Many leaders of nonprofit organizations (NPOs) in the United States do not have plans to adopt cloud computing. However, the factors accounting for their decisions is not known. This correlational study used the extended unified theory of acceptance and use of technology (UTAUT2) to examine whether performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can predict behavioral intention (BI) and use behavior (UB) of NPO information technology (IT) managers towards adopting cloud computing within the Phoenix metropolitan area of Arizona of the U.S. An existing UTAUT2 survey instrument was used with a sample of IT managers (N = 106) from NPOs. A multiple regression analysis confirmed a positive statistically significant relationship between predictors and the dependent variables of BI and UB. The first model significantly predicted BI, F (7,99) =54.239, p -?¤ .001, R^2=.795. Performance expectancy (β = .295, p = .004), social influence (β = .148, p = .033), facilitating conditions (β = .246, p = .007), and habit (β = .245, p = .002) were statistically significant predictors of BI at the .05 level. The second model significantly predicted UB, F (3,103) = 37.845, p -?¤ .001, R^2 = .527. Habit (β = .430, p = .001) was a statistically significant predictor for UB at a .05 level. Using the study results, NPO IT managers may be able to develop strategies to improve the adoption of cloud computing within their organization. The implication for positive social change is that, by using the study results, NPO leaders may be able to improve their IT infrastructure and services for those in need, while also reducing their organization's carbon footprint through use of shared data centers for processing.

The purpose of this study was to determine if the Northwest Evaluation Association (NWEA) Measures of Academic Progress (MAP) reading and math assessments are a valid predictor of performance on the language arts and mathematics Mississippi Curriculum Test, 2 nd Edition (MCT2). Additionally, the researcher sought to determine whether student characteristics of gender, ethnicity, and economically disadvantaged status added statistically to the prediction of MCT2 scores. The researcher used a correlational research design to answer the research questions that guide this study. Regression analyses were performed using IBM Statistical Package for the Social Sciences (SPSS), version 22. Data were collected from a Southern Mississippi school district. Scores from 676 6 th grade students and 659 8 th grade students were used in this study. The results of simple linear regression indicate that NWEA-MAP reading and mathematics assessments are a valid predictor of language arts and mathematics MCT2 scale scores for 6 th and 8 th grade students. Results of multiple regression indicate that the linear combination of fall reading NWEA-MAP RIT scores, spring reading NWEA-MAP RIT scores, student characteristics of gender, ethnicity, and economically disadvantaged status was significantly related to MCT2 language arts scale scores for sixth grade students; likewise, the linear combination of fall reading NWEA-MAP RIT scores, spring reading NWEA-MAP RIT scores, student characteristics of gender, ethnicity, and economically disadvantaged status was significantly related to MCT2 language arts scale scores for eighth grade students. Similarly, multiple regression analyses indicate that the linear combination of fall mathematics NWEA-MAP RIT scores, spring mathematics NWEA-MAP RIT scores, student characteristics of gender, ethnicity, and economically disadvantaged status was significantly related to MCT2 mathematics scale scores for sixth grade students; similarly, the linear combination of fall mathematics NWEA-MAP RIT scores, spring mathematics NWEA-MAP RIT scores, student characteristics of gender, ethnicity, and economically disadvantaged status was significantly related to MCT2 mathematics scale scores for eighth grade students.

The scope of the current study focuses on the relationship between direct and indirect methods of measuring social skills in children. Participants included 33 children between the ages of 6 and 11 years old. The sample drew from elementary schools in 2 Southern states in the U.S., as well as social skills groups from a university-based clinic. While some participants had been previously identified has having disabilities impacting social performance, it was not an inclusionary requirement and the majority of children were not identified as having a disability clinically or through a special education eligibility domain. Teachers and clinicians leading social skills groups completed indirect measures, the Behavior Assessment Scale for Children Third Edition (BASC-3) and the Social Skills Improvement System (SSIS) related to the participants’ social skills. Direct observations of participants were completed using the Social Observation System (SOS) by graduate level research assistants. Hierarchical multiple regression analyses were conducted to determine the predictive value of the teacher informed indirect measures on the direct method of observation. Additionally, simple linear regression analyses were conducted to examine the reverse relationship of the direct observation’s ability to predict the variance observed in each indirect measure. Results indicated that both the indirect and direct methods of social skills assessment can significantly predict the other. However, while significant, a low to moderate amount of variance in the direct measure is explained by the indirect measures of social skills. The results and implications of the finding are discussed, as well as limitations and future directions.

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Public buildings and other public facilities are essential for the functioning and quality of life in modern societies, but they also frequently have a significant negative impact on the natural environment. Public agencies, with their large portfolios of facilities, have faced considerable challenges in recent years in minimizing their negative environmental impacts and energy consumption and coping with shortages of financial capital to invest in new facilities and operate and maintain existing ones, while still meeting their mission goals. These range from the need to provide a quality workplace for their staff to providing a public service and long term benefits to the public. The concept of green building has emerged as a set of objectives and practices designed to reduce negative environment impacts and other challenges while enhancing the functionality of built facilities. However, the prevailing belief related to implementing green building is that incorporating Green Building Strategies and Technologies (GBSTs) increases the initial cost of constructing a facility while potentially reducing its life cycle costs. Thus, this research deals with optimizing the design of individual facilities to balance the initial cost investment for GBSTs versus their potential Life Cycle Cost (LCC) savings without the need to conduct detailed life cycle cost analysis during the early capital planning and budget phases in public sector projects. The purpose of this study is to develop an approach for modeling the general relationship between investments in initial costs versus savings in LCCs involved in implementing green building strategies in public capital projects. To address the research question, this study developed multiple regression models to identify the relationships between GBSTs and their initial cost premiums, operating costs, and LCCs. The multiple regression models include dummy variables because this is a convenient way of applying a single regression equation to represent several nominal variables, which here consist of initial, operating, maintenance, and repair and replacement costs, and ordinal variables, which in this case are the GBST alternatives considered. These new regression models can be used to identify the relationship between GBST alternatives, initial cost premiums, annual operating costs and LCC in the earliest stage of a project, when public agencies are at the capital planning and budgeting stages of facility development, without necessarily needing to know the precise details of design and implementation for a particular building. In addition, this study also proposes and tests a method to generate all the necessary cost data based on building performance models and industry accepted standard cost data. This statistical approach can easily be extended to accommodate additional GBSTs that were not included in this study to identify the relationship between their initial cost premium and their potential LCC saving at the earliest stage of facility development. In addition, this approach will be a useful tool for other institutional facility owners who manage large facility portfolios with significant annual facility investments and over time should help them minimize the environmental impacts caused by their facilities. / Ph. D.

Dry abrasive blasting is a commonly used surface preparation operation by many process industries to clean up metallic surfaces and achieve surface finishes suitable for future adhesion. Abrasives used in this process can be recyclable or expendable. This study was undertaken to evaluate the performance of three recyclable abrasives: garnet, barshot and steel grit/shot in terms of productivity (area cleaned per unit time), consumption (amount of abrasive used per unit area cleaned) and uncontrolled total particulate matter (TPM) emission factors (in terms of mass of pollutant emitted per unit area cleaned and mass of pollutant emitted per unit mass of abrasive consumed). Though there have been various attempts in the past to evaluate the performance of these abrasives, there has not been a streamlined approach to evaluate these parameters in the commonly used range of process conditions, or to identify and model the influences of key process variables on these performance parameters. The first step in this study was to evaluate the performance of these three abrasives in blasting painted steel panels under enclosed blasting conditions and using USEPA recommended protocols. The second step was to model the influences of blast pressure and abrasive feed rate, two most critical parameters on productivity, consumption and emission factors. Two and three dimensional models were obtained using multiple linear regression techniques to express productivity, consumption and TPM emission factors in terms of blast pressure and abrasive feed rate. Barshot was found to have high productivities over all and steel grit/shot demonstrated the least emission potential at almost all of the tested pressure and feed rate conditions. The data will help fill the gaps in literature currently available for dry abrasive blasting performance. The models obtained will help industries, the research community and the regulatory agencies to make accurate estimates of the performance parameters. Estimating productivity and consumption will help industries identify best management practices by optimizing the process conditions to achieve high productivity and low consumption rates. Emission factor determination will help in reducing the emissions to the atmosphere by choosing process conditions corresponding to minimum emissions. The performance parameters once optimized can result in reduction in material, labor, energy, emission and disposal costs, lower resource utilization and hence reduction in overall life cycle costs of dry abrasive process. The developed models will help industries in making environmentally preferable purchases thereby promoting source reduction options. PM emissions estimated using the models presented here will aid studies on health risk associated with inhalation of atmospheric PM.

Background: Stroke is the second leading cause of disability and mortality in the U.K., therefore research investigating stroke has been highlighted by the National Stroke Strategy to develop studies which are longitudinal and focus on outcome. A comprehensive systematic review (Study One) was undertaken to investigate the role of psychological factors on stroke recovery. This informed the development of the research study (Study Two). The aim of this study was to investigate the role of psychological and cognitive factors on psychological and physical recovery from acute stroke, in a longitudinal study as directed by the National Stroke Strategy. The current study additionally incorporates cognitive neuropsychological elements along with measures of mood, personality and coping. This is the first study to the authors’ knowledge which has investigated repressive coping and Type D personality with stroke. Method: Longitudinal data collection was conducted in two NHS hospitals, with a clinical sample at Time 1 (0-6 weeks post stroke), followed up at Time 2 (3 months post stroke) and Time 3 (6 months post stroke), in the participants’ homes or in nursing homes. Measures used to test independent variables were: Centre for Epidemiologic Studies Short Depression Scale (CES-D 10), Perceived Stress Scale (PSS), Multidimensional Scale of Perceived Social Support (MPSS), Standard Assessment of Negative Affectivity, Social Inhibition, and Type D Personality (DS 14, Type D personality), Marlowe-Crowne Form B & 6 Item STAI (for repressive coping), 3 item Sense of Coherence (SoC) scale, line bi-section & Bells cancellation task (visual neglect), forward digit span (verbal short term memory), Rivermead Behavioural Memory Test (visual short term memory) and the colour word Stroop test (executive function), along with demographic data, stroke markers and health behaviours. Dependent variables were: Quality of life (measured by the SF-36) and physical recovery (modified Rankin Scale). Results: The main analysis used hierarchical multiple regression analyses and mediation analysis to test a series of hypotheses. Physical recovery outcome was predicted by stroke severity, age, stress, repressive coping, social support and visual neglect at different time points. Depression and visual memory were reported as mediators at Time 2. Quality of life outcome was predicted by stroke severity, age, stress, social support, depression and visual neglect at different time points. Conclusions: The results of this study indicate that psychological factors do have an impact on both physical and psychological outcome from stroke. Stress, repressive coping and visual neglect were the most consistent predictors of outcome. Depression and social support played a smaller role, whereas Type D personality was nonsignificant across analyses.

O crescimento populacional e a degradação dos corpos d’água vêm exercendo pressão à agricultura moderna, a proporcionar respostas mais eficientes quanto ao uso racional da água. Para uma melhor utilização dos recursos hídricos, faz-se necessário compreender o movimento da água na natureza, onde o conhecimento prévio dos fenômenos atmosféricos constitui uma importante ferramenta no planejamento de atividades que utilizam os recursos hídricos como fonte primária de abastecimento. Nesse trabalho foram realizadas previsões de longo prazo com antecedência de sete meses e intervalo de tempo mensal de níveis no Sistema Hidrológico do Taim, utilizando previsões de precipitação geradas por um modelo de circulação global. Para realizar as previsões foi elaborado um modelo hidrológico empírico de regressão, onde foram utilizadas técnicas estatísticas de análise e manipulação de séries históricas para correlacionar os dados disponíveis aos níveis (volumes) de água no banhado. Partindo do pressuposto que as previsões meteorológicas são a maior fonte de incerteza na previsão hidrológica, foi utilizada a técnica de previsão por conjunto (ensemble) e dados do modelo COLA, com 30 membros, para quantificar as incertezas envolvidas. Foi elaborado um algoritmo para gerar todas as possibilidades de regressão linear múltipla com os dados disponíveis, onde oito equações candidatas foram selecionadas para realizar as previsões. Numa análise preliminar dos dados de entrada de precipitações previstas foi observado que o modelo de circulação global não representou os extremos observados de forma satisfatória, sendo executado um processo de remoção do viés. O modelo de empírico de simulação foi posteriormente executado em modo continuo, gerando previsões de longo prazo de níveis para os próximos sete meses, para cada mês no período de junho/2004 a dezembro/2011. Os resultados obtidos mostraram que a metodologia utilizada obteve bons resultados, com desempenho satisfatórios até o terceiro mês, decaindo seu desempenho nos meses posteriores, mas configurando-se em uma ferramenta para auxílio à gestão dos recursos hídricos do local de estudo. / Population growth and degradation of water bodies have been pressuring modern agriculture, to provide more efficient responses about the rational use of water. For a better use of water resources, it is necessary to understand the movement of water in nature, where prior knowledge of atmospheric phenomena is an important tool in planning activities that use water as the primary source of supply. In this study were performed long-term forecasts of water levels (seven months of horizon, monthly time-step) in the Hydrological System Taim, using rainfall forecasts generated by a global circulation model as input. To perform predictions was developed an empirical hydrological regression model. This model was developed based on statistical techniques of analysis and manipulation of historical data to correlate the input data available to the levels (volume) of water in a wetland. Assuming that weather forecasts are a major source of uncertainty in hydrological forecasting, we used an ensemble forecast from COLA 2.2 with 30 members to quantify the uncertainties involved. An algorithm was developed to generate all the multiple linear regression models with the available data, where eight candidates equations were selected for hydrological forecasting. In a preliminary analysis of the precipitation forecast was observed that the global circulation model did not achieve a good representation of extremes values, thus a process of bias removal was carried out. Then the empirical model was used to generate water levels forecast for the next seven months, in each month of the period june/2004 to december/2011. The results showed that the methodology used has a satisfactory performance until the lead time three (third month in the future) where the performance starts to show lower values. Beside the sharply lost of performance in the last lead times, the model is a support tool that can help the decision making in the management of water resources for the study case.

Magister Artium - MA (Psychology) / Families in South Africa are faced with manifold hardships that negatively impact the family as a unit. However, there are a variety of protective factors that have been identified as meaningful resources that facilitates healing and growth within a family unit. The study aims to investigate whether age, gender, employment status and level of education significantly predicts family resilience. The study utilised secondary data compromised of (N=656) participants from a low socio-economic rural community in South Africa. Family resilience views the family as a functional system of which provides positive adaption to family members who have experienced stressful events. Walsh's key processes in family resilience is outlined, highlighting a multi-level developmental systems orientation. The study utilised a multiple regression analysis consisting of four predictor variables namely, age, gender, employment status and level of education to assess whether these variables predict high levels of family resilience. The model found that amongst the four predictor variable, only employment status significantly predicted family resilience.

Areas of California's Central Valley are sinking at rates up to 1 foot per year due to subsidence caused, in part, by the state's years-long drought, challenging growers to locate additional water sources for their crops. Supply and demand theory guided this correlational study. The purpose of the study was to examine the financial impact of drought on almond demand. This study included annualized historical almond industry data for the United States (N = 97), downloaded from a United States Department of Agriculture database. The results of multiple linear regression analysis indicated that the model was capable of predicting almond demand, F(3,92) = 483.579, p < .001, R2 = .940. Both supply and price were statistically significant in the final model, with supply (p < .001) accounting for a higher contribution to the model than price (p = .015). Fine effect's contribution (p = .267) to the model was not statistically significant. The results of this study could enable almond industry leaders to increase profit margins through market predictability understanding and mitigate fiscal risks associated with variable labor and groundwater pumping costs. The implications for positive social change include the potential to restore employment opportunities, stabilize migratory worker prospects, and reduce water utilization to preserve natural resources.