The window insulation always plays an important role in building design and thermal comfort, it is one of the main parts needed to be optimized in building envelop. Window insulation is a basic element which can decide the insulation capacity. The large heat loss from the window is the main part of wasting energy, and simultaneously, there is also difficult to explore the new energy source and to improve the current heat generation device efficiency in this energy crisis century. Therefore, building a proper insulated window system is a good approach to keep an acceptable indoor climate as well as to reduce energy use and negative climate effects. On the other hand, there are amounts of old houses and poorly design houses all over the world either in a well developed country or a developing country. Therefore it can form a better atmosphere to optimize the window worldwide. The aim of the thesis is telling the reader what kind of optimization can be done to get better window insulation. This thesis starts with a briefly introduction to give the basic knowledge of heat loss from windows, and then shows the heat loss level in comparison with other parts of building. Afterwards it gives the optimization strategy to make good window glazing and window frames. For frame design, the hollow frame material property and the benefits of insulants filling window system are described. There are still some problems which exist for the energy efficient windows, such as condensation problem. The thesis also shows this kind of problem and the solution approach.

Central Arizona Project (CAP) water was treated using the process of slowsand filtration, chemical pretreatment and RO membrane. Both bench scale plate and frame reactor and pilot scale tests suggested RO membrane fouling by clay and organic matter with minor scaling by CaCO3 and BaSO4. Several strategies were studied to reduce RO membrane fouling and scaling. The first is choosing optimized operation conditions through bench scale tests. The second is to modify the traditional concentration polarization model for a better fouling/scaling prediction. This modified model was also used to optimize concentrate spacer design, which leads to reduced concentration polarization index. The third is to develop a method for anti-scalant test and comparison, which can be used for anti-scalant selection and dose optimization.Additional to these strategies, pre-oxidation pretreatment for RO membrane in water reuse application was investigated at bench and pilot scale. In the MBR-Ozone-RO train study, ozone showed certain impact on RO membrane fouling, but no significant difference was made on membrane cleaning frequency. UV and UV/AOP impacts on RO membrane fouling tests were done on plate and frame reactor. UV did not show any competency to reduce membrane fouling, while UV/AOP tests showed promising results by reducing RO membrane fouling rate by 50%.

A prototype treatment developed to significantly reduce symptoms of stress among inservice teachers was tested in this experiment. Thirty participants selected for high stress levels were randomly assigned to treatment and control groups. They were assessed on environmental, personality, and emotional variables, using self-report and expert-judge measures, both pre- and posttreatment. The experimental treatment was holistic, incorporating all processes previously found to be related to reducing teacher stress. At posttreatment, the treatment group averaged 1.02 standard deviations lower on the stress measures than the control group. Significant differences in the posttest means, favoring the experimental group, were found for 23 of the 39 variables measured on the three self-report instruments. As a group the participants demonstrated substantially lower stress levels than the control group after the treatment, with a substantial decrease from their pretreatment stress levels. Since the control group received no treatment, some of the difference nay be due to Hawthorne Effect.

At the rate the population is growing it is important to find ways to be more efficient with the energy and water we use. The increase in population increases the need for electricity and water, but the way we are using our sources will not leave us with enough for future generations. The constant use of "dirty energy", energy that emits CO2 and other chemicals into the atmosphere, will continue to harm our environment. A new system is needed to help preserve water and produce green energy that will not harm the only earth we have.

Faculty of Health Sciences Degree of Master of Science in Physiotherapy 9601486g / A study was conducted to determine the optimum position of the handlebar on the Johnny G. Spinning® bicycle to reduce low back pain in cyclists participating in a Spinning® class. A three period open label cross over design, involving thirty six subjects was conducted. Each subject participated in three Spinning® classes with a different handlebar height at each session. The saddle angle, saddle post height and fore/aft position of the saddle remained fixed to eliminate variability. The cyclists’ pain perception was measured via the Visual Analogue Scale, Lickert Scale and the McGill Pain Questionnaire. The results were analysed with respect to the change in the mean and standard deviation of the Visual Analogue Scale, the Lickert scale and the McGill Pain Questionnaire. The significance of the study was set at the 0.05 level. A zero value was recorded with respect to pain experienced by the cyclists during a Spinning® class on the VAS, Lickert scale and on the McGill Pain scale, when the handlebars were placed in the high handlebar height position on the Spinning® bicycle; this is the most important outcome of the study conducted. In conclusion, there is a statistically meaningful difference (p<0.001) between the mean values of pain recorded by participants of the low handlebar height compared to the normal handlebar height, with the normal handlebar height being the better position. The standard deviation remains relatively constant. No pain was recorded on the VAS, Lickert scale and on the McGill Pain scale when the handlebars were placed in the high handlebar height position on the Spinning® bicycle. The results of the study indicate that the high handlebar height position is the best position for participants in a Spinning® class.

Supply chain disruptions are more prevalent in Ghana as businesses develop and become more complex. While business leaders recognize the importance of reducing the exposure to disruptions, 83 % of companies still suffer a form of disruption. Using the systems theory as the conceptual framework, the purpose of this qualitative case study was to explore strategies supply chain managers in Ghana use to reduce disruptions in the supply chain. The data collection was through face-to-face, semi-structured interviews from 12 supply chain managers who gave tape-recorded interview responses to 8 questions. Transcript evaluation, member checking, and methodological triangulation ensured reliability and strengthened the credibility of the data collected. Data analysis revealed 6 themes, which included identification of disruptions before they occur, information sharing and collaboration between partners, management strategies to mitigate supply chain disruptions, inventory optimization, availability of human capital, energy, and finance problems. The identification of disruptions was important to participants because they believed if they identified potential disruptions they might prevent them from happening. The collaboration between partners was important because participants believed that the speed at which stakeholders work together after a disruption determines the extent of the cost and the recovery period. Business leaders could apply the findings to provide insight to businesses to reduce disruptions, improve best practices, and increase business profits. Implications for social change include the enhancement of company profits given efficiencies in the supply chain. Such increase in profits leads to increases in taxes, which contributes to the overall betterment of the local communities.

There were 133 billion pounds of food that went to waste in the United States in 2014, leading to $161.6 billion in economic loss. Of this waste, 89 billion pounds occurred in restaurants and other food service facilities. A case study was used to explore the strategies small, independent, family-owned restaurants owners used to reduce food waste. Four small independent, family-owned restaurants owners located in the Washington, DC, metro area participated in the study. These owners were selected based on their revenue and years of survival. Stakeholders theory was the conceptual framework in which the study was grounded. Face-to-face interviews with participants and company financial documents comprised the data. Interview transcripts, member checking results, and financial documents were analyzed for emergent themes. The 3 themes that emerged from this study are employee training, communication among stakeholders, and customer loyalty. The implications for social change include the potential to provide new strategies that can help small, independent, family-owned restaurants reduce food waste, increase profits, and improve the economic conditions of communities in the Washington, DC metro area.

Standard recovery solution on a failed task in Hadoop systems is to execute the task again. After retrying for a configured number of times, it is marked as failure. With significant amount of data, complicated Map and Reduce functions, recovering corrupted or unfinished data from a failed job can be more efficient than re-executing the same job. This paper is an extension of [1] by applying fusion-based technique [7][8] in Hadoop MapReduce tasks execution to enhance its fault tolerance. Multiple data sets are executed through Hadoop MapReduce with and without fusion in various pre-defined failure scenarios for comparison. As the complexity of the Map and Reduce function relative to the Recover function increases, it becomes more efficient to utilize fusion and users can tolerate faults by incurring less than ten percent of extra execution time. / text

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When a cloud user allocates a cluster to execute a map-reduce workload, the user must determine the number and type of virtual machine instances to minimize the workload's financial cost. The cloud user may rent on-demand instances at a fixed price or spot instances at a variable price to execute the workload. Although the cloud user may bid on spot virtual machine instances at a reduced rate, the spot market auction may delay the workload's start or terminate the spot instances before the workload completes. The cloud user requires a forecast for the workload's financial cost and completion time to analyze the trade-offs between on-demand and spot instances. While existing estimation tools predict map-reduce workloads' completion times and costs, these tools do not provide spot instance estimates because a spot market auction determines the instance's start time and duration. The ephemeral spot instances impact execution time estimates because the spot market auction forces the map-reduce workloads to use different storage strategies to persist data after the spot instances terminate. The spot market also reduces the existing tools' completion time and cost estimate accuracy because the tool must factor in spot instance wait times and early terminations. This dissertation updated an existing tool to forecast map-reduce workload's monetary cost and completion time based on spot market historical traces. The enhanced estimation tool includes three new enhancements over existing tools. First, the estimation tool models the impact to the execution from new storage strategies. Second, the enhanced tool calculates additional execution time from early spot instance termination. Finally, the enhance tool predicts the workloads wait time and early termination probabilities from historic traces. Based on two historical Amazon EC2 spot market traces, the enhancements reduce the average completion time prediction error by 96% and the average monetary cost prediction error by 99% over existing tools.