Substance abuse has become a major problem in the USA in the past decade, with immense public health and societal consequences. Methamphetamine (meth) use has grown due to an increased number of meth production and distribution markets. Border states such as Arizona and California are especially concerned with Mexico’s production and distribution of meth to their residents. A mathematical model for meth use and markets was developed and then analyzed to track multiple types of drug markets and drug-related arrests for possession or distribution. The importance of social influences as a major causal factor in the onset of illicit drug use is explicitly incorporated. The model parameters are then estimated using meth-related data from California and Arizona. A parameter sensitivity analysis on the model output was carried out. The results suggest that law enforcement policy aimed at marketers will be significantly more effective than targeting current users. Moreover, local unorganized markets have a greater role in maintaining the endemic level of meth users. Whereas, global organized markets play a role in initiating meth use outbreaks. Some implications for interventions and health promotion for the two states are also discussed.

The premise of this thesis developed from my personal interests and undergraduate educational experiences in both industrial engineering and design studies, particularly those related to product design. My education has stressed the differences in the ways that engineers and designers approach problem solving and creating solutions, but I am most interested in marrying the two mindsets of designers and engineers to better solve problems creatively and efficiently.
This thesis focuses on the recent appearance of generative design technology into the world of industrial design and engineering as it relates to product development. An introduction to generative design discusses the uses and benefits of this tool for both designers and engineers and also addresses the challenges of this technology. The relevance of generative design to the world of product development is discussed as well as the implications of how this technology will change the roles of designers and engineers, and especially their traditional design processes. The remainder of this paper is divided into two elements. The first serves as documentation of my own exploration of using generative design software to solve a product design challenge and my reflections on the benefits and challenges of using this tool. The second element addresses the need for employing quantitiative methodologies within the generative design process to aid designers in selecting the most advantageous design option when presented with generative outcomes. Both sections aim to provide more context to this new design process and seek to answer questions about some of the ambiguous processes of generative design.

Woodland/Alloy Casting, Inc. is an aluminum foundry known for providing high-quality molds to their customers in industries such as aviation, electrical, defense, and nuclear power. However, as the company has grown larger during the past three years, they have begun to struggle with the on-time delivery of their orders. Woodland prides itself on their high-grade process that includes core processing, the molding process, cleaning process, and heat-treat process. To create each mold, it has to flow through each part of the system flawlessly. Throughout this process, significant bottlenecks occur that limit the number of molds leaving the system. To combat this issue, this project uses a simulation of the foundry to test how best to schedule their work to optimize the use of their resources. Simulation can be an effective tool when testing for improvements in systems where making changes to the physical system is too expensive. ARENA is a simulation tool that allows for manipulation of resources and process while also allowing both random and selected schedules to be run through the foundry’s production process. By using an ARENA simulation to test different scheduling techniques, the risk of missing production runs is minimized during the experimental period so that many different options can be tested to see how they will affect the production line. In this project, several feasible scheduling techniques are compared in simulation to determine which schedules allow for the highest number of molds to be completed.

Rapid advancements in Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are widening the playing field for automated decision assistants in healthcare. The field of radiology offers a unique platform for this technology due to its repetitive work structure, ability to leverage large data sets, and high position for clinical and social impact. Several technologies in cancer screening, such as Computer Aided Detection (CAD), have broken the barrier of research into reality through successful outcomes with patient data (Morton, Whaley, Brandt, & Amrami, 2006; Patel et al, 2018). Technologies, such as the IBM Medical Sieve, are growing excitement with the potential for increased impact through the addition of medical record information ("Medical Sieve Radiology Grand Challenge", 2018). As the capabilities of automation increase and become a part of expert-decision-making jobs, however, the careful consideration of its integration into human systems is often overlooked. This paper aims to identify how healthcare professionals and system engineers implementing and interacting with automated decision-making aids in Radiology should take bureaucratic, legal, professional, and political accountability concerns into consideration. This Accountability Framework is modeled after Romzek and Dubnick’s (1987) public administration framework and expanded on through an analysis of literature on accountability definitions and examples in military, healthcare, and research sectors. A cohesive understanding of this framework and the human concerns it raises helps drive the questions that, if fully addressed, create the potential for a successful integration and adoption of AI in radiology and ultimately the care environment.

Advancements in healthcare and the emergence of an aging population has led to an increase in the number of prosthetic joint procedures in the United States. According to Healthcare Cost and Utilization Project, 660,876 and 348,970 total hip and knee arthroplasties were performed in 2014[1].The percentage of total hip or knee procedures that are revised due to an infection is 1.23% and 1.21% respectively[3], [4]. Although the percent of infections may be small, an infection can have a tremendous burden on the patient and healthcare system. It is expected that prosthetic joint infections (PJIs) will cost the healthcare system an estimated $1.62 billion by 2020[5]. PJIs are often difficult to treat due to the formation of biofilm at the site of the infection. A large majority of PJIs are the result of a bacterial biofilm, but around 1% of PJIs are due to fungal infections[3]. The current method of treatment is to surgically remove all infected tissue at the site of infection through a process called debridement and then insert a medicated bone cement spacer[7], [10]–[12]. One such medication that is loaded into the bone cement is caspofungin, a member of the echinocandin class of compounds that inhibit the synthesis of 1,3-β-D-glucan which is a crucial element of the cell wall of the target fungi[13]–[15]. For the studies reported herein, the caspofungin-loaded bone cement samples were made at 5 dosage strengths according to standard operating room practices. The elution of the drug was analyzed using ultraviolet spectrophotometry. The elution profiles were analyzed for 19 days consecutively, during which the 70 mg, 1 g, and 5 g dosage groups showed a prolonged, sustained release of the caspofungin. The 70 mg and 1 g dosage cumulative mass release profiles were not statistically significant, but it is unlikely that the difference would not have a clinical significance especially in the treatment of a fungal biofilm infection. The determination of the elution profile for caspofungin from loaded-bone cement can provide clinicians with a basis for how the drug will release into the infected joint.

With a rapidly decreasing amount of resources for construction, wood and bamboo have been suggested as renewable materials for increased use in the future to attain sustainability. Through a literature review, bamboo and wood growth, manufacturing and structural attributes were compared and then scored in a weighted matrix to determine the option that shows the higher rate of sustainability. In regards to the growth phase, which includes water usage, land usage, growth time, bamboo and wood showed similar characteristics overall, with wood scoring 1.11% higher than bamboo. Manufacturing, which captures the extraction and milling processes, is experiencing use of wood at levels four times those of bamboo, as bamboo production has not reached the efficiency of wood within the United States. Structural use proved to display bamboo’s power, as it scored 30% higher than wood. Overall, bamboo received a score 15% greater than that of wood, identifying this fast growing plant as the comparatively more sustainable construction material.

The listing price of residential rental real estate is dependent upon property specific attributes. These attributes involve data that can be tabulated as categorical and continuous predictors. The forecasting model presented in this paper is developed using publicly available, property specific information sourced from the Zillow and Trulia online real estate databases. The following fifteen predictors were tracked for forty-eight rental listings in the 85281 area code: housing type, square footage, number of baths, number of bedrooms, distance to Arizona State University’s Tempe Campus, crime level of the neighborhood, median age range of the neighborhood population, percentage of the neighborhood population that is married, median year of construction of the neighborhood, percentage of the population commuting longer than thirty minutes, percentage of neighborhood homes occupied by renters, percentage of the population commuting by transit, and the number of restaurants, grocery stores, and nightlife within a one mile radius of the property. Through regression analysis, the significant predictors of the listing price of a rental property in the 85281 area code were discerned. These predictors were used to form a forecasting model. This forecasting model explains 75.5% of the variation in listing prices of residential rental real estate in the 85281 area code.

This study aims to explore the prevalence of smartphone, smartwatch, and fitness tracker ownership among college students, and compare the popularity of each device in tracking health-related habits such as physical activity, eating, and sleep. In addition, this study aims to analyze the effectiveness of each device for achieving personal health goals in all three categories. Research for this study was conducted using an Institutional Review Board (IRB) approved survey that was distributed electronically to various Greek and student organizations around Arizona State University campuses. In total, 183 responses were considered, with participants ranging from ages 18 to 23. Participants were required to own or possess a smartphone to be eligible to complete the survey. After seven days of data collection, the results were then analyzed using Qualtrics. The results revealed that smartwatch and fitness tracker ownership is not prevalent within the Arizona State University demographic. In addition, after comparing device popularity across each habit-tracking category, it is apparent that the smartphone is the most used device for tracking. Finally, when looking at device effectiveness in relation to achieving health goals, smartwatches consistently scored higher than smartphones. Supplemental research should be conducted to further explore the prevalence and effectiveness of habit tracking. This research should include a larger sample size and a more evenly spread gender demographic.

At Arizona State University, the Disability Resource Center provides disabled students transportation around campus. This transportation service known as DART is composed of approximately 20 student workers and 9 carts that pick riders up based on pre-determined pick-up times and locations. With the current system, the scheduling of drivers to riders is inefficient, and in response, a tool was developed to schedule the rides in a faster manner. A demonstration of the new tool resulted in a time reduction of 98%.