6 resultados para high-performance frontal analysis

em QSpace: Queen's University - Canada


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The purpose of this study was to report the knowledge used by expert high performance gymnastic coaches in the organization of training and competition. In-depth interviews were conducted with 9 coaches who worked with male gymnasts and 8 coaches who worked with female gymnasts. Qualitative analyses showed that coaches of males and coaches of females planned training similarly, except that coaches of females appeared to emphasize esthetic and nutritional issues to a greater extent. Coaches of males revealed more concerns about the organization of gymnasts' physical conditioning. Analysis indicated that expert gymnastic coaches of males and females are constantly involved in dynamic social interactions with gymnasts, parents, and assistant coaches. Many areas of coaches' organizational work, such as dealing with the athletes' personal concerns and working with parents, are not part of the structure of coaches' training programs and emerged as crucial tasks of expert gymnastic coaches for developing elite gymnasts.

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This paper proposes a three-step method of evaluating high performance coaches involving feedback from the athletes. First, data are collected using an instrument such as the Coaching Behavior Scale for Sport (CBS-S: Côté, Yardley, Hay, Sedgwick, & Baker, 1999). Second, a summary report is prepared with descriptive information regarding the frequency of behaviors demonstrated by the coach that can be compared to previous results or to a criterion measure. The third step involves appropriate personnel reviewing the report and subsequently providing guidance for individual coach development. This three-step appraisal method provides useful evaluative feedback to coaches and has been used in several sport programs in Canada, the United States, and Australia.

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What experiences are needed to become a high-performance coach? The present study addressed this question through structured retrospective quantitative interviews with 10 team- and 9 individual-sport coaches at the Canadian interuniversity-sport level. Minimum amounts of certain experiences were deemed necessary but not sufficient to become a high-performance coach (e.g., playing the sport they now coach and interaction with a mentor coach for all coaches, leadership opportunities as athletes for team-sport coaches only). Although coaches reported varying amounts of these necessary experiences, general stages of high-performance coach development were traced. Findings serve to identify and support potential high-performance coaches and increase the effectiveness of formal coaching-education programs.

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The purpose of this study is to report the knowledge used in training and competition by 17 expert high-performance gymnastic coaches. A qualitative research methodology was used to collect and inductively analyze the data. The knowledge elicited for the competition component was categorized as competition site, competition floor, and trial competitions. These categories indicated that the coaches are minimally involved with the gymnasts in competition. The knowledge of the coaches elicited within the training component were categorized as coach involvement in training, intervention style, technical skills, mental skills, and simulation. Properties of these categories that were extensively discussed by the expert coaches, such as teaching progressions, being supportive, and helping athletes to deal with stress,are consistent with the literature on coaching and on sport psychology. Other aspects considered important in the sport psychology literature, such as developing concentration skills, were not discussed as thoroughly by the expert coaches.

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Greenhouses have become an invaluable source of year-round food production. Further development of viable and efficient high performance greenhouses is important for future food security. Closing the greenhouse envelope from the environment can provide benefits in space heating energy savings, pest control, and CO2 enrichment. This requires the application of a novel air conditioning system to handle the high cooling loads experienced by a greenhouse. Liquid desiccant air-conditioning (LDAC) have been found to provide high latent cooling capacities, which is perfect for the application of a humid greenhouse microclimate. TRNSYS simulations were undertaken to study the feasibility of two liquid desiccant dehumidification systems based on their capacity to control the greenhouse microclimate, and their cooling performance. The base model (B-LDAC) included a natural gas boiler, and two cooling systems for seasonal operation. The second model (HP-LDAC) was a hybrid liquid desiccant-heat pump dehumidification system. The average tCOPdehum and tCOPtotal of the B-LDAC system increased from 0.40 and 0.56 in January to 0.94 and 1.09 in June. Increased load and performance during a sample summer day improved these values to 3.5 and 3.0, respectively. The average eCOPdehum and eCOPtotal values were 1.0 and 1.8 in winter, and 1.7 and 2.1 in summer. The HP-LDAC system produced similar daily performance trends where the annual average eCOPdehum and eCOPtotal values were 1.3 and 1.2, but the sample day saw peaks of 2.4 and 3.2, respectively. The B-LDAC and HP-LDAC results predicted greenhouse temperatures exceeding 30°C for 34% and 17% of the month of July, respectively. Similarly, humidity levels increased in summer months, with a maximum of 14% of the time spent over 80% in May for both models. The percentage of annual savings in space heating energy associated with closing the greenhouse to ventilation was 34%. The additional annual regeneration energy input was reduced by 26% to 526 kWhm-2, with the implementation of a heat recovery ventilator on the regeneration exhaust air. The models also predicted an electrical energy input of 245 kWhm-2 and 305 kWhm-2 for the B-LDAC and HP-LDAC simulations, respectively.

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Modern software applications are becoming more dependent on database management systems (DBMSs). DBMSs are usually used as black boxes by software developers. For example, Object-Relational Mapping (ORM) is one of the most popular database abstraction approaches that developers use nowadays. Using ORM, objects in Object-Oriented languages are mapped to records in the database, and object manipulations are automatically translated to SQL queries. As a result of such conceptual abstraction, developers do not need deep knowledge of databases; however, all too often this abstraction leads to inefficient and incorrect database access code. Thus, this thesis proposes a series of approaches to improve the performance of database-centric software applications that are implemented using ORM. Our approaches focus on troubleshooting and detecting inefficient (i.e., performance problems) database accesses in the source code, and we rank the detected problems based on their severity. We first conduct an empirical study on the maintenance of ORM code in both open source and industrial applications. We find that ORM performance-related configurations are rarely tuned in practice, and there is a need for tools that can help improve/tune the performance of ORM-based applications. Thus, we propose approaches along two dimensions to help developers improve the performance of ORM-based applications: 1) helping developers write more performant ORM code; and 2) helping developers configure ORM configurations. To provide tooling support to developers, we first propose static analysis approaches to detect performance anti-patterns in the source code. We automatically rank the detected anti-pattern instances according to their performance impacts. Our study finds that by resolving the detected anti-patterns, the application performance can be improved by 34% on average. We then discuss our experience and lessons learned when integrating our anti-pattern detection tool into industrial practice. We hope our experience can help improve the industrial adoption of future research tools. However, as static analysis approaches are prone to false positives and lack runtime information, we also propose dynamic analysis approaches to further help developers improve the performance of their database access code. We propose automated approaches to detect redundant data access anti-patterns in the database access code, and our study finds that resolving such redundant data access anti-patterns can improve application performance by an average of 17%. Finally, we propose an automated approach to tune performance-related ORM configurations using both static and dynamic analysis. Our study shows that our approach can help improve application throughput by 27--138%. Through our case studies on real-world applications, we show that all of our proposed approaches can provide valuable support to developers and help improve application performance significantly.