Assessment of Higher-Education Hospitality Programs

The function of assessment in higher-education hospitality programs is to improve student learning. Although the assessment process is common in higher-education institutions, examples of assessment practices in hospitality programs have not been made available to academic practitioners. This paper describes a method successful at formulating assessment in a hospitality college professional program.

Integrated assessment of water conservation practices for sustainable management strategies

Miami-Dade County implemented a series of water conservation programs, which included rebate/exchange incentives to encourage the use of high efficiency aerators (AR), showerheads (SH), toilets (HET) and clothes washers (HEW), to respond to the environmental sustainability issue in urban areas. This study first used panel data analysis of water consumption to evaluate the performance and actual water savings of individual programs. Integrated water demand model has also been developed for incorporating property’s physical characteristics into the water consumption profiles. Life cycle assessment (with emphasis on end-use stage in water system) of water intense appliances was conducted to determine the environmental impacts brought by each practice. Approximately 6 to 10 % of water has been saved in the first and second year of implementation of high efficiency appliances, and with continuing savings in the third and fourth years. Water savings (gallons per household per day) for water efficiency appliances were observed at 28 (11.1%) for SH, 34.7 (13.3%) for HET, and 39.7 (14.5%) for HEW. Furthermore, the estimated contributions of high efficiency appliances for reducing water demand in the integrated water demand model were between 5 and 19% (highest in the AR program). Results indicated that adoption of more than one type of water efficiency appliance could significantly reduce residential water demand. For the sustainable water management strategies, the appropriate water conservation rate was projected to be 1 to 2 million gallons per day (MGD) through 2030. With 2 MGD of water savings, the estimated per capita water use (GPCD) could be reduced from approximately 140 to 122 GPCD. Additional efforts are needed to reduce the water demand to US EPA’s “Water Sense” conservation levels of 70 GPCD by 2030. Life cycle assessment results showed that environmental impacts (water and energy demands and greenhouse gas emissions) from end-use and demand phases are most significant within the water system, particularly due to water heating (73% for clothes washer and 93% for showerhead). Estimations of optimal lifespan for appliances (8 to 21 years) implied that earlier replacement with efficiency models is encouraged in order to minimize the environmental impacts brought by current practice.

A total quality management methodology for universities

This research document is motivated by the need for a systemic, efficient quality improvement methodology at universities. There exists no methodology designed for a total quality management (TQM) program in a university. The main objective of this study is to develop a TQM Methodology that enables a university to efficiently develop an integral total quality improvement (TQM) Plan. ^ Current research focuses on the need of improving the quality of universities, the study of the perceived best quality universities, and the measurement of the quality of universities through rankings. There is no evidence of research on how to plan for an integral quality improvement initiative for the university as a whole, which is the main contribution of this study. ^ This research is built on various reference TQM models and criteria provided by ISO 9000, Baldrige and Six Sigma; and educational accreditation criteria found in ABET and SACS. The TQM methodology is proposed by following a seven-step metamethodology. The proposed methodology guides the user to develop a TQM plan in five sequential phases: initiation, assessment, analysis, preparation and acceptance. Each phase defines for the user its purpose, key activities, input requirements, controls, deliverables, and tools to use. The application of quality concepts in education and higher education is particular; since there are unique factors in education which ought to be considered. These factors shape the quality dimensions in a university and are the main inputs to the methodology. ^ The proposed TQM Methodology is used to guide the user to collect and transform appropriate inputs to a holistic TQM Plan, ready to be implemented by the university. Different input data will lead to a unique TQM plan for the specific university at the time. It may not necessarily transform the university into a world-class institution, but aims to strive for stakeholder-oriented improvements, leading to a better alignment with its mission and total quality advancement. ^ The proposed TQM methodology is validated in three steps. First, it is verified by going through a test activity as part of the meta-methodology. Secondly, the methodology is applied to a case university to develop a TQM plan. Lastly, the methodology and the TQM plan both are verified by an expert group consisting of TQM specialists and university administrators. The proposed TQM methodology is applicable to any university at all levels of advancement, regardless of changes in its long-term vision and short-term needs. It helps to assure the quality of a TQM plan, while making the process more systemic, efficient, and cost effective. This research establishes a framework with a solid foundation for extending the proposed TQM methodology into other industries. ^

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

Diatom-based Models for Inferring Hydrology and Periphyton Abundance in a Subtropical Karstic Wetland: Implications for Ecosystem-Scale Bioassessment

We developed diatom-based prediction models of hydrology and periphyton abundance to inform assessment tools for a hydrologically managed wetland. Because hydrology is an important driver of ecosystem change, hydrologic alterations by restoration efforts could modify biological responses, such as periphyton characteristics. In karstic wetlands, diatoms are particularly important components of mat-forming calcareous periphyton assemblages that both respond and contribute to the structural organization and function of the periphyton matrix. We examined the distribution of diatoms across the Florida Everglades landscape and found hydroperiod and periphyton biovolume were strongly correlated with assemblage composition. We present species optima and tolerances for hydroperiod and periphyton biovolume, for use in interpreting the directionality of change in these important variables. Predictions of these variables were mapped to visualize landscape-scale spatial patterns in a dominant driver of change in this ecosystem (hydroperiod) and an ecosystem-level response metric of hydrologic change (periphyton biovolume). Specific diatom assemblages inhabiting periphyton mats of differing abundance can be used to infer past conditions and inform management decisions based on how assemblages are changing. This study captures diatom responses to wide gradients of hydrology and periphyton characteristics to inform ecosystem-scale bioassessment efforts in a large wetland.

Loaxahatchee Impoundment Landscape Assessment (LILA): Tree Island Experiments and Management; May 1, 2005 to September 4, 2009: Final Report

Hydrologic modifications have negatively impacted the Florida Everglades in numerous significant ways. The compartmentalization of the once continuously flowing system into the Water Conservation Areas (WCAs) caused disruption of the slow natural flow of water south from Lake Okeechobee through the Everglades to Florida Bay. The ponding of water in the WCAs, the linking of water flow to controlled water levels, and the management of water levels for anthropogenic vs. ecological well-being has caused a reduction in the spatial heterogeneity of the Everglades leading to greater uniformity in topography and vegetation. These effects are noticeable as the degradation in structure of the Everglades Ridge and Slough environment and associated Tree Islands. In aquatic systems water flow is of fundamental importance in shaping the structure and function of the ecosystem. The organized patterns of parallel orientation of ridges, sloughs, and tear-drop shaped tree islands along historic flow paths attest to the importance of water movement in structuring this system. Our main objective was to operate and manage the LILA facility to provide a broad potential as a research platform for an integrated group of multidisciplinary, multi-agency scientists collaborating on multifunctional studies aimed primarily at determining the effects of CERP water management scenarios on the ecology of tree islands and ridge and slough habitats. We support Everglades water management, CERP, and the Long-Term Plan by defining hydrologic regimes that sustain healthy tree islands and ridge and slough ecosystems. Information gained through this project will help to reduce the uncertainty of predicting the tree island and ridge and slough ecosystem response to changes in hydrologic conditions. Additionally, we have developed the LILA site as a visual example of Everglades restoration programs in action.

Economic assessment of best management practices in the Mara River Basin : toward implementing payment for watershed services

The Mara River in East Africa is currently experiencing poor water quality and increased fluctuations in seasonal flow. This study investigated technically effective and economically viable Best Management Practices for adoption in the Mara River Basin of Kenya that can stop further water resources degradation. A survey of 155 farmers was conducted in the upper catchment of the Kenyan side of the river basin. Farmers provided their assessment of BMPs that would best suit their farm in terms of water quality improvement, economic feasibility, and technicalsuitability. Cost data on different practices from farmers and published literature was collected. The results indicated that erosion control structures and runoff management practices were most suitable for adoption. The study estimated the total area that would be improved to restore water quality and reduce further water resources degradation. Farmers were found to incur losses from adopting new practices and would therefore require monetary support.

Land Use and Climate Change Impacts on the Hydrology of the Upper Mara River Basin, Kenya: Results of a Modeling Study to Support Better Resource Management

Some of the most valued natural and cultural landscapes on Earth lie in river basins that are poorly gauged and have incomplete historical climate and runoff records. The Mara River Basin of East Africa is such a basin. It hosts the internationally renowned Mara-Serengeti landscape as well as a rich mixture of indigenous cultures. The Mara River is the sole source of surface water to the landscape during the dry season and periods of drought. During recent years, the flow of the Mara River has become increasingly erratic, especially in the upper reaches, and resource managers are hampered by a lack of understanding of the relative influence of different sources of flow alteration. Uncertainties about the impacts of future climate change compound the challenges. We applied the Soil Water Assessment Tool (SWAT) to investigate the response of the headwater hydrology of the Mara River to scenarios of continued land use change and projected climate change. Under the data-scarce conditions of the basin, model performance was improved using satellite-based estimated rainfall data, which may also improve the usefulness of runoff models in other parts of East Africa. The results of the analysis indicate that any further conversion of forests to agriculture and grassland in the basin headwaters is likely to reduce dry season flows and increase peak flows, leading to greater water scarcity at critical times of the year and exacerbating erosion on hillslopes. Most climate change projections for the region call for modest and seasonally variable increases in precipitation (5–10 %) accompanied by increases in temperature (2.5–3.5 °C). Simulated runoff responses to climate change scenarios were non-linear and suggest the basin is highly vulnerable under low (−3 %) and high (+25 %) extremes of projected precipitation changes, but under median projections (+7 %) there is little impact on annual water yields or mean discharge. Modest increases in precipitation are partitioned largely to increased evapotranspiration. Overall, model results support the existing efforts of Mara water resource managers to protect headwater forests and indicate that additional emphasis should be placed on improving land management practices that enhance infiltration and aquifer recharge as part of a wider program of climate change adaptation.

A State and Territorial Survey Regarding Utilization of Environmental Health Shelter Assessments during Disasters, and a Secondary Analysis of Available Shelter Assessment Data

Disasters are complex events characterized by damage to key infrastructure and population displacements into disaster shelters. Assessing the living environment in shelters during disasters is a crucial health security concern. Until now, jurisdictional knowledge and preparedness on those assessment methods, or deficiencies found in shelters is limited. A cross-sectional survey (STUSA survey) ascertained knowledge and preparedness for those assessments in all 50 states, DC, and 5 US territories. Descriptive analysis of overall knowledge and preparedness was performed. Fisher’s exact statistics analyzed differences between two groups: jurisdiction type and population size. Two logistic regression models analyzed earthquakes and hurricane risks as predictors of knowledge and preparedness. A convenience sample of state shelter assessments records (n=116) was analyzed to describe environmental health deficiencies found during selected events. Overall, 55 (98%) of jurisdictions responded (states and territories) and appeared to be knowledgeable of these assessments (states 92%, territories 100%, p = 1.000), and engaged in disaster planning with shelter partners (states 96%, territories 83%, p = 0.564). Few had shelter assessment procedures (states 53%, territories 50%, p = 1.000); or training in disaster shelter assessments (states 41%, 60% territories, p = 0.638). Knowledge or preparedness was not predicted by disaster risks, population size, and jurisdiction type in neither model. Knowledge: hurricane (Adjusted OR 0.69, 95% C.I. 0.06-7.88); earthquake (OR 0.82, 95% C.I. 0.17-4.06); and both risks (OR 1.44, 95% C.I. 0.24-8.63); preparedness model: hurricane (OR 1.91, 95% C.I. 0.06-20.69); earthquake (OR 0.47, 95% C.I. 0.7-3.17); and both risks (OR 0.50, 95% C.I. 0.06-3.94). Environmental health deficiencies documented in shelter assessments occurred mostly in: sanitation (30%); facility (17%); food (15%); and sleeping areas (12%); and during ice storms and tornadoes. More research is needed in the area of environmental health assessments of disaster shelters, particularly, in those areas that may provide better insight into the living environment of all shelter occupants and potential effects in disaster morbidity and mortality. Also, to evaluate the effectiveness and usefulness of these assessments methods and the data available on environmental health deficiencies in risk management to protect those at greater risk in shelter facilities during disasters.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.