We present methods of calculating the value of two performance parameters for multipath, multistage interconnection networks: the normalized throughput and the probability of successful message transmission. We develop a set of exact equations for the loading probability mass functions of network channels and a program for solving them exactly. We also develop a Monte Carlo method for approxmiate solution of the equations, and show that the resulting approximation method will always calculate the values of the performance parameters more quickly than direct simulation.

This thesis considers statistical issues in the analysis of data in the studies of chronic diseases which involve modeling dependencies between life history processes using copula functions. Many disease processes feature recurrent events which represent events arising from an underlying chronic condition; these are often modeled as point processes. In addition, however, there often exists a random variable which is realized upon the occurrence of each event, which is called a mark of the point process. When considered together, such processes are called marked point processes. A novel copula model for the marked point process is described here which uses copula functions to govern the association between marks and event times. Specifically, a copula function is used to link each mark with the next event time following the realization of that mark to reflect the pattern in the data wherein larger marks are often followed by longer time to the next event. The extent of organ damage in an individual can often be characterized by ordered states, and interest frequently lies in modeling the rates at which individuals progress through these states. Risk factors can be studied and the effect of therapeutic interventions can be assessed based on relevant multistate models. When chronic diseases affect multiple organ systems, joint modeling of progression in several organ systems is also important. In contrast to common intensity-based or frailty-based approaches to modelling, this thesis considers a copula-based framework for modeling and analysis. Through decomposition of the density and by use of conditional independence assumptions, an appealing joint model is obtained by assuming that the joint survival function of absorption transition times is governed by a multivariate copula function. Different approaches to estimation and inference are discussed and compared including composite likelihood and two-stage estimation methods. Special attention is paid to the case of interval-censored data arising from intermittent assessment. Attention is also directed to use of copula models for more general scenarios with a focus on semiparametric two-stage estimation procedures. In this approach nonparametric or semiparametric estimates of the marginal survivor functions are obtained in the first stage and estimates of the association parameters are obtained in the second stage. Bivariate failure time models are considered for data under right-censoring and current status observation schemes, and right-censored multistate models. A new expression for the asymptotic variance of the second-stage estimator for the association parameter along with a way of estimating this for finite samples are presented under these models and observation schemes.

Supply chain network design (SCND) identifies the production and distribution resources essential to maximizing a network’s profit. Once implemented, a SCND impacts a network’s performance for the long-term. This dissertation extends the SCND literature both in terms of model scope and solution approach. The SCND problem can be more realistically modeled to improve design decisions by including: the location, capacity, and technology attributes of a resource; the effect of the economies of scale on the cost structure; multiple products and multiple levels of supply chain hierarchy; stochastic, dynamic, and correlated demand; and the gradually unfolding uncertainty. The resulting multistage stochastic mixed-integer program (MSMIP) has no known general purpose solution methodology. Two decomposition approaches—end-of-horizon (EoH) decomposition and nodal decomposition—are applied. The developed EoH decomposition exploits the traditional treatment of the end-of-horizon effect. It rests on independently optimizing the SCND of every node of the last level of the scenario-tree. Imposing these optimal configurations before optimizing the design decisions of the remaining nodes produces a smaller and thus easier to solve MSMIP. An optimal solution results when the discount rate is 0 percent. Otherwise, this decomposition deduces a bound on the optimality-gap. This decomposition is neither SCND nor MSMIP specific; it pertains to any application sensitive to the EoH-effect and to special cases of MSMIP. To demonstrate this versatility, additional computational experiments for a two-stage mixed-integer stochastic program (SMIP) are included. This dissertation also presents the first application of nodal decomposition in both SCND and MSMIP. The developed column generation heuristic optimizes the nodal sub-problems using an iterative procedure that provides a restricted master problem’s columns. The heuristic’s computational efficiency rests on solving the sub-problems independently and on its novel handling of the master problem. Conceptually, it reformulates the master problem to avoid the duality-gap. Technologically, it provides the first application of Leontief substitution flow problems in MSMIP and thereby shows that hypergraphs lend themselves to loosely coupled MSMIPs. Computational results demonstrate superior performance of the heuristic approach and also show how this heuristic still applies when the SCND problem is modeled as a SMIP where the restricted master problem is a shortest-path problem.

In the thesis, we study the rearrangeability of the Banyan-type network with crosstalk constraint. Let $x$, $p$ and $c$ be nonnegative integers with $0leq x,cleq n$ and $n,pgeq 1$. $B_{n}(x,p,c)$ is the Banyan-type network with, $2^{n+1}$ inputs, $2^{n+1}$ outputs, $x$ extra-stages, and each connection containing at most $c$ crosstalk switch elements. We give the necessary and sufficient conditions for rearrangeable Banyan-type networks $B_{n}(x,p,c)$.

This study estimates the demand for domestic air travel services in the United States in order to calculate the fare and expenditure elasticities of demand. We segmented the market according to number of operating airlines, distances and traveler types. Using Seemingly Unrelated Regression to estimate the Almost Ideal Demand System (AIDS), we find that the expenditure and uncompensated own-fare elasticities are around unity and consistent with the previous literature. Results reveal a tendency of uncompensated own-fare elasticity to decrease as distance increases, and a tendency of uncompensated own-fare elasticity to increase as number of airlines increases. Due to few observations, business travelers' results are not reliable to make any conclusion. Leisure travelers' results are closer to all travelers' results.

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide, accounting for over 600,000 deaths per year. The most common treatment strategy for intermediate and advanced stage unresectable HCC is transarterial chemoembolization (TACE), which involves the local administration of a chemotherapeutic drug combined with arterial occlusion resulting in ischemic tumor necrosis. However, TACE suffers from inadvertent exposure of noncancerous liver parenchyma to embolic agents resulting in liver injury. In some cases, over-embolization has lead to infection, necrosis of unaffected liver tissue, and even liver failure which suggests the need for a biocompatible, multifunctional embolic material which can deliver anticancer drugs with high target specificity. Our laboratory has recently developed a method to fabricate porous silicon (pSi) microparticles with defined physicochemical properties based on photolithography and anodic etching. These microparticles function as multistage drug delivery systems that can circumvent the biobarriers present in the systemic circulation enabling site-specific localization and release of chemotherapy and imaging agents. The versatility of the fabrication process enables the realization of microparticles ranging in size from 600nm to 116[mu]m in diameter with varying shapes, including discoidal, cylindrical and hemispherical, and varying porosity with pore sizes ranging from 6nm to greater than 50nm in diameter. Nanoparticles, such as quantum dots, siRNA-loaded nanoliposomes, gadolinium-based contrast agents, gold and iron oxide nanoparticles, are loaded in pSi microparticles by tailoring their pore sizes and surface chemistries. This thesis presents preliminary results on the applicability of biocompatible, engineered pSi microparticles as an embolic agent for HCC chemoembolization therapy. Hemispherical microparticles with 116[mu]m diameter were successfully fabricated and suspended in phosphate buffered saline (PBS). A microvascular construct was rapid prototyped in polydimethylsiloxane (PDMS) as an in vitro experimental platform to study the embolization behavior of pSi microparticles. Oxidized pSi microparticles were introduced into the microfluidic device at an appropriate flow rate and time-lapse images were taken showing the formation of occlusions at the bifurcation within minutes of administration. Furthermore, penetration through the bifurcation was completely hindered suggesting that pSi microparticles can potentially be used as a biocompatible, multifunctional chemoembolization agent. Although these results are promising, further investigations are warranted.

This thesis considers statistical issues in the analysis of data in the studies of chronic diseases which involve modeling dependencies between life history processes using copula functions. Many disease processes feature recurrent events which represent events arising from an underlying chronic condition; these are often modeled as point processes. In addition, however, there often exists a random variable which is realized upon the occurrence of each event, which is called a mark of the point process. When considered together, such processes are called marked point processes. A novel copula model for the marked point process is described here which uses copula functions to govern the association between marks and event times. Specifically, a copula function is used to link each mark with the next event time following the realization of that mark to reflect the pattern in the data wherein larger marks are often followed by longer time to the next event. The extent of organ damage in an individual can often be characterized by ordered states, and interest frequently lies in modeling the rates at which individuals progress through these states. Risk factors can be studied and the effect of therapeutic interventions can be assessed based on relevant multistate models. When chronic diseases affect multiple organ systems, joint modeling of progression in several organ systems is also important. In contrast to common intensity-based or frailty-based approaches to modelling, this thesis considers a copula-based framework for modeling and analysis. Through decomposition of the density and by use of conditional independence assumptions, an appealing joint model is obtained by assuming that the joint survival function of absorption transition times is governed by a multivariate copula function. Different approaches to estimation and inference are discussed and compared including composite likelihood and two-stage estimation methods. Special attention is paid to the case of interval-censored data arising from intermittent assessment. Attention is also directed to use of copula models for more general scenarios with a focus on semiparametric two-stage estimation procedures. In this approach nonparametric or semiparametric estimates of the marginal survivor functions are obtained in the first stage and estimates of the association parameters are obtained in the second stage. Bivariate failure time models are considered for data under right-censoring and current status observation schemes, and right-censored multistate models. A new expression for the asymptotic variance of the second-stage estimator for the association parameter along with a way of estimating this for finite samples are presented under these models and observation schemes.

High-Frequency Surface Wave Radar (HFSWR) is a radar technology that offers numerous advantages for surveillance of coastal waters beyond the exclusive economic zone. However, target detection and tracking is primarily limited by ionospheric interference. Ionospheric clutter is characterized by a high degree of nonhomogeneity and nonstationarity, which makes its suppression difficult using conventional processing techniques. Space-time adaptive processing techniques have enjoyed great success in airborne radar, but have not yet been investigated in the context of HFSWR. This thesis is primarily concerned with the evaluation of existing STAP techniques in the HFSWR scenario and the development of a new multistage adaptive processing approach, dubbed the Fast Fully Adaptive (FFA) scheme, which was developed with the particular constraints of the HFSWR interference environment in mind. Three different spatio-temporal partitioning schemes are introduced and a thorough investigation of the performance of the FFA is conducted.

High-Frequency Surface Wave Radar (HFSWR) is a radar technology that offers numerous advantages for surveillance of coastal waters beyond the exclusive economic zone. However, target detection and tracking is primarily limited by ionospheric interference. Ionospheric clutter is characterized by a high degree of nonhomogeneity and nonstationarity, which makes its suppression difficult using conventional processing techniques. Space-time adaptive processing techniques have enjoyed great success in airborne radar, but have not yet been investigated in the context of HFSWR. This thesis is primarily concerned with the evaluation of existing STAP techniques in the HFSWR scenario and the development of a new multistage adaptive processing approach, dubbed the Fast Fully Adaptive (FFA) scheme, which was developed with the particular constraints of the HFSWR interference environment in mind. Three different spatio-temporal partitioning schemes are introduced and a thorough investigation of the performance of the FFA is conducted.

Final publication is available at http://dx.doi.org/10.1007/s10508-014-0429-5. Teeranee Techasrivichien, Niphon Darawuttimaprakorn, Sureeporn Punpuing, Patou Masika Musumari, Bhekumusa Wellington Lukhele, Christina El-saaidi, S. Pilar Suguimoto, Mitchell D. Feldman, Masako Ono-Kihara, Masahiro Kihara "Changes in Sexual Behavior and Attitudes Across Generations and Gender Among a Population-Based Probability Sample From an Urbanizing Province in Thailand" Archives of Sexual Behavior 2014. / 京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18679号 / 医博第3951号 / 新制||医||1007(附属図書館) / 31612 / 京都大学大学院医学研究科医学専攻 / (主査)教授 中山 健夫, 教授 福原 俊一, 教授 中原 俊隆 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM