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Assessing team development and the group process in sport
Roeske-Carlson, David Ray University of Minnesota 2000 해외박사(DDOD)
소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.
The purpose of this research project was to develop and test the validity and reliability of an assessment instrument designed to define group process issues within the team development process. Through correlation analysis and the use of factorial analysis a strong dependable assessment instrument was developed. This research project is the first step in developing a valid and reliable assessment instrument for the use of studying, measuring, and exploring the group process within sport. The assessment instrument used for this study was the Team Development Assessment Package (TDAP) developed by David Roeske-Carlson and Dr. March L. Krotee (1999). The construction of the TDAP derived from a myriad of models, interventions, and research literature. The TDAP was developed to explore team cohesion and adaptability, stage development, leadership behavior, communication patterns, team strengths, and team satisfaction. There was a total of (n = 495) student athletes from (n = 22) fall sport teams that volunteered to participants in this study. There were (n = 288) females; (n = 207) males. The frequency distribution of the type of teams participating in the study include the following: college soccer (n = 5), high school soccer (n = 6), college volleyball (n = 5), high school volleyball (n = 4), and college football (n = 2). The number of respondents per sport were: football (n = 110); soccer (n = 235); and volleyball (n = 150). The results of the study support the empirical research and suggest that the TDAP has bridged the “gulf” between research, theory, and practice within the sport psychology discipline. The results suggest that an assessment instrument has been developed that has the capabilities to be sensitive and specific in determining developmental needs for sport teams, while at the same time, providing valuable research information concerning team development issues within sport.
Carlson, Joel Norman Kristian 서울대학교 융합과학기술대학원 2016 국내석사
Purpose: Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs), a linear accelerator component which modulates the radiation field shape, are an important source of errors in dose distributions during radiotherapy. It is therefore advantageous to be able to predict these errors, and account for them before the plan is delivered to the patient. In this work, we used machine learning techniques to predict these discrepancies, assessed the quality of the predictions, and examined the impact the errors have on quality assurance procedures, and patient dosimetry. Materials and Methods: 114 volumetric modulated arc therapy plans for either head and neck cancer, or prostate cancer were retrospectively chosen. Features hypothesized to be predictive of errors were calculated from the plan files, such as leaf position, velocity and acceleration, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in position between synchronized plan (DICOM-RT) files and machine reported delivery (DynaLog) files acquired during QA were used as a target response for training predictive models. Model accuracy was assessed using a testing set of data which was not used for model training or validation. Predicted positions were then incorporated into the treatment planning system (TPS), and the increase in accuracy from accounting for errors was verified using gamma analysis. Results: The developed models are capable of predicting the errors to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to the delivered positions than were the planned positions. Integration of the predicted positions into dose calculations performed within the TPS was shown to increase gamma passing rates measured against dose distributions delivered during QA at all criteria. The proposed method increased gamma passing rates of head and neck plans with 1%/2mm criteria by 4.17% (SD = 1.54%) on average. Impact on patient dosimetry was assessed using dose volumetric histograms. In all cases, predicted dose volumetric parameters were in closer agreement with delivered parameters than were planned parameter, particularly for organs at risk at the periphery of the treatment field. Conclusions: In this study, we showed that MLC errors can be accurately predicted, and that accounting for these errors gives treatment planners a more realistic view of the dose distribution as it will truly be delivered to the patient.
Stochastic Inference and Bayesian Nonparametric Models in Electrophysiological Time Series
Carlson, David Duke University 2015 해외박사(DDOD)
소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.
This thesis presents novel methods for processing electrophysiological time-series from simultaneously recorded electrodes in a brain, as well as providing new inference techniques that are more generally applicable. On spike sorting, I introduce Bayesian nonparametric methods to process multiple electrodes simultaneously, which improves performance when the electrode spacing is less than 100 microns. Furthermore, by treating the spike sorting problem as a single deconvolutional model instead of the conventional 2-step procedure with detection and clustering steps, the over- lapping spike problem is ameliorated. I then show that these detected neurons and their spike trains have dynamic relationships with local field potentials in distinct brain regions, and that the number of distinct relationships appears to cluster. While these models approach an important scientific problem, it is necessary to have efficient inference in computationally-intensive models. To this end, I intro- duce novel methods for Variational Bayesian inference, as well as introducing a new stochastic inference algorithm called "Stochastic Spectral Descent," which mimics Stochastic Gradient Descent but operates in the Shatten-infinity norm. I show that several common machine learning problems naturally operate in the Shatten-infinity norm, and that this descent method mimics the natural geometry and greatly improves learning efficiency.