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Hybrid system models - those devised from two or more disparate sub-system models - provide a number of benefits in terms of conceptualization, development, and assessment of dynamical systems. The decomposition approach helps to formulate complex interactions that are otherwise difficult or impractical to express. However, hybrid model development and usage can introduce complexity that emerges from the composition itself.
To improve assurance of model correctness, sub-systems using disparate modeling formalisms must be integrated above and beyond just the data and control level; their composition must have model specification and simulation execution aspects as well. Poly-formalism composition is one approach to composing models in this manner.
This dissertation describes a poly-formalism composition between a Discrete EVent System specification (DEVS) model and a Cellular Automata (CA) model types. These model specifications have been chosen for their broad applicability in important and emerging domains. An agent-environment domain exemplifies the composition approach. The inherent spatial relations within a CA make it well-suited for environmental representations. Similarly, the component-based nature of agents fits well within the hierarchical component structure of DEVS.
This composition employs the use of a third model, called an interaction model, that includes methods for integrating the two model types at a formalism level, at a systems architecture level, and at a model execution level. A prototype framework using DEVS for the agent model and GRASS for the environment has been developed and is described. Furthermore, this dissertation explains how the concepts of this composition approach are being applied to a real-world research project.
This dissertation expands the tool set modelers in computer science and other disciplines have in order to build hybrid system models, and provides an interaction model for an on-going research project. The concepts and models presented in this dissertation demonstrate the feasibility of composition between discrete-event agents and discrete-time cellular automata. Furthermore, it provides concepts and models that may be applied directly, or used by a modeler to devise compositions for other research efforts.
The purpose of this project is to implement an on-site mindfulness-based intervention to reduce stress and burnout among mental health care workers. Healthcare professionals are among the most stressed of any profession, and mental health workers are at an extremely high risk for burnout and compassion fatigue (Christopher & Meris, 2010) with an estimated 21% to 67% of mental health workers reporting that they experience high levels of burnout (Salyers et al., 2011).
After researching the literature, it was evident that practicing mindfulness can lead to less stress and higher job satisfaction. In an effort to combat this problem, an on-site mindfulness intervention was implemented at an outpatient psychiatric setting for eight weeks. Twenty-seven mental health workers gave their consent to be part of the study, and eleven were able to complete the study and self-assessment surveys for three time periods. The Maslach Burnout Inventory (MBI) (the Human Service Version) and a 1-item job satisfaction were used to measure the effect of intervention on employees’ levels of stress and job satisfaction.
A non-parametric Friedman test of differences among repeated measures was conducted and findings were not significant when comparing the average total scores of means between pre-, post-, or 1-month follow-up for Emotional Exhaustion (p = .148), Depersonalization (p = .223), Personal Achievement (p = .784) and job satisfaction (p = .422). The positive outcomes cited by participant support the thesis that the on-site mindfulness-based intervention is better than no intervention though the effect was not statistically significant.