Development of an enhanced hydro-geochemical model to address mercury-speciation fate and transport in aquatic environments

An awareness of mercury (Hg) contamination in various aquatic environments around the world has increased over the past decade, mostly due to its ability to concentrate in the biota. Because the presence and distribution of Hg in aquatic systems depend on many factors (e.g., pe, pH, salinity, temperature, organic and inorganic ligands, sorbents, etc.), it is crucial to understand its fate and transport in the presence of complexing constituents and natural sorbents, under those different factors. An improved understanding of the subject will support the selection of monitoring, remediation, and restoration technologies. The coupling of equilibrium chemical reactions with transport processes in the model PHREEQC offers an advantage in simulating and predicting the fate and transport of aqueous chemical species of interest. Thus, a great variety of reactive transport problems could be addressed in aquatic systems with boundary conditions of specific interest. Nevertheless, PHREEQC lacks a comprehensive thermodynamic database for Hg. Therefore, in order to use PHREEQC to address the fate and transport of Hg in aquatic environments, it is necessary to expand its thermodynamic database, confirm it and then evaluate it in applications where potential exists for its calibration and continued validation. The objectives of this study were twofold: 1) to develop, expand, and confirm the Hg database of the hydrogeochemical PHREEQC to enhance its capability to simulate the fate of Hg species in the presence of complexing constituents and natural sorbents under different conditions of pH, redox, salinity and temperature; and 2) to apply and evaluate the new database in flow and transport scenarios, at two field test beds: Oak Ridge Reservation, Oak Ridge, TN and Everglades National Park, FL, where Hg is present and is of much concern. Overall, this research enhanced the capability of the PHREEQC model to simulate the coupling of the Hg reactions in transport conditions. It also demonstrated its usefulness when applied to field situations.

Engaged Scholarship: A Model for Creating an Education Research Lab

Paper Higher education, student affairs and lifelong learning

A "Transforming the School-to-Prison Pipeline" Initiative: Mentoring Model Pilot Project Symposium

Abstract: This informative and interactive teaching symposium posits the Positive Peer Leadership Mentoring Program (PPLM) as an evidence-based wrap-around service for youth and families in Miami-Dade who are involved in the school-to-prison pipeline. Presenters first provide information to initiate the dialogic process of discerning and interpreting the school-to-prison pipeline, impacted by costs of incarceration for Black youth and families and the move toward effective mental health services in the juvenile justice system. Then, participants experience an interactive pedagogical mentoring format set forth in PPLM as the first step toward transforming the school-to-prison pipeline in their own classroom or other educational setting.

A generic model of execution for synthesizing domain-specific models

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

Contribution of lipophilic secondary metabolites to the toxicity of strains of freshwater cyanobacterial harmful algal blooms, identified using the zebrafish (Danio rerio) embryo as a model for vertebrate development

Cyanobacteria ("blue-green algae") are known to produce a diverse repertoire of biologically active secondary metabolites. When associated with so-called "harmful algal blooms", particularly in freshwater systems, a number of these metabolites have been associated—as "toxins", or commonly "cyanotoxins"—with human and animal health concerns. In addition to the known water-soluble toxins from these genera (i.e. microcystins, cylindrospermopsin, and saxitoxins), our studies have shown that there are metabolites within the lipophilic extracts of these strains that inhibit vertebrate development in zebrafish embryos. Following these studies, the zebrafish embryo model was implemented in the bioassay-guided purification of four isolates of cyanobacterial harmful algal blooms, namely Aphanizomenon, two isolates of Cylindrospermopsis, and Microcystis, in order to identify and chemically characterize the bioactive lipophilic metabolites in these isolates. ^ We have recently isolated a group of polymethoxy-1-alkenes (PMAs), as potential toxins, based on the bioactivity observed in the zebrafish embryos. Although PMAs have been previously isolated from diverse cyanobacteria, they have not previously been associated with relevant toxicity. These compounds seem to be widespread across the different genera of cyanobacteria, and, according to our studies, suggested to be derived from the polyketide biosynthetic pathway which is a common synthetic route for cyanobacterial and other algal toxins. Thus, it can be argued that these metabolites are perhaps important contributors to the toxicity of cyanobacterial blooms. In addition to the PMAs, a set of bioactive glycosidic carotenoids were also isolated because of their inhibition of zebrafish embryonic development. These pigmented organic molecules are found in many photosynthetic organisms, including cyanobacteria, and they have been largely associated with the prevention of photooxidative damage. This is the first indication of these compounds as toxic metabolites and the hypothesized mode of action is via their biotransformation to retinoids, some of which are known to be teratogenic. Additional fractions within all four isolates have been shown to contain other uncharacterized lipophilic toxic metabolites. This apparent repertoire of lipophilic compounds may contribute to the toxicity of these cyanobacterial harmful algal blooms, which were previously attributed primarily to the presence of the known water-soluble toxins.^

Vocabulary learning through use of the picture-word inductive model for young English learners in China: A mixed methods examination using Cognitive Load Theory

English has been taught as a core and compulsory subject in China for decades. Recently, the demand for English in China has increased dramatically. China now has the world's largest English-learning population. The traditional English-teaching method cannot continue to be the only approach because it merely focuses on reading, grammar and translation, which cannot meet English learners and users' needs (i.e., communicative competence and skills in speaking and writing). ^ This study was conducted to investigate if the Picture-Word Inductive Model (PWIM), a new pedagogical method using pictures and inductive thinking, would benefit English learners in China in terms of potential higher output in speaking and writing. With the gauge of Cognitive Load Theory (CLT), specifically, its redundancy effect, I investigated whether processing words and a picture concurrently would present a cognitive overload for English learners in China. ^ I conducted a mixed methods research study. A quasi-experiment (pretest, intervention for seven weeks, and posttest) was conducted using 234 students in four groups in Lianyungang, China (58 fourth graders and 57 seventh graders as an experimental group with PWIM and 59 fourth graders and 60 seventh graders as a control group with the traditional method). No significant difference in the effects of PWIM was found on vocabulary acquisition based on grade levels. Observations, questionnaires with open-ended questions, and interviews were deployed to answer the three remaining research questions. A few students felt cognitively overloaded when they encountered too many writing samples, too many new words at one time, repeated words, mismatches between words and pictures, and so on. Many students listed and exemplified numerous strengths of PWIM, but a few mentioned weaknesses of PWIM. The students expressed the idea that PWIM had a positive effect on their English teaching. ^ As integrated inferences, qualitative findings were used to explain the quantitative results that there were no significant differences of the effects of the PWIM between the experimental and control groups in both grade levels, from four contextual aspects: time constraints on PWIM implementation, teachers' resistance, how to use PWIM and PWIM implemented in a classroom over 55 students.^

Use of Homeostatic Model Assessment Indexes for the Identification of Metabolic Syndrome and Insulin Resistance among Cuban-Americans: A Cross Sectional Study

Aim: to determine cut off points for The Homeostatic Model Assessment Index 1 and 2 (HOMA-1 and HOMA-2) for identifying insulin resistance and metabolic syndrome among a Cuban-American population. Study Design: Cross sectional. Place and Duration of Study: Florida International University, Robert Stempel School of Public Health and Social Work, Department of Dietetics and Nutrition, Miami, FL from July 2010 to December 2011. Methodology: Subjects without diabetes residing in South Florida were enrolled (N=146, aged 37 to 83 years). The HOMA1-IR and HOMA2-IR 90th percentile in the healthy group (n=75) was used as the cut-off point for insulin resistance. A ROC curve was constructed to determine the cut-off point for metabolic syndrome. Results: HOMA1-IR was associated with BMI, central obesity, and triglycerides (P3.95 and >2.20 and for metabolic syndrome were >2.98 (63.4% sensitivity and 73.3% specificity) and >1.55 (60.6% sensitivity and 66.7% specificity), respectively. Conclusion: HOMA cut-off points may be used as a screening tool to identify insulin resistance and metabolic syndrome among Cuban-Americans living in South Florida.

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.

Pulsed parabolic equation model of acoustic transmission (PPEMAT)

Underwater sound is very important in the field of oceanography where it is used for remote sensing in much the same way that radar is used in atmospheric studies. One way to mathematically model sound propagation in the ocean is by using the parabolic-equation method, a technique that allows range dependent environmental parameters. More importantly, this method can model sound transmission where the source emits either a pure tone or a short pulse of sound. Based on the parabolic approximation method and using the split-step Fourier algorithm, a computer model for underwater sound propagation was designed and implemented. This computer model differs from previous models in its use of the interactive mode, structured programming, modular design, and state-of-the-art graphics displays. In addition, the model maximizes the efficiency of computer time through synchronization of loosely coupled dual processors and the design of a restart capability. Since the model is designed for adaptability and for users with limited computer skills, it is anticipated that it will have many applications in the scientific community.

Analysis of fractured terrain using remote sensing and geographic information systems : establishing a correlation between fracture network properties and vegetation

This research analyzed the spatial relationship between a mega-scale fracture network and the occurrence of vegetation in an arid region. High-resolution aerial photographs of Arches National Park, Utah were used for digital image processing. Four sets of large-scale joints were digitized from the rectified color photograph in order to characterize the geospatial properties of the fracture network with the aid of a Geographic Information System. An unsupervised landcover classification was carried out to identify the spatial distribution of vegetation on the fractured outcrop. Results of this study confirm that the WNW-ESE alignment of vegetation is dominantly controlled by the spatial distribution of the systematic joint set, which in turn parallels the regional fold axis. This research provides insight into the spatial heterogeneity inherent to fracture networks, as well as the effects of jointing on the distribution of surface vegetation in desert environments.

Development and evaluation of a geographic information system based method to estimate flooding susceptibility in an area of Broward County, Florida

The objective of this study was to develop a GIS-based multi-class index overlay model to determine areas susceptible to inland flooding during extreme precipitation events in Broward County, Florida. Data layers used in the method include Airborne Laser Terrain Mapper (ALTM) elevation data, excess precipitation depth determined through performing a Soil Conservation Service (SCS) Curve Number (CN) analysis, and the slope of the terrain. The method includes a calibration procedure that uses "weights and scores" criteria obtained from Hurricane Irene (1999) records, a reported 100-year precipitation event, Doppler radar data and documented flooding locations. Results are displayed in maps of Eastern Broward County depicting types of flooding scenarios for a 100-year, 24-hour storm based on the soil saturation conditions. As expected the results of the multi-class index overlay analysis showed that an increase for the potential of inland flooding could be expected when a higher antecedent moisture condition is experienced. The proposed method proves to have some potential as a predictive tool for flooding susceptibility based on a relatively simple approach.

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

A model of the hydrothermal system at Casa Diablo in Long Valley, California, based on resistivity profiles and soil mercury analyses

A description and model of the near-surface hydrothermal system at Casa Diablo, with its implications for the larger-scale hydrothermal system of Long Valley, California, is presented. The data include resistivity profiles with penetrations to three different depth ranges, and analyses of inorganic mercury concentrations in 144 soil samples taken over a 1.3 by 1.7 km area. Analyses of the data together with the mapping of active surface hydrothermal features (fumaroles, mudpots, etc.), has revealed that the relationship between the hydrothermal system, surface hydrothermal activity, and mercury anomalies is strongly controlled by faults and topography. There are, however, more subtle factors responsible for the location of many active and anomalous zones such as fractures, zones of high permeability, and interactions between hydrothermal and cooler groundwater. In addition, the near-surface location of the upwelling from the deep hydrothermal reservoir, which supplies the geothermal power plants at Casa Diablo and the numerous hot pools in the caldera with hydrothermal water, has been detected. The data indicate that after upwelling the hydrothermal water flows eastward at shallow depth for at least 2 km and probably continues another 10 km to the east, all the way to Lake Crowley.

A software tool that generates symbolic model verifier (SMV) input language from a predicate transition net

The purpose of this research was to apply model checking by using a symbolic model checker on Predicate Transition Nets (PrT Nets). A PrT Net is a formal model of information flow which allows system properties to be modeled and analyzed. The aim of this thesis was to use the modeling and analysis power of PrT nets to provide a mechanism for the system model to be verified. Symbolic Model Verifier (SMV) was the model checker chosen in this thesis, and in order to verify the PrT net model of a system, it was translated to SMV input language. A software tool was implemented which translates the PrT Net into SMV language, hence enabling the process of model checking. The system includes two parts: the PrT net editor where the representation of a system can be edited, and the translator which converts the PrT net into an SMV program.

Rainfall variability in the upper Napo River Basin, Ecuadorian Amazon

The purpose of this research was to investigate the influence of elevation and other terrain characteristics over the spatial and temporal distribution of rainfall. A comparative analysis was conducted between several methods of spatial interpolations using mean monthly precipitation values in order to select the best. Following those previous results it was possible to fit an Artificial Neural Network model for interpolation of monthly precipitation values for a period of 20 years, with input values such as longitude, latitude, elevation, four geomorphologic characteristics and anchored by seven weather stations, it reached a high correlation coefficient (r=0.85). This research demonstrated a strong influence of elevation and other geomorphologic variables over the spatial distribution of precipitation and the agreement that there are nonlinear relationships. This model will be used to fill gaps in time-series of monthly precipitation, and to generate maps of spatial distribution of monthly precipitation at a resolution of 1km2.