The non-linear acousto-mechanical and thermodynamic response of spherical absorbers to laser pulses of various temporal profiles

This study is to theoretically investigate shockwave and microbubble formation due to laser absorption by microparticles and nanoparticles. The initial motivation for this research was to understand the underlying physical mechanisms responsible for laser damage to the retina, as well as the predict threshold levels for damage for laser pulses with of progressively shorter durations. The strongest absorbers in the retina are micron size melanosomes, and their absorption of laser light causes them to accrue very high energy density. I theoretically investigate how this absorbed energy is transferred to the surrounding medium. For a wide range of conditions I calculate shockwave generation and bubble growth as a function of the three parameters; fluence, pulse duration and pulse shape. In order to develop a rigorous physical treatment, the governing equations for the behavior of an absorber and for the surrounding medium are derived. Shockwave theory is investigated and the conclusion is that a shock pressure explanation is likely to be the underlying physical cause of retinal damage at threshold fluences for sub-nanosecond pulses. The same effects are also expected for non-biological micro and nano absorbers. ^

A non-linear discrete stochastic optimization model for Advanced Access patient appointment scheduling

Access to healthcare is a major problem in which patients are deprived of receiving timely admission to healthcare. Poor access has resulted in significant but avoidable healthcare cost, poor quality of healthcare, and deterioration in the general public health. Advanced Access is a simple and direct approach to appointment scheduling in which the majority of a clinic's appointments slots are kept open in order to provide access for immediate or same day healthcare needs and therefore, alleviate the problem of poor access the healthcare. This research formulates a non-linear discrete stochastic mathematical model of the Advanced Access appointment scheduling policy. The model objective is to maximize the expected profit of the clinic subject to constraints on minimum access to healthcare provided. Patient behavior is characterized with probabilities for no-show, balking, and related patient choices. Structural properties of the model are analyzed to determine whether Advanced Access patient scheduling is feasible. To solve the complex combinatorial optimization problem, a heuristic that combines greedy construction algorithm and neighborhood improvement search was developed. The model and the heuristic were used to evaluate the Advanced Access patient appointment policy compared to existing policies. Trade-off between profit and access to healthcare are established, and parameter analysis of input parameters was performed. The trade-off curve is a characteristic curve and was observed to be concave. This implies that there exists an access level at which at which the clinic can be operated at optimal profit that can be realized. The results also show that, in many scenarios by switching from existing scheduling policy to Advanced Access policy clinics can improve access without any decrease in profit. Further, the success of Advanced Access policy in providing improved access and/or profit depends on the expected value of demand, variation in demand, and the ratio of demand for same day and advanced appointments. The contributions of the dissertation are a model of Advanced Access patient scheduling, a heuristic to solve the model, and the use of the model to understand the scheduling policy trade-offs which healthcare clinic managers must make. ^

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Investigation of escape and negative student behaviors related to Florida state high stakes test preparation in Miami-Dade County Public High Schools

The purpose of this study was to determine whether there was a relationship between pressure to perform on state mandated, high-stakes tests and the rate of student escape behavior defined as the number of school suspensions and absences. The state assigned grade of a school was used as a surrogate measure of pressure with the assumption that pressure increased as the school grade decreased. Student attendance and suspension data were gathered from all 33 of the regular public high schools in Miami-Dade County Public Schools. The research questions were: Is the number of suspensions highest in the third quarter, when most FCAT preparation takes place for each of the 3 school years 2007-08 through 2009-10? How accurately does the high school's grade predict the number of suspensions and number of absences during each of the 4 school years 2005-06 through 2008-09? The research questions were answered using repeated measures analysis of variance for research question #1 and non-linear multiple regression for research question #2. No significant difference could be found between the numbers of suspensions in each of the grading periods nor was there a relationship between the number of suspensions and school grade. A statistically significant relationship was found between student attendance and school grade. When plotted, this relationship was found to be quadratic in nature and formed a loose inverted U for each of the four years during which data were collected. This indicated that students in very high and very low performing schools had low levels of absences while those in the midlevel of the distribution of school performance (C schools) had the greatest rates of absence. Identifying a relationship between the pressures associated with high stakes testing and student escape behavior suggests that it might be useful for building administrators to reevaluate test preparation activities and procedures being used in their building and to include anxiety reducing strategies. As a relationship was found, it sets the foundation for future studies to identify whether testing related activities are impacting some students emotionally and are causing unintended consequences of testing mandates.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Investigation of Escape and Negative Student Behaviors Related to Florida State High Stakes Test Preparation in Miami-Dade County Public High Schools, having been approved in respect to style and intellectual content, is referred to you for judgment

The purpose of this study was to determine whether there was a relationship between pressure to perform on state mandated, high-stakes tests and the rate of student escape behavior defined as the number of school suspensions and absences. The state assigned grade of a school was used as a surrogate measure of pressure with the assumption that pressure increased as the school grade decreased. Student attendance and suspension data were gathered from all 33 of the regular public high schools in Miami-Dade County Public Schools. The research questions were: Is the number of suspensions highest in the third quarter, when most FCAT preparation takes place for each of the 3 school years 2007-08 through 2009-10? How accurately does the high school’s grade predict the number of suspensions and number of absences during each of the 4 school years 2005-06 through 2008-09? The research questions were answered using repeated measures analysis of variance for research question #1 and non-linear multiple regression for research question #2. No significant difference could be found between the numbers of suspensions in each of the grading periods nor was there a relationship between the number of suspensions and school grade. A statistically significant relationship was found between student attendance and school grade. When plotted, this relationship was found to be quadratic in nature and formed a loose inverted U for each of the four years during which data were collected. This indicated that students in very high and very low performing schools had low levels of absences while those in the midlevel of the distribution of school performance (C schools) had the greatest rates of absence. Identifying a relationship between the pressures associated with high stakes testing and student escape behavior suggests that it might be useful for building administrators to reevaluate test preparation activities and procedures being used in their building and to include anxiety reducing strategies. As a relationship was found, it sets the foundation for future studies to identify whether testing related activities are impacting some students emotionally and are causing unintended consequences of testing mandates.