Receiver operating characteristic (ROC) curves are widely used in medical decision making. It was recognized in the last decade that only a specific region of the ROC curve is of clinical interest, which can be summarized by the partial area under the ROC curve (partial AUC). Early statistical methods for evaluating partial AUC assume that the data are from a specified underlying distribution. Nonparametric estimators of the partial AUC emerged recently, but there are theoretical issues to be addressed. In this dissertation, we propose two new nonparametric statistics, partially integrated ROC and partially integrated weighted ROC, for estimating partial AUC. We show that our partially integrated ROC statistic is a consistent estimator of the partial AUC, and derive its asymptotic distribution which is distribution free under the null hypothesis. In the partially integrated ROC statistic, when the ROC curve crosses the Uniform distribution function (CDF) and if the partial area evaluated contains the crossing point, or when there are multiple crossing, the partially integrated ROC statistic might not perform well. To address this issue, we propose the partially integrated weighted ROC statistic. This statistic evaluates the partially weighted AUC, where larger weight is given when the ROC curve is above the Uniform CDF and smaller weight is given when the ROC curve is below the Uniform CDF. We show that our partially integrated weighted ROC statistic is a consistent estimator of the partially weighted AUC. We derive its asymptotic distribution which is distribution free under the null hypothesis. We propose to apply our two nonparametric statistics to functional category analysis in microarray experiments. We define the functional category analysis to be the statistical identification of over-represented functional gene categories in a microarray experiment based on differential gene expression. We compare our statistics with existing methods for the functional category analysis both via simulation study and application to a real microarray data, and demonstrate that our two statistics are effective for identifying over-represented functional gene categories. We also emphasize the essential role of the empirical distribution function plots and the ROC curves in the functional category analysis.

<p>The study evaluated the Mutant Prevention Concentration (MPC) of Pradofloxacin on three Escherichia coli (E.coli) strains, 2 wildtypes and one first-step gyrA resistant mutant. We also measured the value of AUC (Under the Concentration)/MPC that prevents growth of resistant mutants. It is of importance to reach a concentration above MPC that prevent E.coli from developing resistance against the antibiotic.</p><p>We used an in vitro kinetic model where we added bacteria? and antibiotic. The culture flask was attached to a pump with an adjustable pump-speed. This made it possible to dilute the antibiotics in a satisfying elimination half-life (t1/2= 7 hours) pace. Samples were removed with a syringe at different times in the study. The samples where then cultured on agar- plates to enable counting of the viable colonies after incubation.</p><p>The optimal concentration to completely eradicate both E.coli wildtypes Nu14 and MG1655 with Pradofloxacin was Cmax ≥8 times MPC and AUC/MPC then became73. Additional experiments needs to be done on the resistant mutant LM378 before we can determine the optimal concentration. But results so far indicate that the concentration of Cmax would be about 8-12 timesMPC to completely eradicate that mutant.</p>

This dissertation presents efficient, on-line, convergent methods to find defense strategies against attacks in dynamic multi-stage attacker-defender games including adaptive learning. This effort culminated in four papers submitted to high quality journals and a book and they are partially published. The first paper presents a novel fictitious play approach to describe the interactions between the attackers and network administrator along a dynamic game. Multi-objective optimization methodology is used to predict the attacker's best actions at each decision node. The administrator also keeps track of the attacker's actions and updates his knowledge on the attacker's behavior and objectives after each detected attack, and uses this information to update the prediction of the attacker's future actions to find its best response strategies. The second paper proposes a Dynamic game tree based Fictitious Play (DFP) approach to describe the repeated interactive decision processes of the players. Each player considers all possibilities in future interactions with their uncertainties, which are based on learning the opponent's decision process (including risk attitude, objectives). Instead of searching the entire game tree, appropriate future time horizons are dynamically selected for both players. The administrator keeps tracking the opponent's actions, predicts the probabilities of future possible attacks, and then chooses its best moves. The third paper introduces an optimization model to maximize the deterministic equivalent of the random payoff function of a computer network administrator in defending the system against random attacks. By introducing new variables the transformed objective function becomes concave. A special optimization algorithm is developed which requires the computation of the unique solution of a single variable monotonic equation. The fourth paper, which is an invited book chapter, proposes a discrete-time stochastic control model to capture the process of finding the best current move of the defender. The defender's payoffs at each stage of the game depend on the attacker's and the defender's accumulative efforts and are considered random variables due to their uncertainty. Their certain equivalents can be approximated based on their first and second moments which is chosen as the cost functions of the dynamic system. An on-line, convergent, Scenarios based Proactive Defense (SPD) algorithm is developed based on Differential Dynamic Programming (DDP) to solve the associated optimal control problem.

The development of suitable long term flood risk intervention strategies is a challenge. Climate change alone is a significant complication but in addition complexities exist trying to identify the most appropriate set of interventions, the area with the highest economical benefit and the most opportune time for implementation. All of these elements pose difficulties to decision makers. Recently, there has been a shift in the current practice for appraising potential strategies and consideration is now being given to ensure flexible, adaptive strategies to account for the uncertain climatic conditions. Real Options in particular is becoming an acknowledged approach to account for the future uncertainties inherent in a flood risk investment decision. Real Options facilitates adaptive strategies as it enables the value of flexibility to be explicitly included within the decision making process. Opportunities are provided for the decision maker to modify and update investments when knowledge of the future state comes to light. In this thesis the use of Real Options in flood risk management is investigated as a method to account for the uncertainties of climate change. Each Intervention strategy is purposely designed to capture a level of flexibility and have the ability to adapt in the future if required. A state of the art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time. In addition to Real Options, this thesis also explores the use of evolutionary optimisation algorithms to aid the decision making process when identifying the most appropriate long term strategies. Although the risk analysis tool is capable of quantifying the potential benefits attributed to a strategy, it is not necessarily able to identify the most appropriate. Methods are required which can search for the optimal solutions according to a range of performance metrics. Single and multi-objective genetic algorithms are investigated in this thesis as a method to search for the most appropriate long term intervention strategies. The Real Options concepts are combined with the evolutionary multiobjective optimisation algorithm to create a decision support methodology which is capable of searching for the most appropriate long term economical yet robust intervention strategies which are flexible to future change. The methodology is applied to two individual case studies, a section of the Thames Estuary and an area on the River Dodder. The results show the inclusion of flexibility is advantageous while the outputs provide decision makers with supplementary knowledge which previously has not been considered.

This essay aims to investigate the contemporary context in which Bibelns lära om Kristus, authored by Viktor Rydberg in 1862, was written and received. By claiming the human nature of Christ, he evoked massive response. By performing a literal study, I have examined the 19th century society in Sweden; infrastructural alteration, the origin and spread of popular movements such as revivalism, liberalism and Enlightenment, to inquire in what way (if at all) Rydberg is to be considered influenced by their conceptions. The essay emanates from the assumption that the Lutheran reformation in the late 19th century experienced a revival in Sweden. This revival is to be called the new reformation. In my result and reflection, I have come to the conclusion that Viktor Rydberg did not in fact criticize his contemporary church to annihilate it. He was, apart from inspired by the liberal thought of individual, religious and political freedom, simply trying to alter it. The cause for his success is in this essay claimed to be three: the fact that he was the “perfect combination” of radical theology and political liberalism; that he played the role as a spiritual guide; and finally, that he, in the light of his earlier authorship, was by the Swedish nation perceived as a trustworthy authority.

Bakgrund: Skatteplanering är ett aktuellt och omdebatterat ämne. I USA har ett Under-sheltering Puzzle kunnat påvisas då vissa företag avstår från att skatteplanera. Vi frågar oss om fenomenet förekommer i Sverige och i så fall varför vissa företag ej skatteplanerar.  Syfte: Studien avser att förklara förekomsten av The Under-sheltering Puzzle i Sverige. Med hjälp av ett företags olika strategier vill vi påvisa fenomenet och förklara omfattningen av börsföretagens skatteplanering.  Metod: En deduktiv ansats genomförs där teorier testas genom hypotesprövning. Teorierna behandlar ett företags olika strategier samt teorin om Upper Echelon. Datan är insamlad mellan åren 2011 och 2015. Slutsatser: Vi kan i studien påvisa att The Under-sheltering Puzzle förekommer i Sverige då vissa grupper av företag tenderar att betala mer skatt än andra. Företagens marknadsstrategi kan anses mest betydelsefull för att förklara varför ett företag väljer eller avstår från att skatteplanera. / Background: Tax sheltering is a subject of debates and a current issue in society. In the USA however, results have demonstrated that some companies exhibit a phenomenon called The Under-sheltering Puzzle and avoid tax sheltering. We raise the question if the phenomenon also is present in Sweden and if that is the case, why some companies avoid taxes.  Purpose: The thesis pertains to explain The Under-sheltering Puzzle in Sweden. By applying theories which cover the different strategies in a company, we intend to proof the existence of the phenomenon and explain the magnitude of tax sheltering in public corporations. Method: A deductive research approach has been used and theories constitutes the basis for our hypotheses. The theories discuss the different strategies in a corporation and the impact of Upper Echelons. The data is collected between the years of 2011 and 2015.  Conclusions: In this thesis we can proof that The Under-sheltering Puzzle is present in Sweden and that some groups of companies tend to pay higher taxes than others. We believe that the market strategy is the most primal factor in explaining why a company choose or avoid tax sheltering.

Increasing the percentage of wind power in the United States electricity generation mix would facilitate the transition towards a more sustainable, low-pollution, and environmentally-conscious electricity grid. However, this effort is not without cost. Wind power generation is time-variable and typically not synchronized with electricity demand (i.e., load). In addition, the highest-output wind resources are often located in remote locations, necessitating transmission investment between generation sites and load. Furthermore, negative public perceptions of wind projects could prevent widespread wind development, especially for projects close to densely-populated communities. The work presented in my dissertation seeks to understand where it’s best to locate wind energy projects while considering these various factors. First, in Chapter 2, I examine whether energy storage technologies, such as grid-scale batteries, could help reduce the transmission upgrade costs incurred when siting wind projects in distant locations. For a case study of a hypothetical 200 MW wind project in North Dakota that delivers power to Illinois, I present an optimization model that estimates the optimal size of transmission and energy storage capacity that yields the lowest average cost of generation and transmission ($/MWh). I find that for this application of storage to be economical, energy storage costs would have to be $100/kWh or lower, which is well below current costs for available technologies. I conclude that there are likely better ways to use energy storage than for accessing distant wind projects. Following from this work, in Chapter 3, I present an optimization model to estimate the economics of accessing high quality wind resources in remote areas to comply with renewable energy policy targets. I include temporal aspects of wind power (variability costs and correlation to market prices) as well as total wind power produced from different farms. I assess the goal of providing 40 TWh of new wind generation in the Midwestern transmission system (MISO) while minimizing system costs. Results show that building wind farms in North/South Dakota (windiest states) compared to Illinois (less windy, but close to population centers) would only be economical if the incremental transmission costs to access them were below $360/kW of wind capacity (break-even value). Historically, the incremental transmission costs for wind development in North/South Dakota compared to in Illinois are about twice this value. However, the break-even incremental transmission cost for wind farms in Minnesota/Iowa (also windy states) is $250/kW, which is consistent with historical costs. I conclude that for the case in MISO, building wind projects in more distant locations (i.e., Minnesota/Iowa) is most economical. My two final chapters use semi-structured interviews (Chapter 4) and conjoint-based surveys (Chapter 5) to understand public perceptions and preferences for different wind project siting characteristics such as the distance between the project and a person’s home (i.e., “not-in-my-backyard” or NIMBY) and offshore vs. onshore locations. The semi-structured interviews, conducted with members of a community in Massachusetts, revealed that economic benefit to the community is the most important factor driving perceptions about projects, along with aesthetics, noise impacts, environmental benefits, hazard to wildlife, and safety concerns. In Chapter 5, I show the results from the conjoint survey. The study’s sample included participants from a coastal community in Massachusetts and a U.S.-wide sample from Amazon’s Mechanical Turk. Results show that participants in the U.S.-wide sample perceived a small reduction in utility, equivalent to $1 per month, for living within 1 mile of a project. Surprisingly, I find no evidence of this effect for participants in the coastal community. The most important characteristic to both samples was the economic benefits from the project – both to their community through increased tax revenue, and to individuals through reduced monthly energy bills. Further, participants in both samples preferred onshore to offshore projects, but that preference was much stronger in the coastal community. I also find that participants from the coastal community preferred expanding an existing wind projects rather than building an entirely new one, whereas those in the U.S.-wide sample were indifferent, and equally supportive of the two options. These differences are likely driven by the prior positive experience the coastal community has had with an existing onshore wind project as well as their strong cultural identity that favors ocean views. I conclude that preference for increased distance from a wind project (NIMBY) is likely small or non-existent and that offshore wind projects within 5 miles from shore could cause large welfare losses to coastal communities. Finally, in Chapter 6, I provide a discussion and policy recommendations from my work. Importantly, I recommend that future research should combine the various topics throughout my chapters (i.e., transmission requirements, hourly power production, variability impacts to the grid, and public preferences) into a comprehensive model that identifies optimal locations for wind projects across the United States.

The purpose of this case study is to determine if there are any under- or overrepresented industries in CRM case studies. CRM stands for customer relationship management and can be applied as a system to help the sales manager in an industry to control the activities and their customers. CRM enable long-term relationships with customer and to retaining key customer. The top findings in this case study is that the hotel industry is by far the leader of CRM case studies and therefore the overrepresented industry. The underrepresented industries when it comes to amount of CRM case studies is the digital content industry, retailing industry, pharmaceutical industry and the telecommunication industry. The conclusion in this case study is that all the service industries are dependent on the CRM-systems and therefore a lot of case studies have been composed about the subject. Another conclusion is that that CRM-systems cannot be ignored by any industries since the economy is customer orientated.

It is widely acknowledged that under situations of heightened pressure, many expert athletes suffer from performance decrements. This phenomenon has been termed ‘choking under pressure’ and has been the subject of extensive research in sport psychology. Despite this attention, gaps in the literature remain leaving opportunities for further advancements in knowledge about the phenomenon, particularly in relation to its underlying processes and the development of appropriate interventions that can be adopted in order to alleviate, or even prevent choking. The present programme of research, in general terms, aimed to develop and test the efficacy of an intervention tool, based on priming, to alleviate choking under pressure. It was acknowledged that such a tool should be matched to the mechanisms that underlie the choking process and although an abundance of research has provided valuable information about these mechanisms, it was identified that there still remains a lack of consensus regarding the most appropriate explanatory theory. Therefore the initial study in this thesis aimed to provide further insight into the processes that govern choking by examining accounts from elite international swimmers of their experiences of performing under high levels of pressure. The results provided further support for the postulation that choking under pressure occurs as a result of a combination of conscious processing hypothesis (Masters, 1992) and processing efficiency theory (Eysenck & Calvo, 1992) and that an optimum level of skill-focused attention is beneficial to performance. The following studies utilised this information as well as that of the existent theories of choking, to develop and examine an effective priming based intervention tool (a scrambled sentence task). Specifically, Studies 2, 3 and 4 examined the amount of residual working memory available after activation of the prime, the optimisation of the priming task and the efficacy of the tool in promoting performance under high pressure respectively. Results revealed support for the efficacy of the tool in reducing online skill-focused attention and promoting performance under both low- and high-pressure conditions. Finally, the general themes that emerged throughout the whole programme of study are discussed, as well as the limitations and recommendations for future research. Implications for coaches, athletes and practitioners are also presented.

Ethnic/racial minorities in the United States are overrepresented in fatalities from motor vehicle crashes (MVC). Growing evidence indicates that there are differences among racial/ethnic groups in risk of involvement in fatal crashes. Based on previous research, numerous factors may be involved in high racial/ethnic fatality rates from MVCs, including failure to use safety equipment, driving while under the influence of alcohol/drug, red light running, and speeding. Using data from the Office of the Chief Medical Examiner (OCME) and the FR300P Police Crash Report, this project explores differences in variables associated with traffic safety behavior and traffic law obedience between non-White and White road users (drivers, passengers, and pedestrians). Results indicate that there is a significant association between race/ethnicity and driving while under the influence of alcohol/drugs (DUI). Those endeavoring to develop more effective traffic safety prevention and education programs may consider the effect of social/cultural factors in future efforts.