Job satisfaction is an important job-related attitude that has been linked to various outcomes for both the organization and its employees. In spite of this, researchers of the construct disagree about how job satisfaction is defined and measured. This study proposes the use of the Facet Satisfaction Scale, a new scale of measurement for job satisfaction that is based on more recent definitions of the construct. Reliability and preliminary predictive validity studies were conducted in order to determine the utility of this scale. Next steps in scale development are discussed.

Screening, Brief Intervention, and Referral to Treatment (SBIRT) for alcohol use has been shown to reduce rates of alcohol use across multiple clinical settings, and is routinely recommended by the United States Preventative Services Task Force (USPSTF). In 2005 the National Institute on Alcohol Abuse and Alcoholism (NIAAA) recommended implementing a single item screening question (SISQ) for this purpose. Since then the SISQ has been well validated compared to other tools, such as the Alcohol Use Disorders Identification Test (AUDIT). It has not, however, been well studied in particular populations, such as those with comorbid anxiety and/or depressive disorders. Medicare Annual Wellness Visits present a unique opportunity to study the SISQ because while they do inquire about alcohol use, they do not routinely include a SISQ. Our study seeks to investigate the efficacy of implementation of a SISQ during Medicare Annual Wellness Visits in a residency clinic population with anxiety and/or depressive disorders. Data collection is ongoing and will measure rates of referral to treatment before and after the SISQ is implemented, as well as rates of brief interventions given.

BACKGROUND: Health-related interventions informed by behavioral theory have been shown to be more effective in changing behaviors as compared to those that are not. The Theory of Planned Behavior (TPB) has been used to successfully predict and explain a variety of health related behaviors, including sugar-sweetened beverage (SSB) intake and physical activity (PA). The TPB assumes that behavioral intentions are the most important determinant of behavior. Intentions are the function of individual's attitudes toward the behavior (these evaluations can be positive or negative), subjective norms (social standards and expectations surrounding the behavior), and perceived behavioral control (perception of the ease with which the behavior can be performed). According to literature, behavioral intentions predict 20% - 40% of the variance in health behaviors with attitudes beings the strongest predictor of diet, and perceived behavioral control being the strongest predictor of physical activity related intentions. Excessive SSB consumption and inadequate PA have been highly associated with the obesity epidemic, and related comorbidities such as cardiovascular disease and type-2 diabetes. Understanding and targeting these behaviors through application of health behavior theories, such as the TPB, is important. PRIMARY AIMS: This research is embedded within a larger 2-arm randomized-control trial, Talking Health, which targets residents in rural southwest Virginia. Guided by the TPB, the overall goal of the Talking Health trial is to determine the effectiveness of a 6-month intervention aimed at decreasing SSB intake (SIPsmartER) compared to a matched contact control aimed at increasing PA (MoveMore). Each condition includes three classes, one teach-back call, and 11 interactive voice response (IVR) calls. The primary aims of this secondary analysis of Talking Health are to 1) determine if single-item TPB indicators are correlated with multi-item TPB scales for SSB and PA; 2) examine how baseline TPB variables predict participation in the SIPsmartER and MoveMore; 3) determine how the IVR TPB variables assessed during IVR calls predict future SSB and PA behaviors reported in a subsequent IVR call; and 4) explore how TPB variables change over the course of the teach back and 11 IVR calls. METHODS: Eligibility requirements included being 18 years of age or older, having reliable access to a telephone, drinking 200 kilocalories of SSB per day, and having no contraindications for moderate-intensity physical activity. The present research utilizes data from the baseline health assessment, class attendance and IVR and teach back calls completion data, as well as data collected in teach-back and 11 IVR calls. Multi-item TPB constructs for both SSB and PA behaviors were assessed at baseline (measured on a 7-point Likert scale). Each IVR call assessed self-reported past week behavior (ounces of SSB or minutes of PA) and four single-item TPB constructs including behavioral intentions, perceived behavioral control, instrumental attitudes, affective attitudes, and subjective norms. Participation was measured as the number out of 15 activities completed by participants (three classes, one teach back call, and 11 IVR calls). Statistical analysis included descriptive statistics, Chi square tests, independent T-tests, Pearson's correlations, Cronbach's α, and sequential multi-step regression models. Multiple data imputations were used to account for missing data. RESULTS: Of the 301 participants, 81% were female and 93% were Caucasian. The mean age of participants was 48.8 ± 13.5. Additionally, 32% of participants completed high school education, 55% earned < $20,000 per year, 32% had a full time or part time job, and 33% were classified as low health literate. Single-item indicators for both SSB-TPB questions (r > 0.60) and PA-TPB questions (r > 0.69) were highly correlated with their multi-item scales. Baseline TPB variables did not predict the participation rates in either SIPsmartER (F=1.763, R2=0.057, P=0.124) or MoveMore (F=0.815, R2=0.028, P=0.541) conditions. Of the nine SIPsmartER IVR regression models, eight were significant, and the SSB-TPB variables predicted about 30% of the variance in SSB behavior. Of the nine MoveMore IVR regression models, all were significant, and the PA-TPB variables predicted about 20% of the variance in SSB behavior. In both conditions, the majority of variance was explained by behavioral intentions and the addition of other TPB variables (perceived behavioral control, instrumental attitudes, affective attitudes, and subjective norms) explained substantially less variance in the behaviors. There were no notable patterns of change in TPB variables over 11 IVR calls for either SIPsmartER or MoveMore participants. DISCUSSION: Our findings show that single-item indicators can be used as reliable measures of the TPB constructs. The TPB model did not show significant predictive value when it comes to participation in SIPsmartER or MoveMore. On the other hand, our findings show that TPB model explained about 30% (SSB) and about 20% (PA) of variance in behavior. Although significant changes in IVR TPB variables were found between the two time points in several instances for both SSB and PA behavior, there were no patterns of change over time. Based on our findings, assessing behavioral intentions as the goal behavior in each IVR call may be the most useful application of the TPB. Other TPB variables can be assessed using single-item indicators. / Master of Science

This thesis considers two kinds of problems, motivated by practical applications in data center operations and maintenance. Data centers are the brain of the internet, each hosting as many as tens of thousands of IT devices, making them a considerable global energy consumption contributor (more than 1 percent of global power consumption). There is a large body of work at different layers aimed at reducing the total power consumption for data centers. One of the key places to save power is addressing the thermal heterogeneity in data centers by thermal-aware workload distribution. The corresponding optimization problem is challenging due to its combinatorial nature and the computational complexity of thermal models. In this thesis, a holistic theoretical approach is proposed for thermal-aware workload distribution which uses linearization to make the problem model-independent and easier to study. Two general optimization problems are defined. In the first problem, several cooling parameters and heat recirculation effects are considered, where two red-line temperatures are defined for idle and fully utilized servers to allow the cooling effort to be reduced. The resulting problem is a mixed integer linear programming problem which is solved approximately using a proposed heuristic. Numerical results confirm that the proposed approach outperforms commonly considered baseline algorithms and commercial solvers (MATLAB) and can reduce the power consumption by more than 10 percent. In the next problem, additional operational costs related to reliability of the servers are considered. The resulting problem is solved by a generalization of the proposed heuristics integrated with a Model Predictive Control (MPC) approach, where demand predictions are available. Finally, in the second type of problems, we address a problem in inventory management related to data center maintenance, where we develop an efficient dynamic programming algorithm to solve a lot-sizing problem. The algorithm is based on a key structural property that may be of more general interest, that of a just-in-time ordering policy. / Thesis / Doctor of Philosophy (PhD) / Data centers, each hosting as many as tens of thousands of IT devices, contribute to a considerable portion of energy usage worldwide (more than 1 percent of global power consumption). They also encounter other operational costs mostly related to reliability of devices and maintenance. One of the key places to reduce energy consumption is through addressing the thermal heterogeneity in data centers by thermal-aware work load distribution for the servers. This prevents hot spot generation and addresses the trade-off between IT and cooling power consumption, the two main power consump tion contributors. The corresponding optimization problem is challenging due to its combinatorial nature and the complexity of thermal models. In this thesis, we present a holistic approach for thermal-aware workload distribution in data centers, using lin earization to make the problem model-independent and simpler to study. Two quite general nonlinear optimization problems are defined. The results confirm that the proposed approach completed by a proposed heuristic solves the problems efficiently and with high precision. Finally, we address a problem in inventory management related to data center maintenance, where we develop an efficient algorithm to solve a lot-sizing problem that has a goal of reducing data center operational costs.

Social ångest beskrivs som en obehagskänsla kopplat till sociala situationer där individen upplever en risk att bli negativt bedömd av andra (APA, 2013).Det finns ett stort behov av att effektivisera mätning av social ångest samt att på ett precist sätt mäta dess symtom. Vidare råder det svårigheter att mäta psykologiska konstrukt på ett tillförlitligt sätt. Borg centiMaxskalan® (CR100) är en skattningsskala som utvecklats för att mäta subjektiva upplevelser med större precision. Syftet med denna studieär att studera begreppsvaliditeten för single-item -mätning av social ångest med Borg centiMaxskalan®samt att se om single-item-mätning mäter social ångest på ett likvärdigt sätt som ett multi-item. För att undersöka detta jämförs single-item-mätning med centiMax för social ångest (SÅ-SI-cM) mot SPIN och socialångestskalan med centiMax (SÅS-MI-cM). Data samlades in digitalt via ett bekvämlighetsurval (N = 382). Resultatet visar ett positivt samband mellan SPIN och single-item-mätning med centiMax för social ångest(r = .77, p &lt;.001) samt ett positivt samband mellan single-item-mätning med centiMax och multi-item-mätning med SÅS-cMax (r = .77, p &lt;.001). Sammanfattningsvis tyder resultatet på att single-item-mätning med centiMaxmäter social ångest på ett likvärdigt sätt som SPIN och SÅS-MI-cM.Resultatet i denna studie indikerar på att vidare studier med single-item-mätningav social ångest med centiMax (SÅ-SI-cM) är av intresseför att vidare validera detta mätinstrument

The main subject of this diploma thesis is elaboration of construction and technological preparation for the main structure of the Smart Light shop in Bratislava. For main structure has been processed time schedule of the construction, single item budget, machine configuration design, drawing of building site, coordination situation of the building with connection to the infrastructure, safety and health protection during work on building site. Part of the thesis is processing study of main construction technological parts. Diploma thesis in technological prescript focuses on implementation of floor structure with cast epoxy walking surface. There has been elaborated testing and quality plan of this technological part. Additional chapter approximates built-in technology – cooling ceiling structure. For elaboration of this diploma thesis were used programs AutoCAD, CONTEC, BuildPowerS, Microsoft Excel, Microsoft Word.