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The goal of this research was to analyze the academic impact of the implementation of the Value Added Assessment Model. The researcher analyzed the Value Added Assessment Models in the three Value Added Assessment Model states that had implemented the Value Added Assessment Model for more than five years. Additionally, the research was done by analyzing the academic impact as measured by the eighth grade reading NAEP and the eighth grade mathematics NAEP. The researcher paired the three states that had implemented Value Added Assessment Model for more than five years, with three demographically matched states that had not implemented Value Added Assessment Model. The states were matched as follows: Ohio (Value Added Assessment Model implementing state) with Michigan (non Value Added Assessment Model state), Pennsylvania (Value Added Assessment Model implementing state) with Virginia (non Value Added Assessment Model state) and Tennessee (Value Added Assessment Model implementing state) with Georgia (non Value Added Assessment Model state). The mean composite scale score in NAEP from the following categories of students were compared and analyzed: 1) All students 2) White students 3) Black students 4) National School Lunch Program Eligible Students 5) National School Lunch Program Ineligible Students 6) Exceptional Education students. The results of the study indicated that the impact of Value Added Assessment Model on academic impact as measured by the eighth grade reading NAEP and the eighth grade mathematics NAEP was negligible.

In search of a better investment metric, researchers began to study Economic Value Added, or EVA, which was introduced in 1991 by Stern Stewart & Co in their book, "The Quest for Value" (Turvey, 2000). Stern Stewart & Co devised EVA as a better alternative to evaluate investment projects within the corporate finance field, later to be considered for use as a performance metric for investor use. A wide array of multinational corporations, such as Coca-Cola, Briggs and Stratton, and AT&T adopted the EVA method, which led to EVA's worldwide acclaim. Several points in the study reveal that EVA does not offer less risk, higher returns, and more adaptability for an investor. In fact, EVA underperformed the traditional portfolio performance metrics in key measurements including mean returns, and confidence intervals. EVA is a difficult performance metric to calculate, with several complex components that can be calculated in several different ways such as NOPAT, cost of equity, and cost of debt. Any information that is inaccurate or lacking can significantly impact the outcomes. Traditional performance metrics, on the other hand, such as ROA, ROE, and E/P are simple to calculate with few components, and only one way to calculate them.

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One method of estimating returns to agricultural research as an alternative to the commonly used aggregate output production function approach is to calculate returns based on estimates from a value-added production function. Value added is the value of consumed inputs in the production process subtracted from the value of total production. The value-added function alleviates several problems found in the aggregate production function, allowing for more reliable estimates. One objective of this study is to compare the rates of return to research based on estimates from the value added and gross production functions. The estimation of the research coefficients are conducted through the use of the Ordinary Least Squares and Ridge Regression of a Cobb-Douglas production function. Four commodity groups are examined; cash grains, livestock, poultry, and dairy using cross-sectional 1978 data for the United States. The results of the analysis show that in most cases the research variable was more significant and therefore explained the dependent variable better in the value-added functions than in the gross production functions. A larger internal rate of return to research in the value-added function for models excluding a spillover of research variable suggests an underinvestment in research dollars oriented towards improving value added. Therefore, future research should concentrate on improving the efficiency of fixed and non-consumed inputs used in the production of most commodity groups in general. Also, results from both the value added and production functions show an underinvestment of research in the cash grain commodity group relative to the other three commodity groups in this study. More research should be conducted in the area of improving the efficiency of all inputs used in the cash grain production process. / Master of Science

Currently, 17.1% of 2-19 year olds are obese. While obesity is a multifactorial disease, energy imbalance is commonly cited as a primary etiology. Excess consumption of added sugar (AS) from corn and cane sweeteners has been implicated as a leading contributor to weight gain in youth and adults. Children and adolescents are among the highest consumers of AS, which account for 16% of their total daily calories (~318 calories/d), which is above American Heart Association, World Health Organization, and Dietary Guidelines for Americans recommendations. Although a strong temporal relationship has been established between weight gain and increased consumption of corn and cane sweeteners, a causal relationship is difficult to determine due to the inherent limitations of self-report dietary assessments (i.e., measurement errors such as underreporting). Further, obtaining accurate dietary intake data from children and adolescents is challenging due to the high dietary variability observed in this population. To overcome the limitations of self-report dietary assessments, the Institute of Medicine has recognized the need to develop and validate objective biomarkers of dietary intake.One such biomarker is the delta (δ) 13C biomarker; preliminary studies suggest that the δ13C biomarker is a valid, objective indicator of AS intake in adults and holds promise for children and adolescents. Establishing δ13C as a valid, reliable and sensitive means for assessing habitual AS intake in children and adolescents provides valuable objective dietary information with the potential to address a pressing public health concern, which is the relationship between AS intake and health. / Master of Science

Around 46.2 million Americans living in rural areas are disproportionately burdened by health disparities. Likewise, obesity and obesity-associated diseases (e.g., diabetes, cardiovascular disease) are much higher for rural residents when compared to their urban counterparts. There is a high need to understand and address the nutritional determinants of these health inequities among adults and adolescents. One area of concern in rural dietary habits pertains to added sugars and more specifically, sugar-sweetened beverages (SSB). Excessive added sugars and SSB intake have been strongly linked to many of the nutrition and chronic disease disparities impacting rural residents. Moreover, studies conducted in rural populations have found high consumptions of these in both adults and adolescents. There is an opportunity to better understand added sugars and SSB patterns in rural populations to inform the development of culturally relevant, multi-level interventions that address high consumption. Study #1 is a cross-sectional study that explores top food and beverage sources of added sugars in the diet of adults (n = 301) living in rural areas of Southwest Virginia. Study #2 uses a nationally representative sample of adolescents (n = 1,560) from the Family Life, Activity, Sun, Health and Eating (FLASHE) study sponsored by the National Cancer Institute, to explore factors across the levels of the socioecological model associated with adolescent SSB intake. Study #3 utilizes focus groups and a pilot trial to understand language preferences, acceptability and use of SMS aimed at caregivers to reduce SSB intake in both caregivers and adolescents living in rural areas of Southwest Virginia (n = 33). Collectively, these three studies offer recommendations and culturally relevant strategies for future large-scale trials aimed at reducing SSB intake among adolescents and caregivers in rural communities and ultimately reducing rural health disparities. / Doctor of Philosophy / Rural populations in the United States are at higher risk for being diagnosed with and dying from preventable and obesity-associated diseases like heart disease and cancer. Excessive added sugars and sugary drink (i.e. sodas, sweet tea/coffee, energy drinks, sweetened fruit drinks, sports drinks) intake have been strongly linked to many of the chronic diseases afflicting rural residents. Moreover, studies conducted in rural populations have found high consumptions of these, in both adults and adolescents. There is a great need to better understand added sugars and sugary drink patterns in rural populations so that we can develop programs to reduce consumption that are also culturally well received. Study #1 in this dissertation explores top food and beverage sources of added sugars in the diet of 301 adults living in rural areas of Southwest Virginia. Study #2 uses a nationally representative sample of 1,560 adolescents to explain why adolescent SSB intake might be higher. Study #3 aims to understand language preferences, acceptability and use of a text message program to reduce sugary drink intake in both caregivers and adolescents living in rural areas of Southwest Virginia. Collectively, these three studies offer recommendations and culturally relevant strategies for future large scale trials aimed at reducing sugary drink intake among adolescents and caregivers in rural communities and ultimately improving rural health.

Abstract Since the launch and hot market of innovative products such as iPod, iPhone and iPad, it has created a PND operational interface revolutionary wave. The beloved keyboard, mouse or Passive Touch Panel are gradually replaced by Capacitive Touch Panel(CTP) which became one of the hottest trends in consumer electronic products. No matter in transmittance, hardness, response time, operating temperature resistance, UV resistance or beauty of outlook, CTP products surpass traditional Passive Touch Panel. The market of CTP products is so huge but there is big difference of respective company¡¦s performance. Some companies earn 2 or 3 times of company capital per year but some companies still are at a net loss stage. It¡¦s worth to study why there is a significant difference of their business performance. Further, it is beneficial to study the topic of what are the key successful factors of these outstanding companies. By selecting six representitive samples of CTP companies to research, we will study the development of this industry, the evolution of product technology and the difference of business performance, especially focusing on their sales scale, future growth rate, customer potential growth, ROE, ROA, EPS, EVA and MVA to analyze and compare. We confirm TPK Holding Co., Ltd. and Young Fast Optoelectronics Co., Ltd. are leaders among Taiwan CTP industry. We will study the key successful factors of this industry through interviews with senior management and researchers of this industry. The conclusion of this study is as follows, 1. Glass-type CTP will be the mainstream of CTP product structure, especially in the consumer electronic products such as smartphone and tablet computers. 2. On Glass Solution, Flexible Subtrate and In Cell technology will be the future development of CTP products. 3. If compared with other financial indicators, Economic Value Added (EVA) will fully evaluate and predict the corporate value. 4. There are 7 key successful factors of CTP panels: I. Innovative ability of advanced products and production process technology. II. Emphasize the patent layout to lead the advanced technology. III. Capacity and yield rate. IV. Main customers¡¦ ability to create market. V. Rapid and complete technology supported ability. VI. Provide value added service and products which surpassed other competitors. VII. Establish huge human resource and sound financial structure. Key words¡GCapacitive Touch Panel(CTP)¡BBusiness Performance¡BEconomic Value Added (EVA)¡BMarket Value Added (EVA)¡BKey Successful Factors(KSF)