This study evaluated outcomes of an inpatient program designed to reduce severe agitated behavior in geriatric patients with dementia who could not be successfully treated on an outpatient basis. An individualized treatment plan was created for each patient (N = 250) that involved pharmacological and nonpharmacological interventions with behavioral, environmental, and psychological components. Assessment of behavioral, cognitive, and functional status was conducted for each patient on admission to the program and at discharge. Significant improvements on these assessments were observed. We conclude that the longitudinal, multidisciplinary approach used in this study was effective in significantly reducing intrusive and dangerous behaviors while preserving or enhancing patients' cognitive and functional abilities.

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The need to develop systems that exploit multi and many-core architectures to reduce wasteful heat generation is of utmost importance in compute-intensive applications. We propose an energy-conscious approach to multicore scheduling known as non-preemptive dynamic window (NPDW) scheduling that achieves effective load and temperature balancing over chip multiprocessors. NPDW utilizes the concept of dynamic time windows to accumulate tasks and find an optimal stable matching between accumulated tasks and available processor cores using a modified Gale-Shapely algorithm. The metrics of window and matching performance are defined to create a dynamic window heuristic to determine the next time window size based on the current and previous window sizes. Based on derived formulation and experimental results, we show that our NPDW scheduler is able to distribute the computational and thermal load throughout the processors in a multicore environment better than baseline schedulers. We believe that within multicore compute applications requiring temperature and energy-conscious system design, our scheduler may be employed to effectively disperse system load and prevent excess core heating.

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