46 resultados para seminar-based training


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This paper presents an experimental research on the use of eddy current testing (ECT) and artificial neural networks (ANNs) in order to identify the gauge and position of steel bars immersed in concrete structures. The paper presents details of the ECT probe and concrete specimens constructed for the tests, and a study about the influence of the concrete on the values of measured voltages. After this, new measurements were done with a greater number of specimens, simulating a field condition and the results were used to generate training and validation vectors for multilayer perceptron ANNs. The results show a high percentage of correct identification with respect to both, the gauge of the bar and of the thickness of the concrete cover. © 2013 Copyright Taylor and Francis Group, LLC.

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Objective: This study aimed to determine the energy expenditure (EE) in terms of caloric cost and metabolic equivalents (METs) of two sessions of an exercise protocol. Methods: Fifteen subjects (51.0 ± 5.5years) performed the exercise sessions (80min), which were composed by (warming, walking and flexibility exercises; Session A) and (warming, walking and local muscular endurance exercises; Session B). Heart hate (HR) was measured during each part of the sessions. In laboratory environment, maximal oxygen consumption (VO2max) and oxygen uptake in rest and exercise conditions (using mean HR obtained in classes) were measured on different days, using indirect calorimetry. Exercise METs were obtained by dividing VO2 in exercise (mL.kg-1.min-1) by VO2 in rest (mL.kg-1.min-1). The EE of the exercises was calculated by the formula: MET x Weight(kg) x Time(min)/60. The results were analyzed by ANOVA with Tuckey post hoc test (p < 0.05). Results: One MET for this group was 2.7 ± 0.1mL.kg-1.min-1. The mean METs of exercises were 4,7 ± 0,8 (warming), 5,8 ± 0,9 (walking) and 3,6 ± 0,7 (flexibility) on session A, and 4,6 ± 1,2 (warming), 5,6 ± 1,0 (walking) and 4.8 ± 1,0 (local muscular endurance exercises) on Session B. The training sessions showed similar energy cost (A: 398 ± 86.72 kcal and B: 404 ± 38.85 kcal; p > 0,05). None of activities were classified into vigorous intensity (> 7 METs). There were no differences on VO2 between walking (15,6 ± 2,8 or 15,4 ± 2,6 mL.kg-1.min-1) and local muscular endurance exercises (13,2 ± 2,9 mL.kg-1.min-1), although both were higher (p > 0.05) than flexibility exercises (10.1 ± 2.2 mL.kg-1.min-1). Conclusion: The proposed protocol achieves the physical activity needed by healthy adults to improve and maintain health, by their structure, moderate intensity, duration, frequency and caloric expenditure.

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Due to shortage of time and limited availability of faculty surgeons to teach basic surgical skills during medical graduation, the search for alternative ways of simulated training with feedback is needed. The purpose of this study was to compare the simulated teaching of suture skills to novice medical students by senior medical students and by experienced faculty surgeons. Forty-eight novice medical students were randomly assigned to three practice conditions on bench model (n = 16): self-directed suture training (control), senior medical student-directed suture skills' training, or experienced faculty surgeon-directed suture skills' training. Pre- and post-tests were applied. Global Rating Scale with blinded evaluation and self-perceived confidence based on Likert scale were used to assess all suture performances in pre- and post-training. Effect size was also calculated. The analysis made after training showed that the students who received feedback from the instructors had better performance based on the Global Rating Scale (all p < 0.0000) and felt more confident to carry out sutures (all p < 0.0000) when compared to the control. There was no significant difference (all p > 0.05) between the student-directed teaching and faculty-directed teaching groups. The magnitude of the effect (instructor-directed training suture) was considered large (>0.80) in all measurements. The acquisition of suture skills after student-directed training was similar to the training supervised by faculty surgeon, and the increase in suture performances of trainees that received instructor administered training was superior to self-directed learning. © 2013 Springer-Verlag Italia.

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Purpose. To verify the effects of resistance training at the electromyographic fatigue threshold (EMGFT) based on one-repetition maximum strength (1RM), heart rate (HR), rate of perceived exertion (PE) and endurance time (EndT). Methods. Nineteen subjects (training group [TG]: n = 10; control group [CG]: n = 9), performed 1-min bicep curl exercises sets at 25%, 30%, 35% and 40% 1RM. Electromyography (biceps brachii and brachiorradialis), HR and PE were registered. Biceps brachii EMGFT was used to create a load index for an eight-week resistance training programme (three sets until exhaustion/session, two sessions/week) for the TG. The CG only attended one session in the first week and another session in the last week of the eight-week training period for EndT measurement. EndT was determined from the number of repetitions of each of the three sets performed in the first and last training sessions. After training, 1RM, EMGFT, EndT, HR and PE at the different bicep curl load intensities were again measured for both groups. Results. Increases in 1RM (5.9%, p < 0.05) and EndT (> 60%, p < 0.001) after training were found. In addition, PE was reduced at all load intensities (p < 0.05), while no changes were found for HR and EMGFT after training. Conclusions. Strength-endurance training based on the EMGFT improved muscular endurance and also, to a lesser extent, muscular strength. Moreover, the reduced levels of physical exertion after training at the same intensity suggest that endurance training exercises may improve comfort while performing strength exercises.

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To assess the effects of continuous exercise training at intensities corresponding to 80 and 90 % of the lactate minimum test (LM), we evaluated antioxidant activity, hormone concentration, biochemical analyses and aerobic and anaerobic performance, as well as glycogen stores, during 12 weeks of swimming training in rats. One-hundred rats were separated into three groups: control (CG, n = 40), exercise at 80 (EG80, n = 30) and 90 % (EG90, n = 30) of LM. The training lasted 12 weeks, with sessions of 60 min/day, 6 days/week. The intensity was based at 80 and 90 % of the LM. The volume did not differ between training groups (Ẋ of EG80 = 52 ± 4 min; Ẋ of EG90 = 56 ± 2 min). The glycogen concentration (mg/100 mg) in the gastrocnemius increased after the training in EG80 (0.788 ± 0.118) and EG90 (0.795 ± 0.157) in comparison to the control (0.390 ± 0.132). The glycogen stores in the soleus enhanced after the training in EG90 (0.677 ± 0.230) in comparison to the control (0.343 ± 0.142). The aerobic performance increased by 43 and 34 % for EG80 and EG90, respectively, in relation to baseline. The antioxidant enzymes remain unchanged during the training. Creatine kinase (U/L) increased after 8 weeks in both groups (EG80 = 427.2 ± 97.4; EG90 = 641.1 ± 90.2) in relation to the control (246.9 ± 66.8), and corticosterone (ng/mL) increased after 12 weeks in EG90 (539 ± 54) in comparison to the control (362 ± 44). The continuous exercise at 80 and 90 % of the LM has a marked aerobic impact on endurance performance without significantly biomarkers changes compared to control. © 2013 Springer-Verlag Berlin Heidelberg.

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An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

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The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

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Background and Purpose: The evidence of the benefits from regular physical activity to hypertensives is based on dry land training studies. Therefore, the aim of this study is to compare the effect of aquatic exercise with dry land training on hypertensive women. Methods: This is a randomized controlled study with 52 post-menopausal hypertensive women. The patients were randomly allocated in three groups: water aerobic training group (n=19), dry land aerobic training group (n=19) and a non-intervention control group (n=14). The training protocol was performed by 12weeks. Results: There were no differences among the three groups concerning basal blood pressure (BP) and biochemical variables. In water group, there was a statistically significant reduction of systolic BP from 136±16mmHg at zero week to 124±18mm Hg at 11th week and 124±15mmHg at 12th week. In dry land training group, there was a statistically significant reduction of systolic BP from 138±15mmHg at zero week to 125±10mmHg at 7th week, 127±10mmHg at 10th week and 126±9mmHg at 12th week. The control group presented no change in any of the assessed variables. No changes were carried out in any antihypertensive medications during study. Discussion: This is a randomized controlled study that demonstrates the antihypertensive efficacy of aerobic aquatic exercise. © 2013 John Wiley & Sons, Ltd.

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In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

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BACKGROUND: Organizations are increasingly required to reduce their environmental impact through the adoption of environmental management, which requires the support of human resource practices.OBJECTIVE: The objective of this study is to determine whether human resource management practices, especially training, are supporting environmental management practices at four hotels located in Brazil.METHODS: This research is qualitative, based on the analysis of four hotels in Brazil.RESULTS: Based on the systematized empirical evidence collected from four hotels (Hotels A, B, C, and D), it can be concluded that: (1) human resource management is still not fully aligned with environmental objectives at the hotels studied; (2) only Hotel B has implemented environmental management practices and aligned with human resource management in a more developed manner, which may indicate that these two variables of analysis could have interrelations; (3) environmental training as a human resource management practice was verified in all hotels analyzed.CONCLUSIONS: The greening of human resources practices is not fully aligned with environmental objectives in the hotels studied. If these hotels really wish to "go green," environmental training will be necessary. Hotel stakeholders play a major role in implementing the greening of the hotel industry.

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Since the beginning, some pattern recognition techniques have faced the problem of high computational burden for dataset learning. Among the most widely used techniques, we may highlight Support Vector Machines (SVM), which have obtained very promising results for data classification. However, this classifier requires an expensive training phase, which is dominated by a parameter optimization that aims to make SVM less prone to errors over the training set. In this paper, we model the problem of finding such parameters as a metaheuristic-based optimization task, which is performed through Harmony Search (HS) and some of its variants. The experimental results have showen the robustness of HS-based approaches for such task in comparison against with an exhaustive (grid) search, and also a Particle Swarm Optimization-based implementation.

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Objective: This study aimed to analyze and compare the role of a water-based exercise program versus a combination of water and callisthenic exercises on postural control, functional independence, and freezing of gait (FOG) in patients with mild to moderate Parkinson disease.Methods: Twenty-five community-dwelling participants with idiopathic Parkinson disease were recruited. Of these, 9 participants took part in a water-based program of physical exercises and the other 16 participants took part in a combined program that consisted of callisthenic exercises plus an aquatic exercise session. Both programs were 16 weeks in duration. The clinical evaluation assessed the festination by means of the FOG score test; postural control was verified by means of the balance test of the short physical performance battery, and the Spanish validated version of the Unified Parkinson's Disease Rating Scale part 2 was used to assess functional independence. Participants were evaluated before and after 16 weeks of both proposed programs.Results: The results showed improvement in FOG for both groups, although a significant main effect was observed only in the patients who performed the callisthenic exercise plus an aquatic exercise program. Postural control did not show significant improvements after both proposed physical exercise programs as soon as functional autonomy. Our preliminary results suggest that training sessions with the combination of water plus callisthenic exercises may be a useful physical rehabilitation strategy for individuals with mild to moderate Parkinson disease who have FOG.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The purpose of this paper is mainly to show how training may support low-carbon operations and production management in a more sustainable organizational context. Design/methodology/approach – A conceptual framework to facilitate the integration between training and low-carbon operations and production is presented. Findings – To accomplish better training in a low-carbon organization, some steps should be followed. Challenges may occur, including the necessity of collaboration across the supply chain. Research limitations/implications – The proposed framework should be applied and improved based on the actual conditions in organizations.