HELPING YOUTH WITH AUTISM SPECTRUM DISORDER DEVELOP SOCIAL COMPETENCE IN COMMUNITY-BASED SETTINGS

The purpose of the current dissertation is to identify the features of effective interventions by exploring the experiences of youth with ASD who participate in such interventions, through two intervention studies (Studies 1 and 2) and one interview study (Study 3). Studies 1 and 2 were designed to support the development of social competence of youth with ASD through Structured Play with LEGO TM (Study 1, 12 youths with ASD, ages 7–12) and Minecraft TM (Study 2, 4 youths with ASD, ages 11–13). Over the course of the sessions, the play of the youth developed from parallel play (children playing alone, without interacting) to co-operative play (playing together with shared objectives). The results of Study 2 showed that rates of initiations and levels of engagement increased from the first session to the final session. In Study 3, 12 youths with ASD (ages 10–14) and at least one of their parents were interviewed to explore what children and their parents want from programs designed to improve social competence, which activities and practices were perceived to promote social competence by the participants, and which factors affected their decisions regarding these programs. The adolescents and parents looked for programs that supported social development and emotional wellbeing, but did not always have access to the programs they would have preferred, with factors such as cost and location reducing their options. Three overarching themes emerged through analysis of the three studies: (a) interests of the youth; (b) structure, both through interactions and instruction; and (c) naturalistic settings. Adolescents generally engage more willingly in interventions that incorporate their interests, such as play with Minecraft TM in Study 2. Additionally, Structured Play and structured instruction were crucial components of providing safe and supportive contexts for the development of social competence. Finally, skills learned in naturalistic settings tend to be applied more successfully in everyday situations. The themes are analysed through the lens of Vygotsky’s (1978) perspectives on learning, play, and development. Implications of the results for practitioners and researchers are discussed.

Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.

A cloud-based system for measuring radiation treatment plan similarity

PURPOSE: Radiation therapy is used to treat cancer using carefully designed plans that maximize the radiation dose delivered to the target and minimize damage to healthy tissue, with the dose administered over multiple occasions. Creating treatment plans is a laborious process and presents an obstacle to more frequent replanning, which remains an unsolved problem. However, in between new plans being created, the patient's anatomy can change due to multiple factors including reduction in tumor size and loss of weight, which results in poorer patient outcomes. Cloud computing is a newer technology that is slowly being used for medical applications with promising results. The objective of this work was to design and build a system that could analyze a database of previously created treatment plans, which are stored with their associated anatomical information in studies, to find the one with the most similar anatomy to a new patient. The analyses would be performed in parallel on the cloud to decrease the computation time of finding this plan. METHODS: The system used SlicerRT, a radiation therapy toolkit for the open-source platform 3D Slicer, for its tools to perform the similarity analysis algorithm. Amazon Web Services was used for the cloud instances on which the analyses were performed, as well as for storage of the radiation therapy studies and messaging between the instances and a master local computer. A module was built in SlicerRT to provide the user with an interface to direct the system on the cloud, as well as to perform other related tasks. RESULTS: The cloud-based system out-performed previous methods of conducting the similarity analyses in terms of time, as it analyzed 100 studies in approximately 13 minutes, and produced the same similarity values as those methods. It also scaled up to larger numbers of studies to analyze in the database with a small increase in computation time of just over 2 minutes. CONCLUSION: This system successfully analyzes a large database of radiation therapy studies and finds the one that is most similar to a new patient, which represents a potential step forward in achieving feasible adaptive radiation therapy replanning.