Climate, Land Use and Hydrologic Sensitivities of Stormwater Quantity and Quality in Complex Coastal Urban Watersheds

The study analyzed hydro-climatic and land use sensitivities of stormwater runoff and quality in the complex coastal urban watershed of Miami River Basin, Florida by developing a Storm Water Management Model (EPA SWMM 5). Regression-based empirical models were also developed to explain stream water quality in relation to internal (land uses and hydrology) and external (upstream contribution, seawater) sources and drivers in six highly urbanized canal basins of Southeast Florida. Stormwater runoff and quality were most sensitive to rainfall, imperviousness, and conversion of open lands/parks to residential, commercial and industrial areas. In-stream dissolved oxygen and total phosphorus in the watersheds were dictated by internal stressors while external stressors were dominant for total nitrogen and specific conductance. The research findings and tools will be useful for proactive monitoring and management of storm runoff and urban stream water quality under the changing climate and environment in South Florida and around the world.

Hydroperiod and seasonal effects on fish decomposition in an oligotrophic Everglades marsh

In the Everglades, the majority of fish detrital inputs occur during the dry scason, when waterlevel drawdown reduces aquatic habitat. While these mortality events are highly seasonal, the remineralization and recycling of fish detrital nutrients may represent an important stimulus to the ecosystem in the following wet season. The goal of this study was to quantify the rate of detrital fish decomposition during three periods of the year to determine seasonal variations in decomposition patterns in this ecosystem. A multiple regression analysis showed that hydroperiod and water depth both played a role in determining fish decomposition rates within this ecosystem. Decomposition rates ranged from a low of 13% day−1 in December 2000 to a high of 50% day−1 in April 2001, the height of the dry season. Phosphorus analysis showed that Gambusia holbrooki, the dominant small fish species in the Everglades, contains 7.169±1.46 mg P g−1 wet fish weight. Based on the observed decomposition rates and the average biomass added, the estimafed daily flux of phosphorus from the experimental detrital loading ranged from a low of 27.04 mg P day−1 to a high of 108.14 mg P day−1 during the decomposition period. We estimated that these inputs could represent an input of 43 μg P m−2 day−1 to the total temporal Everglades phosphorus budget. Although much of this phosphorus is likely incorporated into the macroinvertebrate pool, detrital inputs peak during the dry season when nutrients are most likely to be incorporated into the soil and occur when decomposition of vegetative material is moisture-limited. These inputs may therefore play an important role in stimulating vegetative production during the early wet season.

Estimation of average annual daily traffic (AADT) using geographically weighted regression (GWR) method and geographic information system (GIS)

Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

An exergy based engineering and economic analysis of sustainable building

To achieve the goal of sustainable development, the building energy system was evaluated from both the first and second law of thermodynamics point of view. The relationship between exergy destruction and sustainable development were discussed at first, followed by the description of the resource abundance model, the life cycle analysis model and the economic investment effectiveness model. By combining the forgoing models, a new sustainable index was proposed. Several green building case studies in U.S. and China were presented. The influences of building function, geographic location, climate pattern, the regional energy structure, and the technology improvement potential of renewable energy in the future were discussed. The building’s envelope, HVAC system, on-site renewable energy system life cycle analysis from energy, exergy, environmental and economic perspective were compared. It was found that climate pattern had a dramatic influence on the life cycle investment effectiveness of the building envelope. The building HVAC system energy performance was much better than its exergy performance. To further increase the exergy efficiency, renewable energy rather than fossil fuel should be used as the primary energy. A building life cycle cost and exergy consumption regression model was set up. The optimal building insulation level could be affected by either cost minimization or exergy consumption minimization approach. The exergy approach would cause better insulation than cost approach. The influence of energy price on the system selection strategy was discussed. Two photovoltaics (PV) systems—stand alone and grid tied system were compared by the life cycle assessment method. The superiority of the latter one was quite obvious. The analysis also showed that during its life span PV technology was less attractive economically because the electricity price in U.S. and China did not fully reflect the environmental burden associated with it. However if future energy price surges and PV system cost reductions were considered, the technology could be very promising for sustainable buildings in the future.

How do partnerships lead to a competitive advantage? Applying the resource based view to nascent social ventures

This dissertation is one of the earliest to systematically apply and empirically test the resource-based view (RBV) in the context of nascent social ventures in a large scale study. Social ventures are entrepreneurial ventures organized as nonprofit, for-profit, or hybrid organizations whose primary purpose is to address unmet social needs and create social value. Nascent social ventures face resource gaps and engage in partnerships or alliances as one means to access external resources. These partnerships with different sectors facilitate social venture innovative and earned income strategies, and assist in the development of adequate heterogeneous resource conditions that impact competitive advantage. Competitive advantage in the context of nascent social ventures is achieved through the creation of value and the achievement of venture development activities and launching. The relationships between partnerships, heterogeneous resource conditions, strategies, and competitive advantage are analyzed in the context of nascent social ventures that participated in business plan competitions. A content analysis of 179 social venture business plans and an exploratory follow-up survey of 72 of these ventures are used to analyze these relationships using regression, ANOVA, correlations, t-tests, and non-parametric statistics. The findings suggest a significant positive relationship between competitive advantage and partnership diversity, heterogeneous resource conditions, social innovation, and earned income. Social capital is the type of resource most significantly related to competitive advantage. Founder previous start-up experience, client location, and business plan completeness are also found to be significant in the relationship between partnership diversity and competitive advantage. Finally the findings suggest that hybrid social ventures create a greater competitive advantage than nonprofit or for-profit social ventures. Consequently, this dissertation not only provides academics further insight into the factors that impact nascent social value creation, venture development, and ability to launch, but also offers practitioners guidance on how best to organize certain processes to create a competitive advantage. As a result more insight is gained into the nascent social venture creation process and how these ventures can have a greater impact on society.

Color-based surface reflectance separation for scene illumination estimation and rendering

Given the importance of color processing in computer vision and computer graphics, estimating and rendering illumination spectral reflectance of image scenes is important to advance the capability of a large class of applications such as scene reconstruction, rendering, surface segmentation, object recognition, and reflectance estimation. Consequently, this dissertation proposes effective methods for reflection components separation and rendering in single scene images. Based on the dichromatic reflectance model, a novel decomposition technique, named the Mean-Shift Decomposition (MSD) method, is introduced to separate the specular from diffuse reflectance components. This technique provides a direct access to surface shape information through diffuse shading pixel isolation. More importantly, this process does not require any local color segmentation process, which differs from the traditional methods that operate by aggregating color information along each image plane. ^ Exploiting the merits of the MSD method, a scene illumination rendering technique is designed to estimate the relative contributing specular reflectance attributes of a scene image. The image feature subset targeted provides a direct access to the surface illumination information, while a newly introduced efficient rendering method reshapes the dynamic range distribution of the specular reflectance components over each image color channel. This image enhancement technique renders the scene illumination reflection effectively without altering the scene’s surface diffuse attributes contributing to realistic rendering effects. ^ As an ancillary contribution, an effective color constancy algorithm based on the dichromatic reflectance model was also developed. This algorithm selects image highlights in order to extract the prominent surface reflectance that reproduces the exact illumination chromaticity. This evaluation is presented using a novel voting scheme technique based on histogram analysis. ^ In each of the three main contributions, empirical evaluations were performed on synthetic and real-world image scenes taken from three different color image datasets. The experimental results show over 90% accuracy in illumination estimation contributing to near real world illumination rendering effects. ^

Robert Twilley sewing decomposition bags for mangrove root decomposition experiment

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Distribution of Diatoms and Development of Diatom-Based Models for Inferring Salinity and Nutrient Concentrations in Florida Bay and Adjacent Coastal Wetlands of South Florida (USA)

The composition and distribution of diatom algae inhabiting estuaries and coasts of the subtropical Americas are poorly documented, especially relative to the central role diatoms play in coastal food webs and to their potential utility as sentinels of environmental change in these threatened ecosystems. Here, we document the distribution of diatoms among the diverse habitat types and long environmental gradients represented by the shallow topographic relief of the South Florida, USA, coastline. A total of 592 species were encountered from 38 freshwater, mangrove, and marine locations in the Everglades wetland and Florida Bay during two seasonal collections, with the highest diversity occurring at sites of high salinity and low water column organic carbon concentration (WTOC). Freshwater, mangrove, and estuarine assemblages were compositionally distinct, but seasonal differences were only detected in mangrove and estuarine sites where solute concentration differed greatly between wet and dry seasons. Epiphytic, planktonic, and sediment assemblages were compositionally similar, implying a high degree of mixing along the shallow, tidal, and storm-prone coast. The relationships between diatom taxa and salinity, water total phosphorus (WTP), water total nitrogen (WTN), and WTOC concentrations were determined and incorporated into weighted averaging partial least squares regression models. Salinity was the most influential variable, resulting in a highly predictive model (r apparent 2  = 0.97, r jackknife 2  = 0.95) that can be used in the future to infer changes in coastal freshwater delivery or sea-level rise in South Florida and compositionally similar environments. Models predicting WTN (r apparent 2  = 0.75, r jackknife 2  = 0.46), WTP (r apparent 2  = 0.75, r jackknife 2  = 0.49), and WTOC (r apparent 2  = 0.79, r jackknife 2  = 0.57) were also strong, suggesting that diatoms can provide reliable inferences of changes in solute delivery to the coastal ecosystem.

The relationship between faculty perceptions and implementation of elements of transactional distance theory and online web-based course completion rates

The purpose of this study was to explore the relationship between faculty perceptions, selected demographics, implementation of elements of transactional distance theory and online web-based course completion rates. This theory posits that the high transactional distance of online courses makes it difficult for students to complete these courses successfully; too often this is associated with low completion rates. Faculty members play an indispensable role in course design, whether online or face-to-face. They also influence course delivery format from design through implementation and ultimately to how students will experience the course. This study used transactional distance theory as the conceptual framework to examine the relationship between teaching and learning strategies used by faculty members to help students complete online courses. Faculty members' sex, number of years teaching online at the college, and their online course completion rates were considered. A researcher-developed survey was used to collect data from 348 faculty members who teach online at two prominent colleges in the southeastern part of United States. An exploratory factor analysis resulted in six factors related to transactional distance theory. The factors accounted for slightly over 65% of the variance of transactional distance scores as measured by the survey instrument. Results provided support for Moore's (1993) theory of transactional distance. Female faculty members scored higher in all the factors of transactional distance theory when compared to men. Faculty number of years teaching online at the college level correlated significantly with all the elements of transactional distance theory. Regression analysis was used to determine that two of the factors, instructor interface and instructor-learner interaction, accounted for 12% of the variance in student online course completion rates. In conclusion, of the six factors found, the two with the highest percentage scores were instructor interface and instructor-learner interaction. This finding, while in alignment with the literature concerning the dialogue element of transactional distance theory, brings a special interest to the importance of instructor interface as a factor. Surprisingly, based on the reviewed literature on transactional distance theory, faculty perceptions concerning learner-learner interaction was not an important factor and there was no learner-content interaction factor.

The relationship of aptitude, course grades, and program length, to performance on a standards based test (NCLEX -RN)

The purpose of this study was threefold: first, to investigate variables associated with learning, and performance as measured by the National Council Licensure Examination for Registered Nurses (NCLEX-RN). The second purpose was to validate the predictive value of the Assessment Technologies Institute (ATI) achievement exit exam, and lastly, to provide a model that could be used to predict performance on the NCLEX-RN, with implications for admission and curriculum development. The study was based on school learning theory, which implies that acquisition in school learning is a function of aptitude (pre-admission measures), opportunity to learn, and quality of instruction (program measures). Data utilized were from 298 graduates of an associate degree nursing program in the Southeastern United States. Of the 298 graduates, 142 were Hispanic, 87 were Black, non-Hispanic, 54 White, non-Hispanic, and 15 reported as Others. The graduates took the NCLEX-RN for the first time during the years 2003–2005. This study was a predictive, correlational design that relied upon retrospective data. Point biserial correlations, and chi-square analyses were used to investigate relationships between 19 selected predictor variables and the dichotomous criterion variable, NCLEX-RN. The correlation and chi square findings indicated that men did better on the NCLEX-RN than women; Blacks had the highest failure rates, followed by Hispanics; older students were more likely to pass the exam than younger students; and students who passed the exam started and completed the nursing program with a higher grade point average, than those who failed the exam. Using logistic regression, five statistical models that used variables associated with learning and student performance on the NCLEX-RN were tested with a model adapted from Bloom's (1976) and Carroll's (1963) school learning theories. The derived model included: NCLEX-RNsuccess = f (Nurse Entrance Test and advanced medical-surgical nursing course grade achieved). The model demonstrates that student performance on the NCLEX-RN can be predicted by one pre-admission measure, and a program measure. The Assessment Technologies Institute achievement exit exam (an outcome measure) had no predictive value for student performance on the NCLEX-RN. The model developed accurately predicted 94% of the student's successful performance on the NCLEX-RN.

Identification of characteristic volatile organic compounds released during the decomposition process of human remains and analogues

The manner in which remains decompose has been and is currently being researched around the world, yet little is still known about the generated scent of death. In fact, it was not until the Casey Anthony trial that research on the odor released from decomposing remains, and the compounds that it is comprised of, was brought to light. The Anthony trial marked the first admission of human decomposition odor as forensic evidence into the court of law; however, it was not "ready for prime time" as the scientific research on the scent of death is still in its infancy. This research employed the use of solid-phase microextraction (SPME) with gas chromatography-mass spectrometry (GC-MS) to identify the volatile organic compounds (VOCs) released from decomposing remains and to assess the impact that different environmental conditions had on the scent of death. Using human cadaver analogues, it was discovered that the environment in which the remains were exposed to dramatically affected the odors released by either modifying the compounds that it was comprised of or by enhancing/hindering the amount that was liberated. In addition, the VOCs released during the different stages of the decomposition process for both human remains and analogues were evaluated. Statistical analysis showed correlations between the stage of decay and the VOCs generated, such that each phase of decomposition was distinguishable based upon the type and abundance of compounds that comprised the odor. This study has provided new insight into the scent of death and the factors that can dramatically affect it, specifically, frozen, aquatic, and soil environments. Moreover, the results revealed that different stages of decomposition were distinguishable based upon the type and total mass of each compound present. Thus, based upon these findings, it is suggested that the training aids that are employed for human remains detection (HRD) canines should 1) be characteristic of remains that have undergone decomposition in different environmental settings, and 2) represent each stage of decay, to ensure that the HRD canines have been trained to the various odors that they are likely to encounter in an operational situation.

Predicting Lodging Manager Annual Salary Based on Metropolitan Statistical Area Attributes and Lodging Industry Performance: Exploring the Concept

Using multiple regression analysis, lodging managers’ annual mean salaries in 143 Metropolitan Statistical Areas (MSA) within the U.S. were analyzed to identify what relationships existed with variables related to general MSA characteristics, along with the lodging industry’s size and performance. By examining the relationship between these variables, the authors predict the long-term possibility of predicting lodging industry managers’ salaries. These predictions may have an impact on financial performance of an individual lodging property or organization. Through this paper, this concept was applied and explored within U.S. MSAs. These findings may have value for a variety of stakeholders, including human resources practitioners, the hospitality education community, and individuals considering lodging management careers.

Workplace Aggression: A Reconceptualization Of The Construct & An Exploration Of Strain Based Outcomes

The examination of Workplace Aggression as a global construct conceptualization has gained considerable attention over the past few years as organizations work to better understand and address the occurrence and consequences of this challenging construct. The purpose of this dissertation is to build on previous efforts to validate the appropriateness and usefulness of a global conceptualization of the workplace aggression construct. This dissertation has been broken up into two parts: Part 1 utilized a Confirmatory Factor Analysis approach in order to assess the existence of workplace aggression as a global construct; Part 2 utilized a series of correlational analyses to examine the relationship between a selection of commonly experienced individual strain based outcomes and the global construct conceptualization assessed in Part 1. Participants were a diverse sample of 219 working individuals from Amazon’s Mechanical Turk participant pool. Results of Part 1 did not show support for a one-factor global construct conceptualization of the workplace aggression construct. However, support was shown for a higher-order five-factor model of the construct, suggesting that it may be possible to conceptualize workplace aggression as an overarching construct that is made up of separate workplace aggression constructs. Results of Part 2 showed support for the relationships between an existing global construct workplace aggression conceptualization and a series of strain-based outcomes. Utilizing correlational analyses, additional post-hoc analyses showed that individual factors such as emotional intelligence and personality are related to the experience of workplace aggression. Further, utilizing moderated regression analysis, the results demonstrated that individuals experiencing high levels of workplace aggression reported higher job satisfaction when they felt strongly that the aggressive act was highly visible, and similarly, when they felt that there was a clear intent to cause harm. Overall, the findings of this dissertation do support the need for a simplification of its current state of measurement. Future research should continue to examine workplace aggression in an effort to shed additional light on the structure and usefulness of this complex construct.

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Workplace Aggression: A Reconceptualization of The Construct & an Exploration of Strain Based Outcomes

The examination of Workplace Aggression as a global construct conceptualization has gained considerable attention over the past few years as organizations work to better understand and address the occurrence and consequences of this challenging construct. The purpose of this dissertation is to build on previous efforts to validate the appropriateness and usefulness of a global conceptualization of the workplace aggression construct. This dissertation has been broken up into two parts: Part 1 utilized a Confirmatory Factor Analysis approach in order to assess the existence of workplace aggression as a global construct; Part 2 utilized a series of correlational analyses to examine the relationship between a selection of commonly experienced individual strain based outcomes and the global construct conceptualization assessed in Part 1. Participants were a diverse sample of 219 working individuals from Amazon’s Mechanical Turk participant pool. Results of Part 1 did not show support for a one-factor global construct conceptualization of the workplace aggression construct. However, support was shown for a higher-order five-factor model of the construct, suggesting that it may be possible to conceptualize workplace aggression as an overarching construct that is made up of separate workplace aggression constructs. Results of Part 2 showed support for the relationships between an existing global construct workplace aggression conceptualization and a series of strain-based outcomes. Utilizing correlational analyses, additional post-hoc analyses showed that individual factors such as emotional intelligence and personality are related to the experience of workplace aggression. Further, utilizing moderated regression analysis, the results demonstrated that individuals experiencing high levels of workplace aggression reported higher job satisfaction when they felt strongly that the aggressive act was highly visible, and similarly, when they felt that there was a clear intent to cause harm. Overall, the findings of this dissertation do support the need for a simplification of its current state of measurement. Future research should continue to examine workplace aggression in an effort to shed additional light on the structure and usefulness of this complex construct.