After decades of power generating units increasing in size, there is currently a growing focus on distributed generation, power generation close to energy loads. Investments in large-scale units have been driven by economy of scale, but recent technological improvements on small generating plants have made it possible to exploit the benefits of local power generation to a larger extent than previously. Distributed generation can improve power system efficiency because heat can be recovered from thermal units to supply heat and thermally activated cooling, and because small-scale renewables have a promising end-user market. Further benefits of distributed generation include improved reliability, deferral of often controversial and costly grid investments and reduction of grid losses. The new appeal of small-scale power generation means that there is a need for new tools to analyze distributed generation, both from a system perspective and from the perspective of potential developers. In this thesis, the focus is on the value of power generation for end-users. The thesis identifies how an end-user can find optimal distributed generation systems and investment strategies under a variety of economic and regulatory scenarios. The final part of the thesis extends the analysis with a bottom-up model of how the economics of distributed generation for a representative set of building types can transfer to technology diffusion in a market. Four separate research papers make up the thesis. In the first paper, Optimal Investment Strategies in Decentralized Renewable Power Generation under Uncertainty, a method for evaluation of investments in renewable power units under price uncertainty is presented. It is assumed the developer has a building with an electricity load and a renewable power resource. The case study compares a set of wind power systems with different capacity and finds that capacity depends on the electricity price and that there under uncertain prices can be a significant value in postponing investment until larger projects are profitable. In the second paper, Combined Heat and Power in Commercial Buildings: Investment and Risk Analysis, a Monte Carlo simulation program to find the value and risk characteristics of combined heat and power units is presented. Using historical price data to estimate price process parameters, it is shown that uncertain prices should not be a barrier for investment, since on-site generators can adapt to uncertain prices and reduce the total energy cost risks. In, Optimizing Distributed Generation Systems for Commercial Buildings, which uses a mixed integer linear program, distributed generation portfolios that maximize profitability are tailored to a building's energy load. Distributed generation with heat recovery and thermally activated cooling are found profitable in an office and a health care building, using current generator data and energy tariffs from California. With the fourth paper, Distributed Energy Resources Market Diffusion Model, the analysis is taken a step further to predict distributed generation market diffusion. Market penetration is assumed to depend on economic attractiveness and knowledge and trust in the technologies. A case study based on the U.S. commercial sector depicts a large market for reciprocating engines and microturbines, with the West and Northeast regions driving market diffusion. Technology research and outreach programs can speed up and change the path of capacity expansion. The thesis presents three different models for analyzing investments in distributed generation, all of which have benefits and disadvantages. Choice of model depends on the specific application, but the different approaches can be used on the same problem to analyze it from different viewpoints. The cases in the thesis indicate that distributed generation can reduce expected energy costs while at the same time improve cost predictability. Further, the thesis identifies several important factors and potential barriers to distributed generation adoption. Analyzing distributed generation from the end-user perspective is important also for policy makers, because of the importance of estimating how the market will react to potential policy measures. The thesis shows that small-scale generating capacity has the potential to increase in the near future. Further research should increase the understanding of economic and environmental issues related to distributed generation, while policy makers should aim to construct and implement measures that make it attractive for end-users to invest in efficient local generating capacity.

Syftet med uppsatsen är att undersöka om det förkommer underprissättning och om den består de första fem månaderna samt finna sannolikheten för en positiv utveckling efter börsintroduktionen. Eftersom vi har studerat kursförändringar efter börsintroduktioner har vi valt att använda en kvantitativ metodansats i form av en eventstudie. Teorierna behandlar främst förklaringar till varför en underprissättning sker samt hur publicitet och informations asymmetri påverkar prissättningen. Teorier som berörs är bland annat ”Winners Curse”, ”Cascade theory” och ”Signalling theory”. Våra empiriska data kommer främst från bolagens prospekt samt kursinformation från Affärsvärlden och Stockholmsbörsen. Prospekten har visat kursintervall och introduktionsdatum, val av garant med mera. Affärsvärlden visar branschtillhörighet samt bransch- och indexhistorik. Stockholmsbörsen tillför slutkurser för samtliga bolag samt information om noteringskrav och -process. Studien visar att de nyintroducerade bolagen har varit underprissatta med i genomsnitt 2,17% under perioden 2001-2006, vilket är betydligt lägre än vad tidigare studier visat. Detta beror främst på att de tidigare studierna har baserats på den amerikanska marknaden och under en tidsperiod då större underprissättning har förekommit än under 2001-2006. Den genomsnittliga utvecklingen efter 5 dagar, 1 månad, 3 månader, 5 månader respektive 1 år har varit 2,40%, 2,46%, 3,06%, 3,73% respektive 5,01%. Det har även förkommit stora skillnader mellan olika branscher, garanter, introduktionsår, introduktionspris och överteckningsgrad. / The purpose of this essay is to examine if it occurs under pricing and if it last the first five month and find the probability of a positive share development after a initial public offering. Since we have studied exchange fluctuation after initial public offerings have we chosen to use an quantitative method approach. The theories consider explanations to why under pricing occurs and how publicity and information asymmetry affect the price determination. ”Winners Curse”, ”Cascade theory” and ”Signalling theory” are some of the theories that are mentioned. Our empirical data is mainly captured from the companies prospects and share price information from Affärsvärlden and the Stockholm Exchange. The prospects has shown share price intervals, initial public offering dates, underwriter etc. Affärsvärlden show type of business and index history. The Stockholm Exchange contribute closing prices for all of the studied companies and information about demands for quotation and the process regarding initial public offerings. Our study show that initial public offerings have been under priced with an average of 2,17% during the period 2001-2006, which is considerably lower than earlier studies. The reason is mainly because the earlier studies has based on the American stock market and under a period when larger under pricing have existed. The average development after 5 days, 1 month, 3 months, 5 months respectively 1 year has been 2,40%, 2,46%, 3,06%, 3,73% respectively 5,01%. It has also occurred big differences between different type of business, underwriters, year of introduction, oversubscribtion and initial share price.

Since the global budget system carried out by National Health Insurance Bureau in 2003, hospital autonomy management practiced in 2003 as well as reviews carried by specialized doctors system established, local hospitals have faced critical impacts. The fluctuating point reimbursement was applied, it not only restrains the reimbursement received by the hospitals, but also causes management difficulties for local hospital as the fluctuating points shrink year by year. The number of western medical hospitals is decreasing. The number in 2000 was 575, but now only 500 local hospitals operate in Taiwan. For survival, local hospitals have to establish sound financial system in order to deal with changing national health insurance policies. On the other hand, they are encouraged to use strategy management theories to promote the competitiveness for local hospitals to well control their expenditures and create their income. The study has analyzed the statistical data from Statistical Office, Department of Health and National Health Insurance Bureau and integrated related literatures to understand the management strategies and responses of local hospitals under national health insurance policies and the economic, demographic and political environment. Results of the study will be provided for reference. Beside statistic data and literatures, strategy management concepts and theories were also adopted to clarify current policies of health insurance, reimbursement system and the situation of local hospitals to probe into the difficulties and possible solutions. Local hospitals were chosen to be the study subjects, and through SWOT Analysis, Poter¡¦s Five Force Analysis, Blue Sea Strategy and the findings from literature review, we found that (1)the financial gap of health insurance has transferred to medical organizations, especially local hospitals; (2)under global budget system, local hospitals have to increase income and decrease expenditures by transformation, running pay business or joint outpatient service; ¡]3¡^cost management is critical for local hospitals to establish internal strength; (4) referring to blue sea strategy to develop distinguished business and differential products can create niche for local hospitals to break through the bottleneck.

Analysis, design and implementation of cooperative control strategies for multi-robot systems under communication constraints is the topic of this thesis. Motivated by a rapidly growing number of applications with networked robots and other vehicles, fundamental limits on the achievable collaborative behavior are studied for large teams of autonomous agents. In particular, a problem is researched in detail in which the group of agents is supposed to agree on a common state without any centralized coordination. Due to the dynamics of the individual agents and their varying connectivity, this problemis an extension of the classical consensus problemin computer science. It captures a crucial component of many desirable features of multi-robot systems, such as formation, flocking, rendezvous, synchronizing and covering. Analytical bounds on the convergence rate to consensus are derived for several systemconfigurations. It is shown that static communication networks that exhibit particular symmetries yield slow convergence, if the connectivity of each agent does not scale with the total number of agents. On the other hand, some randomly varying networks allow fast convergence even if the connectivity is low. It is furthermore argued that if the data being exchanged between the agents are quantized, it may heavily degrade the performance. The extent to which certain quantization schemes are more suitable than others is quantified through relations between the number of agents and the required total network bit rate. The design of distributed coordination and estimation schemes based on the consensus algorithm is presented. A receding horizon coordination strategy utilizing subgradient optimization is developed. Robustness and implementation aspects are discussed. A new collaborative estimation method is also proposed. The implementation of multi-robot control systems is difficult due to the high systemcomplexity. In the final part of this thesis, a hierarchical control architecture appropriate for a class of coordination tasks is therefore suggested. It allows a formal verification of the correctness of the implemented control algorithms. / QC 20100920

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Emotional information is commonly assumed to be recognized more accurately than neutral information. While emotionality enhances recognition accuracy, it also induces a more liberal response bias. In this study, the effects of arousal and valence axes of emotion on recognition memory accuracy and liberal bias were examined in young and older adults, for emotional words. For this purpose, memory was assessed with a surprise old/new recognition task, based on Signal Detection Theory. There are also some factors regarding words that influence visual word recognition. One is the dissociation between consonants and vowels / consonants and vowels are processed differently. In the study session, the participants were instructed to count vowels within a word under incidental encoding. Since vowels constrain lexical selection less tightly than consonants, deciding how many vowels each word contained is compatible with the idea that the vowels should be processed faster. The results of the recognition session showed that young adults recognized more accurately as compared to older adults, replicating the age effect. Valence differences of words also showed a significant effect on memory performance, that is positive words were recognized better in both groups. On the other hand, it was observed that there was a significant bias to respond as &lsquo / old&rsquo / only to positive words in older group / whereas young adults showed a liberal bias to negative words. This age-related difference suggested that older adults regulated their emotion in favour of maintaining well-being, even incidentally. Considering individual differences and mood state in emotional word processing is essential so personality traits and current mood states were assessed by commonly used Five Factor Personality Inventory and Positive and Negative Affect Schedule respectively. Except &lsquo / openness to experience&rsquo / , other personality dimensions did not predict recognition memory performance for emotional words.

The growth and development of two sorghum drought tolerant genotypes BTx642 (post-flowering drought tolerant, “stay green”) and RTx7000 (pre-flowering drought tolerant) were characterized and compared. Differences in phenology and the growth and development of leaves and stalks were identified that could contribute to variation in shoot biomass, grain yield and response to water deficit. An F12 recombinant inbred line (RIL) population derived from the two parents was genotyped using the Illumina Genome Analyzer II platform and the information used to generate a genetic map useful for analysis of quantitative trait loci (QTL). Seventy-two different traits were measured in the RIL population at anthesis and at grain maturity. Plants were grown in well-watered environments in greenhouse conditions and in field conditions near College Station, TX in 2008-2010. QTL mapping was used to analyze the genetic basis of trait variation in the population and to detect associations between traits. A total of 477 QTL were identified that in combination modulate leaf size (length, width, and area), shoot biomass accumulation (shoot, stalk, stem, leaf, and leaf sheath), panicle weight, root size and architecture (length, surface area, and volume, number of tips, forks and nodal roots, and root biomass), stalk and stem length, and flowering time. Six flowering time QTL were identified and variation in time to anthesis affected the expression of several other traits including leaf size and biomass accumulation. However, QTL infrequently had an impact on traits associated with different organs. The specificity observed is consistent with independent genetic control of traits associated with leaves, stems and roots. Nine QTL that modulated shoot biomass accumulation were detected that were not affected by flowering time. Of these, four shoot biomass QTL co-localized with leaf size traits. Eight QTL for panicle biomass were detected with two coincident with QTL for upper leaf size. A QTL for leaf width at anthesis was found to co-localize with a stay green locus.

This dissertation examined the experiences of African American and Hispanic community college transfers studying in a predominantly White institution (PWI) teacher education program. Extant literature has demonstrated that there is an increasing shortage of public school teachers of color. To address this pervasive crisis, universities must recruit larger numbers of under-represented students from America's community colleges to fill the gap. Additionally, under-represented students in predominantly White institution (PWI) teacher education programs must be retained and graduated in higher numbers to impact teacher production. To understand the needs and experiences of these students, three African American and four Hispanic students successfully studying in the College of Education and Human Development at Texas A & M University were interviewed to examine their experiences as they transitioned into and through the pre-service teacher education program. This study utilized a case study analysis and data collected through interviews, observations, and document analysis were analyzed utilizing constant comparative techniques. Key themes that emerged through data analysis included (1) Student Perceptions of the Transfer Process, (2) The Role of Intimate Support Structures, (3) The Role of Institutional Support Structures, (4) Situational Factors Faced by Under-represented Transfers, and (5) Strategies Employed by Under-represented Transfers in a Predominantly White, Teacher Education Program. Findings from this study suggest that Texas A & M University should improve the marketing of academic support resources and provide narrowly tailored services to meet the needs of under-represented transfer students. Additionally, Schlossberg's Transition Theory is amended to better represent the assets and liabilities utilized by these populations in this particular PWI.

Les fictions contemporaines, plus particulièrement les téléséries américaines, semblent actuellement accorder une place prépondérante à la thématique de la mort au sein de leur récit. Il semble ainsi possible de discerner une véritable tendance aux thanatofictions dans le paysage médiatique américain d'aujourd'hui. Considérant la place importante que revêt la thématique de la finitude dans toute culture ainsi que le rôle que les médias contemporains peuvent avoir concernant la formation du rapport social à la mort, nous avons décidé d'interroger la représentation de la mort qui est proposée dans une télésérie américaine contemporaine. Pour ce faire, nous avons choisi d'analyser la télésérie américaine Six Feet Under, présentée sur les ondes de la chaîne câblée HBO de 2001 à 2005. À travers cette analyse de Six Feet Under, nous avions pour objectif de chercher à mieux comprendre quelles significations une télésérie américaine d'aujourd'hui véhicule au sujet de la mort. Plus encore, nous cherchions à comprendre comment Six Feet Under représente le rapport de la culture américaine actuelle à la mortalité. La télésérie Six Feet Under fut analysée grâce à une méthode herméneutique, ce qui nous a permis d'étudier le récit, les récurrences narratives ainsi que les représentations de la mort véhiculées à travers les discours visuels et verbaux de cette fiction. À l'issue de notre analyse, nous avons pu conclure que la télésérie Six Feet Under propose une représentation de la mort en tant que phénomène universel, ontologique et essentiel. Cette représentation s'accompagne également d'une critique de l'attitude dénégatoire des Américains d'aujourd'hui concernant la mort. Plus globalement, notre interprétation révèle que Six Feet Under propose une représentation alternative et plus positive de la mort, laquelle pourrait avoir une pertinence sociale particulière dans notre paysage télévisuel actuel. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Mort, Représentation, Télésérie, Fiction, Herméneutique

In order to control any system one needs to know the system's current state. In many real-world scenarios the state of the system cannot be determined with certainty due to the sensors being noisy or simply missing. In cases like these one needs to use probabilistic inference techniques to compute the likely states of the system and because such cases are common, there are lots of techniques to choose from in the field of Artificial Intelligence. Formally, we must compute a probability distribution function over all possible states. Doing this exactly is difficult because the number of states is exponential in the number of variables in the system and because the joint PDF may not have a closed form. Many approximation techniques have been developed over the years, but none ideally suited the problem we faced. Particle filtering is a popular scheme that approximates the joint PDF over the variables in the system by a set of weighted samples. It works even when the joint PDF has no closed form and the size of the sample can be adjusted to trade off accuracy for computation time. However, with many variables the size of the sample required for a good approximation can still become prohibitively large. Factored particle filtering uses the structure of variable dependencies to split the problem into many smaller subproblems and scales better if such decomposition is possible. However, our problem was unusual because some normally independent variables would become strongly correlated for short periods of time. This dynamically-changing dependency structure was not handled effectively by existing techniques. Considering variables to be always correlated meant the problem did not scale, considering them to be always independent introduced errors too large to tolerate. It was necessary to develop an approach that would utilize variables' independence whenever possible, but not introduce large errors when variables become correlated. We have developed a new technique for monitoring the state of the system for a class of systems with context-specific correlations. It is based on the idea of caching the context in which correlations arise and otherwise keeping the variables independent. Our evaluation shows that our technique outperforms existing techniques and is the first viable solution for the class of problems we consider.