127 resultados para Early Development in Sport


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"In the past few years, many career theorists have noted the dearth of literature in the area of career development in childhood and adolescence. A growing need for integrating theory and research on the early stages of vocational development within a systemic, life-span developmental approach has been articulated. This volume, the first book dedicated to career development of children and adolescents, provides a broad and comprehensive overview of the current knowledge about the key career processes that take place in this age group. Each of the eighteen chapters represents an in-depth examination of a specific aspect of career development with a focus on integrating modern career theory and ongoing research and further developing theory-practice connections in understanding child and adolescent career behaviour. Twenty-six authors, leading experts from eight countries, provide a state-of-the-art summary of the current thinking in the field and outline directions for future empirical work and practice."--publisher website

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Objectives The aim of this position paper is to discuss the role of affect in designing learning experiences to enhance expertise acquisition in sport. The design of learning environments and athlete development programmes are predicated on the successful sampling and simulation of competitive performance conditions during practice. This premise is captured by the concept of representative learning design, founded on an ecological dynamics approach to developing skill in sport, and based on the individual-environment relationship. In this paper we discuss how the effective development of expertise in sport could be enhanced by the consideration of affective constraints in the representative design of learning experiences. Conclusions Based on previous theoretical modelling and practical examples we delineate two key principles of Affective Learning Design: (i) the design of emotion-laden learning experiences that effectively simulate the constraints of performance environments in sport; (ii) recognising individualised emotional and coordination tendencies that are associated with different periods of learning. Considering the role of affect in learning environments has clear implications for how sport psychologists, athletes and coaches might collaborate to enhance the acquisition of expertise in sport.

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During development of the primary olfactory system, axon targeting is inaccurate and axons inappropriately project within the target layer or overproject into the deeper layers of the olfactory bulb. As a consequence there is considerable apoptosis of primary olfactory neurons during embryonic and postnatal development and axons of the degraded neurons need to be removed. Olfactory ensheathing cells (OECs) are the glia of the primary olfactory nerve and are known to phagocytose axon debris in the adult and postnatal animal. However, it is unclear when phagocytosis by OECs first commences. We investigated the onset of phagocytosis by OECs in the developing mouse olfactory system by utilizing two transgenic reporter lines: OMP-ZsGreen mice which express bright green fluorescent protein in primary olfactory neurons, and S100β-DsRed mice which express red fluorescent protein in OECs. In crosses of these mice, the fate of the degraded axon debris is easily visualized. We found evidence of axon degradation at embryonic day (E)13.5. Phagocytosis of the primary olfactory axon debris by OECs was first detected at E14.5. Phagocytosis of axon debris continued into the postnatal animal during the period when there was extensive mistargeting of olfactory axons. Macrophages were often present in close proximity to OECs but they contributed only a minor role to clearing the axon debris, even after widespread degeneration of olfactory neurons by unilateral bulbectomy and methimazole treatment. These results demonstrate that from early in embryonic development OECs are the primary phagocytic cells of the primary olfactory nerve.

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The Asia‐Pacific region is characterised by rapid population growth and urbanisation. These trends often result in an increasing consumption of land, which in turn lead to spatially expansive and discontinuous urban development. As a consequence, local communities and the environment face strong pressures. Many cities in the region have developed policies to tackle the issue of rapid growth and its associated consequences, for example climate change. The broad aim of this paper is to identify the nature, trends and strategies of growth management in major Asia‐Pacific city‐regions, and their implications for natural resource management and infrastructure provision. More specifically, this research seeks to provide insights on sustainable urban development practice, particularly on the promotion of compact urbanisation within the Asia‐Pacific’s fastest growing regions. The methodology of the paper includes a detailed literature review and a comparative analysis of existing strategies and policies. The literature review focuses on the key concepts related to sustainable urban growth management. It also includes existing applications of urban growth management approaches and planning information system in managing growth. Following the literature review, the paper undertakes a comparative analysis of the strategies of major Asia‐Pacific city‐regions of Kuala Lumpur and Hong Kong in terms of their approaches to sustainable urban development. The findings of the paper provide a clear understanding of the necessity of sustainable urban development practices. It contributes to the development of a substantial base for further research. Ultimately, this research aims to shed light on sustainable urban development by providing insights on the management of growth, natural resources and urban infrastructures.

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Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

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Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

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Interactive educational courseware has been adopted in diverse education sectors such as primary, secondary, tertiary education, vocational and professional training. In Malaysian educational context, the ministry of education has implemented Smart School Project that aims to increase high level of academic achievement in primary and secondary schools by using interactive educational courseware. However, many researchers have reported that many coursewares fail to accommodate the learner and teacher needs. In particular, the interface design is not appropriately designed in terms of quality of learning. This paper reviews educational courseware development process in terms of defining quality of interface design and suggests a conceptual model of interface design through the integration of design components and interactive learning experience into the development process. As a result, it defines the concept of interactive learning experience in a more practical approach in order to implement each stage of the development process in a seamless and integrated way.

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The aims of this chapter are twofold. First, we show how experiments related to nonlinear dynamical systems theory can bring about insights on the interconnectedness of different information sources for action. These include the amount of information as emphasised in conventional models of cognition and action in sport and the nature of perceptual information typically emphasised in the ecological approach. The second aim was to show how, through examining the interconnectedness of these information sources, one can study the emergence of novel tactical solutions in sport; and design experiments where tactical/decisional creativity can be observed. Within this approach it is proposed that perceptual and affective information can be manipulated during practice so that the athlete's cognitive and action systems can be transposed to a meta-stable dynamical performance region where the creation of novel action information may reside.

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Interactive educational courseware has been adopted in diverse education sectors such as primary, secondary, tertiary education, vocational and professional training. In Malaysian educational context, the ministry of education has implemented Smart School Project that aims to increase high level of academic achievement in primary and secondary schools by using interactive educational courseware. However, many researchers have reported that many coursewares fail to accommodate the learner and teacher needs. In particular, the interface design is not appropriately designed in terms of quality of learning. This paper reviews educational courseware development process in terms of defining quality of interface design and suggests a conceptual model of interface design through the integration of design components and interactive learning experience into the development process. As a result, it defines the concept of interactive learning experience in a more practical approach in order to implement each stage of the development process in a seamless and integrated way.