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Like other scientists, hydrologists encode mathematical formulations that simulate various hydrologic processes as computer programs so that problems with water resource management that would otherwise be manually intractable can be solved efficiently. These computer models are typically developed to answer specific questions within a specific study domain. For example, one computer model may be developed to solve for magnitudes of water flow and water levels in an aquifer while another may be developed to solve for magnitudes of water flow through a water distribution network of pipes and reservoirs. Interactions between different processes are often ignored or are approximated using overly simplistic assumptions. The increasing complexity of the water resources challenges society faces, including stresses from variable climate and land use change, means that some of these models need to be stitched together so that these challenges are not evaluated myopically from the perspective of a single research discipline or study domain. The research in this dissertation presents an investigation of the various approaches and technologies that can be used to support model integration. The research delves into some of the computational challenges associated with model integration and suggests approaches for dealing with these challenges. Finally, it advances new software that provides data structures that water resources modelers are more accustomed to and allows them to take advantage of advanced computing resources for efficient simulations.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18255号 / 工博第3847号 / 新制||工||1590(附属図書館) / 31113 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 准教授 宇野 伸宏, 准教授 松島 格也 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

<p>  </p> <p>Ebola virus (EBOV) infection remains to be a challenge to human health by its high mortality rate. Though it has been discovered for almost 50 years, there are only two antibody-based therapies approved today, and the mortality rate is still greater than 30% with the treatment. Authentic EBOV studies are strictly limited to biosafety level-4 (BSL-4) labs, which slows the development of treatment. While more simple and safer systems have been developed to understand different stages of EBOV infection, such as the matrix protein (VP40) virus-like particle (VLP) and minigenome systems, we still lack a systematical view of EBOV infection. On the other hand, mathematical modeling has been used to assist biological and medical studies for many years, as it has the advantage of integrating data and providing quantitative insight to a biosystem. In our study, we took advantage of mathematical modeling and build the primary ordinary differential equation-based (ODE-based) model of EBOV at subcellular level step by step. We built the budding pathway of EBOV VP40 first, calibrated and validated our model with experimental data. We proposed that phosphatidylserine (PS) can directly influence the stability of VP40 filaments and the budding process of VLPs. Also, the oligomerization of VP40 filaments may follow the nucleation-elongation process. Next, we conducted in-silico simulation to evaluate the treatment efficiency of fendiline, a drug lowering cell membrane PS level, in treating EBOV. We found that while in general, fendiline can decrease VLP production, there can be fendiline-induced VLP production increases at certain time points due to slow filament growth or fast VLP budding rates. Also, we concluded that fendiline is relatively more effective when applied in the budding stage of EBOV life cycle. Moreover, fendiline efficacy may increase when applied with a VLP budding step targeted treatment. Finally, we integrated nucleoprotein (NP) into our model. We reproduced the two-stage interaction between NP and VP40 and predict that NP increases VLP production through influencing filament oligomerization and VLP budding steps. Also, the dual-effect of NP on VLP production may exist, as a too high NP/VP40 production ratio can decrease VLP production. From the aspect of protein expression time, we found that a bit earlier NP production than VP40 production is beneficial for both inclusion body-containing (IB-containing) VLP production and prevention in energy waste on production of VLPs without IBs. Overall, we have built a solid foundation towards a mathematical EBOV model and demonstrated the value of models in assisting experimental EBOV studies.</p>

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Quorum sensing (QS) is a type of microbial communication used by bacteria to coordinate their behavior based on population density, regulating complex processes like biofilm formation and virulence, among other behaviors. Quorum quenching (QQ), on the other hand, disrupts this communication, usually by degradation of the QS signaling molecule. QQ offers a potential strategy for controlling bacterial behaviors linked to pathogenicity and biofouling. Despite significant advances in understanding and modeling the spatial-temporal behavior of QS, predictive modeling of QQ remains nascent, with a notable gap in the quantitative assessment of QQ's impact on QS. Here we show quantitative evaluation and characterization of the effect of QQ on QS in agar-based experiments, combined with an experimentally validated computational model. This research utilizes green fluorescence in E. coli MG 1655 as an indicator of QS activation, focusing on the degradation of Acyl-Homoserine Lactone (AHL), a key QS molecule in Gram-negative bacteria linked to pathogenicity, by the AiiA enzyme in engineered AiiA-producing Salmonella Typhimurium 14028. Our findings suggest that QQ more effectively influences QS in spatial configurations of the populations with larger interaction surfaces and shorter diffusion distances. Contrary to our initially held hypothesis, the primary effect of QQ is not a delay in QS onset but rather an attenuation of QS activity, with the area-under-the-curve of fluorescence serving as a quantitative metric. This study also introduces, to the best of our knowledge, one of the first instances of experimentally validated predictive modeling for QQ, applied to agar-based experimental setups. We posit that the quantitative experimental characterization and modeling framework presented in this research will enhance the understanding of bacterial community interactions. Enhanced comprehension of QQ and QS behaviors holds significant promise for advancing practical applications, particularly in mitigating or diminishing undesirable QS-associated activities. This is especially relevant in areas like biofouling, waste treatment, and the reduction of infections and progression of diseases in plants and animals, areas increasingly important as concerns about drug resistance in microbes and food security escalates. / Master of Science / One of the ways bacteria communicate with each other is called quorum sensing (QS), where they use chemical signals to organize and time group behavior, including forming communities encapsulated in protective layers, called biofilms, and engaging in virulent attacks against hosts. Quorum quenching (QQ) in bacteria, however, disrupts this communication system, usually by breaking down the chemical signals that bacteria use to send messages to each other. Even though QS has been studied extensively, determining how to predict and control QQ is still a nascent area of research. Here, we studied and characterized how QQ affects QS by doing experiments with bacteria populations in agar (a jelly-like substance) and applied a computational model to explain and ultimately predict the experimental observations. Engineered QS population (E. coli MG 1655) produced Acyl-Homoserine Lactone (AHL) signaling molecules, and engineered QQ bacteria (S. Tm 14028) used the Autoinducer Inactivation A (AiiA) enzyme to break down the AHL. According to our results, QQ doesn't delay the QS bacteria's group behaviors (in our case, green fluorescent signal production); it weakens the signal instead. Understanding QQ and QS better, especially through measurements and modeling, could lead to expanded methods of deterring harmful bacterial behavior, managing waste better, and stopping diseases in plants, animals, and humans, especially with the concerning rise of drug-resistant microbes and food security. One exciting possibility is using QQ to protect plants from bacterial infections. This could be a way to shield our crops without always relying on antibiotics.

In this thesis, a physics-based single particle modeling is presented to analyze a proposed direct coupled hybrid energy storage modules using lithium-ion battery and ultracapacitor. Firstly, a state of the art for the energy storage system in the electrified vehicles are summarized. Several energy storage elements including lead-acid battery, nickel-metal hydride battery, lithium-ion battery, ultracapacitor, and lithium-ion capacitor are reviewed. Requirements of the energy storage systems in electric, hybrid electric, and plug-in hybrid electric vehicles are generalized. Typical hybrid energy storage system topologies are also reviewed. Moreover, these energy storage elements and hybrid energy storage system topologies are compared to the requirements of the energy storage systems in terms of specific power and specific energy. Secondly, the performance of different battery balancing topologies, including line shunting, ring shunting, synchronous flyback, multi-winding, and dissipative shunting are analyzed based on a linear programming methodology. As a traction battery in an electric or plug-in electric vehicle, high voltage lithium-ion packs are typically configured in a modular fashion, therefore, the analysis considers the balancing topologies at module level and cell level and focuses on minimum balancing time, minimum plug-in charge time, minimum energy loss, and component counts of every balancing topology for the entire battery pack. Thirdly, different modeling techniques for the lithium-ion battery and ultracapacitor are presented. One of the main contributions of this thesis is the development of a physics-based single particle modeling embedded with a solid-electrolyte interface growth model for a lithium-ion battery in battery management system. This development considers the numerical solution of diffusion equation, cell level quantities, parametrization method, effects of number of shells in a spherical particle, SOC-SOH estimation algorithms, and aging effects. The accuracy of the modeling is validated by experimental results of a Panasonic NCR18650A lithium-ion battery cell. Fourthly, the physics-based modeling is applied to analyze the performance of a proposed direct coupled hybrid energy storage module topology based on the Panasonic NCR18650A lithium-ion battery and Maxwell BCAP0350 ultracapacitor. There are many ways to directly connect battery cells and ultracapacitor cells in a module which would influence the performance of the module. The results show that a module has 9 cells in a battery string and 14 cells in an ultracapacitor string can obtain the highest power capability and utilize the most of the energy in an ultracapacitor. More ultracapacitor strings connected in parallel would increase the power density but reduce the energy density. Moreover, the simulation and experimental results indicate that the direct coupled hybrid modules can extend the operating range and slow the capacity fade of lithium-ion battery. An SOC-SOH estimation algorithm for the hybrid module is also developed based on the physics-based modeling. Finally, a pack design methodology is proposed to meet U.S. Advanced Battery Consortium LLC PHEV-40, power-assist, and 48V HEV performance targets for the battery packs or the proposed direct coupled topologies. In order to explore replacement tradeoffs between the battery and ultracapacitor, a case study of the direct coupled topologies is presented. From the case study, ultracapacitors enhance the power capability for short term pulse power and marginally reduce the cost of an entire energy storage system. Moreover, the hybrid module topologies can keep a relatively long all-electric range when the batteries degrade. / Dissertation / Doctor of Philosophy (PhD)

To evaluate voting rules, it would be good to know what universe election outcomes are drawn from. Election theorists have postulated that elections might be drawn from various stochastic preference models, including the IC and IAC conditions, but these models induce empirically contradicted predictions. We use two distinct data sets, FairVote and German Politbarometer survey. Based on the data information, we suggest approaches that differ from those probabilistic models to better approximate the actual data in Chapter 3 and 4. Chapter 5 applies the spatial model for four-candidate in a three-dimensional setting. We also offer a significant gap between the actual and simulated data under the IAC conditions by comparing their statistical characteristics. / Doctor of Philosophy / Through the 1884 Third Reform Act, the plurality rule (or first-past-the-post system) runs to elect parliament members for the first time. More than a hundred years passed after the Act, and election theorists have suggested various alternatives, the plurality rule is the second most used rule worldwide for national elections for now. One main reason is that researchers do not reach an agreement on the best alternative rule. Theorists have evaluated different voting rules under probabilistic assumptions, but real-world examples contradict the predictions of these models. In this dissertation, we suggest different approaches provide a better approximation to the actual data. In Chapter 3 and 4, we go backward: analyze how voters of each preference order are distributed in real data first, then set a model for estimating the frequency of paradox. In chapter 5, we extend an existing model with higher dimensionality. Then using the model, we offer empirical evidence showing the gap between the actual and simulated data under a popular probabilistic model.

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