In this paper, a novel model of Hotelling duopoly is introduced that explains horizontal product variety as the result of consumer preferences, expanding on and meshing the works of Hotelling (1929) and Neven (1985). From this model, two opposing forces from consumer preferences are found that impact the variety and price decisions of firms: market share revenues and price revenues. As firms face consumers with highly linear (weak) preferences over variety, the profit incentive is to simply capture the market by offering products that appeal to the middle consumer. However, as firms face consumers with highly quadratic (strong) preferences over variety, the profit incentive is to carve out and exploit a market segment by offering a distinct variety. Thus, observed product variety between minimal and maximal differentiation is emergent from consumer preferences, as firms face a balance of price and market share incentives.

This thesis details a Python-based software designed to calculate the Jones polynomial, a vital mathematical tool from Knot Theory used for characterizing the topological and geometrical complexity of curves in 3-space, which is essential in understanding physical systems of filaments, including the behavior of polymers and biopolymers. The Jones polynomial serves as a topological invariant capable of distinguishing between different knot structures. This capability is fundamental to characterizing the architecture of molecular chains, such as proteins and DNA. Traditional computational methods for deriving the Jones polynomial have been limited by closure-schemes and high execu- tion costs, which can be impractical for complex structures like those that appear in real life. This software implements methods that significantly reduce calculation times, allowing for more efficient and practical applications in the study of biological poly- mers. It utilizes a divide-and-conquer approach combined with parallel computing and applies recursive Reidemeister moves to optimize the computation, transitioning from an exponential to a near-linear runtime for specific configurations. This thesis provides an overview of the software’s functions, detailed performance evaluations using protein structures as test cases, and a discussion of the implications for future research and potential algorithmic improvements.

In the realm of supply chain management, student organizations play a crucial role in shaping the future leaders of the field. The Supply Chain Management Association at ASU (SCMA at ASU) stands as a testament to the impact such organizations can have. Recognized as a powerhouse within the highly competitive landscape of student-run organizations at the W.P. Carey School of Business, SCMA at ASU not only facilitates networking opportunities but also serves as a channel for industry insights, professional growth, and the development of an engaged student community. The purpose of this guidebook is to distill the collective experience of SCMA at ASU into a comprehensive resource that can guide and inspire the establishment, maintenance, and growth of student-led supply chain organizations at universities nationwide. To create the most informative guidebook possible, this resource will not only draw upon the rich experiences and achievements of SCMA at ASU but also incorporate insights and information from supply chain student organizations across various universities. This inclusion ensures a diverse range of successful strategies, innovative practices, and practical advice, reflecting what has worked best for these organizations in different academic and operational contexts. By pooling knowledge from a broad spectrum of successful SCM student organizations, this guidebook aims to serve as an essential tool for any university looking to enhance its supply chain program through student-led initiatives, fostering a new generation of supply chain professionals equipped to navigate and lead in an ever-changing global landscape.

Recent satellite and remote sensing innovations have led to an eruption in the amount and variety of geospatial ice data available to the public, permitting in-depth study of high-definition ice imagery and digital elevation models (DEMs) for the goal of safe maritime navigation and climate monitoring. Few researchers have investigated texture in optical imagery as a predictive measure of Arctic sea ice thickness due to its cloud pollution, uniformity, and lack of distinct features that make it incompatible with standard feature descriptors. Thus, this paper implements three suitable ice texture metrics on 1640 Arctic sea ice image patches, namely (1) variance pooling, (2) gray-level co-occurrence matrices (GLCMs), and (3) textons, to assess the feasibly of a texture-based ice thickness regression model. Results indicate that of all texture metrics studied, only one GLCM statistic, namely homogeneity, bore any correlation (0.15) to ice freeboard.

Deforestation in the Amazon rainforest has the potential to have devastating effects on ecosystems on both a local and global scale, making it one of the most environmentally threatening phenomena occurring today. In order to minimize deforestation in the Amazon and its consequences, it is helpful to analyze its occurrence using machine learning architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that has shown significant capability in performing semantic segmentation. It is built upon a symmetric series of downsampling and upsampling layers that propagate feature information into higher spatial resolutions, allowing for the precise identification of features on the pixel scale. Such an architecture is well-suited for identifying features in satellite imagery. In this thesis, we construct and train a U-Net to identify deforested areas in satellite imagery of the Amazon through semantic segmentation.

With uses in fields such as medicine, agriculture, and biotechnology, halogenases are useful enzymes in nature which add or substitute halogens onto other molecules. By doing so, they become necessary for biosynthesis and cross-coupling reactions. Halogenases can be classified by three main types of mechanisms: nucleophilic, radical, and electrophilic. From there, they can be further broken down by the halogen involved, the substrate needed, other proteins used, or molecules generated. A notable example is PrnA which is a tryptophan-7 halogenase that falls under the flavin-dependent definition with an electrophilic mechanism. Historically, research on these enzymes was slow until the use of bioinformatics rapidly accelerated discoveries to the point where halogenases like VirX1 can be identified from viruses. By reviewing the literature available on halogenase since their first analysis, a better understanding of their functions can be obtained. Also, with the application of bioinformatics, a phylogenetic analysis on the halogenases present in cyanobacteria can be conducted and compared.

This project involved research into solar thermal and geothermal energy generation as possible solutions to the growing U.S. energy crisis. Background research into this topic revealed the effects of climate and environmental impacts as major variables in determining optimal states. Delving into thermodynamic engineering analyses, the main deliverables of this research were mathematical models to analyze plant efficiency improvements in order to optimize the cost of operating solar thermal and geothermal power plants. The project concludes with possible future research areas relating to this field.

The purpose of this research is to create predictive models for a leading sustainability certification - the B Corporation certification issued by the non-profit company B Lab based on the B Impact Assessment. This certification is one of many that are currently being used to assess sustainability in the corporate world, and this research seeks to understand the relationships between a corporation's characteristics (e.g. market, size, country) and the B Certification. The data used for the analysis comes from a B Lab upload to data.world, providing descriptive information on each company, current certification status, and B Impact Assessment scores. Further data engineering was used to include attributes on publicly traded status and years certified. Comparing Logistic Regression and Random Forest Classification machine learning methods, a predictive model was produced with 87.58% accuracy discerning between certified and de-certified B Corporations.