied, but the strongest relationships showed that higher training loads increased various measures of sleep. Practitioners should be aware of the increased requirement for sleep during intensified training periods, using this information in the planning and implementation of training and individualized recovery modalities.

Models of Pilot Behavior and Their Use to Evaluate the State of Pilot Training

NASA Astrophysics Data System (ADS)

Jirgl, Miroslav; Jalovecky, Rudolf; Bradac, Zdenek

2016-07-01

This article discusses the possibilities of obtaining new information related to human behavior, namely the changes or progressive development of pilots' abilities during training. The main assumption is that a pilot's ability can be evaluated based on a corresponding behavioral model whose parameters are estimated using mathematical identification procedures. The mean values of the identified parameters are obtained via statistical methods. These parameters are then monitored and their changes evaluated. In this context, the paper introduces and examines relevant mathematical models of human (pilot) behavior, the pilot-aircraft interaction, and an example of the mathematical analysis.

Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

NASA Technical Reports Server (NTRS)

Wang, L.; Shin, R. T.; Kong, J. A.; Yueh, S. H.

1993-01-01

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

PubMed Central

Hass, Joachim; Hertäg, Loreen; Durstewitz, Daniel

2016-01-01

The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition. PMID:27203563

Using virtual reality simulation to study navigation in a complex environment as a functional-cognitive task; A pilot study.

PubMed

Kizony, R; Zeilig, G; Krasovsky, T; Bondi, M; Weiss, P L; Kodesh, E; Kafri, M

2017-01-01

Navigation skills are required for performance of functional complex tasks and may decline due to aging. Investigation of navigation skills should include measurement of cognitive-executive and motor aspects, which are part of complex tasks. to compare young and older healthy adults in navigation within a simulated environment with and without a functional-cognitive task. Ten young adults (25.6±4.3 years) and seven community dwelling older men (69.9±3.8 years) were tested during a single session. After training on a self-paced treadmill to navigate in a non-functional simulation, they performed the Virtual Multiple Errands Test (VMET) in a mall simulation. Outcome measures included cognitive-executive aspects of performance and gait parameters. Younger adults' performance of the VMET was more efficient (1.8±1.0) than older adults (5.3±2.7; p < 0.05) and faster (younger 478.1±141.5 s, older 867.6±393.5 s; p < 0.05). There were no differences between groups in gait parameters. Both groups walked slower in the mall simulation. The shopping simulation provided a paradigm to assess the interplay between motor and cognitive aspects involved in the efficient performance of a complex task. The study emphasized the role of the cognitive-executive aspect of task performance in healthy older adults.

Using fixed-parameter and random-parameter ordered regression models to identify significant factors that affect the severity of drivers' injuries in vehicle-train collisions.

PubMed

Dabbour, Essam; Easa, Said; Haider, Murtaza

2017-10-01

This study attempts to identify significant factors that affect the severity of drivers' injuries when colliding with trains at railroad-grade crossings by analyzing the individual-specific heterogeneity related to those factors over a period of 15 years. Both fixed-parameter and random-parameter ordered regression models were used to analyze records of all vehicle-train collisions that occurred in the United States from January 1, 2001 to December 31, 2015. For fixed-parameter ordered models, both probit and negative log-log link functions were used. The latter function accounts for the fact that lower injury severity levels are more probable than higher ones. Separate models were developed for heavy and light-duty vehicles. Higher train and vehicle speeds, female, and young drivers (below the age of 21 years) were found to be consistently associated with higher severity of drivers' injuries for both heavy and light-duty vehicles. Furthermore, favorable weather, light-duty trucks (including pickup trucks, panel trucks, mini-vans, vans, and sports-utility vehicles), and senior drivers (above the age of 65 years) were found be consistently associated with higher severity of drivers' injuries for light-duty vehicles only. All other factors (e.g. air temperature, the type of warning devices, darkness conditions, and highway pavement type) were found to be temporally unstable, which may explain the conflicting findings of previous studies related to those factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Perceptual control models of pursuit manual tracking demonstrate individual specificity and parameter consistency.

PubMed

Parker, Maximilian G; Tyson, Sarah F; Weightman, Andrew P; Abbott, Bruce; Emsley, Richard; Mansell, Warren

2017-11-01

Computational models that simulate individuals' movements in pursuit-tracking tasks have been used to elucidate mechanisms of human motor control. Whilst there is evidence that individuals demonstrate idiosyncratic control-tracking strategies, it remains unclear whether models can be sensitive to these idiosyncrasies. Perceptual control theory (PCT) provides a unique model architecture with an internally set reference value parameter, and can be optimized to fit an individual's tracking behavior. The current study investigated whether PCT models could show temporal stability and individual specificity over time. Twenty adults completed three blocks of 15 1-min, pursuit-tracking trials. Two blocks (training and post-training) were completed in one session and the third was completed after 1 week (follow-up). The target moved in a one-dimensional, pseudorandom pattern. PCT models were optimized to the training data using a least-mean-squares algorithm, and validated with data from post-training and follow-up. We found significant inter-individual variability (partial η 2 : .464-.697) and intra-individual consistency (Cronbach's α: .880-.976) in parameter estimates. Polynomial regression revealed that all model parameters, including the reference value parameter, contribute to simulation accuracy. Participants' tracking performances were significantly more accurately simulated by models developed from their own tracking data than by models developed from other participants' data. We conclude that PCT models can be optimized to simulate the performance of an individual and that the test-retest reliability of individual models is a necessary criterion for evaluating computational models of human performance.

Impact of Fellowship Training Level on Colonoscopy Quality and Efficiency Metrics.

PubMed

Bitar, Hussein; Zia, Hassaan; Bashir, Muhammad; Parava, Pratyusha; Hanafi, Muhammad; Tierney, William; Madhoun, Mohammad

2018-04-18

Previous studies have described variable effects of fellow involvement on the adenoma detection rate (ADR), but few have stratified this effect by level of training. We aimed to evaluate the "fellow effect" on multiple procedural metrics including a newly defined adenoma management efficiency index, which may have a role in documenting colonoscopy proficiency for trainees. We also describe the impact of level of training on moderate sedation use. We performed a retrospective review of 2024 patients (mean age 60.9 ± 10. 94% males) who underwent outpatient colonoscopy between June 2012 and December 2014 at our Veterans Affairs Medical Center. Colonoscopies were divided into 5 groups. The first 2 groups were first year fellows in the first 6 months and last 6 months of the training year. Second and third year fellows and attending only procedures accounted for one group each. We collected data on doses of sedatives used, frequency of adjunctive agent use, procedural times as well as location, size and histology of polyps. We defined the adenoma management efficiency index as average time required per adenoma resected during withdrawal. 1675 colonoscopies involved a fellow. 349 were performed by the attending alone. There was no difference in ADR between fellows according to level of training (P=0.8), or between fellows compared with attending-only procedures (P=0.67). Procedural times decreased consistently during training, and declined further for attending only procedures. This translated into improvement in the adenoma management efficiency index (fellow groups by ascending level of training 23.5 minutes vs 18.3 minutes vs 13.7 minutes vs 13.4 minutes vs attending group 11.7 minutes; P<0.001). There was no difference in the average doses of midazolam and fentanyl used among fellow groups (P=0.16 and P=0.1, respectively). Compared with attending-only procedures, fellow involvement was associated with higher doses of fentanyl and midazolam and more frequent use of diphenhydramine and glucagon (P<0.0001; P=0.0002; P<0.0001; and P=0.01, respectively). ADR was similar at different stages of fellowship training and comparable with the attending group. Efficiency of detecting and resecting polyps improved throughout training without reaching attending level. Fellow involvement led to greater use of moderate sedation, which may relate to a longer procedure duration and an evolving experience in endoscopic technique. Copyright © 2018 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Systematic review on strength training in Parkinson’s disease: an unsolved question

PubMed Central

Ramazzina, Ileana; Bernazzoli, Benedetta; Costantino, Cosimo

2017-01-01

The purpose of this study was to investigate the effectiveness of strength training, performed against a different resistance from body weight, in improving motor and nonmotor symptoms in patients with Parkinson’s disease (PD). The following electronic databases were searched: PubMed, Physiotherapy Evidence Database, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science. The review was conducted and reported in accordance with the PRISMA statement. Thirteen high-quality randomized controlled trials were included. Strength training performed against external resistance is well tolerated and appears to be a suitable physical activity to improve both physical parameters and quality of life parameters of PD subjects. However, although the study intervention included strength training, only a few selected studies assessed the improvement of muscle strength. Despite the encouraging results, it is difficult to establish a correlation between strength training and the improvements made. Our review highlights the lack of common intent in terms of study design and the presence of different primary and secondary outcomes. Accordingly, further studies are needed to support the beneficial effects of different types of strength training in PD subjects and to underline the superiority of strength training in PD patients with respect to other training. PMID:28408811

Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.

PubMed

Wang, Jun; Deng, Zhaohong; Luo, Xiaoqing; Jiang, Yizhang; Wang, Shitong

2016-06-01

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε-insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Path integrals with higher order actions: Application to realistic chemical systems

NASA Astrophysics Data System (ADS)

Lindoy, Lachlan P.; Huang, Gavin S.; Jordan, Meredith J. T.

2018-02-01

Quantum thermodynamic parameters can be determined using path integral Monte Carlo (PIMC) simulations. These simulations, however, become computationally demanding as the quantum nature of the system increases, although their efficiency can be improved by using higher order approximations to the thermal density matrix, specifically the action. Here we compare the standard, primitive approximation to the action (PA) and three higher order approximations, the Takahashi-Imada action (TIA), the Suzuki-Chin action (SCA) and the Chin action (CA). The resulting PIMC methods are applied to two realistic potential energy surfaces, for H2O and HCN-HNC, both of which are spectroscopically accurate and contain three-body interactions. We further numerically optimise, for each potential, the SCA parameter and the two free parameters in the CA, obtaining more significant improvements in efficiency than seen previously in the literature. For both H2O and HCN-HNC, accounting for all required potential and force evaluations, the optimised CA formalism is approximately twice as efficient as the TIA formalism and approximately an order of magnitude more efficient than the PA. The optimised SCA formalism shows similar efficiency gains to the CA for HCN-HNC but has similar efficiency to the TIA for H2O at low temperature. In H2O and HCN-HNC systems, the optimal value of the a1 CA parameter is approximately 1/3 , corresponding to an equal weighting of all force terms in the thermal density matrix, and similar to previous studies, the optimal α parameter in the SCA was ˜0.31. Importantly, poor choice of parameter significantly degrades the performance of the SCA and CA methods. In particular, for the CA, setting a1 = 0 is not efficient: the reduction in convergence efficiency is not offset by the lower number of force evaluations. We also find that the harmonic approximation to the CA parameters, whilst providing a fourth order approximation to the action, is not optimal for these realistic potentials: numerical optimisation leads to better approximate cancellation of the fifth order terms, with deviation between the harmonic and numerically optimised parameters more marked in the more quantum H2O system. This suggests that numerically optimising the CA or SCA parameters, which can be done at high temperature, will be important in fully realising the efficiency gains of these formalisms for realistic potentials.

Identification of drought in Dhalai river watershed using MCDM and ANN models

NASA Astrophysics Data System (ADS)

Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

2017-03-01

An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

Supermodeling by Synchronization of Alternative SPEEDO Models

NASA Astrophysics Data System (ADS)

Duane, Gregory; Selten, Frank

2016-04-01

The supermodeling approach, wherein different imperfect models of the same objective process are dynamically combined in run-time to reduce systematic error, is tested using SPEEDO - a primitive equation atmospheric model coupled to the CLIO ocean model. Three versions of SPEEDO are defined by parameters that differ in a range that arguably mimics differences among state-of-the-art climate models. A fourth model is taken to represent truth. The "true" ocean drives all three model atmospheres. The three models are also connected to one another at every level, with spatially uniform nudging coefficients that are trained so that the three models, which synchronize with one another, also synchronize with truth when data is continuously assimilated, as in weather prediction. The SPEEDO supermodel is evaluated in weather-prediction mode, with nudging to truth. It is found that the supemodel performs better than any of the three models and marginally better than the best weighted average of the outputs of the three models run separately. To evaluate the utility for climate projection, parameters corresponding to green house gas levels are changed in truth and in the three models. The supermodel formed with inter-model connections from the present-CO2 runs no longer give the optimal configuration for the supermodel in the doubled-CO2 realm, but the supermodel with the previously trained connections is still useful as compared to the separate models or averages of their outputs. In ongoing work, a training algorithm is examined that attempts to match the blocked-zonal index cycle of the SPEEDO model atmosphere to truth, rather than simply minimizing the RMS error in the various fields. Such an approach comes closer to matching the model attractor to the true attractor - the desired effect in climate projection - rather than matching instantaneous states. Gradient descent in a cost function defined over a finite temporal window can indeed be done efficiently. Preliminary results are presented for a crudely defined index cycle.

SVD/MCMC Data Analysis Pipeline for Global Redshifted 21-cm Spectrum Observations of the Cosmic Dawn and Dark Ages

NASA Astrophysics Data System (ADS)

Burns, Jack O.; Tauscher, Keith; Rapetti, David; Mirocha, Jordan; Switzer, Eric

2018-01-01

We have designed a complete data analysis pipeline for constraining Cosmic Dawn physics using sky-averaged spectra in the VHF range (40-200 MHz) obtained either from the ground (e.g., the Experiment to Detect Global Epoch of Reionization Signal, EDGES; and the Cosmic Twilight Polarimeter, CTP) or from orbit above the lunar farside (e.g., the Dark Ages Radio Explorer, DARE). In the case of DARE, we avoid Earth-based RFI, ionospheric effects, and radio solar emissions (when observing at night). To extract the 21-cm spectrum, we parametrize the cosmological signal and systematics with two separate sets of modes defined through Singular Value Decomposition (SVD) of training set curves. The training set for the 21-cm spin-flip brightness temperatures is composed of theoretical models of the first stars, galaxies and black holes created by varying physical parameters within the ares code. The systematics training set is created using sky and beam data to model the beam-weighted foregrounds (which are about four orders of magnitude larger than the signal) as well as expected lab data to model receiver systematics. To constrain physical parameters determining the 21-cm spectrum, we apply to the extracted signal a series of consecutive fitting techniques including two usages of a Markov Chain Monte Carlo (MCMC) algorithm. Importantly, our pipeline efficiently utilizes the significant differences between the foreground and the 21-cm signal in spatial and spectral variations. In addition, it incorporates for the first time polarization data, dramatically improving the constraining power. We are currently validating this end-to-end pipeline using detailed simulations of the signal, foregrounds and instruments. This work was directly supported by the NASA Solar System Exploration Research Virtual Institute cooperative agreement number 80ARC017M0006 and funding from the NASA Ames Research Center cooperative agreement NNX16AF59G.

A Study of the Solar Wind-Magnetosphere Coupling Using Neural Networks

NASA Astrophysics Data System (ADS)

Wu, Jian-Guo; Lundstedt, Henrik

1996-12-01

The interaction between solar wind plasma and interplanetary magnetic field (IMF) and Earth's magnetosphere induces geomagnetic activity. Geomagnetic storms can cause many adverse effects on technical systems in space and on the Earth. It is therefore of great significance to accurately predict geomagnetic activity so as to minimize the amount of disruption to these operational systems and to allow them to work as efficiently as possible. Dynamic neural networks are powerful in modeling the dynamics encoded in time series of data. In this study, we use partially recurrent neural networks to study the solar wind-magnetosphere coupling by predicting geomagnetic storms (as measured by the Dstindex) from solar wind measurements. The solar wind, the IMF and the geomagnetic index Dst data are hourly averaged and read from the National Space Science Data Center's OMNI database. We selected these data from the period 1963 to 1992, which cover 10552h and contain storm time periods 9552h and quiet time periods 1000h. The data are then categorized into three data sets: a training set (6634h), across-validation set (1962h), and a test set (1956h). The validation set is used to determine where the training should be stopped whereas the test set is used for neural networks to get the generalization capability (the out-of-sample performance). Based on the correlation analysis between the Dst index and various solar wind parameters (including various combinations of solar wind parameters), the best coupling functions can be found from the out-of-sample performance of trained neural networks. The coupling functions found are then used to forecast geomagnetic storms one to several hours in advance. The comparisons are made on iterating the single-step prediction several times and on making a non iterated, direct prediction. Thus, we will present the best solar wind-magnetosphere coupling functions and the corresponding prediction results. Interesting Links: Lund Space Weather and AI Center

Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation

PubMed Central

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J.

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16th century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines. PMID:23144601

StarNet: An application of deep learning in the analysis of stellar spectra

NASA Astrophysics Data System (ADS)

Kielty, Collin; Bialek, Spencer; Fabbro, Sebastien; Venn, Kim; O'Briain, Teaghan; Jahandar, Farbod; Monty, Stephanie

2018-06-01

In an era when spectroscopic surveys are capable of collecting spectra for hundreds of thousands of stars, fast and efficient analysis methods are required to maximize scientific impact. These surveys provide a homogeneous database of stellar spectra that are ideal for machine learning applications. In this poster, we present StarNet: a convolutional neural network model applied to the analysis of both SDSS-III APOGEE DR13 and synthetic stellar spectra. When trained on synthetic spectra alone, the calculated stellar parameters (temperature, surface gravity, and metallicity) are of excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. While StarNet was developed using the APOGEE observed spectra and corresponding ASSeT synthetic grid, we suggest that this technique is applicable to other spectral resolutions, spectral surveys, and wavelength regimes. As a demonstration of this, we present a StarNet model trained on lower resolution, R=6000, IR synthetic spectra, describing the spectra delivered by Gemini/NIFS and the forthcoming Gemini/GIRMOS instrument (PI Sivanandam, UToronto). Preliminary results suggest that the stellar parameters determined from this low resolution StarNet model are comparable in precision to the high-resolution APOGEE results. The success of StarNet at lower resolution can be attributed to (1) a large training set of synthetic spectra (N ~200,000) with a priori stellar labels, and (2) the use of the entire spectrum in the solution rather than a few weighted windows, which are common methods in other spectral analysis tools (e.g. FERRE or The Cannon). Remaining challenges in our StarNet applications include rectification, continuum normalization, and wavelength coverage. Solutions to these problems could be used to guide decisions made in the development of future spectrographs, spectroscopic surveys, and data reduction pipelines, such as for the future MSE.

Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentation.

PubMed

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16(th) century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines.

Measuring Learning Resistance to Workplace Training

ERIC Educational Resources Information Center

Taylor, Jonathan E.; Lounsbury, John

2016-01-01

Training Transfer has been a topic bearing considerable mention over the past several decades. This article focuses on the connection between training transfer and learning resistance and presents research findings describing the design, creation, and testing of the Learning Efficiency Inventory (LEI). The LEI was designed to measure learning…

A Feasibility Study of Computer Assisted Instruction in US Army Basic Electronics Training. Final Report.

ERIC Educational Resources Information Center

International Business Machines Corp., Gaithersburg, MD. Federal Systems Div.

A study of computer-assisted instruction (CAI) for US Army basic electronics training at the US Army Signal Center and School establishes the feasibility of CAI as a training technique. Three aspects of CAI are considered: effectiveness, efficiency, and applicability of CAI to basic electronics training. The study explores the effectiveness of the…

A Method for Increasing the Training Effectiveness of Marine Corps Tactical Exercises: A Pilot Study.

ERIC Educational Resources Information Center

Rocklyn, Eugene H.; And Others

Methods for better utilizing simulated combat systems for training officers are required by the Marine Corps to ensure efficient acquisition of combat decision-making skills. In support of this requirement, a review and analysis of several combat training systems helped to identify a set of major training problems. These included the small number…

Mass Customization of Teaching and Training in Organizations: Design Principles and Prototype Evaluation

ERIC Educational Resources Information Center

Nistor, Nicolae; Dehne, Anina; Drews, Frank Thomas

2010-01-01

In search of methods that improve the efficiency of teaching and training in organizations, several authors point out that mass customization (MC) is a principle that covers individual needs of knowledge and skills and, at the same time limits the development costs of customized training to those of mass training. MC is proven and established in…

Training to acquire psychomotor skills for endoscopic endonasal surgery using a personal webcam trainer.

PubMed

Hirayama, Ryuichi; Fujimoto, Yasunori; Umegaki, Masao; Kagawa, Naoki; Kinoshita, Manabu; Hashimoto, Naoya; Yoshimine, Toshiki

2013-05-01

Existing training methods for neuroendoscopic surgery have mainly emphasized the acquisition of anatomical knowledge and procedures for operating an endoscope and instruments. For laparoscopic surgery, various training systems have been developed to teach handling of an endoscope as well as the manipulation of instruments for speedy and precise endoscopic performance using both hands. In endoscopic endonasal surgery (EES), especially using a binostril approach to the skull base and intradural lesions, the learning of more meticulous manipulation of instruments is mandatory, and it may be necessary to develop another type of training method for acquiring psychomotor skills for EES. Authors of the present study developed an inexpensive, portable personal trainer using a webcam and objectively evaluated its utility. Twenty-five neurosurgeons volunteered for this study and were divided into 2 groups, a novice group (19 neurosurgeons) and an experienced group (6 neurosurgeons). Before and after the exercises of set tasks with a webcam box trainer, the basic endoscopic skills of each participant were objectively assessed using the virtual reality simulator (LapSim) while executing 2 virtual tasks: grasping and instrument navigation. Scores for the following 11 performance variables were recorded: instrument time, instrument misses, instrument path length, and instrument angular path (all of which were measured in both hands), as well as tissue damage, max damage, and finally overall score. Instrument time was indicated as movement speed; instrument path length and instrument angular path as movement efficiency; and instrument misses, tissue damage, and max damage as movement precision. In the novice group, movement speed and efficiency were significantly improved after the training. In the experienced group, significant improvement was not shown in the majority of virtual tasks. Before the training, significantly greater movement speed and efficiency were demonstrated in the experienced group, but no difference in movement precision was shown between the 2 groups. After the training, no significant differences were shown between the 2 groups in the majority of the virtual tasks. Analysis revealed that the webcam trainer improved the basic skills of the novices, increasing movement speed and efficiency without sacrificing movement precision. Novices using this unique webcam trainer showed improvement in psychomotor skills for EES. The authors believe that training in terms of basic endoscopic skills is meaningful and that the webcam training system can play a role in daily off-the-job training for EES.

Neuromodulating Attention and Mind-Wandering Processes with a Single Session Real Time EEG.

PubMed

Gonçalves, Óscar F; Carvalho, Sandra; Mendes, Augusto J; Leite, Jorge; Boggio, Paulo S

2018-06-01

Our minds are continuously alternating between external attention (EA) and mind wandering (MW). An appropriate balance between EA and MW is important for promoting efficient perceptual processing, executive functioning, decision-making, auto-biographical memory, and creativity. There is evidence that EA processes are associated with increased activity in high-frequency EEG bands (e.g., SMR), contrasting with the dominance of low-frequency bands during MW (e.g., Theta). The aim of the present study was to test the effects of two distinct single session real-time EEG (rtEEG) protocols (SMR up-training/Theta down-training-SMR⇑Theta⇓; Theta up-training/SMR down-training-Theta⇑SMR⇓) on EA and MW processes. Thirty healthy volunteers were randomly assigned to one of two rtEEG training protocols (SMR⇑Theta⇓; Theta⇑SMR⇓). Before and after the rtEEG training, participants completed the attention network task (ANT) along with several MW measures. Both training protocols were effective in increasing SMR (SMR⇑Theta⇓) and theta (Theta⇑SMR⇓) amplitudes but not in decreasing the amplitude of down-trained bands. There were no significant effects of the rtEEG training in either EA or MW measures. However, there was a significant positive correlation between post-training SMR increases and the use of deliberate MW (rather than spontaneous) strategies. Additionally, for the Theta⇑SMR⇓ protocol, increase in post-training Theta amplitude was significantly associated with a decreased efficiency in the orientation network.

Physical Training, Hemodynamic Parameters and Arterial Stiffness: Friends or Foes of the Hypertensive Patient?

PubMed

Iurciuc, Stela; Avram, Claudiu; Turi, Vladiana; Militaru, Anda; Avram, Adina; Cimpean, Anca Maria; Iurciuc, Mircea

2016-01-01

To evaluate the impact of physical training on central hemodynamic parameters and elasticity of large arteries in hypertensive patients. A total of 129 hypertensive patients were divided into two groups: group A followed lifestyle changes and physical training; and group B acted as a control group; seven parameters were recorded: Pulse wave velocity (PWVao), systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), central aortic systolic blood pressure (SBPao), aortic diastolic blood pressure (DBPao), and central aortic pulse pressure (PPao). The difference between values at 4 months and baseline (Δ) were as follows: ΔPWVao was -1.02 m/s (p<0.001) versus 0.17 m/s (p=0.035), ΔSBPao was -9.6 mmHg (p=0.009) versus 1.6 mmHg (p=0.064), and ΔPPao was -6.8 mmHg (p<0.001) versus 3.2 mmHg, (p=0.029) in group A versus B, respectively. Exercise training improves SBP, PP, SBPao, PPao and may delay arterial ageing. Copyright © 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.