This thesis was conducted to study and analyze the fund allocation process adopted by different states in the United States to reduce the impact of the Covid-19 virus. Seven different states and their funding methodologies were compared against the case count within the state. The study also focused on development of a physical distancing index based on three significant attributes. This index was then compared to the expenditure and case counts to support decision making.
A regression model was developed to analyze and compare how different states case counts played out against the regression model and the risk index.

In an increasingly global economy, companies face challenges with implementing successful business and marketing strategies in cultures different from their own. This paper calls upon previous research to compile a per-country outline of general behaviors and expectations when doing business overseas. Using categorical definitions from Hofstede's 1984 study and those found in the Handbook of Global and Multicultural Negotiation, a table has been prepared to group similar countries based on their cultural biases.

Perfection is extremely difficult to achieve when playing team sports. This is especially true for lacrosse, a sport where dropped passes, missed shots and turnovers are prevalent even at the college and professional levels of the game. In order to improve on mistakes, teams must first recognize where the errors are being made. The purpose of this project is to implement the DMAIC process improvement method into lacrosse, with the goal of identifying and implementing improvements, leading to a more successful team.
In order to use DMAIC, lacrosse was expressed as a process that included five phases: offense, defense, riding, clearing and faceoffs. Data was gathered for each phase using game film from the Arizona State Men’s Club Lacrosse Team over the course of the 2019 and 2020 seasons. The data was then analyzed by comparing the output statistics of each phase to the goal differential, number of goals scored, and number of goals against. Once the areas of improvement were determined, additional analysis was done to determine why these certain areas needed improvement. The results provided what changes needed to be made in order to improve the team. In order to ensure the team sustained their success, control measures were put in place to determine what action needs to be taken and when.

The ASU COVID-19 testing lab process was developed to operate as the primary testing site for all ASU staff, students, and specified external individuals. Tests are collected at various collection sites, including a walk-in site at the SDFC and various drive-up sites on campus; analysis is conducted on ASU campus and results are distributed virtually to all patients via the Health Services patient portal. The following is a literature review on past implementations of various process improvement techniques and how they can be applied to the ABCTL testing process to achieve laboratory goals. (abstract)

This Project Report documents the accomplishments of an extraordinary group of students, faculty, and staff at the Arizona state University, who participated in a year-long, multidisciplinary, first-of-its-kind academic endeavor entitled “The Making of a COVID Lab.” The lab that is the focus of this project is the ASU Biodesign Clinical Testing Laboratory, known simply as the ABCTL.

The definition of "beauty" can be interpreted in many ways but when defining it literally, it is considered as such: "a combination of qualities that pleases the intellect or moral sense" (Oxford Dictionaries). Beauty simply "pleases" the intellect; it does not say that intellect is a factor of beauty itself. Beauty is nice to look at, a mere pleasure to experience in the "moral sense." It does not have anything to do with one's actions, principles or intelligence, but instead the way one presents himself or herself. If someone is deemed as intellectual, does that mean they are not viewed as beautiful? Is that why beauty is rarely associated with brains and vice versa? Has a history of stereotypes and media interference convinced us that these two concepts cannot coexist? And what if we found out that they did? Could we take that person seriously? I decided to challenge the idea of beauty and brains, and see if beauty is in fact measured by its literal definition and controlled by its assumed stereotype, or if other factors apply when deciphering someone's "beauty." First, I will analyze the perceptions and stereotypes of engineers. By looking at the public opinion of both engineers and women engineers, I can show the common struggles engineers face. Next I will look at perceptions of cheerleaders, in particular, professional cheerleaders. Through analyzing current stereotypes and gender roles associated with these women in the spotlight, I can establish how opinions of these women are formed. I will also look at a survey of a sample of Arizona State University students in which we can confirm or deny the results found through research of previous studies. We will also be able to gather personal opinions about why these stereotypes exist and how to break them down. Finally, we will look at personal accounts of current or retired National Football League (NFL) cheerleaders. These will give first-hand examples of what it is like to be both a cheerleader and a woman in STEM (science, technology, engineering, and mathematics).

Zero Waste Management at Arizona State University is an initiative that aims to divert 90% of the waste that goes into the landfills. In order to do this, it is important to focus on the biggest generator of waste every year, which is "Food and Catering". One of the biggest challenges facing the food and catering industry is the lack of efficient and standard processes which results in immense waste every year. As a result, this thesis takes a Lean Six Sigma approach into ASU's zero waste event processes and identifies possible gaps that could be improved. It uses the DMAIC methodology to dive into a standard process for requesting and handling a zero waste event at ASU and concentrates on the logistics behind those zero waste events.

In this paper, a literature review is presented on the application of Bayesian networks applied in system reliability analysis. It is shown that Bayesian networks have become a popular modeling framework for system reliability analysis due to the benefits that Bayesian networks have the capability and flexibility to model complex systems, update the probability according to evidences and give a straightforward and compact graphical representation. Research on approaches for Bayesian network learning and inference are summarized. Two groups of models with multistate nodes were developed for scenarios from constant to continuous time to apply and contrast Bayesian networks with classical fault tree method. The expanded model discretized the continuous variables and provided failure related probability distribution over time.

The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value for δ of the call and put option, the %B value of the stock, and the amount of time until expiration of the stock option. The %B value is the most important. The purpose of analyzing the data is to see the relationship between the variables and, given certain values, what is the probability the trade makes money. This result will be used in finding the probability certain trades make money over a period of time.

Since options are so dependent on probability, this research specifically analyzes stock options rather than stocks themselves. Stock options have value like stocks except options are leveraged. The most common model used to calculate the value of an option is the Black-Scholes Model [1]. There are five main variables the Black-Scholes Model uses to calculate the overall value of an option. These variables are θ, δ, γ, v, and ρ. The variable, θ is the rate of change in price of the option due to time decay, δ is the rate of change of the option’s price due to the stock’s changing value, γ is the rate of change of δ, v represents the rate of change of the value of the option in relation to the stock’s volatility, and ρ represents the rate of change in value of the option in relation to the interest rate [2]. In this research, the %B value of the stock is analyzed along with the time until expiration of the option. All options have the same δ. This is due to the fact that all the options analyzed in this experiment are less than two months from expiration and the value of δ reveals how far in or out of the money an option is.

The machine learning technique used to analyze the data and the probability



is support vector machines. Support vector machines analyze data that can be classified in one of two or more groups and attempts to find a pattern in the data to develop a model, which reliably classifies similar, future data into the correct group. This is used to analyze the outcome of stock options.