RESPONSES OF HEMATOLOGICAL PARAMETERS AND AEROBIC PERFORMANCE OF ELITE MEN AND WOMEN SWIMMERS DURING A 14-WEEK TRAINING PROGRAM

Santhiago, V, da Silva, ASR, Papoti, M, and Gobatto, CA. Responses of hematological parameters and aerobic performance of elite men and women swimmers during a 14-week training program. J Strength Cond Res 23(4): 1097-1105, 2009-The main purpose of the present investigation was to verify the responses of hematological parameters in men and women competitive swimmers during a 14-week training program. Twenty-three Olympic and international athletes were evaluated 4 times during the experiment: at the beginning of the endurance training phase (T1), at the end of the endurance training phase (T2), at the end of the quality phases (T3), and at the end of the taper period (T4). On the first day at 8:00 AM, each swimmer had a blood sample taken for the determination of hematological parameters. At 3:00 PM, the athletes had their aerobic performance measured by anaerobic threshold. On the second day at 8: 00 AM, the swimmers had their aerobic performance measured by critical velocity. Hematocrit and mean corpuscular volume diminished (p <= 0.05) from T1 to T2 (men: 5.8 and 7.2%; women: 11.6 and 6.8%), and increased (p <= 0.05) from T2 to T3 (men: 7.2 and 6.0%; women: 7.4 and 5.2%). These results were related to the plasma volume changes of the athletes. However, these alterations do not seem to affect the swimmers` aerobic performance. For practical applications, time-trial performance is better than aerobic performance (i.e., anaerobic threshold and critical velocity) for monitoring training adaptations.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

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 natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Training effects with the observer-rated cognitive errors and the coping action patterns scales

The use of observer-rated scales requires that raters be trained until they have become reliable in using the scales. However, few studies properly report how training in using a given rating scale is conducted or indeed how it should be conducted. This study examined progress in interrater reliability over 6 months of training with two observer-rated scales, the Cognitive Errors Rating Scale and the Coping Action Patterns Rating Scale. The evolution of the intraclass correlation coefficients was modeled using hierarchical linear modeling. Results showed an overall training effect as well as effects of the basic training phase and of the rater calibration phase, the latter being smaller than the former. The results are discussed in terms of implications for rater training in psychotherapy research.

Reliability of P-drive in occupational therapy following a short training session: A promising instrument measuring seniors' on-road driving competencies

Introduction Occupational therapists could play an important role in facilitating driving cessation for ageing drivers. This, however, requires an easy-to-learn, standardised on-road evaluation method. This study therefore investigates whether use of P-drive' could be reliably taught to occupational therapists via a short half-day training session. Method Using the English 26-item version of P-drive, two occupational therapists evaluated the driving ability of 24 home-dwelling drivers aged 70 years or over on a standardised on-road route. Experienced driving instructors' on-road, subjective evaluations were then compared with P-drive scores. Results Following a short half-day training session, P-drive was shown to have almost perfect between-rater reliability (ICC2,1=0.950, 95% CI 0.889 to 0.978). Reliability was stable across sessions including the training phase even if occupational therapists seemed to become slightly less severe in their ratings with experience. P-drive's score was related to the driving instructors' subjective evaluations of driving skills in a non-linear manner (R-2=0.445, p=0.021). Conclusion P-drive is a reliable instrument that can easily be taught to occupational therapists and implemented as a way of standardising the on-road driving test.

Effect of the time-of-day of training on explicit memory

Studies have shown a time-of-day of training effect on long-term explicit memory with a greater effect being shown in the afternoon than in the morning. However, these studies did not control the chronotype variable. Therefore, the purpose of this study was to assess if the time-of-day effect on explicit memory would continue if this variable were controlled, in addition to identifying the occurrence of a possible synchronic effect. A total of 68 undergraduates were classified as morning, intermediate, or afternoon types. The subjects listened to a list of 10 words during the training phase and immediately performed a recognition task, a procedure which they repeated twice. One week later, they underwent an unannounced recognition test. The target list and the distractor words were the same in all series. The subjects were allocated to two groups according to acquisition time: a morning group (N = 32), and an afternoon group (N = 36). One week later, some of the subjects in each of these groups were subjected to a test in the morning (N = 35) or in the afternoon (N = 33). The groups had similar chronotypes. Long-term explicit memory performance was not affected by test time-of-day or by chronotype. However, there was a training time-of-day effect [F (1,56) = 53.667; P = 0.009] with better performance for those who trained in the afternoon. Our data indicated that the advantage of training in the afternoon for long-term memory performance does not depend on chronotype and also that this performance is not affected by the synchronic effect.

RESPONSES of HEMATOLOGICAL PARAMETERS and AEROBIC PERFORMANCE of ELITE MEN and WOMEN SWIMMERS DURING A 14-WEEK TRAINING PROGRAM

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

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.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Harmony Search applied for Support Vector Machines Training Optimization

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.

Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Aprender a governar: a aprendizagem de funcion??rios p??blicos para as carreiras de estado

Esta tese investiga como dois grupos de funcion??rios p??blicos seleciona dos para as carreiras de Estado no Brasil aprendem a desempenhar as suas fun????es. Deste m odo, busca compreender os processos de aprendizagem, formais e informais, da e ntrada desses servidores p??blicos como alunos nos espa??os destinados ?? sua forma????o, at?? sua inser ????o nos espa??os de trabalho, como aprendizes. A pesquisa justifica-se porque, sendo as am ostras estudadas consideradas boas refer??ncias no servi??o p??blico, faz-se necess??ri o compreender como se d?? a aprendizagem para o exerc??cio dessas carreiras de Estado. Quatro dimens??es s??o consideradas para investigar a aprendizagem dos funcion??rios p??blicos: primeira, o surgimento e evolu????o da forma????o em administra????o p??bli ca no Pa??s. Segunda, as escolas de governo, lugares prop??cios ?? aprendizagem, influente s para a administra????o p??blica do Pa??s, inclinados ?? discuss??o de novas solu????es e cr??ticos na identifica????o, an??lise e apropria????o das experi??ncias verificadas em outras realidades. Te rceira, os cursos de forma????o inicial, espa??os para o primeiro contato com a fun????o escol hida e os seus desafios, saberes necess??rios e ferramentas dispon??veis. Quarta, a inse r????o no ambiente de trabalho, decisiva para o processo de aprendizagem social dos funcion??rios novatos por interm??dio das comunidades de pr??tica.

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Tapering for marathon and cardiac autonomic function.

The purpose of this study was to investigate changes in post-exercise heart rate recovery (HRR) and heart rate variability (HRV) during an overload-tapering paradigm in marathon runners and examine their relationship with running performance. 9 male runners followed a training program composed of 3 weeks of overload followed by 3 weeks of tapering (-33±7%). Before and after overload and during tapering they performed an exhaustive running test (Tlim). At the end of this test, HRR variables (e.g. HRR during the first 60 s; HRR60 s) and vagal-related HRV indices (e.g. RMSSD5-10 min) were examined. Tlim did not change during the overload training phase (603±105 vs. 614±132 s; P=0.992), but increased (727±185 s; P=0.035) during the second week of tapering. Compared with overload, RMSSD5-10 min (7.6±3.3 vs. 8.6±2.9 ms; P=0.045) was reduced after the 2(nd) week of tapering. During tapering, the improvements in Tlim were negatively correlated with the change in HRR60 s (r=-0.84; P=0.005) but not RMSSD5-10 min (r=-0.21; P=0.59). A slower HRR during marathon tapering may be indicative of improved performance. In contrast, the monitoring of changes in HRV as measured in the present study (i.e. after exercise on a single day), may have little or no additive value.

Selecting Wavelet Filter for Compression of Spectra from Multispectral Images

The problem of selecting anappropriate wavelet filter is always present in signal compression based on thewavelet transform. In this report, we propose a method to select a wavelet filter from a predefined set of filters for the compression of spectra from a multispectral image. The wavelet filter selection is based on the Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter for each spectrum has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectra from multispectral images. When a new image is to be compressed, a set of spectra from that image is selected, the spectra are classified by the trained LVQand the filter associated to the largest class is selected for the compression of every spectrum from the multispectral image. The results show, that almost inevery case our method finds the most suitable wavelet filter from the pre-defined set for the compression.