Ertapenem is an antibiotic commonly used to treat a broad spectrum of infections, which is part of a broader class of antibiotics called carbapenem. Unlike other carbapenems, ertapenem has a longer half-life and thus only has to be administered once a day. A physiologically-based pharmacokinetic (PBPK) model was developed to investigate the uptake, distribution, and elimination of ertapenem following a single one gram dose. PBPK modeling incorporates known physiological parameters such as body weight, organ volumes, and blood ow rates in particular tissues. Furthermore, ertapenem is highly bound in human blood plasma; therefore, nonlinear binding is incorporated in the model since only the free portion of the drug can saturate tissues and, hence, is the only portion of the drug considered to be medicinally effective. Parameters in the model were estimated using a least squares inverse problem formulation with published data for blood concentrations of ertapenem for normal height, normal weight males. Finally, an uncertainty analysis of the parameter estimation and model predictions is presented.

In this dissertation, we show two significant applications of combinatorial branch-and-bound as an exact solution methodology in combinatorial optimization problems. In the first problem, we propose a set of new group centrality metrics and show their performance in estimating protein importance in protein-protein interaction networks. The centrality metrics introduced here are extensions of well-known nodal metrics (degree, betweenness, and closeness) for a set of nodes which is required to induce a specific pattern. The structures investigated range from the ``stricter'' induced stars and cliques, to a ``looser'' definition of a representative structure. We derive the computational complexity for each of the newly proposed metrics. Then, we provide mixed integer programming formulations to solve the problems exactly; due to the computational complexity of the problem and the sheer size of protein-protein interaction networks, using a commercial solver with the formulations is not always a viable option. Hence, we also propose a combinatorial branch-and-bound approach to solve the problems introduced. Finally, we conclude this work with a presentation of the performance of the proposed centrality metrics in identifying essential proteins in helicobacter pylori. In the second problem, we introduce the asymmetric probabilistic minimum-cost Hamiltonian cycle problem (APMCHCP) where arcs and vertices in the graph are possible to fail. APMCHCP has applications in many emerging areas, such as post-disaster recovery, electronic circuit design, and security maintenance of wireless sensor networks. For each vertex, we define a chance-constraint to guarantee that the probability of arriving at the vertex must be greater than or equal to a given threshold. Four mixed-integer programming (MIP) formulations are proposed for modeling the problem, including two direct formulations and two recursive formulations. A combinatorial branch-and-bound (CBB) algorithm is proposed for solving the APMCHCP, where data preprocessing steps, feasibility rules, and approaches of finding upper and lower bounds are developed. In the numerical experiments, the CBB algorithm is compared with formulations on a test-bed of two popular benchmark instance sets. The results show that the proposed CBB algorithm significantly outperforms Gurobi solver in terms of both the size of optimally solved instances and the computing time.

Tourism is one of the fastest growing industries in the world, employing approximately 220 million people and generating over 9.4% of the world's GDP. The growing contribution of tourism is accompanied by an increased interest in understanding the major factors which influence visitation levels to those countries. Therefore, finding the right variables to understand and estimate tourism demand becomes very important and challenging in policy formulations. The purpose of this study is to introduce Economic Sentiment Indicator (ESI) to the field of tourism demand studies. Using ESI in demand analysis, this study will assist in the ability to tap into individuals' hopes and/or worries for the present and future. The study developed a demand model in which the number of tourist arrivals to Turkey from select EU countries is used as the dependent variable. ESI along with more traditional variables such as Interest Rate, Relative Price, and Relative Exchange Rate were brought into the model as the independent demand determinants. The study utilized such econometric models as ARIMA for seasonality adjustment and ARDL Bound test approach to cointegration for the long and short-run elasticities. ESI was statistically significant in 8 countries out of 13, three of those countries had a negative coefficient and five had a positive sign as proposed by the study. The study posits that ESI is a good indicator to gauge and monitor tourism demand and adding the visitors' state of mind into the demand equation could reduce errors and increase variance in arrivals. Policy makers should monitor ESI as it fluctuates over time. Since we do not have direct influence on travelers' demand for tourism, it is imperative that we use indirect approaches such as price adjustment and creating new packages or promotional expenditures in order to influence or induce demand. Using this information generated from the study, government officials and tourism suppliers could adjust their promotional activities and expenditures in origin countries accordingly. / Master of Science

This applied dissertation was designed to identify the characteristics of students enrolled in a GED® preparation program who transitioned to postsecondary programs at the same institution after passing the GED® test. The characteristics studied included age; gender; ethnicity; prematriculation scores in reading, language, and math in the Test of Adult Basic Education (TABE); and hours spent preparing for the GED® test in an open-entry, open-exit remedial laboratory environment. Through the use of binary logistic regressions to answer the research questions, a prediction model was constructed. The variables that are able to predict an increased likelihood of transition to postsecondary programs were being between the ages of 16 and 24 at the time of enrollment in the GED® program and having an ethnicity category of Asian, White/Caucasian, Hispanic, or Black/African American as opposed to the category of No Report. The variables that significantly predicted a lessened likelihood of transition to postsecondary programs were a grade equivalent of less than 8.9 in the prematriculation TABE reading, language, and math scores. Spending less than 16 hours preparing for the GED® test was also found to lessen the likelihood of transition. The findings of this study are important to adult education practitioners, tutors, teachers, and administrators who are responsible for GED® programs. Through application of the prediction model in a similar environment, supportive and interventional mechanisms can be created to increase the number of GED® earners who transition to credit, college preparation, and vocational programs.

RSS-based localization is a promising solution for estimating the position of a non-collaborative emitter using a network of collaborative sensors. This paper examines RSS-based localization and differential RSS (DRSS) localization in the presence of correlated shadowing with no knowledge of the emitter's reference power.  A new non-linear least squares (NLS) DRSS location estimator that uses correlated shadowing information to improve performance is introduced. The existing maximum likelihood (ML) estimator and Cram\' er Rao lower bound (CRLB) for RSS-based localization given do not account for correlated shadowing. This paper presents a new ML estimator and CRLB for RSS-based localization that account for spatially correlated shadowing and imperfect knowledge of the emitter's reference power. The performance of the ML estimator is compared to the CRLB under different simulation conditions. The ML estimator is shown to be biased when the number of sensors is small or the shadowing variance is large. The effects of correlated shadowing on an RSS-based location estimator are thoroughly examined. It is proven that an increase in correlated shadowing will improve the accuracy of an RSS-based location estimator. It is also demonstrated that the ideal sensor geometry which minimizes the average error becomes more compact as correlation is increased. A geometric dilution of precision (GDOP) formulation is derived that provides a metric for the effect of the position of the sensors and emitter on the location estimator performance. A measurement campaign is conducted that characterizes the path loss at 3.4 GHz. The measurements are compared to the log-distance model. The errors between the model and the measurements, which should theoretically be Gaussian, have a Kurtosis value of 1.31. The errors were determined to be spatially correlated with an average correlation coefficient of 0.5 at a distance of 160 meters. The performance of the location estimators in simulation is compared to the performance using measurements from the measurement campaign. The performance is very similar, with the largest difference between the simulated and actual results in the ML estimator. In both cases, the new NLS DRSS estimator outperformed the other estimators and achieved the CRLB. / Master of Science

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18787号 / 理博第4045号 / 新制||理||1582(附属図書館) / 31738 / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 永江 知文, 教授 谷森 達, 准教授 成木 恵 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24626号 / 工博第5132号 / 新制||工||1981(附属図書館) / 京都大学大学院工学研究科材料化学専攻 / (主査)教授 田中 勝久, 教授 三浦 清貴, 教授 藤田 晃司 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

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Many women self-report discomfort, depression, mood changes, and irritability in conjunction with menstruation which has been termed Premenstrual Syndrome (PMS). Prior to the creation of the disease/disorder category PMS, disorders with similar symptoms like “hysteria” and “involutional melancholia” were ascribed to women reporting those types of complaints. These diagnoses were based on archaic claims about women’s anatomy and behavior. Modern medical researchers contend that women’s complaints have a physiological basis, yet they cannot definitively tie PMS to any specific physiological etiological pathway, either hormonal or neurological. This thesis explores the argument that the social norms for women’s roles and their associated behaviors are related to the appearance of a disease/disorder category named PMS in the United Kingdom and the United States. Many of women’s complaints may instead be symptoms of social problems (with social remedies) related to role conflict or role strain.