Findings suggest that AMT + RELAX was beneficial in reducing anger symptoms and promoting efficient use of the between-session practice; however, AMT + RELAX did not outperform AMT. This study is an important contribution as it is one of the first randomized controlled trials to study the efficacy of a technology-enhanced, evidence-based psychotherapy for anger management. Findings are limited because of small sample size and modifications to the technology during the trial. However, the results highlight the possible benefits of mobile app-supported treatment, including increasing the accessibility of treatment, lowering therapist workload, reducing costs of treatment, reducing practice time, and enabling new activities and types of treatments. This study presents preliminary evidence that mobile apps can be a valuable addition to treatment for patients with anger difficulties. Future research should evaluate how much therapist involvement is needed to support anger management.

Our findings support using an app as an adjunct to traditional anger management.

The U.S. military medical community spends a great deal of time and resources training its personnel to provide them with the knowledge and skills necessary to perform life-saving tasks, both on the battlefield and at home. However, personnel may fail to retain specialized knowledge and skills if they are not applied during the typical periods of nonuse within the military deployment cycle, and retention of critical knowledge and skills is crucial to the successful care of warfighters. For example, we researched the skill and knowledge loss associated with specialized surgical skills such as those required to perform laparoscopic surgery (LS) procedures. These skills are subject to decay when military surgeons perform combat casualty care during their deployment instead of LS. This article describes our preliminary research identifying critical LS skills, as well as their acquisition and decay rates. It introduces models that identify critical skills related to laparoscopy, and proposes objective metrics for measuring these critical skills. This research will provide insight into best practices for (1) training skills that are durable and resistant to skill decay, (2) assessing these skills over time, and (3) introducing effective refresher training at appropriate intervals to maintain skill proficiency.

Individual-based, spatially explicit models provide a mechanism to understand distributions of individuals on the landscape; however, few models have been coupled with population genetics. The primary benefits of such a combination is to assess performance of populationgenetic estimators in realistic situations. KERNELPOP represents a flexible framework to implement almost any arbitrary population-genetic and demographic model in a spatially explicit context using a variety of dispersal kernels. Estimates of type I error associated with genome scans in metapopulations are provided as an illustration of this software's utility.

NIEHAUS, JAMES M. Cognitive Models of Discourse Comprehension for Narrative Generation. (Under the direction of Professor R. Michael Young). Recent work in the area of narrative generation has sought to develop systems that automatically produce experiences for a user that are understood as stories. Much of this prior work, however, has focused on the structural aspects of narrative rather than the process of narrative comprehension undertaken by readers. Cognitive theories of narrative discourse comprehension define explicit models of a reader's mental state during reading. These cognitive models are created to test hypotheses and explain empirical results about the comprehension processes of readers. They do not often contain sufficient precision for implementation on a computer, and thus, they are not yet suitable for computational generation purposes. This dissertation employs cognitive models of narrative discourse comprehension to define an explicit computational model of a reader's comprehension process during reading, predicting aspects of narrative focus and inferencing with precision. This computational model is employed in a narrative discourse generation system to select content from an event log, creating discourses that satisfy comprehension criteria. The results I would like to thank