971 resultados para Supervised training


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Pós-graduação em Ciências da Motricidade - IBRC

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Pós-graduação em Ciências da Motricidade - IBRC

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The present study aims to reflect on the importance of supervised internship in Physical Education in the context of elementary education; establishing, therefore, a path of definitions of concepts ranging from the disquieting comprehension regarding the association between theory and practice to the most intricate details of the content to be treated in Physical Education classes, as well as the formative process of the learners who perform the supervised training. Thus, it was possible to build a vigorous dialogue with the different theorists and scholars of both Education and Physical Education. Since then, various thematic issues that punctuate the supervised internship in all its dimensions have aroused

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The present study aims to reflect on the importance of supervised internship in Physical Education in the context of elementary education; establishing, therefore, a path of definitions of concepts ranging from the disquieting comprehension regarding the association between theory and practice to the most intricate details of the content to be treated in Physical Education classes, as well as the formative process of the learners who perform the supervised training. Thus, it was possible to build a vigorous dialogue with the different theorists and scholars of both Education and Physical Education. Since then, various thematic issues that punctuate the supervised internship in all its dimensions have aroused

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The present study aims to reflect on the importance of supervised internship in Physical Education in the context of elementary education; establishing, therefore, a path of definitions of concepts ranging from the disquieting comprehension regarding the association between theory and practice to the most intricate details of the content to be treated in Physical Education classes, as well as the formative process of the learners who perform the supervised training. Thus, it was possible to build a vigorous dialogue with the different theorists and scholars of both Education and Physical Education. Since then, various thematic issues that punctuate the supervised internship in all its dimensions have aroused

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Introducción. El número de personas que padecen síndrome metabólico ha incrementado a nivel mundial durante las últimas dos décadas. Existen numerosos estudios que tratan de comparar prevalencias según los diferentes criterios y estimaciones del riesgo metabólico. De ellos se puede concluir que el principal hallazgo ha sido recalcar la necesidad de una definición estándar universal. A pesar de estas discrepancias no hay lugar a duda sobre el problema de salud pública que esto conlleva. Se necesitan medidas y estrategias urgentes para prevenir y controlar esta emergente epidemia global y para ello se debe prestar especial atención a los cambios en el estilo de vida, fundamentalmente dieta y ejercicio. A pesar de todo, existe a día de hoy una importante controversia sobre el tipo de ejercicio más efectivo y su combinación con la dieta para conseguir mejoras en la salud. Objetivos. Estudiar los índices de riesgo metabólico empleados en la literatura científica y las terapias basadas en dieta y ejercicio para el tratamiento de los factores del síndrome metabólico en adultos con sobrepeso. Diseño de investigación. Los datos empleados en el análisis de esta tesis son, primeramente un estudio piloto, y posteriormente parte del estudio “Programas de Nutrición y Actividad Física para el tratamiento de la obesidad” (PRONAF). El estudio PRONAF es un proyecto consistente en un estudio clínico sobre programas de nutrición y actividad física para el sobrepeso y la obesidad, desarrollado en España durante varios años de intervenciones. Fue diseñado, en parte, para tratar de comparar protocolos de entrenamiento de resistencia, cargas y combinado en igualdad de volumen e intensidad, con el objetivo de evaluar su impacto en los factores de riesgo y la prevalencia del síndrome metabólico en personas con sobrepeso y obesidad. El diseño experimental es un control aleatorio y el protocolo incluye 3 modos de ejercicio (entrenamiento de resistencia, con cargas y combinado) y restricción dietética sobre diversas variables determinantes del estado de salud. Las principales variables para la investigación que comprende esta tesis fueron: actividad física habitual, marcadores de grasa corporal, niveles de insulina, glucosa, triglicéridos, colesterol total, colesterol HDL, colesterol LDL, presión arterial y parámetros relacionados con el ejercicio. Conclusiones. A) Los índices de riesgo metabólico estudiados presentan resultados contradictorios en relación al riesgo metabólico en un individuo, dependiendo de los métodos matemáticos empleados para el cálculo y de las variables introducidas, tanto en mujeres sanas como en adultos en sobrepeso. B) El protocolo de entrenamiento combinado (de cargas y de resistencia) junto con la dieta equilibrada propuesto en este estudio fue la mejor estrategia para la mejora del riesgo de síndrome metabólico en adultos con sobrepeso. C) Los protocolos de entrenamiento supervisado de resistencia, con cargas y combinado junto con la restricción nutricional, no obtuvieron mejoras sobre el perfil lipídico, más allá de los cambios conseguidos con el protocolo de dieta y recomendaciones generales de actividad física habitual en clínica, en adultos con sobrepeso. Background. Over the past two decades, a striking increase in the number of people with the MetS worldwide has taken place. Many studies compare prevalences using different criteria and metabolic risk estimation formulas, and perhaps their main achievement is to reinforce the need for a standardized international definition. Although these discrepancies, there is no doubt it is a public health problem. There is urgent need for strategies to prevent and manage the emerging global epidemic, special consideration should be given to behavioral and lifestyle, mainly diet and exercise. However, there is still controversy about the most effective type of exercise and diet combination to achieve improvements. Objectives. To study the metabolic risk scores used in the literature and the diet and exercise therapies for the treatment of the MetS factors in overweight adults. Research design. The data used in the analysis was collected firstly in a pilot study and lately, as a part of the “Programas de Nutrición y Actividad física para el tratamiento de la obesidad” study (PRONAF). The PRONAF Study is a clinical research project in nutrition and physical activity programs for overweight and obesity, carried out in Spain (2008-2011). Was designed, in part, to attempt to match the volume and intensity of endurance, strength and combined training protocols in order to evaluate their impact on risk factors and MetS prevalence in overweight and obese people. The design and protocol included three exercise modes (endurance, strength and combined training) and diet restriction, in a randomized controlled trial concerning diverse health status variables. The main variables under investigation were habitual physical activity, markers of body fat, fasting serum levels of insulin, glucose, triglycerides, total, LDL and HDL cholesterol, blood pressure and diet and exercise parameters. Main outcomes. A) The metabolic risk scores studied presented contradictory results in relation to the metabolic risk of an individual, depending on the mathematical method used and the variables included, both in healthy women and overweight adults. B) The protocol proposed for combination of strength and endurance training combined with a balance diet was the optimal strategy for the improvement of MetS risk in overweight adults. C) The intervention program of endurance, strength or combined supervised training protocol with diet restriction did not achieved further improvements in lipid profile than a habitual clinical practice protocol including dietary advice and standard physical activity recommendations, in overweight adults.

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En este proyecto estudia la posibilidad de realizar una verificación de locutor por medio de la biometría de voz. En primer lugar se obtendrán las características principales de la voz, que serían los coeficientes MFCC, partiendo de una base de datos de diferentes locutores con 10 muestras por cada locutor. Con estos resultados se procederá a la creación de los clasificadores con los que luego testearemos y haremos la verificación. Como resultado final obtendremos un sistema capaz de identificar si el locutor es el que buscamos o no. Para la verificación se utilizan clasificadores Support Vector Machine (SVM), especializado en resolver problemas biclase. Los resultados demuestran que el sistema es capaz de verificar que un locutor es quien dice ser comparándolo con el resto de locutores disponibles en la base de datos. ABSTRACT. Verification based on voice features is an important task for a wide variety of applications concerning biometric verification systems. In this work, we propose a human verification though the use of their voice features focused on supervised training classification algorithms. To this aim we have developed a voice feature extraction system based on MFCC features. For classification purposed we have focused our work in using a Support Vector Machine classificator due to it’s optimization for biclass problems. We test our system in a dataset composed of various individuals of di↵erent gender to evaluate our system’s performance. Experimental results reveal that the proposed system is capable of verificating one individual against the rest of the dataset.

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Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.

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To acting in emergencies it is important that health professionals develop specific and differentiated skills, which shows us the importance of training in emergency planning. So undergraduate courses in medicine and nursing should encourage the development of these skills and evaluate them through various instruments targeted to the different fields. The aim of this study was to implement an optional and interprofessional curricular component, focusing on interprofessional education in pre-hospital emergency for medical and nursing courses Federal University of Rio Grande do Norte (UFRN). This is an exploratory descriptive study, with 24 medical and nursing graduates of last year undergraduate of supervised training, who underwent theoretical and practical training in the care of pre-hospital emergency services. There were theoretical and practical lessons per week for one school semester, taught by doctors and nurses of the Emergency Medical Service (EMS), where the topics discussed were: basic and advanced life support, safe transport in clinical emergencies, trauma, gynecological, obstetric, pediatric and psychiatric diseases, and have been carried out practical activities in ambulances. The students were evaluated by pre-test, post-test and practical stations made through the Objective Structured Clinical Evaluation (OSCE), in the skills laboratory of the Health Sciences Center. During the activities the students were encouraged to critical and reflective thinking, highlighting the importance of integration between the various health care professionals. It was observed that 88% of the students had a score increase over the pre-test. In the evaluation process carried out by medical students and nursing UFRN have similar expectations regarding the essential skills acquired during the training activity. The results of this study will form the basis for the organization of interprofessional education activity in pre-hospital emergency medical students and nursing, as well as helped to organize practices stations, identifying basic clinical skills, and implementing student assessment tools UFRN.

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Correctness of information gathered in production environments is an essential part of quality assurance processes in many industries, this task is often performed by human resources who visually take annotations in various steps of the production flow. Depending on the performed task the correlation between where exactly the information is gathered and what it represents is more than often lost in the process. The lack of labeled data places a great boundary on the application of deep neural networks aimed at object detection tasks, moreover supervised training of deep models requires a great amount of data to be available. Reaching an adequate large collection of labeled images through classic techniques of data annotations is an exhausting and costly task to perform, not always suitable for every scenario. A possible solution is to generate synthetic data that replicates the real one and use it to fine-tune a deep neural network trained on one or more source domains to a different target domain. The purpose of this thesis is to show a real case scenario where the provided data were both in great scarcity and missing the required annotations. Sequentially a possible approach is presented where synthetic data has been generated to address those issues while standing as a training base of deep neural networks for object detection, capable of working on images taken in production-like environments. Lastly, it compares performance on different types of synthetic data and convolutional neural networks used as backbones for the model.

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Supervised exercise training has been shown to improve walking capacity in several studies of patients with intermittent claudication. However, data on long-term outcome are quite limited. The aim of this prospective study was to evaluate long-term effects of supervised exercise training on walking capacity and quality of life in patients with intermittent claudication. Patients and methods: Sixty-seven consecutive patients with intermittent claudication who completed a supervised 12-week exercise training program were asked for follow up evaluation 39 +/- 20 months after program completion. Pain-free walking distance (PWD) and maximum walking distances (MWD) were assessed by treadmill test and several questionnaires. Results: Forty (60%) patients agreed to participate, 22 (33%) refused participation, and 5 (7%) died during follow-up. PWD and MWD significantly improved at completion of 12-weeks supervised exercise training as compared to baseline (PWD 114 +/- 100 vs. 235 +/- 248, p = 0.002; MWD 297 +/- 273 vs. 474 +/- 359, p = 0.001). Improvement of PWD and MWD could be maintained at follow up (197 +/- 254, p = 0.014; 390 +/- 324, p = 0.035, respectively) with non-smokers showing significantly better sustained PWD and MWD improvement as compared to baseline. Overall, walking capacity correlated with functional status of quality of life. Conclusions: Major findings of this investigation were that improvement in walking capacity is sustained after completion of supervised exercise training program with best results in patients who quitted or never smoked. Improved walking capacity is associated with increased functional status of quality of life.

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The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.

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Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

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Latin America is characterized by ethnic, geographical, cultural, and economic diversity; therefore, training in gastroenterology in the region must be considered in this context. The continent's medical education is characterized by a lack of standards and the volume of research continues to be relatively small. There is a multiplicity of events in general gastroenterology and in sub-disciplines, both at regional and local levels, which ensure that many colleagues have access to information. Medical education programs must be based on a clinical vision and be considered in close contact with the patients. The programs should be properly supervised, appropriately defined, and evaluated on a regular basis. The disparity between the patients' needs, the scarce resources available, and the pressures exerted by the health systems on doctors are frequent cited by those complaining of poor professionalism. Teaching development can play a critical role in ensuring the quality of teaching and learning in universities. Continuing professional development programs activities must be planned on the basis of the doctors' needs, with clearly defined objectives and using proper learning methodologies designed for adults. They must be evaluated and accredited by a competent body, so that they may become the basis of a professional regulatory system. The specialty has made progress in the last decades, offering doctors various possibilities for professional development. The world gastroenterology organization has contributed to the speciality through three distinctive, but closely inter-related, programs: Training Centers, Train-the-Trainers, and Global Guidelines, in which Latin America is deeply involved. (C) 2011 Baishideng. All rights reserved.

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Aims. In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. Methods. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set to optimize the performance when applied to the CoRoT data. Results. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.