Graduate program culture and intention to persist: Working adults in cohort and non-cohort programs

Cohort programs have been instituted at many universities to accommodate the growing number of mature adult graduate students who pursue degrees while maintaining multiple commitments such as work and family. While it is estimated that as many as 40–60% of students who begin graduate study fail to complete degrees, it is thought that attrition may be even higher for this population of students. Yet, little is known about the impact of cohorts on the learning environment and whether cohort programs affect graduate student retention. Retention theory stresses the importance of the academic department, quality of faculty-student relationships and student involvement in the life of the academic community as critical determinants in students' decisions to persist to degree completion. However, students who are employed full-time typically spend little time on campus engaged in the learning environment. Using academic and social integration theory, this study examined the experiences of working adult graduate students enrolled in cohort (CEP) and non-cohort (non-CEP) programs and the influence of these experiences on intention to persist. The Graduate Program Context Questionnaire was administered to graduate students (N = 310) to examine measures of academic and social integration and intention to persist. Sample t tests and ANOVAs were conducted to determine whether differences in perceptions could be identified between cohort and non-cohort students. Multiple linear regression was used to identify variables that predict students' intention to persist. While there were many similarities, significant differences were found between CEP and non-CEP student groups on two measures. CEP students rated peer-student relationships higher and scored higher on the intention to persist measure than non-CEP students. The psychological integration measure, however, was the strongest predictor of intention to persist for both the CEP and non-CEP groups. This study supports the research literature which suggests that CEP programs encourage the development of peer-student relationships and promote students' commitment to persistence.

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.

A longitudinal study of Project Trio: A dropout prevention program in a large urban environment

The subject of dropout prevention/reduction is deservedly receiving attention as a problem that, if not resolved, could threaten our national future.^ This study investigates a small segment of the overall dropout problem, which has apparently unique features of program design and population selection. The evidence presented here should add to the knowledge bank of this complicated problem.^ Project Trio was one of a number of dropout prevention programs and activities which were conducted in Dade County school years 1984-85 and 1985-86, and it is here investigated longitudinally through the end of the 1987-88 school year. It involved 17 junior and senior high schools, and 27 programs, 10 the first year and 17 the second, with over 1,000 total students, who had been selected by the schools from a list of the "at risk" students provided by the district, and were divided approximately evenly into the classical research design of an experimental group and the control group, which following standard procedure was to take the regular school curriculum. No school had more than 25 students in either group.^ Each school modified the basic design of the project to accommodate the individual school characteristics and the perceived needs of their students; however all schools projects were to include some form of academic enhancement, counseling and career awareness study.^ The conclusion of this study was that the control group had a significantly lower dropout rate than the experimental group. Though impossible to make a certain determination of the reasons for this unexpected result, it appears from evidence presented that one cause may have been inadequate administration at the local level.^ This study was also a longitudinal investigation of the "at risk" population as a whole for the three and four year period, to determine if academic factors were present in records may be used to identify dropout proneness.^ A significant correlation was found between dropping out and various measures including scores on the Quality of School Life Instrument, attendance, grade point averages, mathematics grades, and overage in grade, important identifiers in selection for dropout prevention programs. ^

Practical secure information flow in programming languages

If we classify variables in a program into various security levels, then a secure information flow analysis aims to verify statically that information in a program can flow only in ways consistent with the specified security levels. One well-studied approach is to formulate the rules of the secure information flow analysis as a type system. A major trend of recent research focuses on how to accommodate various sophisticated modern language features. However, this approach often leads to overly complicated and restrictive type systems, making them unfit for practical use. Also, problems essential to practical use, such as type inference and error reporting, have received little attention. This dissertation identified and solved major theoretical and practical hurdles to the application of secure information flow. ^ We adopted a minimalist approach to designing our language to ensure a simple lenient type system. We started out with a small simple imperative language and only added features that we deemed most important for practical use. One language feature we addressed is arrays. Due to the various leaking channels associated with array operations, arrays have received complicated and restrictive typing rules in other secure languages. We presented a novel approach for lenient array operations, which lead to simple and lenient typing of arrays. ^ Type inference is necessary because usually a user is only concerned with the security types for input/output variables of a program and would like to have all types for auxiliary variables inferred automatically. We presented a type inference algorithm B and proved its soundness and completeness. Moreover, algorithm B stays close to the program and the type system and therefore facilitates informative error reporting that is generated in a cascading fashion. Algorithm B and error reporting have been implemented and tested. ^ Lastly, we presented a novel framework for developing applications that ensure user information privacy. In this framework, core computations are defined as code modules that involve input/output data from multiple parties. Incrementally, secure flow policies are refined based on feedback from the type checking/inference. Core computations only interact with code modules from involved parties through well-defined interfaces. All code modules are digitally signed to ensure their authenticity and integrity. ^

Statistical analysis and optimal classification of blood cell populations using Gaussian distributions

This dissertation develops a new figure of merit to measure the similarity (or dissimilarity) of Gaussian distributions through a novel concept that relates the Fisher distance to the percentage of data overlap. The derivations are expanded to provide a generalized mathematical platform for determining an optimal separating boundary of Gaussian distributions in multiple dimensions. Real-world data used for implementation and in carrying out feasibility studies were provided by Beckman-Coulter. It is noted that although the data used is flow cytometric in nature, the mathematics are general in their derivation to include other types of data as long as their statistical behavior approximate Gaussian distributions. ^ Because this new figure of merit is heavily based on the statistical nature of the data, a new filtering technique is introduced to accommodate for the accumulation process involved with histogram data. When data is accumulated into a frequency histogram, the data is inherently smoothed in a linear fashion, since an averaging effect is taking place as the histogram is generated. This new filtering scheme addresses data that is accumulated in the uneven resolution of the channels of the frequency histogram. ^ The qualitative interpretation of flow cytometric data is currently a time consuming and imprecise method for evaluating histogram data. This method offers a broader spectrum of capabilities in the analysis of histograms, since the figure of merit derived in this dissertation integrates within its mathematics both a measure of similarity and the percentage of overlap between the distributions under analysis. ^

Ethnic groups and institutions: A study of institutional engineering in four Central and Eastern European countries

The issue of institutional engineering has gained a renewed interest with the democratic transitions of the Central and Eastern European countries, as for some states it has become a matter of state survival. The four countries examined in the study – Macedonia, Slovakia, Romania and Bulgaria – exemplify the difficulty in establishing a stable democratic society in the context of the resurgence of national identity. The success of ethnonational minorities in achieving the desired policies affirming or expanding their rights as a group was conditioned upon the cohesion of the minority as well as the permissiveness of state institutions in terms of participation and representation of minority members. The Hungarian minorities in Slovakia and Romania, the Turkish minority in Bulgaria, and the Albanian minority in Macedonia, formed their political organizations to represent their interests. However, in some cases the divergence of strategies or goals between factions of the minority group seriously impeded its ability to obtain the desired concessions from the majority. The difficulty in the pursuit of policies favoring the expansion of minority rights was further exacerbated in some of the cases by the impermissiveness of political institutions. The political parties representing the interest of ethnonational minorities were allowed to participate in elections, although not without suspicions about their intent and even strong opposition from majority groups, but participation in elections and subsequent representation in legislative bodies did not translate into adoption of the desired policies. The ethnonational minorities' inability to effectively influence the decision-making process was the result of the inadequacy of democratic institutions to process these demands and channel them through the normal political process in the absence of majority desire to accommodate them. Despite the promise of democratic institutions to bring about a major overhaul of the policies of forceful assimilation and disregard for minority rights, the four cases analyzed in the study demonstrate that in effect ethnonational minorities continued to be at the mercy of the majority, especially if the minority was unable to position itself as a balancing actor.

U.S. Army front line artillery observation during the 1945 Okinawa campaign: An oral history case study

This dissertation explores the role of artillery forward observation teams during the battle of Okinawa (April–June 1945). It addresses a variety of questions associated with this front line artillery support. First, it examines the role of artillery itself in the American victory over the Japanese on Okinawa. Second, it traces the history of the forward observer in the three decades before the end of World War II. Third, it defines the specific role of the forward observation teams during the battle: what they did and how they did it during this three-month duel. Fourth, it deals with the particular problems of the forward observer. These included coordination with the local infantry commander, adjusting to the periodic rotation between the front lines and the artillery battery behind the line of battle, responding to occasional problems with "friendly fire" (American artillery falling on American ground forces), dealing with personnel turnover in the teams (due to death, wounds, and illness), and finally, developing a more informal relationship between officers and enlisted men to accommodate the reality of this recently created combat assignment. Fifth, it explores the experiences of a select group of men who served on (or in proximity to) forward observation teams on Okinawa. Previous scholars and popular historians of the battle have emphasized the role of Marines, infantrymen, and flame-throwing armor. This work offers a different perspective on the battle and it uses new sources as well. A pre-existing archive of interviews with Okinawan campaign forward observer team members conducted in the 1990s forms the core of the oral history component of this research project. The verbal accounts were checked against and supplemented by a review of unit reports obtained from the U.S. National Archives and various secondary sources. The dissertation concludes that an understanding of American artillery observation is critical to a more complete comprehension of the battle of Okinawa. These mid-ranking (and largely middle class) soldiers proved capable of adjusting to the demands of combat conditions. They provide a unique and understudied perspective of the entire battle.

The construction and optimization of an ion mobility spectrometer for the analysis of explosives and drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs.^ In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug.^ Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). ^ The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.^

Restaurant Industry Perspectives on Pro-social Rule Breaking: Intent versus Action

The resounding message extracted from the service literature is that employees serve pivotal functions in the overall guest experience. This is of course due to the simultaneous delivery of personalized service provision with resultant consumption of those services. This simultaneous delivery and consumption cycle is at times challenged by a perceived desire to accommodate guest request that may violate, to a greater or lesser degree, an organizational rule. This is important to note because increased interactions with customers enable frontline employees to have a better sense of what customers want from the company as well as from the company itself (Bitner, et al, 1994). With that platform established, then why are some employees willing to break organizational rules and risk disciplinary action to better service a customer? This study examines the employee personality, degree of autonomy, job meaning, and co-worker influence on an employee's decision to break organizational rules. The results of this study indicate that co-worker influence exerted a minimal influence on employee decision to break rules while the presence of societal consciousness exerted a much stronger influence. Women reported that they were less likely to engage in rule divergence, and significant correlations were present when filtered by years in current position, and years in the industry.

The Travel Industry: What's in a Name?

The travel and tourism industry is enormous in both size and importance. There can be little doubt that the field is striving to accommodate the diversity of opinion concerning what the industry is and how it can be improved and enlarged even further. Resistance to critiquing long-held beliefs about the industry may inhibit its future. Deconstruction, a postmodern method of analysis, is proposed as one tool with which to engage in reflection upon what the industry is and where it may be headed.

Cultural perceptions of space and place in the South Florida *environment

Public opinion polls in the United States reveal that a great majority of Americans are aware and show concern about ecological issues and the need to preserve natural areas. In South Florida, natural resources have been subjected to enormous strain as the pressure to accommodate a growing population has led to rapid development. Suburbs have been built on areas that were once natural wetlands and farmlands, and the impact today shows a landscape where natural places have all but disappeared. This dissertation examines the intersection between the perceptions that individuals living in the South Florida region have with respect to the natural environment and local ecological problems with where their relationship to nature takes place. ^ The research is based upon both quantitative and qualitative data. The principal methodology used in this research is the ethnographic method, which employed the data gathering techniques of in-depth interviewing and participant observation. The objective of the qualitative portion of the study was to determine how people perceive and relate to their immediate environment. The quantitative portion of the study employed telephone survey data from the FIU/Florida Poll 2000. Data collected through this survey provided the basis to statistically test responses to the research questions. ^ The findings show that people in South Florida have a general idea of the relationship between the human population and the environment but very little knowledge of how they individually affect each other. The experience of private places and public spaces in everyday life permits people to compartmentalize cultural values and understandings of the natural world in separate cognitive schemas. The appreciation of the natural world has almost no connection to their personal sense of obligation to preserve the environment. That obligation is only felt in their home space even though the South Florida environment overall struggles desperately with water shortages, land encroachment, and a rapidly expanding human population whose activities continuously aggravate an already delicate natural balance. ^

Restaurant Industry Perspectives on Pro-social Rule Breaking: Intent versus Action

The resounding message extracted from the service literature is that employees serve pivotal functions in the overall guest experience. This is of course due to the simultaneous delivery of personalized service provision with resultant consumption of those services. This simultaneous delivery and consumption cycle is at times challenged by a perceived desire to accommodate guest request that may violate, to a greater or lesser degree, an organizational rule. This is important to note because increased interactions with customers enable frontline employees to have a better sense of what customers want from the company as well as from the company itself (Bitner, et al, 1994). With that platform established, then why are some employees willing to break organizational rules and risk disciplinary action to better service a customer? This study examines the employee personality, degree of autonomy, job meaning, and co-worker influence on an employee's decision to break organizational rules. The results of this study indicate that co-worker influence exerted a minimal influence on employee decision to break rules while the presence of societal consciousness exerted a much stronger influence. Women reported that they were less likely to engage in rule divergence, and significant correlations were present when filtered by years in current position, and years in the industry.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

The Construction and Optimization on an Ion Mobility Spectrometer for the Analysis of Explosives and Drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs. In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug. Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.