acase@tulane.edu / Chlamydia trachomatis (Ct) is the most common bacterial sexually transmitted infection (STI) in the United States and is major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. Despite decades of screening women for Ct, rates continue to increase among them in high prevalent areas such as New Orleans. A pilot study in New Orleans found approximately 11% of 14-24 year old of African Americans (AAs) were infected with Ct. Our goal is to mathematically model the impact of different interventions for AA men resident in New Orleans on the general rate of Ct among women resident at the same region. We create and analyze mathematical models such as multi-risk and continuous-risk compartmental models and agent-based network model to first help understand the spread of Ct and second evaluate and estimate behavioral and biomedical interventions including condom-use, screening, partner notification, social friend notification, and rescreening. Our compartmental models predict the Ct prevalence is a function of the number of partners for a person, and quantify how this distribution changes as a function of condom-use. We also observe that although increased Ct screening and rescreening, and treating partners of infected people will reduce the prevalence, these mitigations alone are not sufficient to control the epidemic. A combination of both sexual partner and social friend notification is needed to mitigate Ct. / 1 / Asma Aziz Boroojeni

As technology advances, many practical applications require human-controlled robots. For such applications, it is useful to determine the optimal number of robots an operator should control to maximize human efficiency given different situations. One way to achieve this is through computer simulations of team performance. In order to factor in various parameters that may affect team performance, an agent-based model will be used. Agent-based modeling is a computational method that enables a researcher to create, analyze, and experiment with models composed of agents that interact within an environment [12]. We construct an agent-based model of humans interacting with robots, and explore how team performance relates to different agent parameters and team organizational structures [21]. Prior work describes interaction between a single operator and multiple robots, while this work includes multi-operator performance and coordination. Model parameters include neglect time, interaction time, operator slack time, level of robot autonomy, etc. Understanding the parameters that influence team performance will be a step towards finding ways to maximize performance in real life human-robot systems.

archives@tulane.edu / The patellar tendon transmits loads from the quadriceps to the tibia promoting locomotion. The main etiological factor behind patellar tendinopathies is thought to be excessive loading and unloading during athletic activity (Pearson & Hussain, 2014). The extracellular matrix (ECM) composition and fibroblast-like tenocytes dictate tendon’s uniaxial mechanical properties (Kannus, 2000). Following injury, a flood of inflammatory cells and spike in certain gene expressions work together to remove damaged tissue, trigger fibroblast proliferation, and deposit a provisional collagen matrix (Thomopoulos et al., 2015). Despite these processes, healed tendons demonstrate significant functional deficits (Mienaltowski et al., 2016). Moreover decrease in cell migration and fiber alignment with age further hampers healing outcomes(Dunkman et al., 2013). Efforts to restore tendon function are impeded by a lack of understanding of the early healing process, which may be age- and sex-dependent (Fryhofer et al., 2016; Mienaltowski et al., 2016). The tendon healing process can be further understood using an agent-based model (ABM). ABMs simulate individual agents and the interactions between them and their environment. This approach has the advantage of building complexity from the ground up, mimicking the underlying tendon physiology (Conte & Paolucci, 2014). Therefore, the objectives of this study were to 1) formulate a literature based ABM of murine patellar tendon healing with varying initial conditions to recapitulate changes observed with aging, and 2) Conduct simulations to determine whether ABM recapitulated salient features of healing, and to make predictions about healing outcomes. / 1 / Jordan Robinson

The world continues to face outbreaks of disease due to natural causes as well as the threat of biological warfare. Mathematical modeling provides an avenue by which to predict and ultimately prevent widespread outbreaks. A wide variety of modeling tools have been used in the study of the spread of diseases, including Ordinary Differential Equations, Partial Differential Equations, and Difference Equations. In this study, an agent-based model is used to study the spread and control of epidemics and is based on Sirakoulis, et al. [1]. The computer program NetLogo [2] is used for simulation. The development and set-up procedures for this model are fully discussed. The model is used to study the effectiveness of vaccination and quarantine as methods of epidemic control. It is determined that the most effective means of controlling an epidemic is to quarantine individuals with symptoms. In addition, the effect of the adjacent contact coefficient in the model is examined and further development and uses of the model are discussed.

This research complements and combines market microstructure theory and mechanism design to optimize the market structure of financial markets systematically. We develop an agent-based model featuring near-zero-intelligence traders operating in a call market with a wide range of trading rules governing the determination of prices, which orders are executed as well as a range of parameters regarding market intervention by market makers and the presence of informed traders. The market structure which generates the best market performance is determined by applying the search technique Population-based Incremental Learning, guided by a number of performance measures, including maximizing trading volume or price, minimizing bid-ask spread or return volatility. We investigate the credibility of our model by observing the trading behavior of near-zero-intelligence traders with stylized facts in real markets. Based on computer simulations, we conform that the model is capable to reproduce some of the most important stylized facts found in financial markets. Thereafter, we investigate the best found market structure using both single-objective optimization and multi-objective optimization techniques. Our results suggest that the best-found combination of trading rules used to enhance trading volume may not be applied to achieve other objectives, such as reducing bid-ask spread. The results of single-objective optimization experiments show that significantly large tick sizes appear in the best market structures in most cases, except for the case of maximizing trading volume. The tick size is always correlated with the selection of multi-price rules. Though there is no particular combination of priority rule and multiprice rule achieving the best market performance, the time priority rule and the closest multi-price rule are the most frequently obtained rules. The level of market transparency and the extend of market maker intervention show ambiguous results as their representative parameter values change in a wide range. We also nd that the results of multi-objective optimization experiments are much similar to those obtained in the single-objective optimization experiments, except for the market transparency represented by the fraction of informed trader, which shows a clear trend in the multi-objective optimization. Using the results obtained from this research we can derive recommendations for exchanges and regulators on establishing the optimal market structure; for securities issuers on choosing the best exchange for their listing; and for investors on choosing the most suitable exchange for trading.

Nowadays, facility managers and related staffs are facing with much maintenance requests every day. The more complicated building system generates the more diverse and complex maintenance issues. With the limited budget and staff, not all the maintenance requests can be solved immediately. To schedule the maintenance work, facility managers first consider the impact of requested problem on system failure and life safety. Besides these two factors, the author proposed the importance of considering the energy efficiency and occupant satisfaction based on the former research for sustainability. This paper firstly tries to quantify the occupant satisfaction for normal daily maintenance requests which will provide the facility managers with suggestions on work prioritization. For a long time, it is a difficult task to quantify the occupant satisfaction, even though there are enough researches concerning the people satisfaction. In this research, author first designed a structured questionnaire including normal maintenance issues and they are measured by different factors such as thermal impact, acoustic impact, and so on. Then based on the classical disconfirmation theory, a framework was built to prioritize numerous works based on occupant satisfaction. For energy efficiency, due to the limitation of collecting real measured data, this paper referred the work from Lawrance Lab. They conducted the research to simulate the daily HVAC faults to quantify the energy impact through EnergyPlus, which provided the data of energy increase for some daily HVAC faults. An agent based model is proposed to both consider these two factors. Simulation was used to verify the framework and the result showed that the total satisfaction level and energy efficiency can be increased by 30% and 97% respectively.

Many problems facing policymakers are complex and cannot be understood by reducing them to their component parts. However, many of the policy responses to complex problems continue to be based on simple, reductionist methods. Agent-based modeling (ABM) is one alternative method for informing policy that is well-suited to analyzing complex problems. ABM has practical implications for different stages of the policy process, such as testing alternatives, assisting with evaluation by setting up a counterfactual, and agenda setting. The objective of the research presented in this dissertation is to explore the opportunity for using ABM to examine complex problems of relevance for policy. To do so, three separate models were developed to investigate different aspects of food safety inspection systems. Complex problems involve interrelated feedback loops, many actors, exponential growth, asymmetric information, and uncertainty in outcomes and data, and food safety exhibits these traits, providing an interesting case study for the use of ABM. The first model explores three inspection scenarios incorporating access to information. The main finding was that the number of sick consumers is greatly reduced by giving consumers and inspectors more information about whether a retail outlet is contaminated, even if that information may be uncertain. The second model incorporated theories on risk and the role of transparency in encouraging consumer trust by giving consumers access to inspection scores. Overall, the findings were more nuanced: having access to restaurant inspection scores results in a slightly higher mean number of sick consumers, but less variation overall in the number of sick consumers. As well, a greater number of compliant restaurants results in fewer sick consumers. Rather than investigating the structure of the inspection system, the third model examines the potential for mobile technology to crowdsource information about suspected foodborne illness. This model illustrates the potential for health-oriented mobile technologies to improve the surveillance system for foodborne illness. Overall, the findings from the three models support using stylized ABMs to study various aspects of food safety inspection systems, and show that these models can be used to generate insight for policy choices and evidence-based decision making in this area.

Context. Agent based models are used to simulate complex systems by using multiple agents that follow a set of rules. One such model is the boid model which is used to simulate movements of synchronized groups of animals. Executing agent based models partially or fully on the GPU has previously shown to increase performance, opening up the possibility for larger simulations. However, few articles have previously compared a full GPU implementation of the boid model with a multi-threaded CPU implementation. Objectives. The objectives of this thesis are to find how parallel execution of boid model performs when executed on the CPU and GPU respectively, based on the variables frames per second and average boid computation time per frame. Methods. A performance benchmark experiment will be set up where three implementations of the boid model are implemented and tested. Results. The collected data is summarized in both tables and graphs, showing the result of the experiment for frames per second and average boid computation time per frame. Additionally, the average results are summarized in two tables. Conclusions. For the largest flock size the GPGPU implementation performs the best with an average FPS of 42 times over the single-core implementation while the multi-core implementation performs with an average FPS 6 times better than the single-core implementation. For the smallest flock size the single-core implementation is most efficient while the GPGPU implementation has 1.6 times slower average update time and the multi-cor eimplementation has an average update time of 11 times slower compared to the single-core implementation.

Adverse post-natural disaster outcomes in low-income regions, like elevated internal migration levels and low consumption levels, are the result of market failures, poor mechanisms for stabilizing income, and missing insurance markets, which force the affected population to respond, and adapt to the shock they face. In a spatial environment, with multiple locations with independent but interconnected markets, these transitions quickly become complex and highly non-linear due to the feedback loops between the micro individual-level decisions and the meso location-wise market decisions. To capture these continuously evolving micro-meso interactions, this paper presents a spatially explicit bottom-up agent-based model to analyze natural disaster-like shocks to low-income regions. The aim of the model is to temporally and spatially track how population distributions, income, and consumption levels evolve, in order to identify low-income workers that are "food insecure". The model is applied to the 2005 earthquake in northern Pakistan, which faced catastrophic losses and high levels of displacement in a short time span, and with market disruptions, resulted in high levels of food insecurity. The model is calibrated to pre-crisis trends, and shocked using distance-based output and labor loss functions to replicate the earthquake impact. Model results show, how various factors like existing income and saving levels, distance from the fault line, and connectivity to other locations, can give insights into the spatial and temporal emergence of vulnerabilities. The simulation framework presented here, leaps beyond existing modeling efforts, which usually deals with macro long-term loss estimates, and allows policy makers to come up with informed short-term policies in an environment where data is non-existent, policy response is time dependent, and resources are limited.

京都大学 / 新制・課程博士 / 博士(エネルギー科学) / 甲第23294号 / エネ博第419号 / 新制||エネ||79(附属図書館) / 京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻 / (主査)教授 手塚 哲央, 教授 宇根﨑 博信, 准教授 MCLELLAN Benjamin / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DFAM