Discrete time models of the term structure of interest rates and interest rate contingent claims

In this study, discrete time one-factor models of the term structure of interest rates and their application to the pricing of interest rate contingent claims are examined theoretically and empirically. The first chapter provides a discussion of the issues involved in the pricing of interest rate contingent claims and a description of the Ho and Lee (1986), Maloney and Byrne (1989), and Black, Derman, and Toy (1990) discrete time models. In the second chapter, a general discrete time model of the term structure from which the Ho and Lee, Maloney and Byrne, and Black, Derman, and Toy models can all be obtained is presented. The general model also provides for the specification of an additional model, the ExtendedMB model. The third chapter illustrates the application of the discrete time models to the pricing of a variety of interest rate contingent claims. In the final chapter, the performance of the Ho and Lee, Black, Derman, and Toy, and ExtendedMB models in the pricing of Eurodollar futures options is investigated empirically. The results indicate that the Black, Derman, and Toy and ExtendedMB models outperform the Ho and Lee model. Little difference in the performance of the Black, Derman, and Toy and ExtendedMB models is detected. ^

Developing performance measures of mangrove wetlands using simulation models of hydrology, nutrient biogeochemistry, and community dynamics

The goal of mangrove restoration projects should be to improve community structure and ecosystem function of degraded coastal landscapes. This requires the ability to forecast how mangrove structure and function will respond to prescribed changes in site conditions including hydrology, topography, and geophysical energies. There are global, regional, and local factors that can explain gradients of regulators (e.g., salinity, sulfides), resources (nutrients, light, water), and hydroperiod (frequency, duration of flooding) that collectively account for stressors that result in diverse patterns of mangrove properties across a variety of environmental settings. Simulation models of hydrology, nutrient biogeochemistry, and vegetation dynamics have been developed to forecast patterns in mangroves in the Florida Coastal Everglades. These models provide insight to mangrove response to specific restoration alternatives, testing causal mechanisms of system degradation. We propose that these models can also assist in selecting performance measures for monitoring programs that evaluate project effectiveness. This selection process in turn improves model development and calibration for forecasting mangrove response to restoration alternatives. Hydrologic performance measures include soil regulators, particularly soil salinity, surface topography of mangrove landscape, and hydroperiod, including both the frequency and duration of flooding. Estuarine performance measures should include salinity of the bay, tidal amplitude, and conditions of fresh water discharge (included in the salinity value). The most important performance measures from the mangrove biogeochemistry model should include soil resources (bulk density, total nitrogen, and phosphorus) and soil accretion. Mangrove ecology performance measures should include forest dimension analysis (transects and/or plots), sapling recruitment, leaf area index, and faunal relationships. Estuarine ecology performance measures should include the habitat function of mangroves, which can be evaluated with growth rate of key species, habitat suitability analysis, isotope abundance of indicator species, and bird census. The list of performance measures can be modified according to the model output that is used to define the scientific goals during the restoration planning process that reflect specific goals of the project.

When One Dimensional Models Fail: Complexifying Models of Knowledge Construction

One dimensional models of reflective practice do not incorporate spirituality and social responsibility. Theological reflection, a form of reflective practice, is contextualized by a vision of social responsibility and the use of spirituality. An alternative model of reflective practice is proposed for spirituality and socially responsive learning at work.

Potential for Cell-Transplant Therapy with Human Neuronal Precursors to Treat Neuropathic Pain in Models of PNS and CNS Injury: Comparison of hNT2.17 and hNT2.19 Cell Lines

Effective treatment of sensory neuropathies in peripheral neuropathies and spinal cord injury (SCI) is one of the most difficult problems in modern clinical practice. Cell therapy to release antinociceptive agents near the injured spinal cord is a logical next step in the development of treatment modalities. But few clinical trials, especially for chronic pain, have tested the potential of transplant of cells to treat chronic pain. Cell lines derived from the human neuronal NT2 cell line parentage, the hNT2.17 and hNT2.19 lines, which synthesize and release the neurotransmitters gamma-aminobutyric acid (GABA) and serotonin (5HT), respectively, have been used to evaluate the potential of cell-based release of antinociceptive agents near the lumbar dorsal (horn) spinal sensory cell centers to relieve neuropathic pain after PNS (partial nerve and diabetes-related injury) and CNS (spinal cord injury) damage in rat models. Both cell lines transplants potently and permanently reverse behavioral hypersensitivity without inducing tumors or other complications after grafting. Functioning as cellular minipumps for antinociception, human neuronal precursors, like these NT2-derived cell lines, would likely provide a useful adjuvant or replacement for current pharmacological treatments for neuropathic pain.

The effects of student -centered and traditional models of teaching on reading skills

A pre-test, post-test, quasi-experimental design was used to examine the effects of student-centered and traditional models of reading instruction on outcomes of literal comprehension and critical thinking skills. The sample for this study consisted of 101 adult students enrolled in a high-level developmental reading course at a large, urban community college in the Southeastern United States. The experimental group consisted of 48 students, and the control group consisted of 53 students. Students in the experimental group were limited in the time spent reading a course text of basic skills, with instructors using supplemental materials such as poems, news articles, and novels. Discussions, the reading-writing connection, and student choice in material selection were also part of the student-centered curriculum. Students in the control group relied heavily on a course text and vocabulary text for reading material, with great focus placed on basic skills. Activities consisted primarily of multiple-choice questioning and quizzes. The instrument used to collect pre-test data was Descriptive Tests of Language Skills in Reading Comprehension; post-test data were taken from the Florida College Basic Skills Exit Test. A MANCOVA was used as the statistical method to determine if either model of instruction led to significantly higher gains in literal comprehension skills or critical thinking skills. A paired samples t-test was also used to compare pre-test and post-test means. The results of the MANCOVA indicated no significant difference between instructional models on scores of literal comprehension and critical thinking. Neither was there any significant difference in scores between subgroups of age (under 25 and 25 and older) and language background (native English speaker and second-language learner). The results of the t-test indicated, however, that students taught under both instructional models made significant gains in on both literal comprehension and critical thinking skills from pre-test to post-test.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Acoustic Manipulation and Alignment of Particles for Applications in Separation, Micro-Templating, and Device Fabrication

This dissertation studies the manipulation of particles using acoustic stimulation for applications in microfluidics and templating of devices. The term particle is used here to denote any solid, liquid or gaseous material that has properties, which are distinct from the fluid in which it is suspended. Manipulation means to take over the movements of the particles and to position them in specified locations. Using devices, microfabricated out of silicon, the behavior of particles under the acoustic stimulation was studied with the main purpose of aligning the particles at either low-pressure zones, known as the nodes or high-pressure zones, known as anti-nodes. By aligning particles at the nodes in a flow system, these particles can be focused at the center or walls of a microchannel in order to ultimately separate them. These separations are of high scientific importance, especially in the biomedical domain, since acoustopheresis provides a unique approach to separate based on density and compressibility, unparalleled by other techniques. The study of controlling and aligning the particles in various geometries and configurations was successfully achieved by controlling the acoustic waves. Apart from their use in flow systems, a stationary suspended-particle device was developed to provide controllable light transmittance based on acoustic stimuli. Using a glass compartment and a carbon-particle suspension in an organic solvent, the device responded to acoustic stimulation by aligning the particles. The alignment of light-absorbing carbon particles afforded an increase in visible light transmittance as high as 84.5%, and it was controlled by adjusting the frequency and amplitude of the acoustic wave. The device also demonstrated alignment memory rendering it energy-efficient. A similar device for suspended-particles in a monomer enabled the development of electrically conductive films. These films were based on networks of conductive particles. Elastomers doped with conductive metal particles were rendered surface conductive at particle loadings as low as 1% by weight using acoustic focusing. The resulting films were flexible and had transparencies exceeding 80% in the visible spectrum (400-800 nm) These films had electrical bulk conductivities exceeding 50 S/cm.