129 research outputs found

    Model-based supervisory control synthesis of cyber-physical systems

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    Essays in Applied Microeconomics

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    This doctoral dissertation comprises essays in Applied Microeconomics with focus in Health and Regional Economics. The first investigates a neo-classical hospital production model for cost and quality implications by payment source in the context of the 2010 Affordable Care Act. The second essay demonstrates positive crime effects induced by Hurricane Katrina population migration. Specifically, the first essay evaluates hospital cost efficiecies emanating from changes in public reimbursement levels and/or shifts in hospital care demand or health care budgets. Using 2000-2008 data from Tennessee Joint Annual Reports of Hospitals, hybrid generalized translog multi-product cost functions were estimated with controls for multi-dimensional quality, diagnostic mix, and hopital heterogeneity. The production technology cost model, accounting for technological change and geographic effects, was estimated using the Iterative Seemingly Unrelated Regression methodology. Factor demand elasticities, alternative conceptual measures of the elasticites of substitution, scale and scope economies were evaluated. This is the first study to quantify opportunities for exploiting scope economies by payer type (e.g., Medicaid/Tenncare with private payers). Policy implications were explored. Using a natural experiment, the second essay tests an empirical link between the forced evacuation and crime types countrywide and in Houston, TX, while avoiding concerns of endogeneity due to selection or simultaneity. Few prior economic studies of Katrina probed impacts on host labor markets or on evacuees\u27 labor and schooling outcomes, overlooking potential effects on local crime in spite of anecdotal evidence. To ensure identification with a Difference-in-Difference specification, the number of evacuees going to a metropolitan area was instrumented by its distance to New Orleans, LA. Katrina immigration was found to rise the incidence of murder and non-negligent manslaughter, robbery, and motor vehicle theft. The analysis of Houston post-shelter consequences of Katrina on crime showed increases murder, aggravated assault, illegal possession of weapons, and arson. While the regional analysis was based on the Current Population Survey and data from the Federal Bureau of Investigation, the Houston study used data provided by the Police Department. Robustness checks evaluating self-selection utilized the Displaced New Orleans Resident Pilot survey. It remained undetermined whether the crimes were committed by the evacuees, or triggered by their presence

    Hybrid dragonfly algorithm with neighbourhood component analysis and gradient tree boosting for crime rates modelling

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    In crime studies, crime rates time series prediction helps in strategic crime prevention formulation and decision making. Statistical models are commonly applied in predicting time series crime rates. However, the time series crime rates data are limited and mostly nonlinear. One limitation in the statistical models is that they are mainly linear and are only able to model linear relationships. Thus, this study proposed a time series crime prediction model that can handle nonlinear components as well as limited historical crime rates data. Recently, Artificial Intelligence (AI) models have been favoured as they are able to handle nonlinear and robust to small sample data components in crime rates. Hence, the proposed crime model implemented an artificial intelligence model namely Gradient Tree Boosting (GTB) in modelling the crime rates. The crime rates are modelled using the United States (US) annual crime rates of eight crime types with nine factors that influence the crime rates. Since GTB has no feature selection, this study proposed hybridisation of Neighbourhood Component Analysis (NCA) and GTB (NCA-GTB) in identifying significant factors that influence the crime rates. Also, it was found that both NCA and GTB are sensitive to input parameter. Thus, DA2-NCA-eGTB model was proposed to improve the NCA-GTB model. The DA2-NCA-eGTB model hybridised a metaheuristic optimisation algorithm namely Dragonfly Algorithm (DA) with NCA-GTB model to optimise NCA and GTB parameters. In addition, DA2-NCA-eGTB model also improved the accuracy of the NCA-GTB model by using Least Absolute Deviation (LAD) as the GTB loss function. The experimental result showed that DA2-NCA-eGTB model outperformed existing AI models in all eight modelled crime types. This was proven by the smaller values of Mean Absolute Percentage Error (MAPE), which was between 2.9195 and 18.7471. As a conclusion, the study showed that DA2-NCA-eGTB model is statistically significant in representing all crime types and it is able to handle the nonlinear component in limited crime rate data well

    Keeping students safe: Student perceptions of campus safety at a mid-sized Virginia university and the impact for prevention, response and risk reduction strategies

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    The nexus of social factors, the college experience, and campus safety research represents an empirical gap in the literature surrounding campus safety issues. There is a need for new and creative outlooks on how to approach this culturally sensitive and complex issue(s); a need this research will begin to fulfill. This study intends to ascertain themes regarding the socially constructed reality of campus safety perceptions and concerns, of both male and female students, at a mid-sized Virginia university. A mixed methods procedure was used which included a focus group interview as well as a survey. As Kelly and Torres (2006) wrote, “The perception, just as much as the actual experience was what shaped women students fear for their campus safety” (p. 28), thus it will be the perceptions of the students that will shape their concerns of campus safety. This study will utilize unmatched count technique as the quantitative data collection method and a social constructivist framework to adapt to the sensitive and personal nature of campus safety issues, including sexual assault and interpersonal violence. Thematic analysis of the qualitative data allowed the researcher to determine that students’ perceptions could be categorized and defined in a number of ways. There is a clear need for further research on the subject in order to implement culturally appropriate and effective prevention, response and risk reduction strategies

    Evidence-based Development of Trustworthy Mobile Medical Apps

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    abstract: Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of health data, long term operation of wearable sensors and ensuring no harm to the user before actual marketing. Traditionally, clinical studies are used to validate the trustworthiness of medical systems. However, they can take long time and could potentially harm the user. Such evidences can be generated using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging. This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes: • Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment. • Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology. • Automatic code generation for MMAs: Investigating methods for automatically • Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.Dissertation/ThesisDoctoral Dissertation Computer Science 201

    Modeling posttraumatic stress disorder among victimized women on probation and parole : examining the impact of childhood victimization.

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    Women are the fastest growing segment of the criminal justice population in the United States (Minton, 2013; Pew Center on the States, 2009). Research is needed to understand Posttraumatic Stress Disorder (PTSD) among women involved with the criminal justice system to inform prevention and rehabilitation efforts. Despite findings suggesting that a mental health diagnosis of post-traumatic stress disorder (PTSD) is common among women in this population (Lynch, DeHart, Belknap, & Green, 2012; Salina, Lesondak, Razzano, & Weilbaecher, 2007), little research has examined the presence of this disorder among women involved with the criminal justice system with experiences of childhood victimization. Extant research indicates that women take different pathways toward involvement with the criminal justice system than men (Daly, 1992). This approach, the gendered pathways perspective (Salisbury & Van Voorhis, 2009), recognizes that women who become involved with the criminal justice system often have lives characterized by impoverished backgrounds, multiple victimization experiences, psychological distress and mental illness with self-medication as a means of coping. This research examined the structure of PTSD among 406 women on probation and parole with a history of victimization using the Post-traumatic Diagnostic Scale (PDS). Structural equation modeling was used to verify the structure of the PDS through five models: a one-factor model, numbing model, dysphoria model, dysphoric arousal model and DSM-5 model. Findings indicated that the dysphoric arousal model provided good fit to the data (X2 (109) =302.26, p \u3c .001; CFI = .93; TLI = .91; RMSEA = .07; SRMR = .04). Next, multiple indicators multiple causes (MIMC) analyses were conducted to examine differences in factor structure based upon exposure to childhood victimization (childhood physical or sexual victimization and childhood sexual victimization) controlling for sociodemographic variables. Findings from the first MIMIC analysis (X2 (181) =503.67, p \u3c .001; CFI = .91; TLI = .89; RMSEA = .07; SRMR = .06) provided adequate fit to the data, but indicated that symptom structure and severity was not significantly different for women based upon exposure to childhood physical and/or sexual victimization verses adult only victimization (B= .25, β = .08, SE= .17, p =.13). Results of the second MIMIC analysis (X2 (147) =439.71, p \u3c .001; CFI = .90; TLI = .89; RMSEA = .07; SRMR = .07) provided good fit to the data and indicated that exposure to childhood sexual victimization versus other types of victimization significantly predicted differences in PTSD symptom structure and greater severity (B= .29, β = .10, SE= .14, p =.04). However, childhood victimization accounted for only 1% of the variance in PTSD symptomology. Implications for assessment and treatment of this highly-victimized and traumatized population are discussed including the usefulness of addressing the symptoms of dysphoric arousal including sleep disturbance, irritability, and difficulty concentrating. Suggestions for public policy include increasing economic insecurity and revisiting current legal climate linking substance use with criminal justice involvement

    A Typology of Preadolescent Sexual Abusers Based on the Emerging Personality Patterns in the Millon Preadolescent Clinical Inventory

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    The purpose of this study was to develop a personality-based typology of preadolescents with sexual behavior problems based the Emerging Personality Patterns in the Millon Preadolescent Clinical Inventory (M-PACI, Millon et al., 2005). Grounding a typology in a theory driven personality system may offer clarity and specificity in understanding preadolescents with sexual behavior problems in a manner that has not yet been explored. A personality and theory driven typology could provide a more comprehensive framework for assessing and treating children who sexually abuse than any of the current taxonomic models. The study used an ex post facto design with test of hypotheses. The research hypotheses were derived through logical and empirical data findings. A sample of thirty-one participants were administered the M-PACI and a mental health professional completed a demographics and clinical information form on each participant. The participants scores on the M-PACI resulted in them being placed into one of three Emerging Personality Patterns groups, Active, Passive, or Unstable. These three groups were analyzed using a multivariate analysis of variance (MANOVA) on seven dependent variables. Results indicated that Active and Unstable Emerging Personality Patterns participants had significantly higher levels of maltreatment experiences and significantly more Current Clinical Signs as measured by the M-PACI, than the Passive Emerging Personality Patterns group. The results indicate that personality is a useful variable in differentiating preadolescents with sexual behavior problems. The implications for this study lend support for the conceptualization of preadolescents with sexual behavior using a personality based typology

    Risk Factors for Revictimization

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    People with a history of childhood maltreatment are two to three times more likely to be victimized again in adulthood compared to people without such a history, a phenomenon called revictimization. Various risk factors such as, post-traumatic stress disorder symptoms, difficulties with emotion regulation, and risky sex behavior are among important risk factors for revictimization. This dissertation shows that the increased risk of further victimization among the survivors of childhood maltreatment applies to the modern context of online dating as well. In this context, using sex to reduce negative emotions and to boost self-esteem connects maltreatment during childhood to sexual victimization in adulthood. Another finding of the present dissertation was that it is important to understand how the risk factors interact with each other. It seems that childhood maltreatment might develop maladaptive thoughts (e.g., negative thoughts about self and others). These negative thoughts are associated with difficulties with emotion regulation, which in turn might be associated with sexual risk-taking. Finally, the risky sex behavior can enhance the risk of victimization in adulthood. These vulnerabilities reflected in non-verbal cues might be used by perpetrators, particularly the ones with psychopathy, to select potential victims which increases the risk of revictimization. Our knowledge about the underlying mechanisms of revictimization is still in infancy phase since available data is limited mostly to cross-sectional studies conducted on a specific population i.e., Caucasian female university students. Longitudinal research on various populations would help us understand risk factors specific to each population in the years to come

    Risk Factors for Revictimization

    Get PDF
    People with a history of childhood maltreatment are two to three times more likely to be victimized again in adulthood compared to people without such a history, a phenomenon called revictimization. Various risk factors such as, post-traumatic stress disorder symptoms, difficulties with emotion regulation, and risky sex behavior are among important risk factors for revictimization. This dissertation shows that the increased risk of further victimization among the survivors of childhood maltreatment applies to the modern context of online dating as well. In this context, using sex to reduce negative emotions and to boost self-esteem connects maltreatment during childhood to sexual victimization in adulthood. Another finding of the present dissertation was that it is important to understand how the risk factors interact with each other. It seems that childhood maltreatment might develop maladaptive thoughts (e.g., negative thoughts about self and others). These negative thoughts are associated with difficulties with emotion regulation, which in turn might be associated with sexual risk-taking. Finally, the risky sex behavior can enhance the risk of victimization in adulthood. These vulnerabilities reflected in non-verbal cues might be used by perpetrators, particularly the ones with psychopathy, to select potential victims which increases the risk of revictimization. Our knowledge about the underlying mechanisms of revictimization is still in infancy phase since available data is limited mostly to cross-sectional studies conducted on a specific population i.e., Caucasian female university students. Longitudinal research on various populations would help us understand risk factors specific to each population in the years to come
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