Programs and Communities

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 goal of this report is to present an overview of the federal workforce and the opportunities that exist for younger generations to be employed by the federal government. We explored what is preventing younger generations from seeking and securing federal employment and shed light on the benefits, value, and opportunities of federal jobs for the younger generation. The report provides a better understanding of how and what policies, for better or worse, influence federal recruitment and hiring processes. We examined the 20-year history and evolution of the federal job portal, USAJOBS, to understand what information is critical to provide an applicant to successfully complete and submit an application for a federal job. We also reviewed the role of the U.S. Office of Personnel Management (OPM), and its responsibility to provide agencies guidance and support in implementing government orders and policies. With research, data, and interview insights, we developed a tangible guide for young job seekers to use to navigate USAJOBS. This guide provides applicable tips gathered from experiences of first-hand users and federal human resources specialists to help prospective applicants decipher federal job announcements and to strategically and completely prepare their applications and resumes to secure federal employment.
Based on these findings, we encourage younger generations, including students and recent graduates, to seek federal jobs not only because ofthe numerous employment benefits, but because of the value and impact younger generations will have in being a federal employee.