One of the petroleum industry?s goals is to reduce the environmental impact of oil and gas operations in environmentally sensitive areas. To achieve this, a number of Environmentally Friendly Drilling (EFD) technologies have been developed to varying degrees. For example, the use of an elevated platform as an alternative to the gravel pad is less intrusive and leads to a more environmentally friendly approach to drilling operations. Elevated drilling platforms will require the use of piles. Another alternative to the gravel pad is the use of composite mats. Since the demand of low impact technologies for drill site construction has rapidly increased, the parametric study for the feasibility of using pile foundations and composite mats is conducted in this research. Even though a number of EFD technologies have already been developed to varying degrees, few have been integrated into a field demonstrable drilling system (i.e., combination of technologies) compatible with ecologically sensitive areas. In general, it is difficult to select the best combination of EFD technologies for a given site because there are many possible combinations and many different evaluation criteria. The proposed technology evaluation method is based on a systems analysis that can be used for integrating current and new EFD technologies into an optimal EFD system. An optimization scheme is suggested based on a combination of multi-attribute utility theory and exhaustively enumerating all possible technology combinations to provide a quantitative rationale and suggest the best set of systems according to a set of criteria, with the relative importance of the different criteria defined by the decision-maker. In this research, the sensitivity of the optimal solution to the weight factors and the effects of the uncertainty of input scores are also discussed using a case study. An application of the proposed approach is described by conducting a case study in Green Lake at McFaddin, TX. The main purpose of this case study is to test the proposed technology evaluation protocol in a real site and then to refine the protocol. This research describes the results of the case study which provided a more logical and comprehensive approach that maximized the economic and environmental goals of both the landowner and the oil company leaseholder.

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For any given reason, every year many countries spend a lot of money purchasing at least one weapon. Due to the secret character of the military, the decision process for specific weapon procurement is shrouded. Moreover, there are several funds loss cases due to mistakes in weapon contractions. Weapon procurement requires very large amounts of money which comes from tax payers. Therefore, an effort to reduce a possible monetary loss is needed. A decision process based on an analytic model can present a better chance to decision makers for better weapon decisions. In general, weapon procurement decision is a multi criteria environment. Decision making in such environments is defined as Multi-Criteria Decision Making (MCDM). MCDM is broadly classified into two areas: Multi-Attribute Decision Making (MADM) and Multi-Objective Decision Making (MODM). MADM methods are used for selecting an alternative from a small explicit list of alternatives. MODM methods are used for designing problems involving an infinite number of alternatives implicitly defined by mathematical constraints. This research is intended to be used by the South Korean Navy when there is a need to select one weapon type among several candidate types. Therefore, MADM methods are used in this research.Many researches for developing an analytical model for better decision-making have been done. However, there is no research for a generalized weapon procurement decision model that is easy to implement. For this reason, whenever there is a need for weapon procurement decision, the Navy has to spend a lot of effort in determining the best weapon. These efforts can be reduced with a generalized model that is proposed in this research for naval weapon procurement. MADM methods determine alternatives ranking orders and the highest ranked alternative is the best one. Various MADM methods are used in computing the alternatives ranking scores.

Risk management plans improve the likelihood of mission success by identifying potential failures early and planning mitigation methods to circumvent any issues. However, in the aerospace industry to date, risk management plans have typically only been used for larger and more expensive satellites, and have rarely been applied to satellites in the shape of 10 x 10 x 10 centimeter cubes, called CubeSats. Furthermore, existing risk management plans typically require experienced personnel and significant time to run the analysis. The purpose of this research was to develop two risk management software tools, the CubeSat Risk Analysis tool and the CubeSat Decision Advisor tool, which could be used by anyone with any level of experience. Moreover, the tools simply require the user to enter their mission-specific data; the software tools calculate the required analysis. The CubeSat Risk Analysis tool was developed for the purpose of reducing the subjectivity associated with estimating the likelihood and consequence of spacecraft mission risks. The tool estimates mission risk in terms of input characteristics, such as satellite form factor, mass, and development cycle. Using a historical database of small satellite missions, which was gathered in the course of this research, the software determines the mission risk root causes which are of the highest concern for the given mission. The CubeSat Decision Advisor tool uses components of decision theory such as decision trees, multi-attribute utility theory, and utility elicitation methods to determine the expected utility of a mitigation technique alternative. Based on the user’s value preference system, assessment of success probabilities, and resources required for a given mitigation technique, the tool suggests the course of action which will normatively yield the most value for the cost, personnel, and time resources required. The goals of this research were met in the development of two easily-accessible and free risk management software tools to assist in university satellite mission development. But more importantly, these tools will reach beyond the academic setting and allow small satellites to continue to evolve as a platform to accomplish educational, scientific, and military objectives. / text

Thesis (Ph. D.)--University of California, San Diego, 2007. / Title from first page of PDF file (viewed October 2, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 88-93).

Thesis (M.S. Eco.)--Baylor University, 2008. / Includes bibliographical references (p. 68-70)

Thesis (doctoral)--Uppsala University, 1991"--T.p. verso. / Includes bibliographical references.

We have developed a game-theory based prediction tool, named Preana, based on a promising model developed by Professor Bruce Beuno de Mesquita. The first part of this work is dedicated to exploration of the specifics of Mesquita's algorithm and reproduction of the factors and features that have not been revealed in literature. In addition, we have developed a learning mechanism to model the players' reasoning ability when it comes to taking risks. Preana can predict the outcome of any issue with multiple stake-holders who have conflicting interests in economic, business, and political sciences. We have utilized game theory, expected utility theory, Median voter theory, probability distribution and reinforcement learning. We were able to reproduce Mesquita's reported results and have included two case studies from his publications and compared his results to that of Preana. We have also applied Preana on Iran's 2013 presidential election to verify the accuracy of the prediction made by Preana.

Pain has a significant burden on individuals and society; however challenges remain in the measurement of pain-related utility. Research was conducted to design a measurement framework for valuing pain states, for use in policy making. Within the theoretical framework of utility theory, the bio-psychosocial model of pain was used to select co-variates impacting the pain-utility relationship. The applicability of two utility theories (prospect theory and hedonist theory) to pain was evaluated, with EQ-5D-5L and utility values for scenarios of different pain intensities elicited using time-trade-off (TTO) and willingness-to-pay (WTP). Prospect theory was tested using the pain-utility relationship in 600 members of the general population (‘genpop’) by examining the presence of a reference point and the relationship between pain decreases/increases (gains/losses) and utility. Hedonist theory was tested through examination of predicted, experienced and remembered utilities, and ‘peak-end’ effects, principally using data from 56 university athletes experiencing training pain. Pain intensity had the greatest influence on EQ-5D values, with present and worst pain showing additive effects. Duration of the pain episode, general health, mood, age and gender also had significant impacts. In addition to pain intensity, TTO ‘genpop’ models included age, income, experienced pain and general health; WTP ‘genpop’ models included residual pain, age and income. The TTO and WTP models did not fit the athletes’ data well and alternative models were developed. The data did not confirm prospect theory: a reference point could not be identified (‘genpop’ and athletes) and the shape of the utility curve contradicted theory (athletes). Results for hedonist theory were inconclusive: predicted utility was consistently higher than experienced utility in athletes but the relationship depended on pain levels in ‘genpop’; remembered and experienced utility differed despite being consistent for pain levels; peak-end effects were not found. Further research in controlled environments is recommended for further theory testing.

CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior / A crescente demanda de mercado por serviÃos sem fio e a escassez de recursos de rÃdio apela mais do que nunca para a melhoria do desempenho dos sistema de comunicaÃÃo sem fio. Desse modo, à obrigatÃrio garantir o provimento de melhores serviÃos de rÃdio e aperfeiÃoar a cobertura e a capacidade, com isso aumentando o nÃmero de consumidores satisfeitos. Esta dissertaÃÃo lida com algoritmos de escalonamento, buscando a maximizaÃÃo e o controle adaptativo do Ãndice de satisfaÃÃo no enlace direto de uma rede de acesso baseado em frequÃncia, OFDMA (do inglÃs Orthogonal Frequency Division Multiple Acess , considerando diferentes modelos de trÃfego para serviÃos de tempo nÃo real, NRT (do inglÃs Non-Real Time ), e de tempo real, RT (do inglÃs Real Time ); e condiÃÃes de canal mais realistas, por exemplo, CSI imperfeitas. Com o intuito de resolver o problema de maximizaÃÃo de satisfaÃÃo com menor complexidade, uma abordagem com otimizaÃÃo de mÃltiplas camadas usa a teoria da utilidade para formular o problema como uma maximizaÃÃo de soma de taxa ponderada. Este estudo à focado no desenvolvimento de um framework baseado em utilidade empregando a funÃÃo log-logÃstica deslocada, que devido Ãs suas caracterÃsticas permite novas estratÃgias de escalonamento de priorizaÃÃo baseada em QoS e oportunismo de canal, para uma alocaÃÃo de potÃncia igualitÃria entre os recursos de frequÃncia. Visando a maximizaÃÃo da satisfaÃÃo de usuÃrios de serviÃos NRT e RT, dois algoritmos de escalonamento sÃo propostos: MTSM e MDSM, respectivamente. A modificaÃÃo dos parÃmetros da funÃÃo de utilidade log-logÃstica descolocada permite a implementaÃÃo de diferentes estratÃgias de distribuiÃÃo de recursos. Buscando controlar os nÃveis de satisfaÃÃo dos usuÃrios de serviÃos NRT, dois algoritmos adaptativos de escalonamento sÃo propostos: ATES e ASC. O algoritmo ATES realiza um controle da satisfaÃÃo mÃdia pela mudanÃa dinÃmica do parÃmetro de escala, permitindo uma estratÃgia estÃvel para lidar com o dilema entre satisfaÃÃo e capacidade. O algoritmo ASC à capaz de garantir uma variaÃÃo dinÃmica do parÃmetro de formato, garantindo um controle rigoroso dos nÃveis de satisfaÃÃo dos usuÃrios. SimulaÃÃes no nÃvel do sistema indicam o cumprimento do objetivo de desenvolvimento de algoritmos de escalonamento eficientes e de baixa complexidade capazes de maximizar e controlar os Ãndices de satisfaÃÃo. Estas estratÃgias podem ser Ãteis para o operador da rede, que se torna capaz de projetar e operar a rede de acordo com um perfil de satisfaÃÃo de usuÃrio. / The increasing market demand for wireless services and the scarcity of radio resources calls more than ever for the enhancement of the performance of wireless communication systems. Nowadays, it is mandatory to ensure the provision of better radio services and to improve coverage and capacity, thereby increasing the number of satisfied subscribers. This thesis deals with scheduling algorithms aiming at the maximization and adaptive control of the satisfaction index in the downlink of an Orthogonal Frequency Division Multiple Access (OFDMA) network, considering different types of traffic models of Non-Real Time (NRT) and Real Time (RT) services; and more realistic channel conditions, e.g., imperfect Channel State Information (CSI). In order to solve the problem of maximizing the satisfaction with affordable complexity, a cross layer optimization approach uses the utility theory to formulate the problem as a weighted sum rate maximization. This study is focused on the development of an utility-based framework employing the shifted log-logistic function, which due to its characteristics allows novel scheduling strategies of Quality of Service (QoS)-based prioritization and channel opportunism, for an equal power allocationn among frequency resources. Aiming at the maximization of the satisfaction of users of NRT and RT services, two scheduling algorithms are proposed: Modified Throughput-based Satisfaction Maximization (MTSM) and Modified Delay-based Satisfaction Maximization (MDSM), respectively. The modification of parameters of the shifted log-logistic utility function enables different strategies of distribution of resources. Seeking to track satisfaction levels of users of NRT services, two adaptive scheduling algorithms are proposed: Adaptive Throughput-based Efficiency-Satisfaction Trade-Off (ATES) and Adaptive Satisfaction Control (ASC). The ATES algorithm performs an average satisfaction control by adaptively changing the scale parameter, using a feedback control loop that tracks the overall satisfaction of the users and keep it around the desired target value, enabling a stable strategy to deal with the trade-off between satisfaction and capacity. The ASC algorithm is able to ensure a dynamic variation of the shape parameter, guaranteeing a strict control of the user satisfaction levels. System level simulations indicate the accomplishment of the objective of development of efficient and low complexity scheduling algorithms able to maximize and control the satisfaction indexes. These strategies can be useful to the network operator who is able to design and operate the network according to a planned user satisfaction profile.