Research plays a large role in the advancement of any industry. It is particularly important that relevant research is performed in an industry as large and critical as the construction industry. A review of literature shows that historically research has not played a major role in the advancement of the construction industry. The work of this thesis was to explore whether there has been a change in this relationship, or not. This thesis explored whether the construction-related research being done is in alignment with the needs of industry. Comparisons of the rankings of construction research and industry views were done to evaluate their correlation to each other. This evaluation was done through a review of literature of four construction research journals. Articles were gathered and classified in common construction themes. Each research theme was given a ranking based on the quantity of articles classified into common construction themes. The research ranking demonstrated that the most popular research theme was training/ human resources, followed by management/ risks and technology/ innovation. A survey was sent to 259 industry professionals asking them to rank the 22 construction themes. A response rate of 14% was achieved through the survey respondents. Survey respondents determine that the most important research theme is constructability, followed by estimating/ bidding and economics/cost control. Findings demonstrate that there was no correlation between the themes that were popularly researched and what is most important to the construction professionals.

This study was conducted in order to determine how mathematics teachers allocate their time in the classroom and the factors that influence teacher priorities in that time allocation. Research has indicated that math teachers may choose not to do certain activities in their classroom because of the amount of time that they take, but other underlying reasons may exist. In order to study this idea, six math teachers were interviewed on their current time allocation and rationale for that allocation, and the results from these interviews were used to create a survey that was sent to 581 math teachers in Utah. The results from the 224 completed surveys showed that many teachers allocate their classroom time in a fairly traditional manner, with an average of about 10% of class time being spent on student-centered activities. 40.63% of teachers spent 0% of their class time on student-centered activities. There is variation in time allocation and influencing factors based on a teacher's schedule, level of teaching, experience, and how student-centered their teaching methods are. Also, the results support the claim that there are factors, other than limited class time, that affect how teachers choose whether or not to do certain activities. Some of the most significant deciding factors found are whether the activities will help students with their end of level tests, if they will keep students working hard mathematically, whether others are using those activities or not, how the activities affect classroom rowdiness. It was also found that teachers who are more teacher-centered tended to choose activities based on how easy they were to implement, including their personal comfort level, ease of preparation, and ease of management with student behavior. More student-centered teachers tended to care more about keeping the students working hard mathematically.

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Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach. / Doctor of Philosophy / Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. When electricity demand is high, Demand Response (DR) is a method that can be used to reduce user loads, restoring the balance between demand and supply of electricity. Based on data from the Energy Information Administration (EIA), half of all commercial buildings in the US measure 5,000 square feet or smaller in size, whereas the majority of the other half is made up of medium-sized commercial buildings measuring in at between 5,001 to 50,000 square feet. This makes these commercial buildings an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique, where power consumption and activity of individual HVACs can be obtained, using a single power meter. The second work focuses on a DR algorithm, that controls single floor HVAC and lighting loads in a commercial building, based on a user generated bid price for electricity, user preferences and load priorities, when electricity demand is at its peak.

Yes / Black, Asian and minority ethnic communities suffer from disproportionately poorer health than the general population. This issue has been recently exemplified by the large numbers of infection rates and deaths caused by covid-19 in BAME populations. Future research has the potential to improve health outcomes for these groups. High quality research priority setting is crucial to effectively consider the needs of the most vulnerable groups of the population. The purpose of this systematic review is to identify existing research priority studies conducted for BAME health and to determine the extent to which they followed good practice principles for research priority setting. Method: Included studies were identified by searching Medline, Cinnahl, PsychINFO, Psychology and Behavioral Sciences Collection, as well as searches in grey literature. Search terms included “research priority setting”, “research prioritisation”, “research agenda”, “Black and minority ethnic”, “ethnic group”. Studies were included if they identified or elicited research priorities for BAME health and if they outlined a process of conducting a research prioritisation exercise. A checklist of Nine Common Themes of Good Practice in research priority setting was used as a methodological framework to evaluate the research priority processes of each study. Results: Out of 1514 citations initially obtained, 17 studies were included in the final synthesis. Topic areas for their research prioritisation exercise included suicide prevention, knee surgery, mental health, preterm birth, and child obesity. Public and patient involvement was included in eleven studies. Methods of research prioritisation included workshops, Delphi techniques, surveys, focus groups and interviews. The quality of empirical evidence was diverse. None of the exercises followed all good practice principles as outlined in the checklist. Areas that were lacking in particular were: the lack of a comprehensive approach to guide the process; limited use of criteria to guide discussion around priorities; unequal or no representation from ethnic minorities, and poor evaluation of their own processes. Conclusions: Research priority setting practices were found to mostly not follow good practice guidelines which aim to ensure rigour in priority setting activities and support the inclusion of BAME communities in establishing the research agenda. Research is unlikely to deliver useful findings that can support relevant research and positive change for BAME communities unless they fulfil areas of good practice such as inclusivity of key stakeholders’ input, planning for implementation of identified priorities, criteria for deciding on priorities, and evaluation of their processes in research priority setting. / This work was supported by the National Institute for Health Research (NIHR) under its Applied Research Collaboration (ARC) Yorkshire and Humber in the form of Ph.D. funding to HI [NIHR200166], the UK Prevention Research Partnership (UKPRP) in the form of funding to JW and RM [MR/S037527/1], the NIHR Clinical Research Network in the form of funding to JW, and the NIHR ARC Yorkshire and Humber in the form of funding to RM.

This thesis explores the impacts of the Partnership for Incentive-Based Budgeting (PIBB) model at Virginia Tech (VT), a land-grant institution. By conducting a mixed-methods approach including semi-structured interviews, document analysis, and a review of political theory, this research examines the differences in perceptions of employees across employee classifications, academic area, and financial experience at VT on the PIBB model, unhealthy internal competition, communications, fiscal policy, and political influence. The PIBB model was adopted at VT as a strategic response to reduced state funding and aims to encourage budget management improvements and collaborative planning across the university. Findings indicate that while the PIBB model is designed to align financial incentives with the university's academic and operational goals, it may also foster unhealthy, internal competition among faculty, staff, and administrative units. This competition arises from the pressure to meet specific performance metrics linked to budgetary allocations, potentially leading to conflicts and misalignment with the university's broader educational objectives –underscoring the need for a balanced approach to budgeting that supports both financial sustainability and academic integrity. Complexities of implementing market-driven budgeting models within academic settings suggests that while such models can drive efficiency, they must be managed carefully to avoid undermining the core mission of educational institutions. This thesis contributes to ongoing discussions about the optimization of resource allocation in public higher education. / Master of Arts / This thesis looks at how the Partnership for Incentive-Based Budgeting (PIBB) model, implemented at Virginia Tech (VT), impacts various aspects of the university. Using a combination of interviews, document analysis, and a look into political theory, the research investigates how different employees at VT perceive the PIBB model, its effects on internal dynamics like competition, communication, financial policies, and political influences. The PIBB model was implemented as a result in the decline in state money going to VT and to help manage the budget better and collaboration across the university. This thesis research found that while the PIBB model tries to link money incentives with university goals, it also creates unhealthy competition among faculty, staff, and units within the university. This competition happens as everyone wants to meet certain goals to get more money, which can take away from the main goal of the university, which is education. The study shows that it is important to find a balance between the university's goals being at the forefront but also managing the money appropriately. It also shows that using budgeting methods based on business ideas in a public university is tricky and needs careful management to keep the university's values intact. Overall, this research adds to the conversation about how we can best use money in public colleges to make them better for everyone.