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The background to this thesis is the most recent and significant company law reforms in England and China, particularly those concerned with minority shareholders' remedies. The implementation of the Companies Act 2006 in England and the Chinese Company Law 2005 makes this comparative research interesting and challenging. At this time, it is especially worthwhile because of England's already sophisticated level of economic development and the current rapid rise of China as a leading economic power with enormous potential. The thesis examines the deficiencies of current laws on minority shareholders' remedies in both England and China, in particular shareholders' personal actions. derivative actions and the unfair prejudice remedies. It proposes for each jurisdiction further reforms which could be carried out to achieve a proper balance between the protection of minority shareholder interests and those of their companies. The latter often suffer from undue interference from litigious activities of the former. By way of comparison, it shows that England and China can learn from each other with regard to future law reforms concerned with minority shareholders' remedies whilst taking into account their different constitutional, political, socio-economic, legal and cultural backgrounds.

Placebo-controlled randomized clinical trials (RCT) have been regarded as the gold standard for guiding the registry of new drugs. However, such trials in rare diseases are invariably small in sample size due to the small number of patients with disease and to enroll in studies with a placebo arm. A small n, sequential, multiple assignment, randomized trial (snSMART) is a design that include placebo and dose levels of a drug to facilitate efficient treatment effect estimates. In the first project, we propose a new snSMART design that investigates the response rates of a drug tested at a low and high dose compared with placebo. Patients are randomized to an initial treatment (stage 1). In stage 2, patients are rerandomized, depending on their initial treatment and their response to that treatment in stage 1, to either the same or a different dose of treatment, with no placebo option. We present a Bayesian approach where information is borrowed between stage 1 and stage 2 to determine the efficacy of the active treatment. We compare our approach to standard methods using only stage 1 data and a log-linear Poisson model that uses data from both stages where parameters are estimated using generalized estimating equations. We observe that our method has smaller root-mean-square-error (rMSE) and 95% credible interval widths than standard methods in the tested scenarios. In the second project, we extend the previous snSMART design with a binary endpoint to a design that considers a continuous outcome. Data from both stages are used to determine the marginal efficacy of the dose levels of the active treatment via a Bayesian model. We also compare the approach with the standard model based solely upon the data from stage 1 and evaluate different prior distributions in model fitting. We demonstrate that the joint stage Bayesian estimators have smaller rMSE and narrower credible intervals. In the third project, we aim to determine the sample size needed to achieve a prespecified significance level and desired statistical power for an snSMART. We focus on the design and Bayesian analytical approach proposed in the second project to detect the difference in the mean outcome between low dose and placebo. We adjust a one stage sample size calculation to account for the extra information extracted from the two-stage design. We also provide an efficient approach to calculate sample size in one step. Through simulations, we demonstrate that the required sample sizes calculated using the two SSD methods both provide the desired power. Both approaches are available in an RShiny App to disseminate the methods to rare disease investigations.

Background: Long-term exposure to fine particulate matter (PM2.5) is an established risk factor for many adverse health outcomes via different mechanisms, and thus predisposes individuals and populations to elevated risks of both infectious and chronic health outcomes. Previous studies reported the association between PM2.5 exposure and COVID-19 outcomes based on arbitrarily cut-off points. This study investigated this association during the first two surges of the pandemic and examined whether non-pharmacologic prevention initiatives might intervene this association. In addition, though vaccines against SARS-CoV-2 were safe and effective, its community-wide impacts on reducing COVID-19 incidence and mortality corresponding to the predominant strain in the population have not yet been studied. Lastly, we aim to confirm the association between PM2.5 and lung cancer susceptibility in Los Angeles, as well as addressing whether PM2.5 is associated with UADT cancers susceptibility, which have been understudied. Objective and Specific Aims: We aimed to evaluate the association between ambient PM2.5 exposure and COVID-19 incidence and lung and UADT cancer susceptibility. The specific aims were: (1) to estimate the association between long-term exposure to ambient PM2.5, facemask mandates, stay home orders and COVID-19 incidence in the United States during the first two surges; (2) to estimate the association between SARS-CoV-2 vaccines coverage and COVID-19 incidence and mortality in the United States during the Alpha, Delta, and Omicron predominance; and (3) to estimate the association between long-term exposure to ambient and indoor PM2.5 and lung and UADT cancers susceptibility. Study Design and Population: For Specific Aims 1 and 2, we employed a nation-wide ecologic study design, including more than 3,000 counties in the US. Analyses were conducted to estimate the associations between ambient PM2.5, non-pharmacologic prevention initiatives, including facemask mandates and stay-home policies, and vaccination coverage at the county-level and COVID-19 incidence and mortality. The study utilized publicly available data. In Specific Aim 3, we aimed to estimate the association between air pollution and susceptibility of lung and UADT cancers, using a population-based case-control study in the Los Angeles County. The study included 577 lung cancer cases, 565 UADT cancers cases, and 983 controls after applying exclusion criteria. Statistical Methods: For Specific Aim 1, we fit negative binomial models to assess COVID-19 incidence in association with PM2.5 and policies during the first two surges of SARS-CoV-2 pandemic, as of September 12th, 2020. Stratified analyses by facemask policy and stay home policy were also performed. For Specific Aim 2, generalized estimating equations were used to estimate associations between US county-level cumulative complete vaccination rates and booster distribution and the daily change in county-wide COVID-19 risks and mortality during Alpha (April 23rd – July 2nd, 2021), Delta (July 3rd – December 1st, 2021) and Omicron (December 2nd, 2021 – March 25th, 2022) predominance. For both Aims, models were adjusted for potential confounders at both county and state level. A 2-week lag and a 4-week lag were introduced to assess vaccination rates impact on incidence and mortality, respectively. For Specific Aim 3, unconditional logistic regressions were applied to estimate the association between air pollution, including ambient PM2.5 one-year before diagnosis, exposure to household air pollution, and an air pollution index, and lung and UADT cancers’ susceptibility, adjusting for potential confounders. Results: For Aim 1, after adjusting for county-level and state-level potential confounders, each 1-�g/m3 increase in annual average concentration of PM2.5 exposure was associated with an increase in COVID-19 risk (relative risk (RR) = 1.0756, 95% CI: 1.0376, 1.1149). Facemask mandates and stay home policies were inversely associated with COVID-19 with adjusted RRs of 0.8466 (95% CI: 0.7598, 0.9432) and 0.9193 (95% CI: 0.8021, 1.0537), respectively. The associations between PM2.5 and COVID-19 were consistent among counties with or without preventive policies. For Aim 2, among 3,073 counties in 48 states, the average county population complete vaccination rates of all age groups were 50.79% as of March 11th, 2022. Each percentage increase in vaccination rates was associated with reduction of 4% (RR = 0.9607, 95% CI: 0.9553, 0.9661) and 3% (RR = 0.9694, 95% CI: 0.9653, 0.9736) in county-wide COVID-19 cases and mortality, respectively, when Alpha was the dominant variant and after adjusting for potential confounders. The associations between county-level vaccine rates and COVID-19 incidence diminished during the Delta (RR = 0.9988, 95% CI: 0.9964, 1.0011) and Omicron (RR = 0.9969, 95% CI: 0.9919, 1.0019) predominance. Vaccination coverage was associated with slightly decreased COVID-19 mortality (RR = 0.9934, 95% CI: 0.9889, 0.9980) when Delta was the most prevalent strain, but with a marginal increase in COVID-19 mortality (RR = 1.0061, 95% CI: 1.0022, 1.0101) when Omicron was circulating. During the Omicron predominance, each percent increase in people receiving a booster shot was associated with reduction of 6% (RR = 0.9356, 95% CI: 0.9235, 0.9479) and 4% (RR = 0.9595, 95% CI: 0.9431, 0.9761) in COVID-19 incidence and mortality in the community, respectively. For Specific Aim 3, each 1-�g/m3 increase in ambient PM2.5 one-year before diagnosis was associated with elevated risks in lung (odds ratio (OR) = 1.02, 95% CI: 0.98, 1.06) and UADT (OR = 1.04, 95% CI: 1.00, 1.09) cancers susceptibility, adjusting potential confounding factors. Conclusions and public health implications: These results add evidence to the associations between PM2.5 with COVID-19 incidence and with lung and UADT cancers susceptibility. Thus, our results underscore the health hazards associated with increased ambient PM2.5 and may be informative for policymaking and program planning for continuing to improve air quality. Moreover, the non-pharmacologic prevention initiatives and increasing vaccination coverage are shown to be effective in reducing COVID-19 incidence and mortality during different outbreaks of the SARS-CoV-2 variants, indicating both NPIs and vaccination are essential decisions in better preparation for the next surge of COVID-19 and even for the next emerging pandemic in the future.

Ultracold atomic system provides us a versatile platform to address problems ranging from quasi-particle excitations supported by a variety of ground states, to cold collisions which could lead to a precise determination of interatomic molecular potential, and to atomtronics focusing on atomic analogues of electronic components. In this dissertation, I describe our efforts in exploring a wide topic range by our \Rb Bose Einstein condensate (BEC) machine and \Li - \Rb mixture machine, including magnons, which are collective spin excitations supported by Rb F = 1 ferromagnetic BEC ground state, cold elastic collisions between Li and Rb co-trapped in a spherical quadrupole trap, and Li spin dynamics in a spin bath formed by Rb.

Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images.

Abstract: Maintenance management in wind energy industry has great impact on overall wind power cost. Optimizing maintenance strategies can substantially reduces the cost and makes wind energy more competitive among the energy resources. Due to the extreme conditions of remote or offshore sites where the wind turbines are installed, corrective maintenance and time-based preventive maintenance are the most adopted strategies in the wind industry in recent years. However, there is need to further reduce wind power cost via maintenance strategy improvement to increase its competitiveness. Industry and research community have been focusing on various maintenance strategies to save the maintenance cost. This thesis is devoted to developing cost-effective maintenance strategies for wind farms, focusing on conventional time-based maintenance optimization, and prognostics and condition-based maintenance (CBM) optimization within the CBM strategy framework. Studies are performed on improving corrective maintenance and time-based preventive maintenance strategies, which are currently widely adopted in wind industry. Opportunistic maintenance methods are proposed, which take advantage of economic dependencies existing among the wind turbines, and corrective maintenance chances, to implement preventive maintenance simultaneously. Imperfect preventive maintenance actions are considered as well. The methods demonstrate the immediate benefits of saving the overall maintenance cost for a wind farm. In the more advanced CBM strategy, the health conditions of components are monitored and predicted, based on which maintenance actions are scheduled to prevent unexpected failures while reducing the maintenance costs. Prognostic techniques are essential in CBM. In particular, the wind direction and speed around wind turbines are changing over time, which leads to instantaneously time-varying load applied to the wind turbines rotors. With focus on gearbox failure due to the gear tooth crack, an integrated prognostics method is developed considering instantaneously varying load condition. The numerical examples demonstrate that the gearbox remaining useful life prediction considering time-varying load is more accurate compared to existing methods under constant-load assumption. In a subsequent extended study, uncertainty in gear tooth crack initiation time is further considered for wind turbine gearbox prognostics method development. The method provides more accurate gearbox remaining useful life prediction compared to the results without considering time-varying load condition. This thesis also proposes a CBM method considering different turbine types and lead times, as well as the production loss during the shutdown time. The capability to accurately estimate the average maintenance cost for a wind farm with diverse turbines is a key contribution of the proposed method. In addition, this thesis accounts for the inaccuracy in the simulation-based algorithms that most complex problems are solved with. A numerical method for CBM optimization of wind farms is developed to avoid the variations in CBM cost evaluation, which leads to a smooth cost function surface and benefits the optimization process. The research in this thesis provides innovative methods for maintenance management in the wind power industry. The developed methods will help to significantly reduce the overall maintenance cost within either conventional maintenance or CBM strategies that the wind farm owners may apply. It will improve the competitive advantage of the wind energy, and promote a clean and sustainable energy future for the society in Canada and worldwide.

This dissertation explores how family and marriage relationships vary according to the culture in which they occur. Based on the individualism/collectivism framework about cultural variations in familial beliefs across countries, I study three topics of family and marriage relationships across cultures. In the first study, I examine how 17 member countries of Organisation of Economic and Co-operation and Development (OECD) differ culturally in older adults' preference for family elder care. I find that older adults from countries with more traditional values that emphasize the importance of a strong parent-child tie are more likely to prefer family care rather than formal care than those from more secular-rational countries with less emphasis on the parent-child tie; the cultural difference gets smaller at a higher level of individual family income. In the second study, I select China as a representative of the collectivist culture, and look into how the collectivist culture and older parents' filial beliefs shape the intergenerational relationship in China. I find that patrilocal and patrilineal traditions are still prevail in China. A highly cohesive intergenerational relationship people idealize in the collectivist culture is more common between older parents and married sons, and least common between older parents and married daughters. In the third study, I compare an individualist society, the U.S., and China, a collectivist society to test whether marriage also isolates people from their informal social network in China as observed in the U.S. I find that marriage does not isolate but integrates people into their informal social network in China, while marriage isolate people in the U.S. The three studies present new evidence on how marriage and family experiences differ due to different cultural beliefs about family, and under what conditions the cultural influences are weakened or reinforced.

Abstract: A technique to visualize miscible displacement in porous media using laser and the analysis of the results for different processes are presented in this thesis. After saturating the model made of different sized glass beads with oil, solvent was introduced either under dynamic (injection and production through a pair of horizontal wells) or static (diffusion of solvent into oil saturated model) conditions. The former simulates the VAPEX (vapor extraction) process dictated by viscous and gravitational forces and the latter (“diffusion experiments”) represents diffusion/gravity (and thereby convection) controlled displacement of oil by the solvent contacting the porous medium saturated with oil. The refractive indices of saturated and injected fluids were made the same by mixing the fluids with lower and higher indices of refraction to make the model fully transparent. Fluorescent dyes that were only visible with excitation of laser were dissolved in the solvent. A laser sheet scanned the model while synchronous pictures were taken by two high speed cameras from two sides of the model. 2-D images obtained through this process were converted to 3-D visual data and qualitative and quantitative analyses were conducted. An optimized injection method for the VAPEX process was determined by testing different -constant and variable- injection rates. The effect of solvent gravity and viscosity on the displacement (chamber growth) process was also analyzed through the 3-D images. Diffusion was the major factor in the transition zone at the edge of solvent chamber, as well as the solvent propagation from fracture to rock matrices. “Diffusion” experiments were done to analyze the sweep and smoothness of the front (diffusion) interface for different pore sizes, solvent/oil gravity and viscosity ratios, and the boundary effects. The box-counting fractal dimension of the solvent diffusion front in 3-D was applied to compare the progress of the solvent-oil interfaces (mixing process) for different conditions.

Oxazolidinones are known as a new class of antibacterial agents. In our group, the 4,5-disubstituted oxazolidinones have been previously identified to exhibit high affinity for the T box antiterminator transcription system. Among those compounds, ANB-22 and ANB-40 emerged to be the lead compounds showing good RNA-binding activity with high affinity and specificity. In medicinal chemistry, conformationally constraining molecules is a useful approach to optimize the biological activities of drug candidates. This dissertation is devoted to the design and synthesis of the fused tricyclic piperazine analogues and tricyclic triazole analogues of ANB-22 and ANB-40. By employing the aziridine-ring-opening and the Mitsunobu reaction, a series of fused bicyclic and tricyclic oxazolidinone-piperazine derivatives have been prepared. Using similar synthetic strategy and subsequent structural modification, the fused tricyclic piperazine analogues of ANB-22/40 have been successfully obtained as well.The application of the intramolecular azide-alkyne cycloaddition leads to the tricyclic fused oxazolidinone-piperazine-triazole compounds. After appropriate structural modification, the triazole analogues of ANB-22/40 have been prepared. By hydrolyzing the oxazolidinone ring of these tricyclic compounds, a series of amino alcohol derivatives with a piperazine-triazole framework have been generated. In addition, the use of copper catalyst allows the occurrence of the intermolecular cycloaddition to afford four macrocyclic dimers.

Type-3 long QT syndrome, which is related to type 5 voltage-gated sodium channel alpha subunit (SCN5A) mutation, has been identified since 1995. LQTS mutation in SCN5A is a gain-of-function mutation producing late sodium current, INa,L. Brugada mutation in SCN5A is a loss-of-function causing INa decrease. Whereas, the mechanism for Dilated Cardiomyopathy mutations in SCN5A is still not fully understood. N1325S is one of the first series of mutations identified for type-3 LQTS. Our lab created a mouse model for LQTS by expressing SCN5A mutation N1325S in the mouse hearts (TG-NS) and a matched experimental control line with overexpression of wild- type SCN5A (TG-WT). There are some interesting findings in TG-NS mice: (i) Intracellular sodium (Na+) level is higher in TG-NS myocytes compared with TG-WT myocytes. (ii) Ca2+ handling is abnormal in TG-NS myocytes, but not in TG-WT myocytes. (iii) Apoptosis was also found in TG-NS mouse heart tissue, but not in TG-WT hearts. These results provoke the hypothesis that gain-of-function mutation N1325S in SCN5A leads to LQTS through abnormal cytosolic Ca2+ homeostasis. Another LQTS mutation in SCN5A R1193Q was identified in 2004 and the electrophysiological property is similar to other gain-of-function SCN5A mutations. The transgenic mouse model for this mutation was also established and the surface Electrocardiogram (ECG) results indicate longer corrected QT interval also present in transgenic mice carrying R1193Q mutation. Besides, quinidine, an anti-arrhythmic medication, can cause arrhythmic symptoms such as premature ventricular contraction (PVC), premature atrial contraction (PAC) and atrioventricular (AV) block in R1193Q transgenic mice.In order to further study the relationship between abnormal Ca2+ handling and the type of SCN5A mutation, either gain-of-function or loss-of-function, we have chosen HL-1 cells, a cell line with indefinite passages in culture with all the adult cardiac phenotypes. The similar abnormal Ca2+ handling was also identified in HL-1 cells expressing N1325S mutation but not in those cells expressing wild-type SCN5A gene. Since we hypothesized that the abnormal Ca2+ handling is caused by INa,L created by gain-of-function mutation, either in HL-1 cells or in isolated TG-NS myocytes, I then use INa,L blocker ranolazine, a clinical trial medication for LQT patients, to specifically block INa,L. After the blockage of INa,L, the abnormal Ca2+ handling was rescued in both isolated myocytes from TG-NS mice and HL-1 cells expressing N1325S mutation. Finally, several different types of SCN5A mutations related to different types of heart diseases were selected and the Ca2+ handling was tested in transfected HL-1 cells.

Solar magnetic fields, produced in the interor and extending all the way into the interplanetary space, connect the Sun with the terrestrial environment. They manifest themselves on the solar surface over a wide range of scales, from ubiquitous ephemeral regions to active regions. The appearance of kilo-Gauss magnetic flux bundles on the photosphere is observationally well studied. However the physical processes that produce observed magnetic structures are yet to be well-understood, due to the lack of information below the solar surface. To illustrate the physics of sub-surface magnetic fields, we carry out numerical simulations of the emergence of magnetic flux ropes from the convection zone through the photosphere and into the corona. The spatial scale of our simulations varies from ephemeral regions to active regions. This study of the formation of magnetic structures shows the importance of the interaction of rising magnetic fields and turbulent convective motions: 1) The first simulation addresses the emergence of a flux rope and formation of an ephemeral region in a shallow convection zone. 2) In another simulation, a flux rope buoyantly rises from 10 Mm below the photosphere, interacts with convective cells of varying scales and forms a small active region with a pair of sunspots. At the beginning, vertical motion dominates the energy transport into the corona when the flux first passes through the photosphere. After that, horizontal motions, i.e., shearing, separation of dipoles, and rotation of polarities take over the energy transport, while vertical motion transports energy back into the convection zone. Strong shearing motions draw the magnetic field parallel to the polarity inversion line. Tether-cutting reconnection transfers the magnetic shear into the corona. Together, with the rotation of sunspots, these processes produce and transport free magnetic energy, up to 8x10^30 ergs, into the corona, providing the energy necessary for solar eruptive events.

A heavy rigid body attached to a relatively light deformable body is a common structure in many mechanical systems. The longterm dynamics of this structure characterize the true features of the structure response to external excitation. Therefore, the study of the longterm dynamics can provide fundamental guidance for engineering design and production. In this thesis, we study a system that describes the longitudinal motion of a light viscoelastic rod carrying a heavy particle. We discretize this problem by replacing the rod with K light particles connected by massless springs. The discretized problem is governed by a system of coupled nonlinear ordinary differential equations. We let ε denote the ratio of the mass of one of the light particles to the mass of the end particle. This introduces a small parameter into the problem. We use a singular perturbation approach developed by O’Malley and Hoppensteadt to analyze this problem. We focus on the K = 1 case; and the longterm dynamics of this case is reduced to the longterm dynamics of the order 1 outer problem based on the matching condition and the approximate solutions. The longterm dynamics of the order 1 outer problem depends on whether the restoring force f of the springs in the discretized system is monotone or non-monotone. For monotone restoring forces, we show that there exists an invariant manifold 퓜 that attracts all solutions to a system equivalent to the order 1 outer problem. The dynamics on 퓜 is governed by a classical second-order ordinary differential equation. We also illustrate that the longterm dynamics on 퓜 determines the longterm dynamics of the order 1 outer problem. For non-monotone restoring forces, the invariant manifold 퓜 still exists. We show by considering a specific example that 퓜 fails to attract all solutions.

In recent years, the rapid development of Internet has significantly changed people’s life style and the way business is conducted, even though accompanied by fear and sceptism. Research on developing new business models regarding to innov ative use of the Internet has become very important to justify the value of Internet. In addition, designing new pricing mechanisms tailored for the new Internet access infrastructures also has great values in supporting and expanding the Internet usage. This dissertation contains three essays exploring those issues. In the first essay, an emerging Internet Business the “Prediction Market” is described and examined. The market is specifically designed for collecting dispersed information from a wide variety of agents. In order to achieve efficient information elicitation and aggregation, the agents are characterized according to their information pre cision and the cost to induce their information. The optimal selection rules are characterized and a betting mechanism which can implement this selection rule is proposed and analyzed. The second essay proposes a new pricing mechanism for price discrimination under demand uncertainty, which can be applied to allocating Internet access. An option framework is used so that users with higher valuation for Internet usage can purchase options beforehand, which gives them the right to exercise the option and get guaranteed demand execution. Such an option framework has three advantages compared with previous congestion pricing mechanism. First, it helps reveal the customers valuation information ex ante and hence allows the service provider to conduct price discrimination. Second, it improves allocation efficiency when capacity is tight. Lastly, it provides customers’ demand information ex ante to facilitate the service provider’s capacity investment decision. Thethirdessayexaminesthepricingissueforanemergingnetworkinfrastruc ture – the Wireless Mesh Network. In such a network, every user can become a router themselves and hence they have the right to decide whether and how much traffic they pass to their neighbors. The overall network quality is highly sensitive to where the users are located and whether they have incentive to share their device capacity with their neighbors. The profitability of a decentralized linear pricing scheme is analyzed under such a network infrastructure. The efficiency loss due to the individual users’ pricing power is estimated and comparison is conducted with traditional Wi-Max infrastructure.

Hepatitis E virus (HEV), the causative agent of hepatitis E, is an important public health concern in many developing countries. It mainly infects young adults and has a mortality of up to 25% in pregnant women. Although hepatitis E is only sporadic in industrialized countries including the United States, a relative high seroprevalence rate has been reported in healthy individuals. Evidence suggests that there exist animal reservoirs for HEV and HEV transmission is zoonotic. Animal strains of HEV, swine HEV and avian HEV have been identified from a pig and a chicken, respectively, in the United States. Studies showed that swine HEV and avian HEV are genetically and antigenically related to human HEV, and that pigs and chickens are useful animal models to study HEV replication, pathogenesis and cross-species infection. The objectives of this dissertation were to genetically characterize both avian HEV and swine HEV, to determine their serological and molecular epidemiology in the United States, to assess the ability of avian HEV cross-species infection in non-human primates, to determine the full-length genomic sequence and genome organization, and to construct an infectious cDNA clone of avian HEV. The prevalence of swine HEV infections in US swine herds and the heterogeneity of swine HEV isolates from different geographic regions of the United States were determined. We found that 35% pigs and 54% swine herds were positive for swine HEV RNA. Partial capsid gene region of twenty-seven US swine HEV isolates was sequenced and was showed to share 88%-100% nucleotide sequence identity to each other and 89-98% identity with the prototype US swine HEV, but only

Vertical pointing Doppler radar profilers are used to explore the vertical structure of precipitation cloud systems and to provide validation information for use in weather research. In this thesis, a theoretical radar rain-backscatter model was developed to simulate profiler Doppler spectra as a function of assumed rain parameters, of which the raindrop size distribution (DSD) is the fundamental quantity used to describe the characteristics of rain. Also, profiler observations during stratiform rain are analyzed to retrieve the corresponding rain DSD’s. In particular, a gamma distribution model is introduced, which uses Rayleigh scattering portion of the Doppler velocity spectrum to estimate the raindrop size distribution. This theoretical scattering model was validated by simulating atmospheric profiles of precipitation Doppler spectra and three moments (reflectivity, mean Doppler velocity and spectral width) and then comparing these with the corresponding measurements from an S-band radar profiler during a NASA conducted Tropical Rainfall Measuring Mission (TRMM) field experiment in Central Florida in 1998. Also, the results of my analysis yielding precipitation retrievals are validated with an independent, simultaneous Joss-Waldvogel Disdrometer rain DSD observations that were collocated with the radar profiler.

We report measurements of branching fractions for charged and neutral B -> ncK decays, and determine the nc mass and width. The results are based on an analysis of 29.1 fb-1 of data collected by the Belle detector at KEKB. We also report a measurement of the CP violation parameter sin(2ø1) in B0 -> ncK0s based on an analysis of a 78 fb-1 of data sample. We report the observation of the decay mode B± -> ppK± based on an analysis of 29.4 fb -1 of data collected by the Belle detector at KEKB. This is the first example of a b -> s transition with baryons in the final state. The pp mass spectrum in this decay is inconsistent with phase space and is peaked at low mass. The branching fraction for this decay is measured to be B(B± -> ppK±) = (4.7(stat+1.1 -0.9) ± 0.5(syst)) x 10-6. We also report upper limits for the decays B0 -> ppKs and B± -> ppπ±.