Vertical Handoff Target Selection in a Heterogeneous Wireless Network Using Fuzzy ELECTRE

Global connectivity is on the verge of becoming a reality to provide high-speed, high-quality, and reliable communication channels for mobile devices at anytime, anywhere in the world. In a heterogeneous wireless environment, one of the key ingredients to provide efficient and ubiquitous computing with guaranteed quality and continuity of service is the design of intelligent handoff algorithms. Traditional single-metric handoff decision algorithms, such as Received Signal Strength (RSS), are not efficient and intelligent enough to minimize the number of unnecessary handoffs, decision delays, call-dropping and blocking probabilities. This research presents a novel approach for of a Multi Attribute Decision Making (MADM) model based on an integrated fuzzy approach for target network selection.

VHITS: Vertical handoff initiation and target selection in a heterogeneous wireless network

Global connectivity, for anyone, at anyplace, at anytime, to provide high-speed, high-quality, and reliable communication channels for mobile devices, is now becoming a reality. The credit mainly goes to the recent technological advances in wireless communications comprised of a wide range of technologies, services, and applications to fulfill the particular needs of end-users in different deployment scenarios (Wi-Fi, WiMAX, and 3G/4G cellular systems). In such a heterogeneous wireless environment, one of the key ingredients to provide efficient ubiquitous computing with guaranteed quality and continuity of service is the design of intelligent handoff algorithms. Traditional single-metric handoff decision algorithms, such as Received Signal Strength (RSS) based, are not efficient and intelligent enough to minimize the number of unnecessary handoffs, decision delays, and call-dropping and/or blocking probabilities. This research presented a novel approach for the design and implementation of a multi-criteria vertical handoff algorithm for heterogeneous wireless networks. Several parallel Fuzzy Logic Controllers were utilized in combination with different types of ranking algorithms and metric weighting schemes to implement two major modules: the first module estimated the necessity of handoff, and the other module was developed to select the best network as the target of handoff. Simulations based on different traffic classes, utilizing various types of wireless networks were carried out by implementing a wireless test-bed inspired by the concept of Rudimentary Network Emulator (RUNE). Simulation results indicated that the proposed scheme provided better performance in terms of minimizing the unnecessary handoffs, call dropping, and call blocking and handoff blocking probabilities. When subjected to Conversational traffic and compared against the RSS-based reference algorithm, the proposed scheme, utilizing the FTOPSIS ranking algorithm, was able to reduce the average outage probability of MSs moving with high speeds by 17%, new call blocking probability by 22%, the handoff blocking probability by 16%, and the average handoff rate by 40%. The significant reduction in the resulted handoff rate provides MS with efficient power consumption, and more available battery life. These percentages indicated a higher probability of guaranteed session continuity and quality of the currently utilized service, resulting in higher user satisfaction levels.

Personality and performance: Assessing the mediating role of mental model formation in the personality-performance relationship

Personality has long been linked to performance. Evolutions in this relationship have brought forward new questions regarding the true nature of how personality impacts performance. Both direct and indirect relationships have been proven significant. This study further investigated potential indirect relationships by including a mediating variable, mental model formation, in the personality-performance relationship. Undergraduate students were assessed in a 6-week period, Time 1 - Time 2 experiment. Conceptualizations of personality included measures of the Big 5 model and Self-efficacy, with performance measured by content quiz and overall course scores. Findings showed that the Big 5 personality traits, extraversion and agreeableness, positively and significantly impacted commonality with the instructor's mental model. However, commonality with the instructor's mental model did not impact performance. In comparison, commonality with an expert mental model positively and significantly impacted performance for both the content quiz and overall course score. Furthermore, similarity with an expert mental model positively and significantly impacted overall course performance. Hypothesized full mediation of mental model formation for the personality-performance relationship was not supported due to a lack of direct effect relationships required for mediation. However, a revised conceptualization of results emerged. Findings from the current study point to the novel and unique role mental models play in the personality-performance relationship. While personality traits do impact mental model formation, accuracy in the mental models formed is critical to performance.

Liquidity, governance and adverse selection in asset pricing

A plethora of recent literature on asset pricing provides plenty of empirical evidence on the importance of liquidity, governance and adverse selection of equity on pricing of assets together with more traditional factors such as market beta and the Fama-French factors. However, literature has usually stressed that these factors are priced individually. In this dissertation we argue that these factors may be related to each other, hence not only individual but also joint tests of their significance is called for. ^ In the three related essays, we examine the liquidity premium in the context of the finer three-digit SIC industry classification, joint importance of liquidity and governance factors as well as governance and adverse selection. Recent studies by Core, Guay and Rusticus (2006) and Ben-Rephael, Kadan and Wohl (2010) find that governance and liquidity premiums are dwindling in the last few years. One reason could be that liquidity is very unevenly distributed across industries. This could affect the interpretation of prior liquidity studies. Thus, in the first chapter we analyze the relation of industry clustering and liquidity risk following a finer industry classification suggested by Johnson, Moorman and Sorescu (2009). In the second chapter, we examine the dwindling influence of the governance factor if taken simultaneously with liquidity. We argue that this happens since governance characteristics are potentially a proxy for information asymmetry that may be better captured by market liquidity of a company's shares. Hence, we jointly examine both the factors, namely, governance and liquidity - in a series of standard asset pricing tests. Our results reconfirm the importance of governance and liquidity in explaining stock returns thus independently corroborating the findings of Amihud (2002) and Gompers, Ishii and Metrick (2003). Moreover, governance is not subsumed by liquidity. Lastly, we analyze the relation of governance and adverse selection, and again corroborate previous findings of a priced governance factor. Furthermore, we ascertain the importance of microstructure measures in asset pricing by employing Huang and Stoll's (1997) method to extract an adverse selection variable and finding evidence for its explanatory power in four-factor regressions.^

A Fuzzy Preprocessing Module for Optimizing the Access Network Selection in Wireless Networks

A heterogeneous wireless network is characterized by the presence of different wireless access technologies that coexist in an overlay fashion. These wireless access technologies usually differ in terms of their operating parameters. On the other hand, Mobile Stations (MSs) in a heterogeneous wireless network are equipped with multiple interfaces to access different types of services from these wireless access technologies. The ultimate goal of these heterogeneous wireless networks is to provide global connectivity with efficient ubiquitous computing to these MSs based on the Always Best Connected (ABC) principle. This is where the need for intelligent and efficient Vertical Handoffs (VHOs) between wireless technologies in a heterogeneous environment becomes apparent. This paper presents the design and implementation of a fuzzy multicriteria based Vertical Handoff Necessity Estimation (VHONE) scheme that determines the proper time for VHO, while considering the continuity and quality of the currently utilized service, and the end-users' satisfaction.

Examining the use of Simarouba glauca Seed Oil as a Feedstock for the Production of Biodiesel using a Small Scale Model Developed in India

Simarouba glauca, a non-edible oilseed crop native to South Florida, is gaining popularity as a feedstock for the production of biodiesel. The University of Agriculture Sciences in Bangalore, India has developed a biodiesel production model based on the principles of decentralization, small scales, and multiple fuel sources. Success of such a program depends on conversion efficiencies at multiple stages. The conversion efficiency of the field-level, decentralized production model was compared with the in-laboratory conversion efficiency benchmark. The study indicated that the field-level model conversion efficiency was less than that of the lab-scale set up. The fuel qualities and characteristics of the Simarouba glauca biodiesel were tested and found to be the standards required for fuel designation. However, this research suggests that for Simarouba glauca to be widely accepted as a biodiesel feedstock further investigation is still required.

Personality and Performance: Assessing the Mediating Role of Mental Model Formation in the Personality-Performance Relationship

Personality has long been linked to performance. Evolutions in this relationship have brought forward new questions regarding the true nature of how personality impacts performance. Both direct and indirect relationships have been proven significant. This study further investigated potential indirect relationships by including a mediating variable, mental model formation, in the personality-performance relationship. Undergraduate students were assessed in a 6-week period, Time 1 - Time 2 experiment. Conceptualizations of personality included measures of the Big 5 model and Self-efficacy, with performance measured by content quiz and overall course scores. Findings showed that the Big 5 personality traits, extraversion and agreeableness, positively and significantly impacted commonality with the instructor’s mental model. However, commonality with the instructor’s mental model did not impact performance. In comparison, commonality with an expert mental model positively and significantly impacted performance for both the content quiz and overall course score. Furthermore, similarity with an expert mental model positively and significantly impacted overall course performance. Hypothesized full mediation of mental model formation for the personality-performance relationship was not supported due to a lack of direct effect relationships required for mediation. However, a revised conceptualization of results emerged. Findings from the current study point to the novel and unique role mental models play in the personality-performance relationship. While personality traits do impact mental model formation, accuracy in the mental models formed is critical to performance.

Liquidity, Governance and Adverse Selection in Asset Pricing

A plethora of recent literature on asset pricing provides plenty of empirical evidence on the importance of liquidity, governance and adverse selection of equity on pricing of assets together with more traditional factors such as market beta and the Fama-French factors. However, literature has usually stressed that these factors are priced individually. In this dissertation we argue that these factors may be related to each other, hence not only individual but also joint tests of their significance is called for. In the three related essays, we examine the liquidity premium in the context of the finer three-digit SIC industry classification, joint importance of liquidity and governance factors as well as governance and adverse selection. Recent studies by Core, Guay and Rusticus (2006) and Ben-Rephael, Kadan and Wohl (2010) find that governance and liquidity premiums are dwindling in the last few years. One reason could be that liquidity is very unevenly distributed across industries. This could affect the interpretation of prior liquidity studies. Thus, in the first chapter we analyze the relation of industry clustering and liquidity risk following a finer industry classification suggested by Johnson, Moorman and Sorescu (2009). In the second chapter, we examine the dwindling influence of the governance factor if taken simultaneously with liquidity. We argue that this happens since governance characteristics are potentially a proxy for information asymmetry that may be better captured by market liquidity of a company’s shares. Hence, we jointly examine both the factors, namely, governance and liquidity – in a series of standard asset pricing tests. Our results reconfirm the importance of governance and liquidity in explaining stock returns thus independently corroborating the findings of Amihud (2002) and Gompers, Ishii and Metrick (2003). Moreover, governance is not subsumed by liquidity. Lastly, we analyze the relation of governance and adverse selection, and again corroborate previous findings of a priced governance factor. Furthermore, we ascertain the importance of microstructure measures in asset pricing by employing Huang and Stoll’s (1997) method to extract an adverse selection variable and finding evidence for its explanatory power in four-factor regressions.

An empirical study of Kirkpatrick's evaluation model in the hospitality industry

This study examined Kirkpatrick’s training evaluation model (Kirkpatrick & Kirkpatrick, 2006) by assessing a sales training program conducted at an organization in the hospitality industry. The study assessed the employees’ training outcomes of knowledge and skills, job performance, and the impact of the training upon the organization. By assessing these training outcomes and their relationships, the study demonstrated whether Kirkpatrick’s theories are supported and the lower evaluation levels can be used to predict organizational impact. The population for this study was a group of reservations sales agents from a leading luxury hotel chain’s reservations center. During the study period from January 2005 to May 2007, there were 335 reservations sales agents employed in this Global Reservations Center (GRC). The number of reservations sales agents who had completed a sales training program/intervention during this period and had data available for at least two months pre and post training composed the sample for this study. The number of agents was 69 ( N = 69). Four hypotheses were tested through paired-samples t tests, correlation, and hierarchical regression analytic procedures. Results from the analyses supported the hypotheses in this study. The significant improvement in the call score supported hypothesis one that the reservations sales agents who completed the training improved their knowledge of content and required skills in handling calls (Level 2). Hypothesis two was accepted in part as there was significant improvement in call conversion, but there was no significant improvement of time usage. The significant improvement in the sales per call supported hypothesis three that the reservations agents who completed the training contributed to increased organizational impact (Level 4), i.e., made significantly more sales. Last, findings supported hypothesis four that Level 2 and Level 3 variables can be used for predicting Level 4 organizational impact. The findings supported the theory of Kirkpatrick’s evaluation model that in order to expect organizational results, a positive change in behavior (job performance) and learning must occur. The examinations of Levels 2 and 3 helped to partially explain and predict Level 4 results.

The order selection and lot sizing problem in the make-to-order environment

This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three subproblems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

Comparison of Some Improved Estimators for Linear Regression Model under Different Conditions

Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis of risk was made among ridge, Liu and LASSO estimators under orthonormal regression model. I found that LASSO dominates least squares, ridge and Liu estimators over a significant portion of the parameter space for large dimension. Secondly, a simulation study was conducted to compare performance of ridge, Liu and two parameter biased estimator by their mean squared error criterion. I found that two parameter biased estimator performs better than its corresponding ridge regression estimator. Overall, Liu estimator performs better than both ridge and two parameter biased estimator.

Development of methods for spatial modeling of sediments quality in Lake Okeechobee, Florida

The major objectives of this dissertation were to develop optimal spatial techniques to model the spatial-temporal changes of the lake sediments and their nutrients from 1988 to 2006, and evaluate the impacts of the hurricanes occurred during 1998–2006. Mud zone reduced about 10.5% from 1988 to 1998, and increased about 6.2% from 1998 to 2006. Mud areas, volumes and weight were calculated using validated Kriging models. From 1988 to 1998, mud thicknesses increased up to 26 cm in the central lake area. The mud area and volume decreased about 13.78% and 10.26%, respectively. From 1998 to 2006, mud depths declined by up to 41 cm in the central lake area, mud volume reduced about 27%. Mud weight increased up to 29.32% from 1988 to 1998, but reduced over 20% from 1998 to 2006. The reduction of mud sediments is likely due to re-suspension and redistribution by waves and currents produced by large storm events, particularly Hurricanes Frances and Jeanne in 2004 and Wilma in 2005. Regression, kriging, geographically weighted regression (GWR) and regression-kriging models have been calibrated and validated for the spatial analysis of the sediments TP and TN of the lake. GWR models provide the most accurate predictions for TP and TN based on model performance and error analysis. TP values declined from an average of 651 to 593 mg/kg from 1998 to 2006, especially in the lake’s western and southern regions. From 1988 to 1998, TP declined in the northern and southern areas, and increased in the central-western part of the lake. The TP weights increased about 37.99%–43.68% from 1988 to 1998 and decreased about 29.72%–34.42% from 1998 to 2006. From 1988 to 1998, TN decreased in most areas, especially in the northern and southern lake regions; western littoral zone had the biggest increase, up to 40,000 mg/kg. From 1998 to 2006, TN declined from an average of 9,363 to 8,926 mg/kg, especially in the central and southern regions. The biggest increases occurred in the northern lake and southern edge areas. TN weights increased about 15%–16.2% from 1988 to 1998, and decreased about 7%–11% from 1998 to 2006.

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.