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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.

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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Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results showed how multi-vehicle interaction enhances usability of the system. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. This testing was able to demonstrate model corrections required to negate the effect of system latencies as well as a demonstration of using SAE message set parsing in a connected vehicle application. Finally, the dissertation develops an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm. The algorithm integrated within an existing framework that minimizes vehicle delay while ensuring vehicles do not collide. A lower-level of control is introduced that minimizes vehicle fuel consumption subject to the arrival times assigned by the upper-level controller. Results show that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining the 80 percent saving in delay when compared to a traditional signalized intersection control. / Ph. D.

<p dir="ltr">During many biological processes, cells move through their surrounding environment by exerting mechanical forces. The mechanical forces are mainly generated by molecular interactions between actin filaments (F-actins) and myosin motors within the actin cytoskeleton. Forces are transmitted to the surrounding extracellular matrix via adhesions. In this thesis, we employed agent-based computational models to study the interactions between F-actins and myosin in the motility assay and the cell migration process. In the first project, the myosin motility assay was employed to study the collective behaviors of F-actins. Unlike most of the previous computational models, we explicitly represent myosin motors. By running simulations under various conditions, we showed how the length, bending stiffness, and concentration affect the collective behavior of F-actins. We found that four distinct structures formed: homogeneous networks, flocks, bands, and rings. In addition, we showed that mobile motors lead to the formation of distinct F-actin clusters that were not observed with immobile motors. In the second project, we developed a 3D migration model to define how cells mechanically interact with their 3D environment during migration. Unlike cells migrating on a surface, cells within 3D extracellular matrix (ECM) must remodel the ECM and/or squeeze their body through the ECM, which causes 3D cell migration to be significantly more challenging than 2D migration. Our model describes realistic structural and rheological properties of ECM, cell protrusion, and focal adhesions between cells and the ECM.</p>

The impact of engineered products is a topic of increasing concern in society. The impact of a product can fall into the categories of economic, environmental, or social impact; the last category is defined as the effect of a product on the daily lives of people. Design teams lack sufficient tools to help improve the impact of products and understand the impact of products at scale in society. This dissertation aims to provide insight and methods for improving the social, environmental, and economic impact of engineered products. The majority of the research focuses on the prediction of product impacts on society, which requires a sociotechnical approach with models that contain aspects of the product and society. This begins with the introduction of an agent-based modeling approach to predict how changes to a design will ultimately impact society. Chapter 3 performs a systematic review of the literature to identify common challenges in product social impact modeling, identifies ways to mitigate the challenges, and provides a general process to create product impact models. Guidance on a general modeling process is essential to enable the widespread use of predictive impact models in engineering design. Chapter 4, provides guidance on creating sociotechnical models using primary survey data and machine learning for impact prediction using a case study of improved cookstoves in Uganda. Chapter 5 presents a method for incorporating environmental impacts, using life cycle assessment and agent-based modeling to properly scale impacts from the functional unit level to the societal level. A limitation of life cycle assessment in the early phases of product design is the difficulty of scaling impacts from the functional unit level to the population level. Using agent-based modeling together with life cycle assessment enables an understanding of the number of functional units required at the population level; allowing for the quantification of the total population-level impact. There are often trade-offs in the social, environmental, and economic sustainability space. To characterize these sustainability trade-offs, Chapter 6 illustrates the modeling of social, environmental, and economic impacts of a product and how to quantify the product sustainability trade-space. Chapter 7, presents work on identifying quantitative factors for selecting engineering global development project locations based on the potential for social impact. Finally, Chapter 8 provides the general contributions of this work, identifies limitations, and provides direction for future work. The research presented in this dissertation is a step toward a future where predictive modeling of the social, environmental, and economic impacts of products is commonplace in engineering design.

Web 2.0時代強調由使用者貢獻內容，並藉由使用者的互動來創造群體智慧的價值。社會性書籤網站統合散佈在各處的網路資訊（尤其是由使用者所產生的部落格文章），承接內容的生產及閱讀，是網路內容價值鏈樞紐；另一方面，從媒體的角度來看，書籤網站可視為是web 2.0下的公民新聞守門人（引路人），以公民取代專業編輯，提供了一個完全不一樣的公民媒體運作方式。本研究針對社會性書籤網站中的內容評價推薦機制，探討其群體智慧運作情形：參考動物群體行為的運作原則，加上文獻的整理及實際案例的觀察，建構出社會性書籤網站推薦機制的模擬運作架構；並透過代理人模擬方法，來找出影響網站群體智慧運作的原則，及相關屬性設定對運作結果的影響。研究結果發現，社會性書籤網站的運作成效，可以分為篩選效果及文章更新效率，兩者之間具有魚與熊掌不可兼得的特性，並可藉由不同的閱讀策略安排來調整。基於web 2.0的特性，使用者同時扮演服務的生產者與消費者。因此，使用者閱讀文章時的閱讀策略安排，可視為是群體智慧運作中的工作分配策略。而群體智慧的運作原則中，正回饋效應可以提升篩選效果，判斷獨立性可以提升文章的更新效率，抑制與負回饋則可以使系統較為穩定。本研究除了為web 2.0網站的群體智慧經營提供具體的參考方針，多重代理人模擬的方法也可做為往後web 2.0相關研究及網站經營時的工具。 / The core spirit for web 2.0 is the contribution of users, and the creation of value through the interaction between users. Social book marking sites integrate all kind of contents on the Internet (especially those generated by users), and play the role of pivot between content production and consumption. From the aspect of media, social bookmarking site can be regarded as news gatekeeper (or gateway) in the web 2.0 era. This study focuses on the rating and recommendation mechanism of social bookmarking sites, trying to find out the effects of collective wisdom with regard to different operations. The principle of collective animal behavior and the existing operations of some social bookmarking sites are first surveyed. Then, an operational model of social bookmarking sites and its recommendation mechanism is built and used for subsequent simulation. / The research findings show that the performance of social bookmarking sites has a tradeoff between sifting effect and efficiency, and that the performance can be controlled through a job allocation strategy. The operation of 「positive feedback」in collective wisdom can lead to sifting effect, 「integrity and variability」 leads to efficiency, and 「negative feedback」, 「inhibition」 lead to system stability. This research is believed to provide some managerial guidelines for web 2.0 sites operation.

A comprehensive literature review was undertaken in order to define and assess the sustainability and resiliency characteristics associated with grass-based and confinement dairy farming. Primarily as a result of reduced input costs, grass-based dairy farming often enhances profitability over confinement systems, especially on small farms. Further, conversion of tilled soil to permanent pasture has been shown to significantly reduce harmful sediment and nutrient transport into waterways. Perennial forage also acts as a carbon sink, curtailing or even negating a grass-based farm's carbon footprint. Finally, social benefits derived from enhanced nutrition and higher quality of life are also associated with grass-based dairy farming. Given that policy goals of the State of Vermont include both bolstering farm viability and reducing farm-related runoff, two questions are then raised. What is the most effective way to incentivize the adoption of rotational grazing in Vermont? And what types of farms are best suited to its use? A series of interviews with dairy experts and farmers was conducted as a preliminary investigation into these questions. This qualitative evidence suggested that farmers generally adopted grass-based dairying after observing a peer's success with the method, suggesting that a key leverage point may be peer-based learning. A behavioral economics game was developed to evaluate the role of peer networks in facilitating decision-making under conditions of uncertainty. A computerized game platform simulated networks of small dairy farm enterprises, with participants acting as farm managers. Treatments varied the size of peer networks, as well as the inclusion of a perfectly-performing automated 'seed player.' Participants could base their decisions upon the successes of their peers. They received a cash incentive based on their farms' performance. Results indicated that players with higher numbers of peers made better economic decisions on average. The inclusion of a 'seed player' within a network, which modeled the ideal behavior, also facilitated better decision-making. Both of these correlations were statistically significant. Furthermore, the shape of the 'diffusion curve' of new adoptees confirmed literature on the dynamics of innovation diffusion. Public policy implications from this work include an increased focus on facilitating peer-to-peer learning among farmers where Best Management Practice adoption is a policy goal. To further evaluate the potential for peer learning to facilitate positive change, the Dairy Farm Transitions Agent Based Model (DFTABM) was developed. The model was calibrated using existing datasets along with the qualitative and quantitative results described above. It forecasts effects on farm profitability, attrition, and soil loss arising from varying assumptions about peer network connectivity, peer emulation, macroeconomic trends, and agri-environmental policy. Nine experimental treatments were assessed. Overall, it was found that high rates of emulation coupled with high rates of connectivity'especially targeted connectivity among smaller farms'yielded the best balance of farm viability and reduction in soil loss. The establishment of a performance-based tax credit had no clear correlation with the resulting soil loss figures predicted by the model. Policy implications from this study include the finding that direct payment schemes for reduction in environmental harm may not always have their intended effects, whereas policies that enhance peer-to-peer learning opportunities, especially among the proprietors of smaller farms, may present an effective and relatively affordable means by which to bolster farm profitability while also reducing environmental degradation.