<p> This paper presents the integration of an intelligent decision support model (IDSM) with a cognitive architecture that controls an autonomous non-deterministic safety-critical system. The IDSM will integrate multi-criteria, decision-making tools via intelligent technologies such as expert systems, fuzzy logic, machine learning, and genetic algorithms. </p><p> Cognitive technology is currently simulated within safety-critical systems to highlight variables of interest, interface with intelligent technologies, and provide an environment that improves the system&rsquo;s cognitive performance. In this study, the IDSM is being applied to an actual safety-critical system, an unmanned surface vehicle (USV) with embedded artificial intelligence (AI) software. The USV&rsquo;s safety performance is being researched in a simulated and a real-world, maritime based environment. The objective is to build a dynamically changing model to evaluate a cognitive architecture&rsquo;s ability to ensure safe performance of an intelligent safety-critical system. The IDSM does this by finding a set of key safety performance parameters that can be critiqued via safety measurements, mechanisms, and methodologies. The uniqueness of this research lies in bounding the decision-making associated with the cognitive architecture&rsquo;s key safety parameters (KSPs). Other real-time applications (RTAs) that would benefit from advancing cognitive science associated with safety are unmanned platforms, transportation technologies, and service robotics. Results will provide cognitive science researchers with a reference for the safety engineering of artificially intelligent safety-critical systems. </p><p>

<p> Cybersecurity event management is critical to the successful accomplishment of an organization&rsquo;s mission. To put it in perspective, in 2016 Symantec tracked over 700 global adversaries and recorded events from 98 million sensors (Aimoto et al., 2017). Studies show that in 2015, more than 55% of the cyberattacks on government operation centers were due to negligence and the lack of skilled personnel to perform network security duties including the failure to properly identify events (Ponemon, 2015a). Practitioners are charged to perform as first responders to any event that affects the network. Inconsistencies and errors that occur at this level can determine the outcome of an event. </p><p> In a time when 91% of Americans believe they have lost control over how information is collected and secured, there is nothing more dangerous than thinking new technology is not vulnerable to attacks (Rainie, 2016). Assailants target those with weak security postures who are unprepared, distracted or lack fundamental elements to identify significant events and secure the environment. </p><p> Under executive order, to address these concerns organizations such as the National Institute of Standards and Technology (NIST) and International Organization of Standards (ISO) developed cybersecurity frameworks, which have been widely accepted as industry standards. These standards focus on business drivers to guide cybersecurity activities and risks within critical infrastructure. It outlines a set of cybersecurity activities, references, and outcomes that can be used to align its cyber activities with business requirements at a high-level. </p><p> This praxis explores the solution provider&rsquo;s role in and method of securing environments through their event management practices. Solution providers are a critical piece of proper event management. They are often contracted to provide solutions that adhere to a NIST type framework with little to no guidance. There are supportive documents and guides for event management but nothing substantive like the Cybersecurity Framework and ISO 27001 has been adopted. Using existing processes and protocols an event management framework is proposed that can be utilized to properly manage events and aid solution providers in their cybersecurity mission. </p><p> Knowledge of event management was captured through subject matter expertise and supported through literature review and investigation. Statistical methods were used to identify deficiencies in cyber operations that would be worth addressing in an event management framework.</p><p>

<p>Abstract Enterprise Resource Planning (ERP) systems present a management problem for various industries including institutions of higher education (IHEs) because they are costly to acquire, challenging to implement, and often fail to meet anticipated expectations. ERP systems are highly complex due to the nature of the operations they support. This dissertation was conducted via a systematic review of the literature from 1998-2017 to synthesize available knowledge in various industries including IHEs in the United States. Through the lenses of Change Management Theory and The Diffusion of Innovations Theory, this systematic review highlighted critical success factors (CSFs) affecting the implementation of ERP systems in various industries and, also, sought to discover CSFs unique to institutions of higher education. Despite the challenges ERP presents to businesses, implementation of ERP systems continues to grow. This dissertation found that for various industries, the most often cited CSFs were: top management support, change management skills, effective communications, and ERP systems user training. For IHEs, institutional governance, staff engagement, lack of internal expertise, and conflicts with other priorities were the critical factors that played a significant role in ERP implementation. These findings suggested that industries must not only pay careful attention to the CSFs in various industries but that they can utilize the specific CSFs in IHEs, even though they may not appear to be as crucial for other industries. Future research may consider the extent to which organizations have mastered the skills necessary to effectively implement ERP systems. Keywords: enterprise resource planning systems, critical success factors, CSFs, implementation, higher education, innovation management

<p> The United States Coast Guard does not adequately perform a Level of Repair Analysis and Business Case Analysis to align vessel maintenance requirements with available resources during the development of an Integrated Logistics Life Cycle Plan (ILSP) which leads to increased vessel sustainment costs and reduced workforce proficiency. The ILSP&rsquo;s maintenance philosophy dictates the designed supportability and maintainability of a vessel throughout its life cycle yet existing ILSPs fail to prescribe the required balance of Coast Guard (internal employees) and non-Coast Guard (contracted) technicians for the execution of hull, mechanical, and electrical depot maintenance. This research develops a standard model to enhance the balance of maintenance resources using a Business Case Analysis (BCA) extension of a Level of Repair Analysis (LORA) informed by Activity-Based Costing Management (ABC/M) theory. These analyses are integral to a complete Life Cycle Cost Analysis (LCCA) and will contain a simplified LORA framework that provides an analysis of alternatives, a decision analysis, and a sensitivity analysis for the development of a maintenance philosophy. This repeatable analysis can be used by engineering managers to develop and sustain cost-effective maintenance plans through all phases of a vessel&rsquo;s life cycle (acquisition, sustainment, and disposal).</p><p>

<p> Government oversight exists to provide the government with the information it needs to evaluate the cost, schedule, and performance of contractors building systems on the government&rsquo;s behalf. The activities required for oversight, while necessary, add additional costs to a program. Stakeholders involved in space system acquisitions debate the extent of the added costs from complying with oversight&mdash;referred to as the burden of government oversight&mdash;with estimates ranging from 2&ndash;5% of a program&rsquo;s total costs to factors of 3-5 times the cost of commercially available alternative products. Higher-end estimates of burden have led some stakeholders to propose acquisition reforms to reduce the amount of oversight. However, recent history has shown that periods of reduced oversight on space system acquisitions can result in significant mission failures. </p><p> Before reasoned action can be taken to determine if parts of oversight should be reformed, there is a need for data about the extent of the burden of government oversight and how oversight manifests at the working-level. To that end, this research sought to answer the question: &ldquo;How much does government oversight burden engineering work and how does that burden manifest?&rdquo; </p><p> We contend that disagreement about the extent of the burden of government oversight stems from both how oversight has been measured in previous studies and ambiguity in the scope of what has been measured as oversight. To address these problems, this dissertation research focused on: 1) improving the measurement of the burden of government oversight on engineering work; 2) analyzing the sources of the burden of government oversight and measuring their impact; and 3) understanding the impact of government oversight on contractor work processes. </p><p> To measure the burden of oversight, we developed a measurement framework that builds on the work sampling method. We created a multi-question survey to instantaneously gather information about engineering work. Through the combination of answers to these questions, researchers can measure the time spent on oversight-related work. </p><p> We used the work sampling method to measure the burden of oversight at a major US aerospace company. The data collected using this method provided an empirically valid estimate of the time spent on government oversight-related work&mdash;which enabled us to test some of the widely-held beliefs about the nature of oversight-related work and to reconcile differences between previously reported estimates of the burden of oversight. </p><p> In addition, we performed qualitative work to understand how oversight impacts contractor engineering work processes more broadly. This research revealed that oversight requests can require cascades of additional activities and can extend the time spent on activities. In addition, frustrations about oversight can lead to objective inefficiencies in terms of added process time. Therefore, to adequately account for the burden of oversight, the scope of accounting needs to be extended. </p><p> Our results provide rich insights into the nature of the burden of oversight on engineering work at the contractor level. Combined, they provide a grounded estimate of the scope of current burden and a basis for more productive discussions about what aspects of oversight could lead to unnecessary burden.</p><p>

<p> The research presented in this dissertation investigates the relationship between critical technologies and technology maturity assessments at a key decision point in the product development life cycle. This study utilizes statistical methods for assessing technology maturity at a key decision point. A regression model is established and utilized for predicting the probability of a system achieving technology maturity. The study disclosed with a 95% confidence that there is statistical evidence that utilization of heritage technology developments, as originally designed, significantly increases the probability of achieving technology maturity at a key decision point. This finding is significance due to the potential for engineers to overestimate technology maturity when utilizing heritage designs. One challenge facing systems engineers is quantifying the impact technology developments have on technology maturity assessments, especially when transitioning from formulation to implementation. Correctly assessing the maturity of a technology is crucial for an organization&rsquo;s ability to manage performance, cost, and schedule. The findings from this research has the potential to reduce unacceptable or unsatisfactory technical performance and programmatic overruns through the minimization of inaccurate maturity determinations.</p><p>

Engineered systems are susceptible to the disruption of intended functionality when unanticipated operating environments and constraints emerge during mission execution. To safeguard intended system functionality, resilient systems which integrate disruption avoidance and mitigation measures are needed. System automation methods have been successfully adopted for disruption avoidance and performance optimization based on anticipated needs and risks. However, given the development of unpredicted or emergent issues, human intervention is often relied on as an operational contingency to system failure or degraded performance. The purpose of this research was to develop and analyze a model for evaluating and selecting system responses to mitigate emergent disruptions without human intervention. The scope of this research addresses risk management within the system lifecycle with focus on system adaptation to unpredictable changes in the operational environment or functional constraints. The Disruption Resilience and Adaptation Model (DREAM) was formulated as an autonomous decision-making and performance evaluation process for mitigating unforeseen or unavoidable system disruptions. Evaluating the system as a whole, the model integrates diagnostic and prognostic heuristics for performance feedback and regulating system responses to actual or potential disruptions. The suitability of rule and utility-based automation methods as system adaptation techniques was also investigated. Via modeling and simulation, the DREAM and existing system automation methods were applied to an Air Traffic Management (ATM) problem and compared based on: (i) mission reliability; (ii) incident rate; (iii) system efficiency; (iv) standard deviation of efficiency; (v) and system stability. Simulation results demonstrate the DREAM yielded statistically significant reductions in the frequency and severity of ATM system disruptions in comparison to the existing rule-based standard and the leading utility-based method. As demonstrated in the ATM case study performed in this research, the DREAM can reduce the frequency and severity of system disruptions that are unanticipated or unpredictable. Furthermore, the DREAM can autonomously enable preventive and corrective action in response to unexpected system disruptions, increasing the likelihood of achieving intended system objectives without the need for human intervention. In practical terms, use of the DREAM could increase the operational availability of safety- and security-critical systems, such as ATM systems, for which disruptions can be catastrophic in nature. Beyond the ATM application in this research, the DREAM is targeted towards engineered systems which are susceptible to inevitable, yet unavoidable disruptions, such as natural disasters, intentional attacks, and human errors.

abstract: Population growth, social interaction, and environmental variability are interrelated facets of the same complex system. Tracing the flow of food, water, information, and energy within these social-ecological systems is essential for understanding their long-term behavior. Leveraging an archaeological perspective of how past societies coevolved with their natural environments will be critical to anticipating the impact of impending climate change on farming communities in the developing world. However, there is currently a lack of formal, quantitative theory rooted in first principles of human behavior that can predict the empirical regularities of the archaeological record in semiarid regions. Through a series of models -- statistical, computational, and mathematical -- and empirical data from two long-term archaeological case studies in the pre-Hispanic American Southwest and Roman North Africa, I explore the feedbacks between population growth and social interaction in water-limited agrarian societies. First, I use a statistical model to analyze a database of 7.5 million artifacts collected from nearly 500 archaeological sites in the Southwest and found that sites located in different climatic zones were more likely to interact with one another than a sites occupying the same zone. Next, I develop a computational model of demography and food production in ancient agrarian societies and, using North Africa as a motivating example, show how the concrete actions and interactions of millions of individual people lead to emergent patterns of population growth and stability. Finally, I build a simple mathematical model of trade and migration among agricultural settlements to determine how the relative costs and benefits of social interaction drive population growth and shape long-term settlement patterns. Together, these studies form the foundation for a unified quantitative approach to regional social-ecological systems. By combining theory and methods from ecology, geography, and climate science, archaeologists can better leverage insights from diverse times and places to fill critical knowledge gaps in the study of food security and sustainability in the drylands of today. / Dissertation/Thesis / Doctoral Dissertation Anthropology 2019

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