A/B testing refers to the statistical procedure of experimental design and analysis to compare two treatments, A and B, applied to different testing subjects. It is widely used by technology companies such as Facebook, LinkedIn, and Netflix, to compare different algorithms, web-designs, and other online products and services. The subjects participating in these online A/B testing experiments are users who are connected in different scales of social networks. Two connected subjects are similar in terms of their social behaviors, education and financial background, and other demographic aspects. Hence, it is only natural to assume that their reactions to online products and services are related to their network adjacency. In this research, we propose to use the conditional autoregressive model (CAR) to present the network structure and include the network effects in the estimation and inference of the treatment effect. The following statistical designs are presented: D-optimal design for network A/B testing, a re-randomization experimental design approach for network A/B testing and covariate-assisted Bayesian sequential design for network A/B testing. The effectiveness of the proposed methods are shown through numerical results with synthetic networks and real social networks.

In a single capture-recapture problem, two new Wilson methods for interval estimation of population size are derived. Classical Chapman interval, Wilson and Wilson-cc intervals are examined and compared in terms of their expected interval width and exact coverage properties in two models. The new approach performs better than the Chapman in each model. Bayesian analysis also gives a different way to estimate population size.

Worldwide globalization has made supply chains more vulnerable to risk factors, increasing the associated costs of outsourcing goods. Outsourcing is highly beneficial for any company that values building upon its core competencies, but the emergence of the COVID-19 pandemic and other crises have exposed significant vulnerabilities within supply chains. These disruptions forced a shift in the production of goods from outsourcing to domestic methods. This paper considers a multi-echelon supply chain model with global and domestic raw material suppliers, manufacturing plants, warehouses, and markets. All levels within the supply chain network are evaluated from a holistic perspective, calculating a total cost for all levels with embedded risk. We formulate the problem as a mixed-integer linear model programmed in Excel Solver linear to solve smaller optimization problems. Then, we create a Tabu Search algorithm that solves problems of any size. Excel Solver considers three small-scale supply chain networks of varying sizes, one of which maximizes the decision variables the software can handle. In comparison, the Tabu Search program, programmed in Python, solves an additional ten larger-scaled supply chain networks. Tabu Search’s capabilities illustrate its scalability and replicability. A quadratic multi-regression analysis interprets the input parameters (iterations, neighbors, and tabu list size) associated with total supply chain cost and run time. The analysis shows iterations and neighbors to minimize total supply chain cost, while the interaction between iterations x neighbors increases the run time exponentially. Therefore, increasing the number of iterations and neighbors will increase run time but provide a more optimal result for total supply chain cost. Tabu Search’s input parameters should be set high in almost every practical case to achieve the most optimal result. This work is the first to incorporate risk and outsourcing into a multi-echelon supply chain, solved using an exact (Excel Solver) and metaheuristic (Tabu Search) solution methodology. From a practical case, managers can visualize supply chain networks of any size and variation to estimate the total supply chain cost in a relatively short time. Supply chain managers can identify suppliers and pick specific suppliers based on cost or risk. Lastly, they can adjust for risk according to external or internal risk factors. Future research directions include expanding or simplifying the supply chain network design, considering multiple parts, and considering scrap or defective products. In addition, one could incorporate a multi-product dynamic planning horizon supply chain. Overall, considering a hybrid method combining Tabu Search with genetic algorithms, particle swarm optimization, simulated annealing, CPLEX, GUROBI, or LINGO, could provide better results in a faster computational time.

Newsvendor Models with Monte Carlo Sampling by Ijeoma Winifred Ekwegh The newsvendor model is used in solving inventory problems in which demand is random. In this thesis, we will focus on a method of using Monte Carlo sampling to estimate the order quantity that will either maximizes revenue or minimizes cost given that demand is uncertain. Given data, the Monte Carlo approach will be used in sampling data over scenarios and also estimating the probability density function. A bootstrapping process yields an empirical distribution for the order quantity that will maximize the expected profit. Finally, this method will be used on a newsvendor example to show that it works in maximizing profit.

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

The determinants of health care use among rural, low-income mothers and their children were assessed using a multi-state, longitudinal data set, Rural Families Speak. The results indicate that rural mothers’ decisions regarding health care utilization for themselves and for their child can be best modeled using a simultaneous systems approach to negative binomial regression. Mothers’ visits to a health care provider increased with higher self-assessed depression scores, increased number of child’s doctor visits, greater numbers of total children in the household, greater numbers of chronic conditions, need for prenatal or post-partum care, development of a new medical condition, and having health insurance (Medicaid/equivalent and HMO/private). Child’s visits to a health care provider, on the other hand, increased with greater numbers of chronic conditions, development of a new medical condition, and increased mothers’ visits to a doctor. Child’s utilization of pediatric health care services decreased with higher levels of maternal depression, greater numbers of total children in the household, if the mother had HMO/private health care coverage, if the mother was pregnant, and if the mother was Latina/African American. Mother’s use of health care services decreased with her age, increased number of child’s chronic conditions, income as a percent of the federal poverty line, and if child had HMO/private health care insurance. The study expands the econometric techniques available for assessing maternal and pediatric health care use and the results contribute to an understanding of how rural, low-income mothers choose the level of health care services use for themselves and for their child. Additionally, the results would assist in formulating policies to reorient the type of health care services provided to this vulnerable population.

View John Huân Vũ's thesis presentation at http://youtu.be/y3bzNmkTr-c. In 2001, the ACM and IEEE Computing Curriculum stated that it was necessary to address "the need to develop implementation models that are international in scope and could be practiced in universities around the world." With increasing connectivity through the internet, the move towards a global economy and growing use of technology places software internationalization as a more important concern for developers. However, there has been a "clear shortage in terms of numbers of trained persons applying for entry-level positions" in this area. Eric Brechner, Director of Microsoft Development Training, suggested five new courses to add to the computer science curriculum due to the growing "gap between what college graduates in any field are taught and what they need to know to work in industry." He concludes that "globalization and accessibility should be part of any course of introductory programming," stating: A course on globalization and accessibility is long overdue on college campuses. It is embarrassing to take graduates from a college with a diverse student population and have to teach them how to write software for a diverse set of customers. This should be part of introductory software development. Anything less is insulting to students, their family, and the peoples of the world. There is very little research into how the subject of software internationalization should be taught to meet the major requirements of the industry. The research question of the thesis is thus, "Is there a framework for software internationalization that has been validated against industry requirements?" The answer is no. The framework "would promote communication between academia and industry ... that could serve as a common reference point in discussions." Since no such framework for software internationalization currently exists, one will be developed here. The contribution of this thesis includes a provisional framework to prepare graduates to internationalize software and a validation of the framework against industry requirements. The requirement of this framework is to provide a portable and standardized set of requirements for computer science and software engineering programs to teach future graduates.