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.

Empirical study of exchange rates in Brazil, Canada and the Caribbean

This dissertation examines the monetary models of exchange rate determination for Brazil, Canada, and two countries in the Caribbean, namely, the Dominican Republic and Jamaica. With the exception of Canada, the others adopted the floating regime during the past ten years.^ The empirical validity of four seminal models in exchange rate economics were determined. Three of these models were entirely classical (Bilson and Frenkel) or Keynesian (Dornbusch) in nature. The fourth model (Real Interest Differential Model) was a mixture of the two schools of economic theory.^ There is no clear empirical evidence of the validity of the monetary models. However, the signs of the coefficients of the nominal interest differential variable were as predicted by the Keynesian hypothesis in the case of Canada and as predicted by the Chicago theorists in the remaining countries. Moreover, in case of Brazil, due to hyperinflation, the exchange rate is heavily influenced by domestic money supply.^ I also tested the purchasing power parity (PPP) for this same set of countries. For both the monetary as well as the PPP hypothesis, I tested for co-integration and applied ordinary least squares estimation procedure. The error correction model was also used for the PPP model, to determine convergence to equilibrium.^ The validity of PPP is also questionable for my set of countries. Endogeinity among the regressors as well as the lack of proper price indices are the contributing factors. More importantly, Central Bank intervention negate rapid adjustment of price and exchange rates to their equilibrium value. However, its forecasting capability for the period 1993-1994 is superior compared to the monetary models in two of the four cases.^ I conclude that in spite of the questionable validity of these models, the monetary models give better results in the case of the "smaller" economies like the Dominican Republic and Jamaica where monetary influences swamp the other determinants of exchange rate. ^

The role of individualism and collectivism as predictors of attributions for unethical work behavior: An empirical examination across two culturally diverse groups

This dissertation addresses how the cultural dimensions of individualism and collectivism affect the attributions people make for unethical behavior at work. The moderating effect of ethnicity is also examined by considering two culturally diverse groups: Hispanics and Anglos. The sample for this study is a group of business graduate students from two universities in the Southeast. A 20-minute survey was distributed to master's degree students at their classroom and later on returned to the researcher. Individualism and collectivism were operationalized as by a set of attitude items, while unethical work behavior was introduced in the form of hypothetical descriptions or scenarios. Data analysis employed multiple group confirmatory factor analysis for both independent and dependent variables, and subsequently multiple group LISREL models, in order to test predictions. Results confirmed the expected link between cultural variables and attribution responses, although the role of independent variables shifted, due to the moderating effect of ethnicity, and to the nuances of each particular situation. ^

Development of operational performance models for truck-lane restrictions on freeway corridors

Highways are generally designed to serve a mixed traffic flow that consists of passenger cars, trucks, buses, recreational vehicles, etc. The fact that the impacts of these different vehicle types are not uniform creates problems in highway operations and safety. A common approach to reducing the impacts of truck traffic on freeways has been to restrict trucks to certain lane(s) to minimize the interaction between trucks and other vehicles and to compensate for their differences in operational characteristics. ^ The performance of different truck lane restriction alternatives differs under different traffic and geometric conditions. Thus, a good estimate of the operational performance of different truck lane restriction alternatives under prevailing conditions is needed to help make informed decisions on truck lane restriction alternatives. This study develops operational performance models that can be applied to help identify the most operationally efficient truck lane restriction alternative on a freeway under prevailing conditions. The operational performance measures examined in this study include average speed, throughput, speed difference, and lane changes. Prevailing conditions include number of lanes, interchange density, free-flow speeds, volumes, truck percentages, and ramp volumes. ^ Recognizing the difficulty of collecting sufficient data for an empirical modeling procedure that involves a high number of variables, the simulation approach was used to estimate the performance values for various truck lane restriction alternatives under various scenarios. Both the CORSIM and VISSIM simulation models were examined for their ability to model truck lane restrictions. Due to a major problem found in the CORSIM model for truck lane modeling, the VISSIM model was adopted as the simulator for this study. ^ The VISSIM model was calibrated mainly to replicate the capacity given in the 2000 Highway Capacity Manual (HCM) for various free-flow speeds under the ideal basic freeway section conditions. Non-linear regression models for average speed, throughput, average number of lane changes, and speed difference between the lane groups were developed. Based on the performance models developed, a simple decision procedure was recommended to select the desired truck lane restriction alternative for prevailing conditions. ^

Liquidity and asset pricing: A comparison between three-factor and three-moment capital asset pricing models

Liquidity is an important attribute of an asset that investors would like to take into consideration when making investment decisions. However, the previous empirical evidence whether liquidity is a determinant of stock return is not unanimous. This dissertation provides a very comprehensive study about the role of liquidity in asset pricing using the Fama-French (1993) three-factor and Kraus and Litzenberger (1976) three-moment CAPM as models for risk adjustment. The relationship between liquidity and well-known determinants of stock returns such as size and book-to-market are also investigated. This study examines the liquidity and asset pricing issues for both intertemporal as well as cross-sectional data. ^ The results indicate an existence of a liquidity premium, i.e., less liquid stocks would demand higher rate of return than more liquid stocks. More specifically, a drop of 1 percent in liquidity is associated with a higher rate of return of about 2 to 3 basis points per month. Further investigation reveals that neither the Fama-French three-factor model nor the three-moment CAPM captures the liquidity premium. Finally, the results show that well-known determinants of stock return such as size and book-to-market do not serve as proxy for liquidity. ^ Overall, this dissertation shows that a liquidity premium exists in the stock market and that liquidity is a distinct effect, and is not influenced by the presence of non-market factors, market factors and other stock characteristics.^

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Three empirical essays on startups' survival using the Kauffman Firm Survey

This dissertation studies newly founded U.S. firms' survival using three different releases of the Kauffman Firm Survey. I study firms' survival from a different perspective in each chapter. ^ The first essay studies firms' survival through an analysis of their initial state at startup and the current state of the firms as they gain maturity. The probability of survival is determined using three probit models, using both firm-specific variables and an industry scale variable to control for the environment of operation. The firm's specific variables include size, experience and leverage as a debt-to-value ratio. The results indicate that size and relevant experience are both positive predictors for the initial and current states. Debt appears to be a predictor of exit if not justified wisely by acquiring assets. As suggested previously in the literature, entering a smaller-scale industry is a positive predictor of survival from birth. Finally, a smaller-scale industry diminishes the negative effects of debt. ^ The second essay makes use of a hazard model to confirm that new service-providing (SP) firms are more likely to survive than new product providers (PPs). I investigate the possible explanations for the higher survival rate of SPs using a Cox proportional hazard model. I examine six hypotheses (variations in capital per worker, expenses per worker, owners' experience, industry wages, assets and size), none of which appear to explain why SPs are more likely than PPs to survive. Two other possibilities are discussed: tax evasion and human/social relations, but these could not be tested due to lack of data. ^ The third essay investigates women-owned firms' higher failure rates using a Cox proportional hazard on two models. I make use of a never-before used variable that proxies for owners' confidence. This variable represents the owners' self-evaluated competitive advantage. ^ The first empirical model allows me to compare women's and men's hazard rates for each variable. In the second model I successively add the variables that could potentially explain why women have a higher failure rate. Unfortunately, I am not able to fully explain the gender effect on the firms' survival. Nonetheless, the second empirical approach allows me to confirm that social and psychological differences among genders are important in explaining the higher likelihood to fail in women-owned firms.^

Globalization and the public provision of higher education: Empirical evidence from selected countries

This research study was designed to examine the relationship between globalization as measured by the KOF index, its related forces (economic, political, cultural and technological) and the public provision of higher education. This study is important since globalization is increasingly being associated with changes in critical aspects of higher education. The public provision of education was measured by government expenditure and educational outcomes; that is participation, gender equity and attainment. The study utilized a non-experimental quantitative research design. Data collected from secondary sources for 139 selected countries was analyzed. The countries were geographically distributed and included both developed and developing countries. The choice of countries for inclusion in the study was based on data availability. The data, which was sourced from international organizations such as the United Nations and the World Bank, were examined for different time periods using five year averages. The period covered was 1970 to 2009.^ The relationship between globalization and the higher education variables was examined using cross sectional regression analysis while controlling for economic, political and demographic factors. The major findings of the study are as follows. For the two spending models, only one revealed a significant relationship between globalization and education with the R 2 s ranging from .222 to .448 over the period. This relationship was however negative indicating that as globalization increased, spending on higher education declined. However, for the education outcomes models, this relationship was not significant. For the sub-indices of globalization, only the political dimension showed significance as shown in the spending model. Political globalization was significant for six periods with R2 s ranging from .31 to .52.^ The study concluded that the results are mixed for both the spending and outcome models. It also found no robust effects of globalization on government education provision. This finding is not surprising given the existing literature which sees mixed results on the social impact of globalization.^

A generic model of execution for synthesizing domain-specific models

Software engineering researchers are challenged to provide increasingly more powerful levels of abstractions to address the rising complexity inherent in software solutions. One new development paradigm that places models as abstraction at the forefront of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code.^ Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process.^ The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources.^ At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM's synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise.^ This dissertation investigates how to decouple the DSK from the MoE and subsequently producing a generic model of execution (GMoE) from the remaining application logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis component of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions.^ This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.^

The role of individualism and collectivism as predictors of attributions for unethical work behavior : an empirical examination across two culturally diverse groups

This dissertation addresses how the cultural dimensions of individualism and collectivism affect the attributions people make for unethical behavior at work. The moderating effect of ethnicity is also examined by considering two culturally diverse groups: Hispanics and Anglos. The sample for this study is a group of business graduate students from two universities in the Southeast. A 20-minute survey was distributed to master's degree students at their classroom and later on returned to the researcher. Individualism and collectivism were operationalized as by a set of attitude items, while unethical work behavior was introduced in the form of hypothetical descriptions or scenarios. Data analysis employed multiple group confirmatory factor analysis for both independent and dependent variables, and subsequently multiple group LISREL models, in order to test predictions. Results confirmed the expected link between cultural variables and attribution responses, although the role of independent variables shifted, due to the moderating effect of ethnicity, and to the nuances of each particular situation.

A Generic Model of Execution for Synthesizing Domain-Specific Models

Software engineering researchers are challenged to provide increasingly more pow- erful levels of abstractions to address the rising complexity inherent in software solu- tions. One new development paradigm that places models as abstraction at the fore- front of the development process is Model-Driven Software Development (MDSD). MDSD considers models as first class artifacts, extending the capability for engineers to use concepts from the problem domain of discourse to specify apropos solutions. A key component in MDSD is domain-specific modeling languages (DSMLs) which are languages with focused expressiveness, targeting a specific taxonomy of problems. The de facto approach used is to first transform DSML models to an intermediate artifact in a HLL e.g., Java or C++, then execute that resulting code. Our research group has developed a class of DSMLs, referred to as interpreted DSMLs (i-DSMLs), where models are directly interpreted by a specialized execution engine with semantics based on model changes at runtime. This execution engine uses a layered architecture and is referred to as a domain-specific virtual machine (DSVM). As the domain-specific model being executed descends the layers of the DSVM the semantic gap between the user-defined model and the services being provided by the underlying infrastructure is closed. The focus of this research is the synthesis engine, the layer in the DSVM which transforms i-DSML models into executable scripts for the next lower layer to process. The appeal of an i-DSML is constrained as it possesses unique semantics contained within the DSVM. Existing DSVMs for i-DSMLs exhibit tight coupling between the implicit model of execution and the semantics of the domain, making it difficult to develop DSVMs for new i-DSMLs without a significant investment in resources. At the onset of this research only one i-DSML had been created for the user- centric communication domain using the aforementioned approach. This i-DSML is the Communication Modeling Language (CML) and its DSVM is the Communication Virtual machine (CVM). A major problem with the CVM’s synthesis engine is that the domain-specific knowledge (DSK) and the model of execution (MoE) are tightly interwoven consequently subsequent DSVMs would need to be developed from inception with no reuse of expertise. This dissertation investigates how to decouple the DSK from the MoE and sub- sequently producing a generic model of execution (GMoE) from the remaining appli- cation logic. This GMoE can be reused to instantiate synthesis engines for DSVMs in other domains. The generalized approach to developing the model synthesis com- ponent of i-DSML interpreters utilizes a reusable framework loosely coupled to DSK as swappable framework extensions. This approach involves first creating an i-DSML and its DSVM for a second do- main, demand-side smartgrid, or microgrid energy management, and designing the synthesis engine so that the DSK and MoE are easily decoupled. To validate the utility of the approach, the SEs are instantiated using the GMoE and DSKs of the two aforementioned domains and an empirical study to support our claim of reduced developmental effort is performed.

Globalization and the Public Provision of Higher Education: Empirical Evidence from Selected Countries

This research study was designed to examine the relationship between globalization as measured by the KOF index, its related forces (economic, political, cultural and technological) and the public provision of higher education. This study is important since globalization is increasingly being associated with changes in critical aspects of higher education. The public provision of education was measured by government expenditure and educational outcomes; that is participation, gender equity and attainment. The study utilized a non-experimental quantitative research design. Data collected from secondary sources for 139 selected countries was analyzed. The countries were geographically distributed and included both developed and developing countries. The choice of countries for inclusion in the study was based on data availability. The data, which was sourced from international organizations such as the United Nations and the World Bank, were examined for different time periods using five year averages. The period covered was 1970 to 2009. The relationship between globalization and the higher education variables was examined using cross sectional regression analysis while controlling for economic, political and demographic factors. The major findings of the study are as follows. For the two spending models, only one revealed a significant relationship between globalization and education with the R2 s ranging from .222 to .448 over the period. This relationship was however negative indicating that as globalization increased, spending on higher education declined. However, for the education outcomes models, this relationship was not significant. For the sub-indices of globalization, only the political dimension showed significance as shown in the spending model. Political globalization was significant for six periods with R2 s ranging from .31 to .52. The study concluded that the results are mixed for both the spending and outcome models. It also found no robust effects of globalization on government education provision. This finding is not surprising given the existing literature which sees mixed results on the social impact of globalization.