583 research outputs found

    The Relationship between Motivations for Physical Activity and Self-Esteem of College Women

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    Physical activity can have a positive impact on several aspects of mental health, including self-esteem. Positive mental health effects of physical activity may be related to increased skill competency and social interaction (Paluska & Schwenk, 2000). Research indicates that self-esteem is also increased by participation in physical activity. Specifically, girls who participate in physical activity have higher self-esteem than girls who do not participate (Wilson & Rodgers, 2002; Schmalz, Deane, Birch, Davidson, 2007). Self-esteem that emerges from physical activity is an important factor in the health and wellbeing of college women. Social, emotional, academic and physical aspects of a young woman\u27s life play a large part in her wellbeing during college (Ahern, Bennett, Kelly & Hetherington, 2011). College women in particular are continually faced with issues of body image dissatisfaction and low self-esteem (Forrest & Stuhdreher, 2007). Research has shown that women who are physically active for extrinsic reasons are less likely to develop strong motivational patterns throughout life and more likely to have overall lower levels of self-worth than women who are motivated intrinsically (Wilson & Rogers, 2002). Influential factors to participate in physical activity include societal impacts from friends, family, and the media. However, specific motivations for physical activity participation are less understood. This study sought to examine the role that appearance motivation plays in the relationship between physical activity participation and self-esteem among college women. The sample consisted of 668 undergraduate women between the ages of 18-24 enrolled in classes at a mid-size southeastern university during the 2012 spring semester. A questionnaire was developed using the International Physical Activity Questionnaire, Motivations for Physical Activity Measure - Revised and the Rosenberg Self-Esteem scale to collect information on physical activity participation, motivations and self-esteem levels of college women. Study results presented no significant relationship between physical activity participation and self-esteem. Physical activity participation and self-esteem were not mediated by appearance motivation; however, there was a significant relationship between physical activity participation and appearance motivations

    Machine learning-based affect detection within the context of human-horse interaction

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    This chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detection and interpretation of human emotion, an application that could significantly benefit quantitative studies in the field of animal assisted therapy. The chapter offers a thorough description, an experimental design, and experimental results on the use of physiological signals, such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals, for the creation and evaluation of machine learning models for the prediction of the emotional state of an individual during interaction with horses

    EDUCATION OF TEACHERSOF THE INITIAL YEARS OF ELEMENTARY SCHOOL IN THE FACE OF OVERCOMING DIFFICULTIES IN MATHEMATICS

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    This research started from the guiding questions: What are the difficulties encountered by teachers of the initial years of Elementary School in the face of the subject of mathematics? How can these difficulties be overcome? Thus, the research aimed to identify the difficulties encountered by teachers in the initial years of Elementary School in relation to the discipline of mathematics, highlighting how they were overcome. The data was collected through the application of a questionnaire to 07 teachers working in the initial years of Elementary School, and was analyzed through Content Analysis. Three categories emerged: (C1) Reports of personal experiences in relation to the subject of mathematics; (C2) Overcoming difficulties in mathematics; (C3) Academic suggestions for teacher education. From the results found, it was noted that in-service education, the exchange of experiences and the availability of materials that address mathematical content are factors that directly affect overcoming the difficulties with the subject.Esta investigación partió de las preguntas orientadoras: ¿Cuáles son las dificultades que encuentran los docentes en los primeros años de la escuela primaria frente a la temática matemática? ¿Cómo se pueden superar estas dificultades? Así, tuvo como objetivo identificar las dificultades encontradas por los/las docentes en los primeros años de la escuela primaria en relación con la disciplina de las matemáticas, destacando cómo fueron superadas. Los datos fueron recolectados a partir de la aplicación de un cuestionario a 07 docentes que trabajaban en los primeros años de la escuela primaria, y analizados a la luz del Análisis de Contenido. Surgieron tres categorías: (C1) Informes de experiencias personales en relación con la asignatura de matemáticas; (C2) Superación de dificultades en matemáticas (C3) Sugerencias académicas para la formación del profesorado. De los resultados encontrados, se encontró que la formación continua, el intercambio de experiencias y la disponibilidad de materiales que abordan contenidos matemáticos son factores que inciden directamente en la superación de dificultades con la disciplina.This research started from the guiding questions: What are the difficulties encountered by teachers of the initial years of Elementary School in the face of the subject of mathematics? How can these difficulties be overcome? Thus, the research aimed to identify the difficulties encountered by teachers in the initial years of Elementary School in relation to the discipline of mathematics, highlighting how they were overcome. The data was collected through the application of a questionnaire to 07 teachers working in the initial years of Elementary School, and was analyzed through Content Analysis. Three categories emerged: (C1) Reports of personal experiences in relation to the subject of mathematics; (C2) Overcoming difficulties in mathematics; (C3) Academic suggestions for teacher education. From the results found, it was noted that in-service education, the exchange of experiences and the availability of materials that address mathematical content are factors that directly affect overcoming the difficulties with the subject

    Balancer genetic algorithm-a novel task scheduling optimization approach in cloud computing

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    Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing

    Heartbeat classification and arrhythmia detection using a multi-model deep-learning technique

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    Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost
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