Development of a mathematical and computer model to assess water quality impacts of hazardous compounds from minor gasoline spills in the inter-tidal zone of the Miami River, Florida

The objective of this study was to develop a model to predict transport and fate of gasoline components of environmental concern in the Miami River by mathematically simulating the movement of dissolved benzene, toluene, xylene (BTX), and methyl-tertiary-butyl ether (MTBE) occurring from minor gasoline spills in the inter-tidal zone of the river. Computer codes were based on mathematical algorithms that acknowledge the role of advective and dispersive physical phenomena along the river and prevailing phase transformations of BTX and MTBE. Phase transformations included volatilization and settling. ^ The model used a finite-difference scheme of steady-state conditions, with a set of numerical equations that was solved by two numerical methods: Gauss-Seidel and Jacobi iterations. A numerical validation process was conducted by comparing the results from both methods with analytical and numerical reference solutions. Since similar trends were achieved after the numerical validation process, it was concluded that the computer codes algorithmically were correct. The Gauss-Seidel iteration yielded at a faster convergence rate than the Jacobi iteration. Hence, the mathematical code was selected to further develop the computer program and software. The model was then analyzed for its sensitivity. It was found that the model was very sensitive to wind speed but not to sediment settling velocity. ^ A computer software was developed with the model code embedded. The software was provided with two major user-friendly visualized forms, one to interface with the database files and the other to execute and present the graphical and tabulated results. For all predicted concentrations of BTX and MTBE, the maximum concentrations were over an order of magnitude lower than current drinking water standards. It should be pointed out, however, that smaller concentrations than the latter reported standards and values, although not harmful to humans, may be very harmful to organisms of the trophic levels of the Miami River ecosystem and associated waters. This computer model can be used for the rapid assessment and management of the effects of minor gasoline spills on inter-tidal riverine water quality. ^

The thermodynamics and statistical mechanics of protein folding

The physics of self-organization and complexity is manifested on a variety of biological scales, from large ecosystems to the molecular level. Protein molecules exhibit characteristics of complex systems in terms of their structure, dynamics, and function. Proteins have the extraordinary ability to fold to a specific functional three-dimensional shape, starting from a random coil, in a biologically relevant time. How they accomplish this is one of the secrets of life. In this work, theoretical research into understanding this remarkable behavior is discussed. Thermodynamic and statistical mechanical tools are used in order to investigate the protein folding dynamics and stability. Theoretical analyses of the results from computer simulation of the dynamics of a four-helix bundle show that the excluded volume entropic effects are very important in protein dynamics and crucial for protein stability. The dramatic effects of changing the size of sidechains imply that a strategic placement of amino acid residues with a particular size may be an important consideration in protein engineering. Another investigation deals with modeling protein structural transitions as a phase transition. Using finite size scaling theory, the nature of unfolding transition of a four-helix bundle protein was investigated and critical exponents for the transition were calculated for various hydrophobic strengths in the core. It is found that the order of the transition changes from first to higher order as the strength of the hydrophobic interaction in the core region is significantly increased. Finally, a detailed kinetic and thermodynamic analysis was carried out in a model two-helix bundle. The connection between the structural free-energy landscape and folding kinetics was quantified. I show how simple protein engineering, by changing the hydropathy of a small number of amino acids, can enhance protein folding by significantly changing the free energy landscape so that kinetic traps are removed. The results have general applicability in protein engineering as well as understanding the underlying physical mechanisms of protein folding. ^

Evaluation of the internal structural validity of the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) anxiety disorders in children and adolescents

The purpose of the present dissertation was to evaluate the internal validity of symptoms of four common anxiety disorders included in the Diagnostic and Statistical Manual of Mental Disorders fourth edition (text revision) (DSM-IV-TR; American Psychiatric Association, 2000), namely, separation anxiety disorder (SAD), social phobia (SOP), specific phobia (SP), and generalized anxiety disorder (GAD), in a sample of 625 youth (ages 6 to 17 years) referred to an anxiety disorders clinic and 479 parents. Confirmatory factor analyses (CFAs) were conducted on the dichotomous items of the SAD, SOP, SP, and GAD sections of the youth and parent versions of the Anxiety Disorders Interview Schedule for DSM-IV (ADIS-IV: C/P; Silverman & Albano, 1996) to test and compare a number of factor models including a factor model based on the DSM. Contrary to predictions, findings from CFAs showed that a correlated model with five factors of SAD, SOP, SP, GAD worry, and GAD somatic distress, provided the best fit of the youth data as well as the parent data. Multiple group CFAs supported the metric invariance of the correlated five factor model across boys and girls. Thus, the present study’s finding supports the internal validity of DSM-IV SAD, SOP, and SP, but raises doubt regarding the internal validity of GAD.^

Formal modeling and analysis techniques for high level Petri nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. ^ There are two issues in using HLPNs—modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. ^ For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. ^ For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. ^ The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.^

Formal Modeling and Analysis Techniques for High Level Petri Nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

Marketing practices and their effectiveness in Taiwan's colleges of technology

The main purpose of this study was to investigate marketing practices in Taiwan's institutions of higher education and their relationship with students' college choice behaviors and attitudes. The study was conducted in 11 Taiwan's colleges of technology. It employed a multistrand conversion mixed model design, consisting of a qualitative and a quantitative strand. Funnel-sequenced interviews were conducted with 19 college administrators and the results were content analyzed using a constant-comparative method. The administrator interview data were also quantitized and used in cluster analysis of the institutions. Questionnaire data were collected from 1474 freshmen students, and analyzed using several univariate and multivariate statistical techniques including factor analysis, MANOVA, and correspondence analysis. ^ Analyses indicated that a weak relationship existed between institutions' marketing intensity and students' college choice. Students did not consider institutions' recruitment activities useful in their college searching process. They also reported little knowledge of their current school when they were deciding to enroll. Data analysis also revealed that students were practically oriented in their college selection. Academic resources, employability after graduation, and tuition were the most important attributes in students' college selection. Parents and students' social network such as friends and high school teachers were significant personal sources in enrollment decisions while institutions' representatives (i.e., recruiters) were considered the least influential. ^ Using cluster analysis, institutions were divided into three groups based on intensity of marketing efforts. Multivariate analysis of variance did not reveal significant differences between the college choice behaviors and attitudes of students who entered these three types of institutions. ^ Content analysis of the administrators' interviews indicated that the majority of them practiced passive marketing. This was primarily as a result of resistance to active marketing, lack of leadership commitment, insufficient financial and human resources, little faculty involvement, and inexperience in marketing. In comparison to public institutions, private institutions showed a more favorable attitude towards marketing concepts. They were well advanced in their recruitment activities while public schools were relatively hesitant to use marketing. Curriculum issues were not well represented in marketing activities and did not seem to be impacted by marketing needs. Based on the analysis of qualitative and quantitative data, it can be concluded that in these colleges, curriculum was more driven by commercial and industrial interest than by students' demands. ^ Theoretical, policy, and methodological implementation of the results were discussed. ^

Molecular markers and human history: A tale of two haplogroup systems

To chronicle demographic movement across African Asian corridors, a variety of molecular (sequence analysis, restriction mapping and denaturing high performance liquid chromatography etc.) and statistical (correspondence analysis, AMOVA, calculation of diversity indices and phylogenetic inference, etc.) techniques were employed to assess the phylogeographic patterns of mtDNA control region and Y chromosomal variation among 14 sub-Saharan, North African and Middle Eastern populations. The patterns of genetic diversity revealed evidence of multiple migrations across several African Asian passageways as well within the African continent itself. The two-part analysis uncovered several interesting results which include the following: (1) a north (Egypt and Middle East Asia) to south (sub-Saharan Africa) partitioning of both mtDNA and Y chromosomal haplogroup diversity, (2) a genetic diversity gradient in sub-Saharan Africa from east to west, (3) evidence in favor of the Levantine Corridor over the Horn of Africa as the major genetic conduit since the Last Glacial Maximum, (4) a substantially higher mtDNA versus Y chromosomal sub-Saharan component in the Middle East collections, (5) a higher representation of East versus West African mtDNA haplotypes in the Arabian Peninsula populations versus no such bias in the Levant groups and lastly, (6) genetic remnants of the Bantu demographic expansion in sub-Saharan Africa. ^

Foundational forensic techniques for cellular and ad hoc multi-hop networks

The Internet has become an integral part of our nation’s critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a ‘distance metric’. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

Towards a Pedagogy of Inferential Statistics in Graduate Education Programs: Insights from Cognitive and Educational Research

This study attempts to understand the nature of knowledge base that supports the ability to select statistical techniques for research situations. Findings showed that the largest component of such knowledge was related to research design. One implication is that techniques should be taught in relation to features of research design.

Career technical education adjunct faculty teacher readiness: An investigation of teacher excellence and variables of preparedness

The purpose of this research was to examine the relationship between teaching readiness and teaching excellence with three variables of preparedness of adjunct professors teaching career technical education courses through student surveys using a correlational design of two statistical techniques; least-squares regression and one-way analysis of variance. That is, the research tested the relationship between teacher readiness and teacher excellence with the number of years teaching, the number of years of experience in the professional field and exposure to teaching related professional development, referred to as variables of preparedness.^ The results of the research provided insight to the relationship between the variables of preparedness and student assessment of their adjunct professors. Concerning the years of teaching experience, this research found a negative inverse relationship with how students rated their professors' teaching readiness and excellence. The research also found no relationship between years of professional experience and the students' assessment. Lastly, the research found a significant positive relationship between the amount of teaching related professional development taken by an adjunct professor and the students' assessment in teaching readiness and excellence.^ This research suggests that policies and practices at colleges should address the professional development needs of adjunct professors. Also, to design a model that meets the practices of inclusion for adjunct faculty and to make professional development a priority within the organization. Lastly, implement that model over time to prepare adjuncts in readiness and excellence. ^

``This is your brain on drugs'': The public policy of anti -drug programming. An exploratory analysis of media and other sources of drug information for adolescents

In the 1980s, government agencies sought to utilize research on drug use prevention to design media campaigns. Enlisting the assistance of the national media, several campaigns were designed and initiated to bring anti-drug use messages to adolescents in the form of public service advertising. This research explores the sources of information selected by adolescents in grades 7 through 12 and how the selection of media and other sources of information relate to drug use behavior and attitudes and perceptions related to risk/harm and disapproval of friends' drug-using activities.^ Data collected from 1989 to 1992 in the Miami Coalition School Survey provided a random selection of secondary school studies. The responses of these students were analyzed using multivariate statistical techniques.^ Although many of the students selected media as the source for most of their information on the effects of drugs on the people who use them, the selection of media was found to be positively related to alcohol use and negatively related to marijuana use. The selection of friends, brothers, or sisters was a statistically significant source for adolescents who smoke cigarettes, use alcohol or marijuana.^ The results indicate that the anti-drug use messages received by students may be canceled out by media messages perceived to advocate substance use and that a more persuasive source of information for adolescents may be friends and siblings. As federal reports suggest that the economic costs of drug abuse will reach an estimated $150 billion by 1997 if current trends continue, prevention policy that addresses the glamorization of substance use remains a national priority. Additionally, programs that advocate prevention within the peer cluster must be supported, as peers are an influential source for both inspiring and possibly preventing drug use behavior. ^

Modeling the role of investigator bias, interrogation techniques, and suspect decision -making on the likelihood of confession

The purpose of the current study was to attempt to model various cognitive and social processes that are believed to lead to false confessions. More specifically, this study manipulated the variables of experimenter expectancy, guilt-innocence of the suspect, and interrogation techniques using the Russano et al. (2005) paradigm. The primary measure of interest was the likelihood of the participant signing the confession statement. By manipulating experimenter expectancy, the current study sought to further explore the social interactions that may occur in the interrogation room. In addition, in past experiments, the interrogator has typically been restricted to the use of one or two interrogation techniques. In the present study, interrogators were permitted to select from 15 different interrogation techniques when attempting to solicit a confession from participants. ^ Consistent with Rusanno et al. (2005), guilty participants (94%) were more likely to confess to the act of cheating than innocent participants (31%). The variable of experimenter expectancy did not effect confessions rates, length of interrogation, or the type of interrogation techniques used. Path analysis revealed feelings of pressure and the weighing of consequences on the part of the participant were associated with the signing of the confession statement. The findings suggest the guilt/innocence of the participant, the participant's perceptions of the interrogation situation, and length of interrogation play a pivotal role in the signing of the confession statement. Further examination of these variables may provide researchers with a better understanding of the relationship between interrogations and confessions. ^

Foundational Forensic Techniques for Cellular and Ad Hoc Multi-hop Networks

The Internet has become an integral part of our nation's critical socio-economic infrastructure. With its heightened use and growing complexity however, organizations are at greater risk of cyber crimes. To aid in the investigation of crimes committed on or via the Internet, a network forensics analysis tool pulls together needed digital evidence. It provides a platform for performing deep network analysis by capturing, recording and analyzing network events to find out the source of a security attack or other information security incidents. Existing network forensics work has been mostly focused on the Internet and fixed networks. But the exponential growth and use of wireless technologies, coupled with their unprecedented characteristics, necessitates the development of new network forensic analysis tools. This dissertation fostered the emergence of a new research field in cellular and ad-hoc network forensics. It was one of the first works to identify this problem and offer fundamental techniques and tools that laid the groundwork for future research. In particular, it introduced novel methods to record network incidents and report logged incidents. For recording incidents, location is considered essential to documenting network incidents. However, in network topology spaces, location cannot be measured due to absence of a 'distance metric'. Therefore, a novel solution was proposed to label locations of nodes within network topology spaces, and then to authenticate the identity of nodes in ad hoc environments. For reporting logged incidents, a novel technique based on Distributed Hash Tables (DHT) was adopted. Although the direct use of DHTs for reporting logged incidents would result in an uncontrollably recursive traffic, a new mechanism was introduced that overcome this recursive process. These logging and reporting techniques aided forensics over cellular and ad-hoc networks, which in turn increased their ability to track and trace attacks to their source. These techniques were a starting point for further research and development that would result in equipping future ad hoc networks with forensic components to complement existing security mechanisms.

The relationship between the teacher's experience, the teacher's college major, and the teacher's level of education in predicting classroom attitudes in high school science students

A review of the literature reveals few research has attempted to demonstrate if a relationship exists between the type of teacher training a science teacher has received and the perceived attitudes of his/her students. Considering that a great deal of time and energy has been devoted by university colleges, school districts, and educators towards refining the teacher education process, it would be more efficient for all parties involved, if research were available that could discern if certain pathways in achieving that education, would promote the tendency towards certain teacher behaviors occurring in the classroom, while other pathways would lead towards different behaviors. Some of the teacher preparation factors examined in this study include the college major chosen by the science teacher, the highest degree earned, the number of years of teaching experience, the type of science course taught, and the grade level taught by the teacher. This study examined how the various factors mentioned, could influence the behaviors which are characteristic of the teacher, and how these behaviors could be reflective in the classroom environment experienced by the students. The instrument used in the study was the Classroom Environment Scale (CES), Real Form. The measured classroom environment was broken down into three separate dimensions, with three components within each dimension in the CES. Multiple Regression statistical analyses examined how components of the teachers' education influenced the perceived dimensions of the classroom environment from the students. The study occurred in Miami-Dade County Florida, with a predominantly urban high school student population. There were 40 secondary science teachers involved, each with an average of 30 students. The total number of students sampled in the study was 1200. The teachers who participated in the study taught the entire range of secondary science courses offered at this large school district. All teachers were selected by the researcher so that a balance would occur in the sample between teachers who were education major versus science major. Additionally, the researcher selected teachers so that a balance occurred in regards to the different levels of college degrees earned among those involved in the study. Several research questions sought to determine if there was significant difference between the type of the educational background obtained by secondary science teachers and the students' perception of the classroom environment. Other research questions sought to determine if there were significant differences in the students' perceptions of the classroom environment for secondary science teachers who taught biological content, or non-biological content sciences. An additional research question sought to evaluate if the grade level taught would affect the students' perception of the classroom environment. Analysis of the multiple regression were run for each of four scores from the CES, Real Form. For score 1, involvement of students, the results showed that teachers with the highest number of years of experience, with masters or masters plus degrees, who were education majors, and who taught twelfth grade students, had greater amounts of students being attentive and interested in class activities, participating in discussions, and doing additional work on their own, as compared with teachers who had lower experience, a bachelors degree, were science majors, and who taught a grade lower than twelfth. For score 2, task orientation, which emphasized completing the required activities and staying on-task, the results showed that teachers with the highest and intermediate experience, a science major, and with the highest college degree, showed higher scores as compared with the teachers indicating lower experiences, education major and a bachelors degree. For Score 3, competition, which indicated how difficult it was to achieve high grades in the class, the results showed that teachers who taught non-biology content subjects had the greatest effect on the regression. Teachers with a masters degree, low levels of experience, and who taught twelfth grade students were also factored into the regression equation. For Score 4, innovation, which indicated the extent in which the teachers used new and innovative techniques to encourage diverse and creative thinking included teachers with an education major as the first entry into the regression equation. Teachers with the least experience (0 to 3 years), and teachers who taught twelfth and eleventh grade students were also included into the regression equation.