Land Grant Application- Rose, Benjamin (Hartland)

Land grant application submitted to the Maine Land Office for Benjamin Rose for service in the Revolutionary War.https://digitalmaine.com/revolutionary_war_me_land_office/1777/thumbnail.jp

Automated Identification of Unhealthy Drinking Using Routinely Collected Data: A Machine Learning Approach

Background: Unhealthy drinking is prevalent in the United States and can lead to serious health and social consequences, yet it is under-diagnosed and under-treated. Identifying unhealthy drinkers can be time-consuming for primary care providers. An automated tool for identification would allow attention to be focused on patients most likely to need care and therefore increase efficiency and effectiveness. Objectives: To build a clinical prediction tool for unhealthy drinking based solely on routinely collected demographic and laboratory data. Methods: We obtained demographic and laboratory data on 89,325 adults seen at the University of Vermont Medical Center from 2011-2017. Logistic regression, support vector machines (SVM), k-nearest neighbor, and random forests were each used to build clinical prediction models. The model with the largest area under the receiver operator curve (AUC) was selected. Results: SVM with polynomials of degree 3 produced the largest AUC. The most influential predictors were alkaline phosphatase, gender, glucose, and serum bicarbonate. The optimum operating point had sensitivity 31.1%, specificity 91.2%, positive predictive value 50.4%, and negative predictive value 82.1%. Application of the tool increased the prevalence of unhealthy drinking from 18.3% to 32.4%, while reducing the target population by 22%. Limitations: Universal screening was not used during the time data was collected. The prevalence of unhealthy drinking among those screened was 60% suggesting the AUDIT-C was administered to confirm rather than screen for unhealthy drinking. Conclusion: An automated tool, using commonly available data, can identify a subset of patients who appear to warrant clinical attention for unhealthy drinking

Optimal Spacecraft Guidance

This book is designed for a one-semester course at Utah State University titled MAE 6570 Optimal Spacecraft Guidance. The class meets for 75 minutes, twice per week, for 14 weeks. There are no prerequisites other than graduate standing in engineering. Proficiency in calculus, differential equations, linear algebra, and computer programming is required. Students find that previous experience in space dynamics, linear multivariable control, or optimal control is helpful. The goal of the book and course is for students to develop fundamental skills needed to do professional work in the area of spacecraft guidance. After working through the book, students should have an understanding of the linear quadratic framework, E-guidance, Q-guidance, Apollo descent guidance, and more. To this end, the book contains seven chapters. An approximate timeline for the course is the following. • Chapter 1 | Week 1 • Chapter 2 | Weeks 2 and 3 • Chapter 3 | Weeks 4 and 5 • Chapter 4 | Weeks 6, 7, and 8 • Chapter 5 | Weeks 9 and 10 • Chapter 6 | Weeks 11 and 12 • Chapter 7 | Weeks 13 and 14 Three dynamical models are used throughout to illustrate the concepts. These models are a nonlinear two-body model, a linear flat planet model, and a linear relative orbital motion model. A key feature of the book is its integration of MATLAB implementations into the text as early as possible. For example, Chapter 1 includes a Q-guidance implementation, Chapter 2 includes a polynomial guidance implementation, and so on. Each chapter ends with a set of problems suitable for independent homework. Several of the chapter problems require modification or extension of these implementations. The final two chapters focus on descent guidance and ascent guidance. By this point, students are expected to be coding independently

Bioefuels from Microalgae: A Study of Growth Conditions in Ettlia oleoabundans

Biofuels derived from algae can provide a source of renewable fuel and facilitate the break from fossil fuel dependency. Algae produce triacylglycerols (TAGs), which can be cost effectively converted into biofuel. Light intensity and temperature affect growth and oil production of algae. Ettlia oleoabundans, an algal species that produces \u3e50% of its weight as TAGs, grew and produced oils at 15 and 25 176C and light intensities under 200 μmol m-2 sec-1, conditions common to New England. Optimizing growth conditions will help in the development of efficient and economical biofuel production

Relationship between China’s Economic Growth and South Africa’s Exports to China

The rapid growth of China’s economy has increased China’s demand for imports required to meet the increased demand for raw materials in its industries. In 2006, China became the largest export destination for South Africa. This paper seeks to examine the relationship between China’s economic growth and South Africa’s exports to China. The paper aims to find out whether there is any significant relationship between China’s economic growth and South Africa’s exports to China. Using an import demand function for China, we employ an ARDL framework to analyze the research problem based on quarterly data covering the period 1992 to 2015. We find no evidence that there is a significant relationship between China’s economic growth and South Africa’s exports to China. However, the results do show that there is a positive long-run relationship between South Africa’s exports to China and the price of South Africa’s exports. The positive coefficient of South Africa’s export prices indicates lack of price sensitivity in terms of South Africa’s exports to China. Based on the results, we recommend that South Africa should find ways to enable its exports sector to benefit more from the high economic growth in China. Among other ways, this could be achieved through negotiating a free trade agreement (FTA) between South Africa and China