ISSP Position Stand: To Sample or to Specialize? Seven Postulates About Youth Sport Activities that Lead to Continued Participation and Elite Performance

A comprehensive approach to sport expertise should consider the entire situation that is comprised of the person, the task, the environment, and the complex interplay of these components (Hackfort, 1986). Accordingly, the Developmental Model of Sport Participation (Côté, Baker, & Abernethy, 2007; Côté & Fraser-Thomas, 2007) provides a comprehensive framework for sport expertise that outlines different pathways of involvement in sport. In pathways one and two, early sampling serves as the foundation for both elite and recreational sport participation. Early sampling is based on two main elements of childhood sport participation: 1) involvement in various sports and 2) participation in deliberate play. In contrast, pathway three shows the course to elite performance through early specialization in one sport. Early specialization implies a focused involvement on one sport and a large number of deliberate practice activities with the goal of improving sport skills and performance during childhood. This paper proposes seven postulates regarding the role that sampling and deliberate play, as opposed to specialization and deliberate practice, can have during childhood in promoting continued participation and elite performance in sport.

The Optimality of Decision Making During Motor Learning

In our daily lives, we often must predict how well we are going to perform in the future based on an evaluation of our current performance and an assessment of how much we will improve with practice. Such predictions can be used to decide whether to invest our time and energy in learning and, if we opt to invest, what rewards we may gain. This thesis investigated whether people are capable of tracking their own learning (i.e. current and future motor ability) and exploiting that information to make decisions related to task reward. In experiment one, participants performed a target aiming task under a visuomotor rotation such that they initially missed the target but gradually improved. After briefly practicing the task, they were asked to select rewards for hits and misses applied to subsequent performance in the task, where selecting a higher reward for hits came at a cost of receiving a lower reward for misses. We found that participants made decisions that were in the direction of optimal and therefore demonstrated knowledge of future task performance. In experiment two, participants learned a novel target aiming task in which they were rewarded for target hits. Every five trials, they could choose a target size which varied inversely with reward value. Although participants’ decisions deviated from optimal, a model suggested that they took into account both past performance, and predicted future performance, when making their decisions. Together, these experiments suggest that people are capable of tracking their own learning and using that information to make sensible decisions related to reward maximization.

Study of Liquid Desiccant Air-Conditioning for High Performance Greenhouses

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.