The North American deer mouse, Peromyscus maniculatus, is the most widespread mammal on the continent. It is of interest to the public health sector as a known host of several viruses which are transmissible to humans and can cause illness, including the acute respiratory disease Hantavirus Pulmonary Syndrome (HPS). This paper seeks to conduct a systematic review of existing literature on virus detection in wild P. maniculatus in order to develop an understanding of virus prevalence and distribution and identify potential areas of further research. Through the analysis of 22 studies published since 2010, it was found that detection effort has been concentrated on the American West with a focus on the spread of Sin Nombre virus, the causative agent of HPS. There are significant gaps in the existing literature both geographically and in regards to the types of viruses being sampled. Moving forward, a systematic approach to widespread sampling should be developed in order to better identify and minimize potential threats to human health.

2018, Google researchers published the BERT (Bidirectional Encoder Representations from Transformers) model, which has since served as a starting point for hundreds of NLP (Natural Language Processing) related experiments and other derivative models. BERT was trained on masked-language modelling (sentence prediction) but its capabilities extend to more common NLP tasks, such as language inference and text classification. Naralytics is a company that seeks to use natural language in order to be able to categorize users who create text into multiple categories – which is a modified version of classification. However, the text that Naralytics seeks to pull from exceed the maximum token length of 512 tokens that BERT supports – so this report discusses the research towards multiple BERT derivatives that seek to address this problem – and then implements a solution that addresses the multiple concerns that are attached to this kind of model.

The growth of fintech companies in developing countries has led to increased levels of economic development and financial inclusion. This thesis explores the reasons for the success of these companies, with a focus on the impact they have on the local economy and their ability to provide financial services to underserved populations. The intent of this thesis is to educate the reader on the overall landscape of financial technology companies in developing countries. The writing will examine the specific types of services offered by these fintech companies that operate in developing countries and the catalysts that make them successful. It will also cover the impact that these companies have on the nations they operate in by looking at contributions to overall economic development and financial inclusion. The results of this literature will have implications for business leaders, policymakers, and investors interested in promoting financial inclusion and economic development through fintech.

Many would contend that the United States healthcare system should be moving towards a state of health equity. Here, every individual is not disadvantaged from achieving their true health potential. However, a variety of barriers currently exist that restrict individuals across the country from attaining equitable health outcomes; one of these is the social determinants of health (SDOH). The SDOH are non-medical factors that influence the health outcomes of an individual such as air pollution, food insecurity, and transportation accessibility. Each of these factors can influence the critical illnesses and health outcomes of individuals and, in turn, diminish the level of health equity in affected areas. Further, the SDOH have a strong correlation with lower levels of health outcomes such as life expectancy, physical health, and mental health. Despite having influenced the United States health care system for decades, the industry has only begun to address its influences within the past few years. Through exploration between the associations of the SDOH and health outcomes, programming and policy-making can begin to address the barrier to health equity that the SDOH create.

Water markets are a promising method for adapting to water scarcity in the western United States, and the Colorado-Big Thompson Project (CBT) market is often held up as a prime example of their potential. While much has been written about the CBT market, the current academic literature tends to eschew structural modeling of supply and demand in favor of fitting hedonic price equations, which ignore many of the market’s unique characteristics. This paper proposes a model of supply and demand for CBT water which accounts for these unique features, including transaction supply, municipality stockpiling, and differences in behavior across different types of water users. The estimation of this model is made possible by novel administrative records data on both transfers and ownership of CBT water, the processing and features of which are described in detail. While the voluminous and messy nature of the data has prevented complete estimation of the model at this point, some preliminary results are presented along with a plan for future work.

As the need for data intelligence becomes more pervasive, companies are facing a shortage of professionals with sufficient analytical skills. It is more important than ever for students in traditional business management programs to understand relevant issues in data analytics. The paper, a teaching case, is split into two parts: the business case and the teaching note. The case addresses the challenges of an electric battery company, Capsule Inc., in adopting new analytics technology. The teaching note provides relevant management concepts and technology adoption framework to analyze the case in a class setting. The case can be used to introduce students to the challenges of implementing automation software in an office, as it focuses on key issues such as process improvement, technology adoption, and cost-reduction.