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. ^

A systems model for community injury prevention

The purpose of this study was to develop, explicate, and validate a comprehensive model in order to more effectively assess community injury prevention needs, plan and target efforts, identify potential interventions, and provide a framework for an outcome-based evaluation of the effectiveness of interventions. A systems model approach was developed to conceptualize the major components of inputs, efforts, outcomes and feedback within a community setting. Profiling of multiple data sources demonstrated a community feedback mechanism that increased awareness of priority issues and elicited support from traditional as well as non-traditional injury prevention partners. Injury countermeasures including education, enforcement, engineering, and economic incentives were presented for their potential synergistic effect impacting on knowledge, attitudes, or behaviors of a targeted population. Levels of outcome data were classified into ultimate, intermediate and immediate indicators to assist with determining the effectiveness of intervention efforts. A collaboration between business and health care was successful in achieving data access and use of an emergency department level of injury data for monitoring of the impact of community interventions. Evaluation of injury events and preventive efforts within the context of a dynamic community systems environment was applied to a study community with examples detailing actual profiling and trending of injuries. The resulting model of community injury prevention was validated using a community focus group, community injury prevention coordinators, and injury prevention national experts. ^

Trichloroethylene fate and transport studies and biodegradation kinetics in the saturated zone

Permeable reactive barriers (PRB) are constructed from soil solid amendments to support the growth of bacteria that are capable of degrading organic contaminants. The objective of this study was to identify low-cost soil solid amendments that could retard the movement of trichloroethylene (TCE) while serving as long-lived carbon sources to foster its biodegradation in shallow groundwater through the use of a PRB. The natural amendments high in organic carbon content such as eucalyptus mulch, compost, wetland peat, organic humus were compared based on their geophysical characteristics, such as pHw, porosity and total organic carbon (TOC), and as well as TCE sorption potentials. The pHw values were within neutral range except for pine bark mulch and wetland peat. All other geophysical characteristics of the amendments showed suitability for use in a PRB. While the Freundlich model showed better fit for compost and pine bark mulch, the linear sorption model was adequate for eucalyptus mulch, wetland peat and Everglades muck within the concentration range studied (0.2-0.8 mg/L TCE). According to these results, two composts and eucalyptus mulch were selected for laboratory column experiments to evaluate their effectiveness at creating and maintaining conditions suitable for TCE anaerobic dechlorination. The columns were monitored for pH, ORP, TCE degradation, longevity of nutrients and soluble TOC to support TCE dechlorination. Native bacteria in the columns had the ability to convert TCE to DCEs; however, the inoculation with the TCE-degrading culture greatly increased the rate of biodegradation. This caused a significant increase in by-product concentration, mostly in the form of DCEs and VC followed by a slow degradation to ethylene. Of the tested amendments eucalyptus mulch was the most effective at supporting the TCE dechlorination. The experimental results of TCE sequential dechlorination took place in eucalyptus mulch and commercial compost from Savannah River Site columns were then simulated using the Hydrus-1D model. The simulations showed good fit with the experimental data. The results suggested that sorption and degradation were the dominant fate and transport mechanisms for TCE and DCEs in the column, supporting the use of these amendments in a permeable reactive barrier to remediate the TCE.

A conceptual model of ecological interactions in the mangrove estuaries of the Florida Everglades

A brackish water ecotone of coastal bays and lakes, mangrove forests, salt marshes, tidal creeks, and upland hammocks separates Florida Bay, Biscayne Bay, and the Gulf of Mexico from the freshwater Everglades. The Everglades mangrove estuaries are characterized by salinity gradients that vary spatially with topography and vary seasonally and inter-annually with rainfall, tide, and freshwater flow from the Everglades. Because of their location at the lower end of the Everglades drainage basin, Everglades mangrove estuaries have been affected by upstream water management practices that have altered the freshwater heads and flows and that affect salinity gradients. Additionally, interannual variation in precipitation patterns, particularly those caused to El Nin˜o events, control freshwater inputs and salinity dynamics in these estuaries. Two major external drivers on this system are water management activities and global climate change. These drivers lead to two major ecosystem stressors: reduced freshwater flow volume and duration, and sea-level rise. Major ecological attributes include mangrove forest production, soil accretion, and resilience; coastal lake submerged aquatic vegetation; resident mangrove fish populations; wood stork (Mycteria americana) and roseate spoonbill (Platelea ajaja) nesting colonies; and estuarine crocodilian populations. Causal linkages between stressors and attributes include coastal transgression, hydroperiods, salinity gradients, and the ‘‘white zone’’ freshwater/estuarine interface. The functional estuary and its ecological attributes, as influenced by sea level and freshwater flow, must be viewed as spatially dynamic, with a possible near-term balancing of transgression but ultimately a long-term continuation of inland movement. Regardless of the spatio-temporal timing of this transgression, a salinity gradient supportive of ecologically functional Everglades mangrove estuaries will be required to maintain the integrity of the South Florida ecosystem.

Chilling damage in a changing climate in coastal landscapes of the subtropical zone: a case study from south Florida

Extensive portions of the southern Everglades are characterized by series of elongated, raised peat ridges and tree islands oriented parallel to the predominant flow direction, separated by intervening sloughs. Tall herbs or woody species are associated with higher elevations and shorter emergent or floating species are associated with lower elevations. The organic soils in this “Ridge-and-Slough” landscape have been stable over millennia in many locations, but degrade over decades under altered hydrologic conditions. We examined soil, pore water, and leaf phosphorus (P) and nitrogen (N) distributions in six Ridge and Slough communities in Shark Slough, Everglades National Park. We found P enrichment to increase and N to decrease monotonically along a gradient from the most persistently flooded sloughs to rarely flooded ridge environments, with the most dramatic change associated with the transition from marsh to forest. Leaf N:P ratios indicated that the marsh communities were strongly P-limited, while data from several forest types suggested either N-limitation or co-limitation by N and P. Ground water stage in forests exhibited a daytime decrease and partial nighttime recovery during periods of surface exposure. The recovery phase suggested re-supply from adjacent flooded marshes or the underlying aquifer, and a strong hydrologic connection between ridge and slough. We therefore developed a simple steady-state model to explore a mechanism by which a phosphorus conveyor belt driven by both evapotranspiration and the regional flow gradient can contribute to the characteristic Ridge and Slough pattern. The model demonstrated that evapotranspiration sinks at higher elevations can draw in low concentration marsh waters, raising local soil and water P concentrations. Focusing of flow and nutrients at the evapotranspiration zone is not strong enough to overcome the regional gradient entirely, allowing the nutrient to spread downstream and creating an elongated concentration plume in the direction of flow. Our analyses suggest that autogenic processes involving the effects of initially small differences in topography, via their interactions with hydrology and nutrient availability, can produce persistent physiographic patterns in the organic sediments of the Everglades.

Chilling damage in a changing climate in coastal landscapes of the subtropical zone: a case study from south Florida

Freeze events significantly influence landscape structure and community composition along subtropical coastlines. This is particularly true in south Florida, where such disturbances have historically contributed to patch diversity within the mangrove forest, and have played a part in limiting its inland transgression. With projected increases in mean global temperatures, such instances are likely to become much less frequent in the region, contributing to a reduction in heterogeneity within the mangrove forest itself. To understand the process more clearly, we explored the dynamics of a Dwarf mangrove forest following two chilling events that produced freeze-like symptoms, i.e., leaf browning, desiccation, and mortality, and interpreted the resulting changes within the context of current winter temperatures and projected future scenarios. Structural effects from a 1996 chilling event were dramatic, with mortality and tissue damage concentrated among individuals comprising the Dwarf forest's low canopy. This disturbance promoted understory plant development and provided an opportunity for Laguncularia racemosa to share dominance with Rhizophora mangle. Mortality due to the less severe 2001 event was greatest in the understory, probably because recovery of the protective canopy following the earlier freeze was still incomplete. Stand dynamics were static over the same period in nearby unimpacted sites. The probability of reaching temperatures as low as those recorded at a nearby meteorological station (≤3 °C) under several warming scenarios was simulated by applying 1° incremental temperature increases to a model developed from a 42-year temperature record. According to the model, the frequency of similar chilling events decreased from once every 1.9 years at present to once every 3.4 and 32.5 years with 1 and 4 °C warming, respectively. The large decrease in the frequency of these events would eliminate an important mechanism that maintains Dwarf forest structure, and promotes compositional diversity.

Variables affecting the success of older applicants for employment in a public organization: An age -cued cognitive model of administrative decision-making

The dissertation takes a multivariate approach to answer the question of how applicant age, after controlling for other variables, affects employment success in a public organization. In addition to applicant age, there are five other categories of variables examined: organization/applicant variables describing the relationship of the applicant to the organization; organization/position variables describing the target position as it relates to the organization; episodic variables such as applicant age relative to the ages of competing applicants; economic variables relating to the salary needs of older applicants; and cognitive variables that may affect the decision maker's evaluation of the applicant. ^ An exploratory phase of research employs archival data from approximately 500 decisions made in the past three years to hire or promote applicants for positions in one public health administration organization. A logit regression model is employed to examine the probability that the variables modify the effect of applicant age on employment success. A confirmatory phase of the dissertation is a controlled experiment in which hiring decision makers from the same public organization perform a simulated hiring decision exercise to evaluate hypothetical applicants of similar qualifications but of different ages. The responses of the decision makers to a series of bipolar adjective scales add support to the cognitive component of the theoretical model of the hiring decision. A final section contains information gathered from interviews with key informants. ^ Applicant age has tended to have a curvilinear relationship with employment success. For some positions, the mean age of the applicants most likely to succeed varies with the values of the five groups of moderating variables. The research contributes not only to the practice of public personnel administration, but is useful in examining larger public policy issues associated with an aging workforce. ^

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.

Development of test-based wind-driven rain intrusion model for hurricane-induced building interior and contents damage

Major portion of hurricane-induced economic loss originates from damages to building structures. The damages on building structures are typically grouped into three main categories: exterior, interior, and contents damage. Although the latter two types of damages, in most cases, cause more than 50% of the total loss, little has been done to investigate the physical damage process and unveil the interdependence of interior damage parameters. Building interior and contents damages are mainly due to wind-driven rain (WDR) intrusion through building envelope defects, breaches, and other functional openings. The limitation of research works and subsequent knowledge gaps, are in most part due to the complexity of damage phenomena during hurricanes and lack of established measurement methodologies to quantify rainwater intrusion. This dissertation focuses on devising methodologies for large-scale experimental simulation of tropical cyclone WDR and measurements of rainwater intrusion to acquire benchmark test-based data for the development of hurricane-induced building interior and contents damage model. Target WDR parameters derived from tropical cyclone rainfall data were used to simulate the WDR characteristics at the Wall of Wind (WOW) facility. The proposed WDR simulation methodology presents detailed procedures for selection of type and number of nozzles formulated based on tropical cyclone WDR study. The simulated WDR was later used to experimentally investigate the mechanisms of rainwater deposition/intrusion in buildings. Test-based dataset of two rainwater intrusion parameters that quantify the distribution of direct impinging raindrops and surface runoff rainwater over building surface — rain admittance factor (RAF) and surface runoff coefficient (SRC), respectively —were developed using common shapes of low-rise buildings. The dataset was applied to a newly formulated WDR estimation model to predict the volume of rainwater ingress through envelope openings such as wall and roof deck breaches and window sill cracks. The validation of the new model using experimental data indicated reasonable estimation of rainwater ingress through envelope defects and breaches during tropical cyclones. The WDR estimation model and experimental dataset of WDR parameters developed in this dissertation work can be used to enhance the prediction capabilities of existing interior damage models such as the Florida Public Hurricane Loss Model (FPHLM).^

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.

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.

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.